diff --git a/inputs/manual_input.csv b/inputs/manual_input.csv index bcfadf8d..c063f330 100644 --- a/inputs/manual_input.csv +++ b/inputs/manual_input.csv @@ -157,7 +157,7 @@ CO2 liquefaction,lifetime,2004,25,years,2004,"Guesstimate, based on CH4 liquefac CO2 liquefaction,carbondioxide-input,0,1,t_CO2/t_CO2,-,Mitsubish Heavy Industries Ltd. and IEA (2004): https://ieaghg.org/docs/General_Docs/Reports/PH4-30%20Ship%20Transport.pdf .,"Assuming a pure, humid, low-pressure input stream. Neglecting possible gross-effects of CO2 which might be cycled for the cooling process." CO2 liquefaction,heat-input,0,0.0067,MWh_th/t_CO2,-,Mitsubish Heavy Industries Ltd. and IEA (2004): https://ieaghg.org/docs/General_Docs/Reports/PH4-30%20Ship%20Transport.pdf .,For drying purposes. CO2 liquefaction,electricity-input,0,0.123,MWh_el/t_CO2,-,Mitsubish Heavy Industries Ltd. and IEA (2004): https://ieaghg.org/docs/General_Docs/Reports/PH4-30%20Ship%20Transport.pdf ., -General liquid hydrocarbon storage (product),investment,2012,160,EUR/m^3,2012,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 - 60 000 m^3 . +General liquid hydrocarbon storage (product),investment,2012,160,EUR/m^3,2012,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 – 60 000 m^3 . General liquid hydrocarbon storage (product),FOM,2012,6.25,%/year,2012,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , figure 7 and pg. 12 .",Assuming ca. 10 EUR/m^3/a (center value between stand alone and addon facility). General liquid hydrocarbon storage (product),lifetime,2012,30,years,2012,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.", General liquid hydrocarbon storage (crude),investment,2012,128,EUR/m^3,2012,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed 20% lower than for product storage. Crude or middle distillate tanks are usually larger compared to product storage due to lower requirements on safety and different construction method. Reference size used here: 80 000 – 120 000 m^3 . @@ -185,15 +185,15 @@ Ammonia cracker,FOM,2050,4.3,%/year,2015,"Ishimoto et al. (2020): 10.1016/j.ijhy Ammonia cracker,ammonia-input,0,1.46,MWh_NH3/MWh_H2,,"ENGIE et al (2020): Ammonia to Green Hydrogen Feasibility Study (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/880826/HS420_-_Ecuity_-_Ammonia_to_Green_Hydrogen.pdf), Fig. 10.",Assuming a integrated 200t/d cracking and purification facility. Electricity demand (316 MWh per 2186 MWh_LHV H2 output) is assumed to also be ammonia LHV input which seems a fair assumption as the facility has options for a higher degree of integration according to the report). methanol-to-olefins/aromatics,investment,2015,2628000,EUR/(t_HVC/h),2015,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a). methanol-to-olefins/aromatics,lifetime,2015,30,years,-,Guesstimate,same as steam cracker -methanol-to-olefins/aromatics,FOM,2015,3,%/year,2015,Guesstimate,same as steam cracker -methanol-to-olefins/aromatics,VOM,2015,30,EUR/t_HVC,2015,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", +methanol-to-olefins/aromatics,FOM,2015,3,%/year,-,Guesstimate,same as steam cracker +methanol-to-olefins/aromatics,VOM,2015,30,€/t_HVC,2015,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", methanol-to-olefins/aromatics,electricity-input,2015,1.3889,MWh_el/t_HVC,-,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), page 69",5 GJ/t_HVC methanol-to-olefins/aromatics,methanol-input,2015,18.03,MWh_MeOH/t_HVC,-,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Sections 4.5 (for ethylene and propylene) and 4.6 (for BTX)","Weighted average: 2.83 t_MeOH/t_ethylene+propylene for 21.7 Mt of ethylene and 17 Mt of propylene, 4.2 t_MeOH/t_BTX for 15.7 Mt of BTX. Assuming 5.54 MWh_MeOH/t_MeOH. " methanol-to-olefins/aromatics,carbondioxide-output,2015,0.6107,t_CO2/t_HVC,-,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Sections 4.5 (for ethylene and propylene) and 4.6 (for BTX)","Weighted average: 0.4 t_MeOH/t_ethylene+propylene for 21.7 Mt of ethylene and 17 Mt of propylene, 1.13 t_CO2/t_BTX for 15.7 Mt of BTX. The report also references process emissions of 0.55 t_MeOH/t_ethylene+propylene elsewhere. " electric steam cracker,investment,2015,10512000,EUR/(t_HVC/h),2015,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a). electric steam cracker,lifetime,2015,30,years,-,Guesstimate, -electric steam cracker,FOM,2015,3,%/year,2015,Guesstimate, -electric steam cracker,VOM,2015,180,EUR/t_HVC,2015,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", +electric steam cracker,FOM,2015,3,%/year,-,Guesstimate, +electric steam cracker,VOM,2015,180,€/t_HVC,2015,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", electric steam cracker,naphtha-input,2015,14.8,MWh_naphtha/t_HVC,-,"Lechtenböhmer et al. (2016): 10.1016/j.energy.2016.07.110, Section 4.3, page 6.", electric steam cracker,electricity-input,2015,2.7,MWh_el/t_HVC,-,"Lechtenböhmer et al. (2016): 10.1016/j.energy.2016.07.110, Section 4.3, page 6.",Assuming electrified processing. electric steam cracker,carbondioxide-output,2015,0.55,t_CO2/t_HVC,-,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), ",The report also references another source with 0.76 t_CO2/t_HVC @@ -219,14 +219,28 @@ methanol-to-kerosene,lifetime,2050,30,years,-,"Concawe (2022): E-Fuels: A techno methanol-to-kerosene,investment,2050,200000,EUR/MW_kerosene,2020,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.", methanol-to-kerosene,FOM,2050,4.5,%/year,2020,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.", methanol-to-kerosene,VOM,2050,1.35,EUR/MWh_kerosene,2020,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.", -Fischer-Tropsch,efficiency,2020,0.7,per unit,,ICCT IRA e-fuels assumptions , -Fischer-Tropsch,investment,2020,1696429,USD/MW_FT,2022,ICCT IRA e-fuels assumptions ,"Well developed technology, no significant learning expected." -Fischer-Tropsch,lifetime,2020,20,years,,ICCT IRA e-fuels assumptions , -Fischer-Tropsch,FOM,2020,4,%/year,2022,ICCT IRA e-fuels assumptions , -Fischer-Tropsch,lifetime,2030,30,years,,ICCT IRA e-fuels assumptions , -Fischer-Tropsch,hydrogen-input,2020,1.43,MWh_H2/MWh_FT,,ICCT IRA e-fuels assumptions ,"0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." -Fischer-Tropsch,electricity-input,2020,0.04,MWh_el/MWh_FT,,ICCT IRA e-fuels assumptions ,"0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." -Fischer-Tropsch,carbondioxide-input,2020,0.32,t_CO2/MWh_FT,,ICCT IRA e-fuels assumptions ,"Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT)." +Fischer-Tropsch,efficiency,2020,0.799,per unit,2017,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.2.", +Fischer-Tropsch,investment,2020,788000,EUR/MW_FT,2017,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected." +Fischer-Tropsch,lifetime,2020,20,years,2017,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.", +Fischer-Tropsch,FOM,2020,3,%/year,2017,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.", +Fischer-Tropsch,investment,2030,677000,EUR/MW_FT,2017,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected." +Fischer-Tropsch,lifetime,2030,20,years,2017,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.", +Fischer-Tropsch,FOM,2030,3,%/year,2017,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.", +Fischer-Tropsch,investment,2050,500000,EUR/MW_FT,2017,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected." +Fischer-Tropsch,lifetime,2050,20,years,2017,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.", +Fischer-Tropsch,FOM,2050,3,%/year,2017,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.", +Fischer-Tropsch,hydrogen-input,2020,1.531,MWh_H2/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." +Fischer-Tropsch,hydrogen-input,2030,1.421,MWh_H2/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." +Fischer-Tropsch,hydrogen-input,2040,1.363,MWh_H2/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." +Fischer-Tropsch,hydrogen-input,2050,1.327,MWh_H2/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." +Fischer-Tropsch,electricity-input,2020,0.008,MWh_el/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." +Fischer-Tropsch,electricity-input,2030,0.007,MWh_el/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." +Fischer-Tropsch,electricity-input,2040,0.007,MWh_el/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." +Fischer-Tropsch,electricity-input,2050,0.007,MWh_el/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output)." +Fischer-Tropsch,carbondioxide-input,2020,0.36,t_CO2/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT)." +Fischer-Tropsch,carbondioxide-input,2030,0.326,t_CO2/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT)." +Fischer-Tropsch,carbondioxide-input,2040,0.301,t_CO2/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT)." +Fischer-Tropsch,carbondioxide-input,2050,0.276,t_CO2/MWh_FT,,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT)." methanolisation,investment,2020,788000,EUR/MW_MeOH,2017,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected." methanolisation,lifetime,2020,20,years,2017,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.", methanolisation,FOM,2020,3,%/year,2017,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.", @@ -244,10 +258,10 @@ csp-tower,investment,2020,159.96,"EUR/kW_th,dp",2020,ATB CSP data (https://atb.n csp-tower,investment,2030,108.37,"EUR/kW_th,dp",2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR." csp-tower,investment,2040,99.97,"EUR/kW_th,dp",2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR." csp-tower,investment,2050,99.38,"EUR/kW_th,dp",2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR." -csp-tower,FOM,2020,1,%/year,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario. -csp-tower,FOM,2030,1.1,%/year,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario. -csp-tower,FOM,2040,1.3,%/year,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario. -csp-tower,FOM,2050,1.4,%/year,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario. +csp-tower,FOM,2020,1,%/year,1,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario. +csp-tower,FOM,2030,1.1,%/year,1.1,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario. +csp-tower,FOM,2040,1.3,%/year,1.3,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario. +csp-tower,FOM,2050,1.4,%/year,1.4,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario. csp-tower,lifetime,2020,30,years,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),- csp-tower,lifetime,2030,30,years,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),- csp-tower,lifetime,2040,30,years,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),- @@ -256,10 +270,10 @@ csp-tower TES,investment,2020,21.43,EUR/kWh_th,2020,ATB CSP data (https://atb.nr csp-tower TES,investment,2030,14.52,EUR/kWh_th,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR." csp-tower TES,investment,2040,13.39,EUR/kWh_th,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR." csp-tower TES,investment,2050,13.32,EUR/kWh_th,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR." -csp-tower TES,FOM,2020,1,%/year,2020,see solar-tower.,- -csp-tower TES,FOM,2030,1.1,%/year,2020,see solar-tower.,- -csp-tower TES,FOM,2040,1.3,%/year,2020,see solar-tower.,- -csp-tower TES,FOM,2050,1.4,%/year,2020,see solar-tower.,- +csp-tower TES,FOM,2020,1,%/year,1,see solar-tower.,- +csp-tower TES,FOM,2030,1.1,%/year,1.1,see solar-tower.,- +csp-tower TES,FOM,2040,1.3,%/year,1.3,see solar-tower.,- +csp-tower TES,FOM,2050,1.4,%/year,1.4,see solar-tower.,- csp-tower TES,lifetime,2020,30,years,2020,see solar-tower.,- csp-tower TES,lifetime,2030,30,years,2020,see solar-tower.,- csp-tower TES,lifetime,2040,30,years,2020,see solar-tower.,- @@ -268,32 +282,29 @@ csp-tower power block,investment,2020,1120.57,EUR/kW_e,2020,ATB CSP data (https: csp-tower power block,investment,2030,759.17,EUR/kW_e,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR." csp-tower power block,investment,2040,700.34,EUR/kW_e,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR." csp-tower power block,investment,2050,696.2,EUR/kW_e,2020,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR." -csp-tower power block,FOM,2020,1,%/year,2020,see solar-tower.,- -csp-tower power block,FOM,2030,1.1,%/year,2020,see solar-tower.,- -csp-tower power block,FOM,2040,1.3,%/year,2020,see solar-tower.,- -csp-tower power block,FOM,2050,1.4,%/year,2020,see solar-tower.,- +csp-tower power block,FOM,2020,1,%/year,1,see solar-tower.,- +csp-tower power block,FOM,2030,1.1,%/year,1.1,see solar-tower.,- +csp-tower power block,FOM,2040,1.3,%/year,1.3,see solar-tower.,- +csp-tower power block,FOM,2050,1.4,%/year,1.4,see solar-tower.,- csp-tower power block,lifetime,2020,30,years,2020,see solar-tower.,- csp-tower power block,lifetime,2030,30,years,2020,see solar-tower.,- csp-tower power block,lifetime,2040,30,years,2020,see solar-tower.,- csp-tower power block,lifetime,2050,30,years,2020,see solar-tower.,- -hydrogen storage tank type 1,investment,2020,16.87,USD/kWh_H2,2022,ICCT IRA e-fuels assumptions , -hydrogen storage tank type 1,FOM,2020,4,%/year,2022,ICCT IRA e-fuels assumptions ,- -hydrogen storage tank type 1,lifetime,2020,30,years,,ICCT IRA e-fuels assumptions ,- -hydrogen storage tank type 1,min_fill_level,2020,6,%,,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",- -hydrogen storage compressor,investment,2020,2.28,USD/kWh_H2,2022,ICCT IRA e-fuels assumptions , -hydrogen storage compressor,FOM,2020,4,%/year,2022,ICCT IRA e-fuels assumptions ,- -hydrogen storage compressor,lifetime,2020,30,years,,ICCT IRA e-fuels assumptions ,- -hydrogen storage compressor,compression-electricity-input,2020,0.05,MWh_el/MWh_H2,,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg. +hydrogen storage tank type 1,investment,2030,13.5,EUR/kWh_H2,2020,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.","450 EUR/kg_H2 converted with LHV to MWh. For a type 1 hydrogen storage tank (steel, 15-250 bar). Currency year assumed 2020 for initial publication of reference; observe note in SI.4.3 that no currency year is explicitly stated in the reference." +hydrogen storage tank type 1,FOM,2030,2,%/year,2020,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",- +hydrogen storage tank type 1,lifetime,2030,20,years,2020,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",- +hydrogen storage tank type 1,min_fill_level,2030,6,%,2020,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",- +hydrogen storage compressor,investment,2030,87.69,EUR/kW_H2,2020,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.","2923 EUR/kg_H2. For a 206 kg/h compressor. Base CAPEX 40 528 EUR/kW_el with scale factor 0.4603. kg_H2 converted to MWh using LHV. Pressure range: 30 bar in, 250 bar out." +hydrogen storage compressor,FOM,2030,4,%/year,2020,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",- +hydrogen storage compressor,lifetime,2030,15,years,2020,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",- +hydrogen storage compressor,compression-electricity-input,2030,0.05,MWh_el/MWh_H2,2020,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg. seawater RO desalination,electricity-input,0,0.003,MWHh_el/t_H2O,,"Caldera et al. (2016): Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",Desalination using SWRO. Assume medium salinity of 35 Practical Salinity Units (PSUs) = 35 kg/m^3. Haber-Bosch,electricity-input,0,0.2473,MWh_el/MWh_NH3,-,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), table 11.",Assume 5 GJ/t_NH3 for compressors and NH3 LHV = 5.16666 MWh/t_NH3. Haber-Bosch,nitrogen-input,0,0.1597,t_N2/MWh_NH3,-,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.",".33 MWh electricity are required for ASU per t_NH3, considering 0.4 MWh are required per t_N2 and LHV of NH3 of 5.1666 Mwh." Haber-Bosch,hydrogen-input,0,1.1484,MWh_H2/MWh_NH3,-,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.","178 kg_H2 per t_NH3, LHV for both assumed." air separation unit,electricity-input,0,0.25,MWh_el/t_N2,-,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), p.288.","For consistency reasons use value from Danish Energy Agency. DEA also reports range of values (0.2-0.4 MWh/t_N2) on pg. 288. Other efficienices reported are even higher, e.g. 0.11 Mwh/t_N2 from Morgan (2013): Techno-Economic Feasibility Study of Ammonia Plants Powered by Offshore Wind ." -direct air capture,years,2020,30,years,,ICCT IRA e-fuels assumptions , -direct air capture,FOM,2020,1.3,%/year,2022,ICCT IRA e-fuels assumptions , -direct air capture,investment,2020,12398844.91,USD/t_CO2/h,2022,ICCT IRA e-fuels assumptions , -direct air capture,electricity-input,2020,0.24,MWh_el/t_CO2,,ICCT IRA e-fuels assumptions , -direct air capture,heat-input,2020,1.17,MWh_th/t_CO2,,ICCT IRA e-fuels assumptions , +direct air capture,electricity-input,0,0.4,MWh_el/t_CO2,-,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","0.4 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 0.182 MWh based on Breyer et al (2019). Should already include electricity for water scrubbing and compression (high quality CO2 output)." +direct air capture,heat-input,0,1.6,MWh_th/t_CO2,-,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","Thermal energy demand. Provided via air-sourced heat pumps. 1.6 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 1.102 MWh based on Breyer et al (2019)." dry bulk carrier Capesize,capacity,2020,180000,t,2020,-,"DWT; corresponds to size of Capesize bulk carriers which have previously docked at the habour in Hamburg, Germany. Short of 200 kt limit for VLBCs." dry bulk carrier Capesize,investment,2020,40000000,EUR,2020,"Based on https://www.hellenicshippingnews.com/dry-bulk-carriers-in-high-demand-as-rates-keep-rallying/, accessed: 2022-12-03.","See figure for ‘Dry Bulk Newbuild Prices’, Capesize at end of 2020. Exchange rate: 1.15 USD = 1 EUR." dry bulk carrier Capesize,FOM,2020,4,%/year,2020,"Based on https://www.hellenicshippingnews.com/capesize-freight-returns-below-operating-expense-levels-but-shipowners-reject-lay-ups/, accessed: 2022-12-03.","5000 USD/d OPEX, exchange rate: 1.15 USD = 1 EUR; absolute value calculate relative to investment cost." @@ -333,7 +344,7 @@ coal,investment,2020,4575.69,EUR/kW_e,2023,"Lazard's levelized cost of energy an coal,FOM,2020,1.31,%/year,2023,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (39.5+91.25) USD/kW_e/a /2 / (1.09 USD/EUR) / investment cost * 100." coal,VOM,2020,3.89908256880734,EUR/MWh_e,2023,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (3+5.5)USD/MWh_e/2 / (1.09 USD/EUR)." coal,lifetime,2020,40,years,2023,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.", -coal,efficiency,2020,0.356,p.u.,2023,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up." +coal,efficiency,2020,0.33,p.u.,2023,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up." coal,fuel,2020,8.4,EUR/MWh_th,2010,"DIW (2013): Current and propsective costs of electricity generation until 2050, http://hdl.handle.net/10419/80348 , pg. 80 text below figure 10, accessed: 2023-12-14.","Based on IEA 2011 data, 99 USD/t." lignite,investment,2020,4575.69,EUR/kW_e,2023,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Higher costs include coal plants with CCS, but since using here for calculating the average nevertheless. Calculated based on average of listed range, i.e. (3200+6775) USD/kW_e/2 / (1.09 USD/EUR). Note: Assume same costs as for hard coal, as cost structure is apparently comparable, see https://diglib.tugraz.at/download.php?id=6093e88b63f93&location=browse and https://iea.blob.core.windows.net/assets/ae17da3d-e8a5-4163-a3ec-2e6fb0b5677d/Projected-Costs-of-Generating-Electricity-2020.pdf ." lignite,FOM,2020,1.31,%/year,2023,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (39.5+91.25) USD/kW_e/a /2 / (1.09 USD/EUR) / investment cost * 100. Note: Assume same costs as for hard coal, as cost structure is apparently comparable, see https://diglib.tugraz.at/download.php?id=6093e88b63f93&location=browse and https://iea.blob.core.windows.net/assets/ae17da3d-e8a5-4163-a3ec-2e6fb0b5677d/Projected-Costs-of-Generating-Electricity-2020.pdf . " @@ -376,114 +387,6 @@ allam,investment,2030,1500,EUR/kW,2020,Own assumption. TODO: Find better technol allam,VOM,2030,2,EUR/MWh,2020,Own assumption. TODO: Find better technology data and cost assumptions, allam,efficiency,2030,0.6,p.u.,2020,Own assumption. TODO: Find better technology data and cost assumptions, allam,lifetime,2030,30,years,2020,Own assumption. TODO: Find better technology data and cost assumptions, -Coal-IGCC,lifetime,2020,40,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal-IGCC-90%-CCS,lifetime,2030,40,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal-95%-CCS,lifetime,2030,40,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal-99%-CCS,lifetime,2030,40,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -NG 2-on-1 Combined Cycle (F-Frame),lifetime,2030,30,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,lifetime,2030,30,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,lifetime,2030,30,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal-95%-CCS,capture_rate,2030,0.95,per unit,-,"NREL, NREL ATB 2024", -Coal-99%-CCS,capture_rate,2030,0.99,per unit,-,"NREL, NREL ATB 2024", -Coal-IGCC-90%-CCS,capture_rate,2030,0.9,per unit,-,"NREL, NREL ATB 2024", -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,capture_rate,2030,0.95,per unit,-,"NREL, NREL ATB 2024", -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,capture_rate,2030,0.97,per unit,-,"NREL, NREL ATB 2024", -Coal-IGCC,efficiency,2020,0.5,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal-IGCC-90%-CCS,efficiency,2030,0.403,p.u.,,"JRC, 01_JRC-EU-TIMES Full model ", -NG 2-on-1 Combined Cycle (F-Frame),efficiency,2020,0.573,p.u.,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -Coal-95%-CCS,efficiency,2030,0.403,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal-99%-CCS,efficiency,2030,0.403,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -NG 2-on-1 Combined Cycle (F-Frame),efficiency,2030,0.573,p.u.,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,efficiency,2030,0.527,p.u.,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,efficiency,2030,0.525,p.u.,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -Coal integrated retrofit 90%-CCS,capture_rate,2030,0.9,per unit,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -Coal integrated retrofit 95%-CCS,capture_rate,2030,0.95,per unit,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -NG Combined Cycle F-Class integrated retrofit 90%-CCS,capture_rate,2030,0.9,per unit,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -NG Combined Cycle F-Class integrated retrofit 95%-CCS,capture_rate,2030,0.95,per unit,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -Coal integrated retrofit 90%-CCS,efficiency,2030,0.386,p.u.,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -Coal integrated retrofit 95%-CCS,efficiency,2030,0.386,p.u.,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -NG Combined Cycle F-Class integrated retrofit 90%-CCS,efficiency,2030,0.536,p.u.,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -NG Combined Cycle F-Class integrated retrofit 95%-CCS,efficiency,2030,0.536,p.u.,-,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""", -Natural gas steam reforming,lifetime,2020,20,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification,lifetime,2020,20,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Heavy oil partial oxidation,lifetime,2020,20,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Alkaline electrolyzer,lifetime,2020,30,years,-,ICCT IRA e-fuels assumptions , -PEM electrolyzer,lifetime,2020,30,years,-,ICCT IRA e-fuels assumptions , -SOEC,lifetime,2020,30,years,-,ICCT IRA e-fuels assumptions , -Solid biomass steam reforming,lifetime,2020,20,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Biomass gasification,lifetime,2020,20,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming CC,lifetime,2020,20,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification CC,lifetime,2020,20,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Biomass gasification CC,lifetime,2020,20,years,-,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming,efficiency,2020,0.75,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming,efficiency,2050,0.787,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification,efficiency,2020,0.56,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification,efficiency,2050,0.787,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Heavy oil partial oxidation,efficiency,2020,0.734,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Alkaline electrolyzer,efficiency,2020,0.65,p.u.,-,ICCT IRA e-fuels assumptions , -Alkaline electrolyzer,efficiency,2030,0.69,p.u.,-,ICCT IRA e-fuels assumptions , -Alkaline electrolyzer,efficiency,2040,0.74,p.u.,-,ICCT IRA e-fuels assumptions , -Alkaline electrolyzer,efficiency,2050,0.78,p.u.,-,ICCT IRA e-fuels assumptions , -PEM electrolyzer,efficiency,2020,0.63,p.u.,-,ICCT IRA e-fuels assumptions , -PEM electrolyzer,efficiency,2030,0.68,p.u.,-,ICCT IRA e-fuels assumptions , -PEM electrolyzer,efficiency,2040,0.71,p.u.,-,ICCT IRA e-fuels assumptions , -PEM electrolyzer,efficiency,2050,0.73,p.u.,-,ICCT IRA e-fuels assumptions , -SOEC,efficiency,2020,0.82,p.u.,-,ICCT IRA e-fuels assumptions , -SOEC,efficiency,2030,0.84,p.u.,-,ICCT IRA e-fuels assumptions , -SOEC,efficiency,2040,0.87,p.u.,-,ICCT IRA e-fuels assumptions , -SOEC,efficiency,2050,0.9,p.u.,-,ICCT IRA e-fuels assumptions , -Solid biomass steam reforming,efficiency,2020,0.712,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Biomass gasification,efficiency,2020,0.35,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Biomass gasification,efficiency,2050,0.525,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming CC,efficiency,2030,0.637,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming CC,efficiency,2050,0.695,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification CC,efficiency,2030,0.532,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification CC,efficiency,2030,0.609,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Biomass gasification CC,efficiency,2030,0.328,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Biomass gasification CC,efficiency,2040,0.514,p.u.,-,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming,investment,2020,186.9,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming,investment,2030,158.31,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification,investment,2020,425.42,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification,investment,2030,351,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Heavy oil partial oxidation,investment,2020,431.73,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Alkaline electrolyzer,investment,2020,1146,USD/kW,2022,ICCT IRA e-fuels assumptions , -Alkaline electrolyzer,investment,2030,936,USD/kW,2022,ICCT IRA e-fuels assumptions , -Alkaline electrolyzer,investment,2040,765,USD/kW,2022,ICCT IRA e-fuels assumptions , -Alkaline electrolyzer,investment,2050,625,USD/kW,2022,ICCT IRA e-fuels assumptions , -PEM electrolyzer,investment,2020,1371,USD/kW,2022,ICCT IRA e-fuels assumptions , -PEM electrolyzer,investment,2030,1120,USD/kW,2022,ICCT IRA e-fuels assumptions , -PEM electrolyzer,investment,2040,915,USD/kW,2022,ICCT IRA e-fuels assumptions , -PEM electrolyzer,investment,2050,748,USD/kW,2022,ICCT IRA e-fuels assumptions , -SOEC,investment,2020,1561,USD/kW,2022,ICCT IRA e-fuels assumptions , -SOEC,investment,2030,1276,USD/kW,2022,ICCT IRA e-fuels assumptions , -SOEC,investment,2040,1042,USD/kW,2022,ICCT IRA e-fuels assumptions , -SOEC,investment,2050,852,USD/kW,2022,ICCT IRA e-fuels assumptions , -Solid biomass steam reforming,investment,2020,519.4,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Biomass gasification,investment,2020,1290.45,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming CC,investment,2030,284.77,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming CC,investment,2030,284.77,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification CC,investment,2030,571.12,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Biomass gasification CC,investment,2030,2651.23,EUR/kW,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming,FOM,2020,0.05,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming,FOM,2030,0.05,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification,FOM,2020,0.06,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification,FOM,2030,0.06,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Heavy oil partial oxidation,FOM,2020,0.05,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Alkaline electrolyzer,FOM,2020,0.04,%/year,2022,ICCT IRA e-fuels assumptions , -Alkaline electrolyzer,FOM,2030,0.04,%/year,2022,ICCT IRA e-fuels assumptions , -Alkaline electrolyzer,FOM,2050,0.04,%/year,2022,ICCT IRA e-fuels assumptions , -PEM electrolyzer,FOM,2020,0.04,%/year,2022,ICCT IRA e-fuels assumptions , -PEM electrolyzer,FOM,2030,0.04,%/year,2022,ICCT IRA e-fuels assumptions , -PEM electrolyzer,FOM,2050,0.04,%/year,2022,ICCT IRA e-fuels assumptions , -SOEC,FOM,2020,0.04,%/year,2022,ICCT IRA e-fuels assumptions , -SOEC,FOM,2030,0.04,%/year,2022,ICCT IRA e-fuels assumptions , -SOEC,FOM,2050,0.04,%/year,2022,ICCT IRA e-fuels assumptions , -Solid biomass steam reforming,FOM,2020,0.04,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Solid biomass steam reforming,FOM,2020,0.05,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming CC,FOM,2030,0.05,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Natural gas steam reforming CC,FOM,2050,0.05,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Coal gasification CC,FOM,2030,0.07,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", -Biomass gasification CC,FOM,2030,0.02,%/year,2010,"JRC, 01_JRC-EU-TIMES Full model ", iron-air battery,FOM,2025,1.02185373078457,%/year,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery,FOM,2030,1,%/year,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery,FOM,2035,1.10628646943421,%/year,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", @@ -491,13 +394,45 @@ iron-air battery,FOM,2040,1.1807773334732,%/year,2020,"Form Energy, FormEnergy_E iron-air battery,investment,2025,23.45,EUR/kWh,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery,investment,2030,15.61,EUR/kWh,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery,investment,2035,11.79,EUR/kWh,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", -iron-air battery,investment,2040,10.4,EUR/kWh,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", -iron-air battery charge,efficiency,2025,0.7,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", +iron-air battery,investment,2040,10.40,EUR/kWh,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", +iron-air battery charge,efficiency,2025,0.70,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery charge,efficiency,2030,0.71,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery charge,efficiency,2035,0.73,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery charge,efficiency,2040,0.74,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery discharge,efficiency,2025,0.59,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", -iron-air battery discharge,efficiency,2030,0.6,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", +iron-air battery discharge,efficiency,2030,0.60,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery discharge,efficiency,2035,0.62,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery discharge,efficiency,2040,0.63,per unit,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", iron-air battery,lifetime,2030,17.5,years,2020,"Form Energy, FormEnergy_Europe_modeling_recommendations_2023.03.pdf, p4", +biogas storage, lifetime, 2020, 25,years,2020,"AU Foulum", +biogas storage, lifetime, 2030, 25,years,2020,"AU Foulum", +biogas storage, lifetime, 2040, 25,years,2020,"AU Foulum", +biogas storage, lifetime, 2050, 25,years,2020,"AU Foulum", +biogas storage, investment, 2020, 14.45, EUR/kWh, 2020,"AU Foulum", +biogas storage, investment, 2030, 14.45, EUR/kWh, 2020,"AU Foulum", +biogas storage, investment, 2040, 14.45, EUR/kWh, 2020,"AU Foulum", +biogas storage, investment, 2050, 14.45, EUR/kWh, 2020,"AU Foulum", +CO2 storage cylinders, lifetime, 2020, 25,years,2020,"AU Foulum", +CO2 storage cylinders, lifetime, 2030, 25,years,2020,"AU Foulum", +CO2 storage cylinders, lifetime, 2040, 25,years,2020,"AU Foulum", +CO2 storage cylinders, lifetime, 2050, 25,years,2020,"AU Foulum", +CO2 storage cylinders, investment, 2020, 77000, EUR/t_CO2, 2020,"AU Foulum", +CO2 storage cylinders, investment, 2030, 77000, EUR/t_CO2, 2020,"AU Foulum", +CO2 storage cylinders, investment, 2040, 77000, EUR/t_CO2, 2020,"AU Foulum", +CO2 storage cylinders, investment, 2050, 77000, EUR/t_CO2, 2020,"AU Foulum", +CO2 storage cylinders, FOM, 2020, 1.0,%/year,2020,"AU Foulum", +CO2 storage cylinders, FOM, 2030, 1.0,%/year,2020,"AU Foulum", +CO2 storage cylinders, FOM, 2040, 1.0,%/year,2020,"AU Foulum", +CO2 storage cylinders, FOM, 2050, 1.0,%/year,2020,"AU Foulum", +CO2_industrial_compressor, lifetime, 2020, 25,years,2020,"AU Foulum", +CO2_industrial_compressor, lifetime, 2030, 25,years,2020,"AU Foulum", +CO2_industrial_compressor, lifetime, 2040, 25,years,2020,"AU Foulum", +CO2_industrial_compressor, lifetime, 2050, 25,years,2020,"AU Foulum", +CO2_industrial_compressor, investment, 2020, 1516000, EUR/t/h_CO2, 2020,"AU Foulum", +CO2_industrial_compressor, investment, 2030, 1516000, EUR/t/h_CO2, 2020,"AU Foulum", +CO2_industrial_compressor, investment, 2040, 1516000, EUR/t/h_CO2, 2020,"AU Foulum", +CO2_industrial_compressor, investment, 2050, 1516000, EUR/t/h_CO2, 2020,"AU Foulum", +CO2_industrial_compressor, FOM, 2020, 4.0,%/year,2020,"AU Foulum", +CO2_industrial_compressor, FOM, 2030, 4.0,%/year,2020,"AU Foulum", +CO2_industrial_compressor, FOM, 2040, 4.0,%/year,2020,"AU Foulum", +CO2_industrial_compressor, FOM, 2050, 4.0,%/year,2020,"AU Foulum", \ No newline at end of file diff --git a/outputs/costs_2020.csv b/outputs/costs_2020.csv index e95ef4fb..836b3416 100644 --- a/outputs/costs_2020.csv +++ b/outputs/costs_2020.csv @@ -1,8 +1,4 @@ technology,parameter,value,unit,source,further description,currency_year -Alkaline electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,efficiency,0.65,p.u.,ICCT IRA e-fuels assumptions ,, -Alkaline electrolyzer,investment,1019.8742,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Ammonia cracker,FOM,4.3,%/year,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 7.","Estimated based on Labour cost rate, Maintenance cost rate, Insurance rate, Admin. cost rate and Chemical & other consumables cost rate.",2015.0 Ammonia cracker,ammonia-input,1.46,MWh_NH3/MWh_H2,"ENGIE et al (2020): Ammonia to Green Hydrogen Feasibility Study (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/880826/HS420_-_Ecuity_-_Ammonia_to_Green_Hydrogen.pdf), Fig. 10.",Assuming a integrated 200t/d cracking and purification facility. Electricity demand (316 MWh per 2186 MWh_LHV H2 output) is assumed to also be ammonia LHV input which seems a fair assumption as the facility has options for a higher degree of integration according to the report)., Ammonia cracker,investment,1123945.3807,EUR/MW_H2,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 6.","Calculated. For a small (200 t_NH3/d input) facility. Base cost for facility: 51 MEUR at capacity 20 000m^3_NH3/h = 339 t_NH3/d input. Cost scaling exponent 0.67. Ammonia density 0.7069 kg/m^3. Conversion efficiency of cracker: 0.685. Ammonia LHV: 5.167 MWh/t_NH3.; and @@ -45,25 +41,18 @@ Battery electric (passenger cars),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL Battery electric (trucks),FOM,14.0,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),investment,204067.0,EUR/LKW,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 -BioSNG,C in fuel,0.324,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,C stored,0.676,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,CO2 stored,0.2479,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C in fuel,0.3162,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C stored,0.6838,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,CO2 stored,0.2569,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BioSNG,FOM,1.608,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Fixed O&M",2020.0 BioSNG,VOM,2.8712,EUR/MWh_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Variable O&M",2020.0 BioSNG,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, BioSNG,efficiency,0.6,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Bio SNG Output",2020.0 BioSNG,investment,2658.5,EUR/kW_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Specific investment",2020.0 BioSNG,lifetime,25.0,years,TODO,"84 Gasif. CFB, Bio-SNG: Technical lifetime",2020.0 -Biomass gasification,efficiency,0.35,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification,investment,1467.7693,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,FOM,0.02,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,efficiency,0.328,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,investment,3015.5325,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -BtL,C in fuel,0.2455,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,C stored,0.7545,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,CO2 stored,0.2767,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C in fuel,0.2396,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C stored,0.7604,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,CO2 stored,0.2857,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BtL,FOM,2.4,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Fixed O&M",2020.0 BtL,VOM,1.1299,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Variable O&M",2020.0 BtL,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -109,11 +98,17 @@ CO2 liquefaction,lifetime,25.0,years,"Guesstimate, based on CH4 liquefaction.",, CO2 pipeline,FOM,0.9,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 pipeline,investment,2116.4433,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch onshore pipeline.,2015.0 CO2 pipeline,lifetime,50.0,years,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 +CO2 storage cylinders, FOM,1.0,%/year,AU Foulum,,2020.0 +CO2 storage cylinders, investment,77000.0, EUR/t_CO2,AU Foulum,,2020.0 +CO2 storage cylinders, lifetime,25.0,years,AU Foulum,,2020.0 CO2 storage tank,FOM,1.0,%/year,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,investment,2584.3462,EUR/t_CO2,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, Table 3.","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,lifetime,25.0,years,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 submarine pipeline,FOM,0.5,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 submarine pipeline,investment,4232.8865,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch offshore pipeline.,2015.0 +CO2_industrial_compressor, FOM,4.0,%/year,AU Foulum,,2020.0 +CO2_industrial_compressor, investment,1516000.0, EUR/t/h_CO2,AU Foulum,,2020.0 +CO2_industrial_compressor, lifetime,25.0,years,AU Foulum,,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,FOM,1.6,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,investment,629102.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 @@ -126,29 +121,6 @@ Charging infrastructure fuel cell vehicles trucks,lifetime,30.0,years,PATHS TO A Charging infrastructure slow (purely) battery electric vehicles passenger cars,FOM,1.8,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,investment,1283.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 -Coal gasification,FOM,0.06,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,efficiency,0.56,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification,investment,483.8765,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,FOM,0.07,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,efficiency,0.532,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,investment,649.5969,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 90%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal-95%-CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -Coal-95%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-95%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,capture_rate,0.99,per unit,"NREL, NREL ATB 2024",, -Coal-99%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,efficiency,0.5,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,capture_rate,0.9,per unit,"NREL, NREL ATB 2024",, -Coal-IGCC-90%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Compressed-Air-Adiabatic-bicharger,FOM,0.9265,%/year,"Viswanathan_2022, p.64 (p.86) Figure 4.14","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 Compressed-Air-Adiabatic-bicharger,efficiency,0.7211,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.52^0.5']}",2020.0 Compressed-Air-Adiabatic-bicharger,investment,946180.9426,EUR/MW,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['Turbine Compressor BOP EPC Management']}",2020.0 @@ -249,15 +221,15 @@ FT fuel transport ship,FOM,5.0,%/year,"Assume comparable tanker as for LOHC tran FT fuel transport ship,capacity,75000.0,t_FTfuel,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,investment,35000000.0,EUR,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,lifetime,15.0,years,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 -Fischer-Tropsch,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +Fischer-Tropsch,FOM,3.0,%/year,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.",,2017.0 Fischer-Tropsch,VOM,5.636,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",102 Hydrogen to Jet: Variable O&M,2020.0 Fischer-Tropsch,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -Fischer-Tropsch,carbondioxide-input,0.32,t_CO2/MWh_FT,ICCT IRA e-fuels assumptions ,"Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", -Fischer-Tropsch,efficiency,0.7,per unit,ICCT IRA e-fuels assumptions ,, -Fischer-Tropsch,electricity-input,0.04,MWh_el/MWh_FT,ICCT IRA e-fuels assumptions ,"0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,hydrogen-input,1.43,MWh_H2/MWh_FT,ICCT IRA e-fuels assumptions ,"0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,investment,1509724.4026,USD/MW_FT,ICCT IRA e-fuels assumptions ,"Well developed technology, no significant learning expected.",2022.0 -Fischer-Tropsch,lifetime,20.0,years,ICCT IRA e-fuels assumptions ,,2020.0 +Fischer-Tropsch,carbondioxide-input,0.36,t_CO2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", +Fischer-Tropsch,efficiency,0.799,per unit,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.2.",,2017.0 +Fischer-Tropsch,electricity-input,0.008,MWh_el/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,hydrogen-input,1.531,MWh_H2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,investment,819108.478,EUR/MW_FT,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected.",2017.0 +Fischer-Tropsch,lifetime,20.0,years,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.",,2017.0 Gasnetz,FOM,2.5,%,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,investment,28.0,EUR/kWGas,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,lifetime,30.0,years,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 @@ -265,7 +237,7 @@ General liquid hydrocarbon storage (crude),FOM,6.25,%/year,"Stelter and Nishida General liquid hydrocarbon storage (crude),investment,137.8999,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed 20% lower than for product storage. Crude or middle distillate tanks are usually larger compared to product storage due to lower requirements on safety and different construction method. Reference size used here: 80 000 – 120 000 m^3 .,2012.0 General liquid hydrocarbon storage (crude),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 General liquid hydrocarbon storage (product),FOM,6.25,%/year,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , figure 7 and pg. 12 .",Assuming ca. 10 EUR/m^3/a (center value between stand alone and addon facility).,2012.0 -General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 - 60 000 m^3 .,2012.0 +General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 – 60 000 m^3 .,2012.0 General liquid hydrocarbon storage (product),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 Gravity-Brick-bicharger,FOM,1.5,%/year,"Viswanathan_2022, p.76 (p.98) Sentence 1 in 4.7.2 Operating Costs","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['1.5 percent of capital cost']}",2020.0 Gravity-Brick-bicharger,efficiency,0.9274,per unit,"Viswanathan_2022, p.77 (p.99) Table 4.36","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.86^0.5']}",2020.0 @@ -341,15 +313,13 @@ HVDC underground,investment,1008.2934,EUR/MW/km,Härtel et al. (2017): https://d HVDC underground,lifetime,40.0,years,Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .,"Based on estimated costs for a NA-EU connector (bidirectional,4 GW, 3000km length and ca. 3000m depth). Costs in return based on existing/currently under construction undersea cables. (same as for HVDC submarine)",2018.0 Haber-Bosch,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 Haber-Bosch,VOM,0.0225,EUR/MWh_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Variable O&M,2015.0 +Haber-Bosch,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +Haber-Bosch,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 Haber-Bosch,electricity-input,0.2473,MWh_el/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), table 11.",Assume 5 GJ/t_NH3 for compressors and NH3 LHV = 5.16666 MWh/t_NH3., Haber-Bosch,hydrogen-input,1.1484,MWh_H2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.","178 kg_H2 per t_NH3, LHV for both assumed.", Haber-Bosch,investment,1785.0713,EUR/kW_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 Haber-Bosch,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 Haber-Bosch,nitrogen-input,0.1597,t_N2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.",".33 MWh electricity are required for ASU per t_NH3, considering 0.4 MWh are required per t_N2 and LHV of NH3 of 5.1666 Mwh.", -Heavy oil partial oxidation,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,efficiency,0.734,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Heavy oil partial oxidation,investment,491.0535,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, HighT-Molten-Salt-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 HighT-Molten-Salt-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 HighT-Molten-Salt-charger,investment,187899.5061,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -466,18 +436,6 @@ Methanol steam reforming,FOM,4.0,%/year,"Niermann et al. (2021): Liquid Organic Methanol steam reforming,investment,18016.8665,EUR/MW_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.","For high temperature steam reforming plant with a capacity of 200 MW_H2 output (6t/h). Reference plant of 1 MW (30kg_H2/h) costs 150kEUR, scale factor of 0.6 assumed.",2020.0 Methanol steam reforming,lifetime,20.0,years,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",,2020.0 Methanol steam reforming,methanol-input,1.201,MWh_MeOH/MWh_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",Assuming per 1 t_H2 (with LHV 33.3333 MWh/t): 4.5 MWh_th and 3.2 MWh_el are required. We assume electricity can be substituted / provided with 1:1 as heat energy., -NG 2-on-1 Combined Cycle (F-Frame),efficiency,0.573,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame),lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,efficiency,0.527,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,capture_rate,0.97,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,efficiency,0.525,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, NH3 (l) storage tank incl. liquefaction,FOM,2.0,%/year,"Guesstimate, based on H2 (l) storage tank.",,2010.0 NH3 (l) storage tank incl. liquefaction,investment,166.8201,EUR/MWh_NH3,"Calculated based on Morgan E. 2013: doi:10.7275/11KT-3F59 , Fig. 55, Fig 58.","Based on estimated for a double-wall liquid ammonia tank (~ambient pressure, -33°C), inner tank from stainless steel, outer tank from concrete including installations for liquefaction/condensation, boil-off gas recovery and safety installations; the necessary installations make only a small fraction of the total cost. The total cost are driven by material and working time on the tanks. While the costs do not scale strictly linearly, we here assume they do (good approximation c.f. ref. Fig 55.) and take the costs for a 9 kt NH3 (l) tank = 8 M$2010, which is smaller 4-5x smaller than the largest deployed tanks today. @@ -488,14 +446,6 @@ NH3 (l) transport ship,FOM,4.0,%/year,"Cihlar et al 2020 based on IEA 2019, Tabl NH3 (l) transport ship,capacity,53000.0,t_NH3,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,investment,81164200.0,EUR,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,lifetime,20.0,years,"Guess estimated based on H2 (l) tanker, but more mature technology",,2019.0 -Natural gas steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,efficiency,0.75,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming,investment,212.5817,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,efficiency,0.637,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,investment,323.8999,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Ni-Zn-bicharger,FOM,2.0701,%/year,"Viswanathan_2022, p.51-52 in section 4.4.2","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Guesstimate 30% assumed of power components every 10 years ']}",2020.0 Ni-Zn-bicharger,efficiency,0.9,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['((0.75-0.87)/2)^0.5 mean value of range efficiency is not RTE but single way AC-store conversion']}",2020.0 Ni-Zn-bicharger,investment,95584.1917,EUR/MW,"Viswanathan_2022, p.59 (p.81) same as Li-LFP","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Power Equipment']}",2020.0 @@ -508,10 +458,6 @@ OCGT,VOM,4.762,EUR/MWh,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx OCGT,efficiency,0.4,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","52 OCGT - Natural gas: Electricity efficiency, annual average",2015.0 OCGT,investment,480.3903,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Specific investment,2015.0 OCGT,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Technical lifetime,2015.0 -PEM electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,efficiency,0.63,p.u.,ICCT IRA e-fuels assumptions ,, -PEM electrolyzer,investment,1220.1113,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, PHS,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 @@ -534,19 +480,15 @@ Pumped-Storage-Hydro-bicharger,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90 Pumped-Storage-Hydro-store,FOM,0.43,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['derived']}",2020.0 Pumped-Storage-Hydro-store,investment,57074.0625,EUR/MWh,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['Reservoir Construction & Infrastructure']}",2020.0 Pumped-Storage-Hydro-store,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 -SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 +SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", SMR,efficiency,0.76,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR,investment,522201.0492,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 -SMR CC,capture_rate,0.9,per unit,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates between 54%-90%, +SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", +SMR CC,capture_rate,0.9,EUR/MW_CH4,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates betwen 54%-90%, SMR CC,efficiency,0.69,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR CC,investment,605753.2171,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR CC,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SOEC,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,efficiency,0.82,p.u.,ICCT IRA e-fuels assumptions ,, -SOEC,investment,1389.2004,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Sand-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 Sand-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 Sand-charger,investment,152624.5646,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -558,10 +500,6 @@ Sand-discharger,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier Sand-store,FOM,0.3308,%/year,"Viswanathan_2022, p 104 (p.126)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['not provided calculated as for hydrogen']}",2020.0 Sand-store,investment,8014.7441,EUR/MWh,"Viswanathan_2022, p.100 (p.122)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['SB and BOS 0.85 of 2021 value']}",2020.0 Sand-store,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['NULL']}",2020.0 -Solid biomass steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,efficiency,0.712,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Solid biomass steam reforming,investment,590.7702,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Steam methane reforming,FOM,3.0,%/year,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 Steam methane reforming,investment,497454.611,EUR/MW_H2,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW). Currency conversion 1.17 USD = 1 EUR.,2015.0 Steam methane reforming,lifetime,30.0,years,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 @@ -604,6 +542,8 @@ Zn-Br-Nonflow-store,FOM,0.2481,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrie Zn-Br-Nonflow-store,investment,276873.6097,EUR/MWh,"Viswanathan_2022, p.59 (p.81) Table 4.14","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['DC storage block']}",2020.0 Zn-Br-Nonflow-store,lifetime,15.0,years,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['NULL']}",2020.0 air separation unit,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 +air separation unit,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +air separation unit,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 air separation unit,electricity-input,0.25,MWh_el/t_N2,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), p.288.","For consistency reasons use value from Danish Energy Agency. DEA also reports range of values (0.2-0.4 MWh/t_N2) on pg. 288. Other efficienices reported are even higher, e.g. 0.11 Mwh/t_N2 from Morgan (2013): Techno-Economic Feasibility Study of Ammonia Plants Powered by Offshore Wind .", air separation unit,investment,1003392.2397,EUR/t_N2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 air separation unit,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 @@ -618,12 +558,13 @@ battery inverter,lifetime,10.0,years,"Danish Energy Agency, technology_data_cata battery storage,investment,245.5074,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Energy storage expansion cost investment,2015.0 battery storage,lifetime,20.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Technical lifetime,2015.0 biochar pyrolysis,FOM,3.4615,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Fixed O&M",2020.0 -biochar pyrolysis,VOM,823.497,EUR/MWh_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 -biochar pyrolysis,efficiency-biochar,0.404,MWh_biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency biochar",2020.0 -biochar pyrolysis,efficiency-heat,0.4848,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency heat",2020.0 -biochar pyrolysis,investment,167272.82,EUR/kW_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 +biochar pyrolysis,VOM,47.6777,EUR/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 +biochar pyrolysis,biomass input,7.6748,MWh_biomass/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Biomass Input",2020.0 +biochar pyrolysis,electricity input,0.3184,MWh_e/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: El-Input",2020.0 +biochar pyrolysis,heat output,3.7859,MWh_th/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: H-Output",2020.0 +biochar pyrolysis,investment,9684528.9742,EUR/t_CO2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 biochar pyrolysis,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Technical lifetime",2020.0 -biochar pyrolysis,yield-biochar,0.0582,ton biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 +biochar pyrolysis,yield-biochar,0.0597,t_biochar/MWh_biomass,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 biodiesel crops,fuel,96.2077,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIORPS1 (rape seed), ENS_BaU_GFTM",,2010.0 bioethanol crops,fuel,62.1519,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOCRP11 (Bioethanol barley, wheat, grain maize, oats, other cereals and rye), ENS_BaU_GFTM",,2010.0 biogas,CO2 stored,0.0868,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, @@ -640,10 +581,17 @@ biogas CC,efficiency,1.0,per unit,Assuming input biomass is already given in bio biogas CC,investment,1032.4577,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Specific investment",2020.0 biogas CC,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Technical lifetime",2020.0 biogas manure,fuel,19.7575,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOGAS1 (manure), ENS_BaU_GFTM",,2010.0 -biogas plus hydrogen,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Fixed O&M,2020.0 -biogas plus hydrogen,VOM,4.5939,EUR/MWh_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 -biogas plus hydrogen,investment,964.7165,EUR/kW_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 +biogas plus hydrogen,Biogas Input,1.1522,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Biogas Consumption,",2020.0 +biogas plus hydrogen,CO2 Input,0.1235,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: CO2 Input,",2020.0 +biogas plus hydrogen,Methane Output,1.9348,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Methane Output,",2020.0 +biogas plus hydrogen,VOM,8.8882,EUR/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 +biogas plus hydrogen,electricity input,0.0217,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: El-Input,",2020.0 +biogas plus hydrogen,heat output,0.2174,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: H-Output,",2020.0 +biogas plus hydrogen,hydrogen input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Hydrogen Consumption,",2020.0 +biogas plus hydrogen,investment,1866.5167,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 biogas plus hydrogen,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Technical lifetime,2020.0 +biogas storage, investment,14.45, EUR/kWh,AU Foulum,,2020.0 +biogas storage, lifetime,25.0,years,AU Foulum,,2020.0 biogas upgrading,FOM,17.0397,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Fixed O&M ",2020.0 biogas upgrading,VOM,4.1613,EUR/MWh output,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Variable O&M",2020.0 biogas upgrading,investment,192.9697,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: investment (upgrading, methane redution and grid injection)",2020.0 @@ -688,9 +636,9 @@ biomass boiler,efficiency,0.82,per unit,"Danish Energy Agency, technologydatafor biomass boiler,investment,722.4205,EUR/kW_th,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Specific investment",2015.0 biomass boiler,lifetime,20.0,years,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Technical lifetime",2015.0 biomass boiler,pelletizing cost,9.0,EUR/MWh_pellets,Assumption based on doi:10.1016/j.rser.2019.109506,,2019.0 -biomass-to-methanol,C in fuel,0.3926,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,C stored,0.6074,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,CO2 stored,0.2227,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C in fuel,0.3832,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C stored,0.6168,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,CO2 stored,0.2317,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, biomass-to-methanol,FOM,1.1111,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Fixed O&M,2020.0 biomass-to-methanol,VOM,21.6979,EUR/MWh_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Variable O&M,2020.0 biomass-to-methanol,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -699,6 +647,15 @@ biomass-to-methanol,efficiency-electricity,0.02,MWh_e/MWh_th,"Danish Energy Agen biomass-to-methanol,efficiency-heat,0.22,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","97 Methanol from biomass gasif.: District heat Output,",2020.0 biomass-to-methanol,investment,5591.3924,EUR/kW_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Specific investment,2020.0 biomass-to-methanol,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Technical lifetime,2020.0 +biomethanation,Biogas Input,1.1444,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Biogas Consumption,",2020.0 +biomethanation,CO2 Input,0.165,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: CO2 Input,",2020.0 +biomethanation,FOM,0.8333,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Fixed O&M ,2020.0 +biomethanation,Hydrogen Input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Hydrogen Input,",2020.0 +biomethanation,Methane Output,1.9673,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Methane Output,",2020.0 +biomethanation,electricity input,0.0417,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: El-Input,",2020.0 +biomethanation,heat output,0.1667,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: H-Output,",2020.0 +biomethanation,investment,7900.0,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Specific investment ,2020.0 +biomethanation,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Technical lifetime,2020.0 cement capture,FOM,3.0,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,capture_rate,0.9,per unit,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,compression-electricity-input,0.1,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 @@ -785,7 +742,7 @@ central solid biomass CHP CC,c_b,0.3489,50°C/100°C,"Danish Energy Agency, tech central solid biomass CHP CC,c_v,1.0,50°C/100°C,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Cv coefficient",2015.0 central solid biomass CHP CC,efficiency,0.2689,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Electricity efficiency, net, annual average",2015.0 central solid biomass CHP CC,efficiency-heat,0.8255,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Heat efficiency, net, annual average",2015.0 -central solid biomass CHP CC,investment,5767.0987,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 +central solid biomass CHP CC,investment,5816.7677,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 central solid biomass CHP CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Technical lifetime",2015.0 central solid biomass CHP powerboost CC,FOM,2.8857,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Fixed O&M",2015.0 central solid biomass CHP powerboost CC,VOM,4.8694,EUR/MWh_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Variable O&M ",2015.0 @@ -812,23 +769,23 @@ central water-sourced heat pump,VOM,1.8942,EUR/MWh,"Danish Energy Agency, techno central water-sourced heat pump,efficiency,3.78,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Total efficiency , net, annual average",2015.0 central water-sourced heat pump,investment,1058.2216,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Specific investment",2015.0 central water-sourced heat pump,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Technical lifetime",2015.0 -clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 +clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, clean water tank storage,investment,69.1286,EUR/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 clean water tank storage,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, coal,CO2 intensity,0.3361,tCO2/MWh_th,Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018,, coal,FOM,1.31,%/year,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (39.5+91.25) USD/kW_e/a /2 / (1.09 USD/EUR) / investment cost * 100.",2023.0 coal,VOM,3.2612,EUR/MWh_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (3+5.5)USD/MWh_e/2 / (1.09 USD/EUR).",2023.0 -coal,efficiency,0.356,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 +coal,efficiency,0.33,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 coal,fuel,9.5542,EUR/MWh_th,"DIW (2013): Current and propsective costs of electricity generation until 2050, http://hdl.handle.net/10419/80348 , pg. 80 text below figure 10, accessed: 2023-12-14.","Based on IEA 2011 data, 99 USD/t.",2010.0 coal,investment,3827.1629,EUR/kW_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Higher costs include coal plants with CCS, but since using here for calculating the average nevertheless. Calculated based on average of listed range, i.e. (3200+6775) USD/kW_e/2 / (1.09 USD/EUR).",2023.0 coal,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 -csp-tower,FOM,1.0,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,2020.0 +csp-tower,FOM,1.0,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,1.0 csp-tower,investment,159.96,"EUR/kW_th,dp",ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower,lifetime,30.0,years,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),-,2020.0 -csp-tower TES,FOM,1.0,%/year,see solar-tower.,-,2020.0 +csp-tower TES,FOM,1.0,%/year,see solar-tower.,-,1.0 csp-tower TES,investment,21.43,EUR/kWh_th,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower TES,lifetime,30.0,years,see solar-tower.,-,2020.0 -csp-tower power block,FOM,1.0,%/year,see solar-tower.,-,2020.0 +csp-tower power block,FOM,1.0,%/year,see solar-tower.,-,1.0 csp-tower power block,investment,1120.57,EUR/kW_e,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower power block,lifetime,30.0,years,see solar-tower.,-,2020.0 decentral CHP,FOM,3.0,%/year,HP, from old pypsa cost assumptions,2015.0 @@ -874,19 +831,18 @@ decentral water tank storage,energy to power ratio,0.15,h,"Danish Energy Agency, decentral water tank storage,investment,433.8709,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Specific investment,2015.0 decentral water tank storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Technical lifetime,2015.0 digestible biomass,fuel,17.0611,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOAGRW1, ENS_Ref for 2040",,2010.0 -digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, digestible biomass to hydrogen,efficiency,0.39,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,investment,4237.1194,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 -direct air capture,FOM,1.3,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,FOM,4.95,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-electricity-input,0.15,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-heat-output,0.2,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,electricity-input,0.24,MWh_el/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 -direct air capture,heat-input,1.17,MWh_th/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 +direct air capture,electricity-input,0.4,MWh_el/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","0.4 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 0.182 MWh based on Breyer et al (2019). Should already include electricity for water scrubbing and compression (high quality CO2 output).",2020.0 +direct air capture,heat-input,1.6,MWh_th/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","Thermal energy demand. Provided via air-sourced heat pumps. 1.6 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 1.102 MWh based on Breyer et al (2019).",2020.0 direct air capture,heat-output,1.25,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,investment,11034260.0394,USD/t_CO2/h,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,investment,7000000.0,EUR/(tCO2/h),"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,lifetime,20.0,years,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,years,30.0,years,ICCT IRA e-fuels assumptions ,, direct firing gas,FOM,1.2121,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Fixed O&M,2019.0 direct firing gas,VOM,0.2845,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Variable O&M,2019.0 direct firing gas,efficiency,1.0,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","312.a Direct firing Natural Gas: Total efficiency, net, annual average",2019.0 @@ -927,8 +883,8 @@ electric boiler steam,VOM,0.8711,EUR/MWh,"Danish Energy Agency, technology_data_ electric boiler steam,efficiency,0.99,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","310.1 Electric boiler steam : Total efficiency, net, annual average",2019.0 electric boiler steam,investment,80.56,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Nominal investment,2019.0 electric boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Technical lifetime,2019.0 -electric steam cracker,FOM,3.0,%/year,Guesstimate,,2015.0 -electric steam cracker,VOM,190.4799,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 +electric steam cracker,FOM,3.0,%/year,Guesstimate,, +electric steam cracker,VOM,190.4799,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 electric steam cracker,carbondioxide-output,0.55,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), ",The report also references another source with 0.76 t_CO2/t_HVC, electric steam cracker,electricity-input,2.7,MWh_el/t_HVC,"Lechtenböhmer et al. (2016): 10.1016/j.energy.2016.07.110, Section 4.3, page 6.",Assuming electrified processing., electric steam cracker,investment,11124025.7434,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -940,14 +896,14 @@ electricity distribution grid,lifetime,40.0,years,TODO, from old pypsa cost assu electricity grid connection,FOM,2.0,%/year,TODO, from old pypsa cost assumptions,2015.0 electricity grid connection,investment,148.151,EUR/kW,DEA, from old pypsa cost assumptions,2015.0 electricity grid connection,lifetime,40.0,years,TODO, from old pypsa cost assumptions,2015.0 -electrobiofuels,C in fuel,0.9245,per unit,Stoichiometric calculation,, -electrobiofuels,FOM,2.4,%/year,combination of BtL and electrofuels,,2015.0 -electrobiofuels,VOM,4.4117,EUR/MWh_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,C in fuel,0.924,per unit,Stoichiometric calculation,, +electrobiofuels,FOM,2.4,%/year,combination of BtL and electrofuels,, +electrobiofuels,VOM,5.184,EUR/MWh_th,combination of BtL and electrofuels,,2017.0 electrobiofuels,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -electrobiofuels,efficiency-biomass,1.3183,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-hydrogen,1.0308,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-tot,0.5785,per unit,Stoichiometric calculation,, -electrobiofuels,investment,1028354.9161,EUR/kW_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,efficiency-biomass,1.3498,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-hydrogen,1.1675,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-tot,0.626,per unit,Stoichiometric calculation,, +electrobiofuels,investment,564215.8653,EUR/kW_th,combination of BtL and electrofuels,,2017.0 electrolysis,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Fixed O&M ,2020.0 electrolysis,efficiency,0.5773,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Hydrogen Output,2020.0 electrolysis,efficiency-heat,0.2762,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: - hereof recoverable for district heating,2020.0 @@ -971,7 +927,7 @@ gas boiler steam,VOM,1.1077,EUR/MWh,"Danish Energy Agency, technology_data_for_i gas boiler steam,efficiency,0.92,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1c Steam boiler Gas: Total efficiency, net, annual average",2019.0 gas boiler steam,investment,54.9273,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Nominal investment,2019.0 gas boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Technical lifetime,2019.0 -gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenance, salt cavern (units converted)",2015.0 +gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenace, salt cavern (units converted)",2015.0 gas storage,investment,0.0348,EUR/kWh,Danish Energy Agency,"150 Underground Storage of Gas, Establishment of one cavern (units converted)",2015.0 gas storage,lifetime,100.0,years,TODO no source,"estimation: most underground storage are already build, they do have a long lifetime",2015.0 gas storage charger,investment,15.1737,EUR/kW,Danish Energy Agency,"150 Underground Storage of Gas, Process equipment (units converted)",2015.0 @@ -995,14 +951,14 @@ hydro,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pyp hydro,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 hydro,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 hydro,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 -hydrogen storage compressor,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg., -hydrogen storage compressor,investment,2.0291,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage compressor,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage tank type 1,investment,15.0133,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage tank type 1,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-, +hydrogen storage compressor,FOM,4.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg.,2020.0 +hydrogen storage compressor,investment,87.69,EUR/kW_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.","2923 EUR/kg_H2. For a 206 kg/h compressor. Base CAPEX 40 528 EUR/kW_el with scale factor 0.4603. kg_H2 converted to MWh using LHV. Pressure range: 30 bar in, 250 bar out.",2020.0 +hydrogen storage compressor,lifetime,15.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage tank type 1,FOM,2.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,investment,13.5,EUR/kWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.","450 EUR/kg_H2 converted with LHV to MWh. For a type 1 hydrogen storage tank (steel, 15-250 bar). Currency year assumed 2020 for initial publication of reference; observe note in SI.4.3 that no currency year is explicitly stated in the reference.",2020.0 +hydrogen storage tank type 1,lifetime,20.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 hydrogen storage tank type 1 including compressor,FOM,1.0526,%/year,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Fixed O&M,2015.0 hydrogen storage tank type 1 including compressor,investment,60.3186,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Specific investment,2015.0 hydrogen storage tank type 1 including compressor,lifetime,25.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Technical lifetime,2015.0 @@ -1049,8 +1005,8 @@ methanol-to-kerosene,hydrogen-input,0.0279,MWh_H2/MWh_kerosene,"Concawe (2022): methanol-to-kerosene,investment,307000.0,EUR/MW_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",,2020.0 methanol-to-kerosene,lifetime,30.0,years,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",, methanol-to-kerosene,methanol-input,1.0764,MWh_MeOH/MWh_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 6.","Assuming LHV 11.94 kWh/kg for kerosene, 5.54 kWh/kg for methanol, 33.3 kWh/kg for hydrogen.", -methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker,2015.0 -methanol-to-olefins/aromatics,VOM,31.7466,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 +methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker, +methanol-to-olefins/aromatics,VOM,31.7466,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 methanol-to-olefins/aromatics,carbondioxide-output,0.6107,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Sections 4.5 (for ethylene and propylene) and 4.6 (for BTX)","Weighted average: 0.4 t_MeOH/t_ethylene+propylene for 21.7 Mt of ethylene and 17 Mt of propylene, 1.13 t_CO2/t_BTX for 15.7 Mt of BTX. The report also references process emissions of 0.55 t_MeOH/t_ethylene+propylene elsewhere. ", methanol-to-olefins/aromatics,electricity-input,1.3889,MWh_el/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), page 69",5 GJ/t_HVC , methanol-to-olefins/aromatics,investment,2781006.4359,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -1077,7 +1033,7 @@ nuclear,investment,8594.1354,EUR/kW_e,"Lazard's levelized cost of energy analysi nuclear,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 offwind,FOM,2.5093,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Fixed O&M [EUR/MW_e/y, 2020]",2020.0 offwind,VOM,0.0212,EUR/MWhel,RES costs made up to fix curtailment order, from old pypsa cost assumptions,2015.0 -offwind,investment,1992.6105,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs subtracted from investment costs",2020.0 +offwind,investment,1992.6105,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs substracted from investment costs",2020.0 offwind,lifetime,27.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",21 Offshore turbines: Technical lifetime [years],2020.0 offwind-ac-connection-submarine,investment,2841.3251,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 offwind-ac-connection-underground,investment,1420.1334,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 @@ -1106,12 +1062,18 @@ organic rankine cycle,FOM,2.0,%/year,"Aghahosseini, Breyer 2020: From hot rock t organic rankine cycle,electricity-input,0.12,MWh_el/MWh_th,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551; Breede et al. 2015: Overcoming challenges in the classification of deep geothermal potential, https://eprints.gla.ac.uk/169585/","Heat-input, Electricity-output. This is a rough estimate, depends on input temperature, implies ~150 C.",2020.0 organic rankine cycle,investment,1376.0,EUR/kW_el,Tartiere and Astolfi 2017: A world overview of the organic Rankine cycle market,"Low rollout complicates the estimation, compounded by a dependence both on plant size and temperature, converted from 1500 USD/kW using currency conversion 1.09 USD = 1 EUR.",2020.0 organic rankine cycle,lifetime,30.0,years,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551",,2020.0 +perennials gbr,FOM,0.0,%year,Own assumption,,2015.0 +perennials gbr,VOM,43.2317,EUR/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,"includes purchase of perennial crops and sales of proteine concentrate, table 8.1 wages, maintenance and auxiliary costs",2015.0 +perennials gbr,biogas-output,0.1947,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,electricity-input,0.0733,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,investment,1371168.1394,EUR/tDM/h,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,includes cost for biogas plant without upgrading,2015.0 +perennials gbr,lifetime,25.0,years,Own assumption,,2015.0 ror,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,investment,3412.2266,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 ror,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 seawater RO desalination,electricity-input,0.003,MWHh_el/t_H2O,"Caldera et al. (2016): Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",Desalination using SWRO. Assume medium salinity of 35 Practical Salinity Units (PSUs) = 35 kg/m^3., -seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2015.0 +seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, seawater desalination,electricity-input,3.0348,kWh/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",, seawater desalination,investment,42561.4413,EUR/(m^3-H2O/h),"Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402), Table 4.",,2015.0 seawater desalination,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, @@ -1137,7 +1099,7 @@ solar-utility,lifetime,35.0,years,"Danish Energy Agency, technology_data_for_el_ solar-utility single-axis tracking,FOM,1.8605,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Fixed O&M [2020-EUR/MW_e/y],2020.0 solar-utility single-axis tracking,investment,650.3522,EUR/kW_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Nominal investment [2020-MEUR/MW_e],2020.0 solar-utility single-axis tracking,lifetime,35.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Technical lifetime [years],2020.0 -solid biomass,CO2 intensity,0.3667,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, +solid biomass,CO2 intensity,0.3757,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, solid biomass,fuel,13.6489,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOWOOW1 (secondary forest residue wood chips), ENS_Ref for 2040",,2010.0 solid biomass boiler steam,FOM,5.4515,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Fixed O&M,2019.0 solid biomass boiler steam,VOM,2.7985,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Variable O&M,2019.0 @@ -1149,7 +1111,7 @@ solid biomass boiler steam CC,VOM,2.7985,EUR/MWh,"Danish Energy Agency, technolo solid biomass boiler steam CC,efficiency,0.89,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1e Steam boiler Wood: Total efficiency, net, annual average",2019.0 solid biomass boiler steam CC,investment,622.5091,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Nominal investment,2019.0 solid biomass boiler steam CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Technical lifetime,2019.0 -solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, solid biomass to hydrogen,efficiency,0.56,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,investment,4237.1194,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 diff --git a/outputs/costs_2025.csv b/outputs/costs_2025.csv index afd40275..6540c7fc 100644 --- a/outputs/costs_2025.csv +++ b/outputs/costs_2025.csv @@ -1,8 +1,4 @@ technology,parameter,value,unit,source,further description,currency_year -Alkaline electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,efficiency,0.67,p.u.,ICCT IRA e-fuels assumptions ,, -Alkaline electrolyzer,investment,926.4302,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Ammonia cracker,FOM,4.3,%/year,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 7.","Estimated based on Labour cost rate, Maintenance cost rate, Insurance rate, Admin. cost rate and Chemical & other consumables cost rate.",2015.0 Ammonia cracker,ammonia-input,1.46,MWh_NH3/MWh_H2,"ENGIE et al (2020): Ammonia to Green Hydrogen Feasibility Study (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/880826/HS420_-_Ecuity_-_Ammonia_to_Green_Hydrogen.pdf), Fig. 10.",Assuming a integrated 200t/d cracking and purification facility. Electricity demand (316 MWh per 2186 MWh_LHV H2 output) is assumed to also be ammonia LHV input which seems a fair assumption as the facility has options for a higher degree of integration according to the report)., Ammonia cracker,investment,1123945.3807,EUR/MW_H2,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 6.","Calculated. For a small (200 t_NH3/d input) facility. Base cost for facility: 51 MEUR at capacity 20 000m^3_NH3/h = 339 t_NH3/d input. Cost scaling exponent 0.67. Ammonia density 0.7069 kg/m^3. Conversion efficiency of cracker: 0.685. Ammonia LHV: 5.167 MWh/t_NH3.; and @@ -45,25 +41,18 @@ Battery electric (passenger cars),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL Battery electric (trucks),FOM,14.0,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),investment,165765.0,EUR/LKW,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 -BioSNG,C in fuel,0.3321,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,C stored,0.6679,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,CO2 stored,0.2449,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C in fuel,0.3242,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C stored,0.6758,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,CO2 stored,0.2539,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BioSNG,FOM,1.6195,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Fixed O&M",2020.0 BioSNG,VOM,2.3395,EUR/MWh_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Variable O&M",2020.0 BioSNG,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, BioSNG,efficiency,0.615,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Bio SNG Output",2020.0 BioSNG,investment,2179.97,EUR/kW_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Specific investment",2020.0 BioSNG,lifetime,25.0,years,TODO,"84 Gasif. CFB, Bio-SNG: Technical lifetime",2020.0 -Biomass gasification,efficiency,0.3792,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification,investment,1467.7693,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,FOM,0.02,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,efficiency,0.328,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,investment,3015.5325,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -BtL,C in fuel,0.2571,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,C stored,0.7429,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,CO2 stored,0.2724,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C in fuel,0.251,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C stored,0.749,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,CO2 stored,0.2814,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BtL,FOM,2.5263,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Fixed O&M",2020.0 BtL,VOM,1.1299,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Variable O&M",2020.0 BtL,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -109,11 +98,17 @@ CO2 liquefaction,lifetime,25.0,years,"Guesstimate, based on CH4 liquefaction.",, CO2 pipeline,FOM,0.9,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 pipeline,investment,2116.4433,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch onshore pipeline.,2015.0 CO2 pipeline,lifetime,50.0,years,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 +CO2 storage cylinders, FOM,1.0,%/year,AU Foulum,,2020.0 +CO2 storage cylinders, investment,77000.0, EUR/t_CO2,AU Foulum,,2020.0 +CO2 storage cylinders, lifetime,25.0,years,AU Foulum,,2020.0 CO2 storage tank,FOM,1.0,%/year,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,investment,2584.3462,EUR/t_CO2,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, Table 3.","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,lifetime,25.0,years,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 submarine pipeline,FOM,0.5,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 submarine pipeline,investment,4232.8865,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch offshore pipeline.,2015.0 +CO2_industrial_compressor, FOM,4.0,%/year,AU Foulum,,2020.0 +CO2_industrial_compressor, investment,1516000.0, EUR/t/h_CO2,AU Foulum,,2020.0 +CO2_industrial_compressor, lifetime,25.0,years,AU Foulum,,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,FOM,1.6,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,investment,527507.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 @@ -126,29 +121,6 @@ Charging infrastructure fuel cell vehicles trucks,lifetime,30.0,years,PATHS TO A Charging infrastructure slow (purely) battery electric vehicles passenger cars,FOM,1.8,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,investment,1126.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 -Coal gasification,FOM,0.06,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,efficiency,0.5978,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification,investment,441.5535,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,FOM,0.07,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,efficiency,0.532,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,investment,649.5969,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 90%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal-95%-CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -Coal-95%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-95%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,capture_rate,0.99,per unit,"NREL, NREL ATB 2024",, -Coal-99%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,efficiency,0.5,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,capture_rate,0.9,per unit,"NREL, NREL ATB 2024",, -Coal-IGCC-90%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Compressed-Air-Adiabatic-bicharger,FOM,0.9265,%/year,"Viswanathan_2022, p.64 (p.86) Figure 4.14","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 Compressed-Air-Adiabatic-bicharger,efficiency,0.7211,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.52^0.5']}",2020.0 Compressed-Air-Adiabatic-bicharger,investment,946180.9426,EUR/MW,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['Turbine Compressor BOP EPC Management']}",2020.0 @@ -249,15 +221,15 @@ FT fuel transport ship,FOM,5.0,%/year,"Assume comparable tanker as for LOHC tran FT fuel transport ship,capacity,75000.0,t_FTfuel,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,investment,35000000.0,EUR,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,lifetime,15.0,years,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 -Fischer-Tropsch,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +Fischer-Tropsch,FOM,3.0,%/year,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.",,2017.0 Fischer-Tropsch,VOM,5.0512,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",102 Hydrogen to Jet: Variable O&M,2020.0 Fischer-Tropsch,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -Fischer-Tropsch,carbondioxide-input,0.32,t_CO2/MWh_FT,ICCT IRA e-fuels assumptions ,"Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", -Fischer-Tropsch,efficiency,0.7,per unit,ICCT IRA e-fuels assumptions ,, -Fischer-Tropsch,electricity-input,0.04,MWh_el/MWh_FT,ICCT IRA e-fuels assumptions ,"0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,hydrogen-input,1.43,MWh_H2/MWh_FT,ICCT IRA e-fuels assumptions ,"0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,investment,1509724.4026,USD/MW_FT,ICCT IRA e-fuels assumptions ,"Well developed technology, no significant learning expected.",2022.0 -Fischer-Tropsch,lifetime,25.0,years,ICCT IRA e-fuels assumptions ,,2020.0 +Fischer-Tropsch,carbondioxide-input,0.343,t_CO2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", +Fischer-Tropsch,efficiency,0.799,per unit,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.2.",,2017.0 +Fischer-Tropsch,electricity-input,0.0075,MWh_el/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,hydrogen-input,1.476,MWh_H2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,investment,761417.4621,EUR/MW_FT,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected.",2017.0 +Fischer-Tropsch,lifetime,20.0,years,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.",,2017.0 Gasnetz,FOM,2.5,%,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,investment,28.0,EUR/kWGas,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,lifetime,30.0,years,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 @@ -265,7 +237,7 @@ General liquid hydrocarbon storage (crude),FOM,6.25,%/year,"Stelter and Nishida General liquid hydrocarbon storage (crude),investment,137.8999,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed 20% lower than for product storage. Crude or middle distillate tanks are usually larger compared to product storage due to lower requirements on safety and different construction method. Reference size used here: 80 000 – 120 000 m^3 .,2012.0 General liquid hydrocarbon storage (crude),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 General liquid hydrocarbon storage (product),FOM,6.25,%/year,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , figure 7 and pg. 12 .",Assuming ca. 10 EUR/m^3/a (center value between stand alone and addon facility).,2012.0 -General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 - 60 000 m^3 .,2012.0 +General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 – 60 000 m^3 .,2012.0 General liquid hydrocarbon storage (product),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 Gravity-Brick-bicharger,FOM,1.5,%/year,"Viswanathan_2022, p.76 (p.98) Sentence 1 in 4.7.2 Operating Costs","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['1.5 percent of capital cost']}",2020.0 Gravity-Brick-bicharger,efficiency,0.9274,per unit,"Viswanathan_2022, p.77 (p.99) Table 4.36","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.86^0.5']}",2020.0 @@ -341,15 +313,13 @@ HVDC underground,investment,1008.2934,EUR/MW/km,Härtel et al. (2017): https://d HVDC underground,lifetime,40.0,years,Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .,"Based on estimated costs for a NA-EU connector (bidirectional,4 GW, 3000km length and ca. 3000m depth). Costs in return based on existing/currently under construction undersea cables. (same as for HVDC submarine)",2018.0 Haber-Bosch,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 Haber-Bosch,VOM,0.0225,EUR/MWh_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Variable O&M,2015.0 +Haber-Bosch,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +Haber-Bosch,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 Haber-Bosch,electricity-input,0.2473,MWh_el/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), table 11.",Assume 5 GJ/t_NH3 for compressors and NH3 LHV = 5.16666 MWh/t_NH3., Haber-Bosch,hydrogen-input,1.1484,MWh_H2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.","178 kg_H2 per t_NH3, LHV for both assumed.", Haber-Bosch,investment,1622.5424,EUR/kW_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 Haber-Bosch,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 Haber-Bosch,nitrogen-input,0.1597,t_N2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.",".33 MWh electricity are required for ASU per t_NH3, considering 0.4 MWh are required per t_N2 and LHV of NH3 of 5.1666 Mwh.", -Heavy oil partial oxidation,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,efficiency,0.734,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Heavy oil partial oxidation,investment,491.0535,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, HighT-Molten-Salt-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 HighT-Molten-Salt-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 HighT-Molten-Salt-charger,investment,166045.8871,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -466,18 +436,6 @@ Methanol steam reforming,FOM,4.0,%/year,"Niermann et al. (2021): Liquid Organic Methanol steam reforming,investment,18016.8665,EUR/MW_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.","For high temperature steam reforming plant with a capacity of 200 MW_H2 output (6t/h). Reference plant of 1 MW (30kg_H2/h) costs 150kEUR, scale factor of 0.6 assumed.",2020.0 Methanol steam reforming,lifetime,20.0,years,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",,2020.0 Methanol steam reforming,methanol-input,1.201,MWh_MeOH/MWh_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",Assuming per 1 t_H2 (with LHV 33.3333 MWh/t): 4.5 MWh_th and 3.2 MWh_el are required. We assume electricity can be substituted / provided with 1:1 as heat energy., -NG 2-on-1 Combined Cycle (F-Frame),efficiency,0.573,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame),lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,efficiency,0.527,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,capture_rate,0.97,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,efficiency,0.525,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, NH3 (l) storage tank incl. liquefaction,FOM,2.0,%/year,"Guesstimate, based on H2 (l) storage tank.",,2010.0 NH3 (l) storage tank incl. liquefaction,investment,166.8201,EUR/MWh_NH3,"Calculated based on Morgan E. 2013: doi:10.7275/11KT-3F59 , Fig. 55, Fig 58.","Based on estimated for a double-wall liquid ammonia tank (~ambient pressure, -33°C), inner tank from stainless steel, outer tank from concrete including installations for liquefaction/condensation, boil-off gas recovery and safety installations; the necessary installations make only a small fraction of the total cost. The total cost are driven by material and working time on the tanks. While the costs do not scale strictly linearly, we here assume they do (good approximation c.f. ref. Fig 55.) and take the costs for a 9 kt NH3 (l) tank = 8 M$2010, which is smaller 4-5x smaller than the largest deployed tanks today. @@ -488,14 +446,6 @@ NH3 (l) transport ship,FOM,4.0,%/year,"Cihlar et al 2020 based on IEA 2019, Tabl NH3 (l) transport ship,capacity,53000.0,t_NH3,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,investment,81164200.0,EUR,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,lifetime,20.0,years,"Guess estimated based on H2 (l) tanker, but more mature technology",,2019.0 -Natural gas steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,efficiency,0.7562,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming,investment,196.3225,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,efficiency,0.637,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,investment,323.8999,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Ni-Zn-bicharger,FOM,2.095,%/year,"Viswanathan_2022, p.51-52 in section 4.4.2","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Guesstimate 30% assumed of power components every 10 years ']}",2020.0 Ni-Zn-bicharger,efficiency,0.9,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['((0.75-0.87)/2)^0.5 mean value of range efficiency is not RTE but single way AC-store conversion']}",2020.0 Ni-Zn-bicharger,investment,88568.8382,EUR/MW,"Viswanathan_2022, p.59 (p.81) same as Li-LFP","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Power Equipment']}",2020.0 @@ -508,10 +458,6 @@ OCGT,VOM,4.762,EUR/MWh,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx OCGT,efficiency,0.405,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","52 OCGT - Natural gas: Electricity efficiency, annual average",2015.0 OCGT,investment,470.4853,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Specific investment,2015.0 OCGT,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Technical lifetime,2015.0 -PEM electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,efficiency,0.655,p.u.,ICCT IRA e-fuels assumptions ,, -PEM electrolyzer,investment,1108.4235,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, PHS,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 @@ -534,19 +480,15 @@ Pumped-Storage-Hydro-bicharger,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90 Pumped-Storage-Hydro-store,FOM,0.43,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['derived']}",2020.0 Pumped-Storage-Hydro-store,investment,57074.0625,EUR/MWh,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['Reservoir Construction & Infrastructure']}",2020.0 Pumped-Storage-Hydro-store,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 -SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 +SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", SMR,efficiency,0.76,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR,investment,522201.0492,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 -SMR CC,capture_rate,0.9,per unit,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates between 54%-90%, +SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", +SMR CC,capture_rate,0.9,EUR/MW_CH4,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates betwen 54%-90%, SMR CC,efficiency,0.69,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR CC,investment,605753.2171,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR CC,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SOEC,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,efficiency,0.83,p.u.,ICCT IRA e-fuels assumptions ,, -SOEC,investment,1262.3836,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Sand-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 Sand-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 Sand-charger,investment,148408.4164,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -558,10 +500,6 @@ Sand-discharger,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier Sand-store,FOM,0.3308,%/year,"Viswanathan_2022, p 104 (p.126)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['not provided calculated as for hydrogen']}",2020.0 Sand-store,investment,7357.7979,EUR/MWh,"Viswanathan_2022, p.100 (p.122)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['SB and BOS 0.85 of 2021 value']}",2020.0 Sand-store,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['NULL']}",2020.0 -Solid biomass steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,efficiency,0.712,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Solid biomass steam reforming,investment,590.7702,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Steam methane reforming,FOM,3.0,%/year,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 Steam methane reforming,investment,497454.611,EUR/MW_H2,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW). Currency conversion 1.17 USD = 1 EUR.,2015.0 Steam methane reforming,lifetime,30.0,years,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 @@ -604,6 +542,8 @@ Zn-Br-Nonflow-store,FOM,0.2362,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrie Zn-Br-Nonflow-store,investment,258047.096,EUR/MWh,"Viswanathan_2022, p.59 (p.81) Table 4.14","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['DC storage block']}",2020.0 Zn-Br-Nonflow-store,lifetime,15.0,years,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['NULL']}",2020.0 air separation unit,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 +air separation unit,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +air separation unit,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 air separation unit,electricity-input,0.25,MWh_el/t_N2,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), p.288.","For consistency reasons use value from Danish Energy Agency. DEA also reports range of values (0.2-0.4 MWh/t_N2) on pg. 288. Other efficienices reported are even higher, e.g. 0.11 Mwh/t_N2 from Morgan (2013): Techno-Economic Feasibility Study of Ammonia Plants Powered by Offshore Wind .", air separation unit,investment,912034.4091,EUR/t_N2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 air separation unit,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 @@ -618,12 +558,13 @@ battery inverter,lifetime,10.0,years,"Danish Energy Agency, technology_data_cata battery storage,investment,197.8874,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Energy storage expansion cost investment,2015.0 battery storage,lifetime,22.5,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Technical lifetime,2015.0 biochar pyrolysis,FOM,3.4615,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Fixed O&M",2020.0 -biochar pyrolysis,VOM,823.497,EUR/MWh_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 -biochar pyrolysis,efficiency-biochar,0.404,MWh_biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency biochar",2020.0 -biochar pyrolysis,efficiency-heat,0.4848,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency heat",2020.0 -biochar pyrolysis,investment,167272.82,EUR/kW_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 +biochar pyrolysis,VOM,47.6777,EUR/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 +biochar pyrolysis,biomass input,7.6748,MWh_biomass/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Biomass Input",2020.0 +biochar pyrolysis,electricity input,0.3184,MWh_e/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: El-Input",2020.0 +biochar pyrolysis,heat output,3.7859,MWh_th/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: H-Output",2020.0 +biochar pyrolysis,investment,9684528.9742,EUR/t_CO2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 biochar pyrolysis,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Technical lifetime",2020.0 -biochar pyrolysis,yield-biochar,0.0582,ton biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 +biochar pyrolysis,yield-biochar,0.0597,t_biochar/MWh_biomass,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 biodiesel crops,fuel,116.9293,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIORPS1 (rape seed), ENS_BaU_GFTM",,2010.0 bioethanol crops,fuel,72.2943,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOCRP11 (Bioethanol barley, wheat, grain maize, oats, other cereals and rye), ENS_BaU_GFTM",,2010.0 biogas,CO2 stored,0.0868,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, @@ -640,10 +581,17 @@ biogas CC,efficiency,1.0,per unit,Assuming input biomass is already given in bio biogas CC,investment,1097.9155,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Specific investment",2020.0 biogas CC,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Technical lifetime",2020.0 biogas manure,fuel,19.8126,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOGAS1 (manure), ENS_BaU_GFTM",,2010.0 -biogas plus hydrogen,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Fixed O&M,2020.0 -biogas plus hydrogen,VOM,4.2111,EUR/MWh_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 -biogas plus hydrogen,investment,884.3234,EUR/kW_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 +biogas plus hydrogen,Biogas Input,1.1522,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Biogas Consumption,",2020.0 +biogas plus hydrogen,CO2 Input,0.1235,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: CO2 Input,",2020.0 +biogas plus hydrogen,Methane Output,1.9348,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Methane Output,",2020.0 +biogas plus hydrogen,VOM,8.1475,EUR/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 +biogas plus hydrogen,electricity input,0.0217,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: El-Input,",2020.0 +biogas plus hydrogen,heat output,0.2174,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: H-Output,",2020.0 +biogas plus hydrogen,hydrogen input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Hydrogen Consumption,",2020.0 +biogas plus hydrogen,investment,1710.9736,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 biogas plus hydrogen,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Technical lifetime,2020.0 +biogas storage, investment,14.45, EUR/kWh,AU Foulum,,2020.0 +biogas storage, lifetime,25.0,years,AU Foulum,,2020.0 biogas upgrading,FOM,17.0397,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Fixed O&M ",2020.0 biogas upgrading,VOM,4.4251,EUR/MWh output,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Variable O&M",2020.0 biogas upgrading,investment,205.2039,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: investment (upgrading, methane redution and grid injection)",2020.0 @@ -688,9 +636,9 @@ biomass boiler,efficiency,0.84,per unit,"Danish Energy Agency, technologydatafor biomass boiler,investment,704.761,EUR/kW_th,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Specific investment",2015.0 biomass boiler,lifetime,20.0,years,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Technical lifetime",2015.0 biomass boiler,pelletizing cost,9.0,EUR/MWh_pellets,Assumption based on doi:10.1016/j.rser.2019.109506,,2019.0 -biomass-to-methanol,C in fuel,0.4028,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,C stored,0.5972,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,CO2 stored,0.219,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C in fuel,0.3931,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C stored,0.6069,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,CO2 stored,0.228,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, biomass-to-methanol,FOM,1.1905,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Fixed O&M,2020.0 biomass-to-methanol,VOM,18.0816,EUR/MWh_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Variable O&M,2020.0 biomass-to-methanol,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -699,6 +647,15 @@ biomass-to-methanol,efficiency-electricity,0.02,MWh_e/MWh_th,"Danish Energy Agen biomass-to-methanol,efficiency-heat,0.22,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","97 Methanol from biomass gasif.: District heat Output,",2020.0 biomass-to-methanol,investment,4348.8608,EUR/kW_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Specific investment,2020.0 biomass-to-methanol,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Technical lifetime,2020.0 +biomethanation,Biogas Input,1.1444,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Biogas Consumption,",2020.0 +biomethanation,CO2 Input,0.165,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: CO2 Input,",2020.0 +biomethanation,FOM,3.8095,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Fixed O&M ,2020.0 +biomethanation,Hydrogen Input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Hydrogen Input,",2020.0 +biomethanation,Methane Output,1.9673,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Methane Output,",2020.0 +biomethanation,electricity input,0.0417,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: El-Input,",2020.0 +biomethanation,heat output,0.1667,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: H-Output,",2020.0 +biomethanation,investment,1728.125,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Specific investment ,2020.0 +biomethanation,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Technical lifetime,2020.0 cement capture,FOM,3.0,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,capture_rate,0.9,per unit,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,compression-electricity-input,0.1,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 @@ -785,7 +742,7 @@ central solid biomass CHP CC,c_b,0.3498,50°C/100°C,"Danish Energy Agency, tech central solid biomass CHP CC,c_v,1.0,50°C/100°C,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Cv coefficient",2015.0 central solid biomass CHP CC,efficiency,0.2694,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Electricity efficiency, net, annual average",2015.0 central solid biomass CHP CC,efficiency-heat,0.825,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Heat efficiency, net, annual average",2015.0 -central solid biomass CHP CC,investment,5617.7823,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 +central solid biomass CHP CC,investment,5666.1929,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 central solid biomass CHP CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Technical lifetime",2015.0 central solid biomass CHP powerboost CC,FOM,2.8762,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Fixed O&M",2015.0 central solid biomass CHP powerboost CC,VOM,4.8603,EUR/MWh_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Variable O&M ",2015.0 @@ -812,23 +769,23 @@ central water-sourced heat pump,VOM,1.7884,EUR/MWh,"Danish Energy Agency, techno central water-sourced heat pump,efficiency,3.8,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Total efficiency , net, annual average",2015.0 central water-sourced heat pump,investment,1058.2216,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Specific investment",2015.0 central water-sourced heat pump,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Technical lifetime",2015.0 -clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 +clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, clean water tank storage,investment,69.1286,EUR/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 clean water tank storage,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, coal,CO2 intensity,0.3361,tCO2/MWh_th,Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018,, coal,FOM,1.31,%/year,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (39.5+91.25) USD/kW_e/a /2 / (1.09 USD/EUR) / investment cost * 100.",2023.0 coal,VOM,3.2612,EUR/MWh_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (3+5.5)USD/MWh_e/2 / (1.09 USD/EUR).",2023.0 -coal,efficiency,0.356,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 +coal,efficiency,0.33,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 coal,fuel,9.5542,EUR/MWh_th,"DIW (2013): Current and propsective costs of electricity generation until 2050, http://hdl.handle.net/10419/80348 , pg. 80 text below figure 10, accessed: 2023-12-14.","Based on IEA 2011 data, 99 USD/t.",2010.0 coal,investment,3827.1629,EUR/kW_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Higher costs include coal plants with CCS, but since using here for calculating the average nevertheless. Calculated based on average of listed range, i.e. (3200+6775) USD/kW_e/2 / (1.09 USD/EUR).",2023.0 coal,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 -csp-tower,FOM,1.05,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,2020.0 +csp-tower,FOM,1.05,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,1.0 csp-tower,investment,134.165,"EUR/kW_th,dp",ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower,lifetime,30.0,years,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),-,2020.0 -csp-tower TES,FOM,1.05,%/year,see solar-tower.,-,2020.0 +csp-tower TES,FOM,1.05,%/year,see solar-tower.,-,1.0 csp-tower TES,investment,17.975,EUR/kWh_th,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower TES,lifetime,30.0,years,see solar-tower.,-,2020.0 -csp-tower power block,FOM,1.05,%/year,see solar-tower.,-,2020.0 +csp-tower power block,FOM,1.05,%/year,see solar-tower.,-,1.0 csp-tower power block,investment,939.87,EUR/kW_e,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower power block,lifetime,30.0,years,see solar-tower.,-,2020.0 decentral CHP,FOM,3.0,%/year,HP, from old pypsa cost assumptions,2015.0 @@ -874,19 +831,18 @@ decentral water tank storage,energy to power ratio,0.15,h,"Danish Energy Agency, decentral water tank storage,investment,433.8709,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Specific investment,2015.0 decentral water tank storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Technical lifetime,2015.0 digestible biomass,fuel,17.0611,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOAGRW1, ENS_Ref for 2040",,2010.0 -digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, digestible biomass to hydrogen,efficiency,0.39,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,investment,3972.2994,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 -direct air capture,FOM,1.3,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,FOM,4.95,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-electricity-input,0.15,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-heat-output,0.2,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,electricity-input,0.24,MWh_el/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 -direct air capture,heat-input,1.17,MWh_th/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 +direct air capture,electricity-input,0.4,MWh_el/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","0.4 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 0.182 MWh based on Breyer et al (2019). Should already include electricity for water scrubbing and compression (high quality CO2 output).",2020.0 +direct air capture,heat-input,1.6,MWh_th/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","Thermal energy demand. Provided via air-sourced heat pumps. 1.6 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 1.102 MWh based on Breyer et al (2019).",2020.0 direct air capture,heat-output,1.25,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,investment,11034260.0394,USD/t_CO2/h,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,investment,7000000.0,EUR/(tCO2/h),"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,lifetime,20.0,years,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,years,30.0,years,ICCT IRA e-fuels assumptions ,, direct firing gas,FOM,1.197,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Fixed O&M,2019.0 direct firing gas,VOM,0.282,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Variable O&M,2019.0 direct firing gas,efficiency,1.0,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","312.a Direct firing Natural Gas: Total efficiency, net, annual average",2019.0 @@ -927,8 +883,8 @@ electric boiler steam,VOM,0.8761,EUR/MWh,"Danish Energy Agency, technology_data_ electric boiler steam,efficiency,0.99,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","310.1 Electric boiler steam : Total efficiency, net, annual average",2019.0 electric boiler steam,investment,75.525,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Nominal investment,2019.0 electric boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Technical lifetime,2019.0 -electric steam cracker,FOM,3.0,%/year,Guesstimate,,2015.0 -electric steam cracker,VOM,190.4799,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 +electric steam cracker,FOM,3.0,%/year,Guesstimate,, +electric steam cracker,VOM,190.4799,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 electric steam cracker,carbondioxide-output,0.55,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), ",The report also references another source with 0.76 t_CO2/t_HVC, electric steam cracker,electricity-input,2.7,MWh_el/t_HVC,"Lechtenböhmer et al. (2016): 10.1016/j.energy.2016.07.110, Section 4.3, page 6.",Assuming electrified processing., electric steam cracker,investment,11124025.7434,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -940,14 +896,14 @@ electricity distribution grid,lifetime,40.0,years,TODO, from old pypsa cost assu electricity grid connection,FOM,2.0,%/year,TODO, from old pypsa cost assumptions,2015.0 electricity grid connection,investment,148.151,EUR/kW,DEA, from old pypsa cost assumptions,2015.0 electricity grid connection,lifetime,40.0,years,TODO, from old pypsa cost assumptions,2015.0 -electrobiofuels,C in fuel,0.9257,per unit,Stoichiometric calculation,, -electrobiofuels,FOM,2.5263,%/year,combination of BtL and electrofuels,,2015.0 -electrobiofuels,VOM,4.011,EUR/MWh_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,C in fuel,0.9251,per unit,Stoichiometric calculation,, +electrobiofuels,FOM,2.5263,%/year,combination of BtL and electrofuels,, +electrobiofuels,VOM,4.714,EUR/MWh_th,combination of BtL and electrofuels,,2017.0 electrobiofuels,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -electrobiofuels,efficiency-biomass,1.32,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-hydrogen,1.047,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-tot,0.5839,per unit,Stoichiometric calculation,, -electrobiofuels,investment,1012250.914,EUR/kW_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,efficiency-biomass,1.3515,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-hydrogen,1.1853,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-tot,0.6315,per unit,Stoichiometric calculation,, +electrobiofuels,investment,516655.5726,EUR/kW_th,combination of BtL and electrofuels,,2017.0 electrolysis,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Fixed O&M ,2020.0 electrolysis,efficiency,0.5874,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Hydrogen Output,2020.0 electrolysis,efficiency-heat,0.264,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: - hereof recoverable for district heating,2020.0 @@ -971,7 +927,7 @@ gas boiler steam,VOM,1.0574,EUR/MWh,"Danish Energy Agency, technology_data_for_i gas boiler steam,efficiency,0.925,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1c Steam boiler Gas: Total efficiency, net, annual average",2019.0 gas boiler steam,investment,50.35,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Nominal investment,2019.0 gas boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Technical lifetime,2019.0 -gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenance, salt cavern (units converted)",2015.0 +gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenace, salt cavern (units converted)",2015.0 gas storage,investment,0.0348,EUR/kWh,Danish Energy Agency,"150 Underground Storage of Gas, Establishment of one cavern (units converted)",2015.0 gas storage,lifetime,100.0,years,TODO no source,"estimation: most underground storage are already build, they do have a long lifetime",2015.0 gas storage charger,investment,15.1737,EUR/kW,Danish Energy Agency,"150 Underground Storage of Gas, Process equipment (units converted)",2015.0 @@ -995,14 +951,14 @@ hydro,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pyp hydro,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 hydro,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 hydro,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 -hydrogen storage compressor,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg., -hydrogen storage compressor,investment,2.0291,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage compressor,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage tank type 1,investment,15.0133,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage tank type 1,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-, +hydrogen storage compressor,FOM,4.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg.,2020.0 +hydrogen storage compressor,investment,87.69,EUR/kW_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.","2923 EUR/kg_H2. For a 206 kg/h compressor. Base CAPEX 40 528 EUR/kW_el with scale factor 0.4603. kg_H2 converted to MWh using LHV. Pressure range: 30 bar in, 250 bar out.",2020.0 +hydrogen storage compressor,lifetime,15.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage tank type 1,FOM,2.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,investment,13.5,EUR/kWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.","450 EUR/kg_H2 converted with LHV to MWh. For a type 1 hydrogen storage tank (steel, 15-250 bar). Currency year assumed 2020 for initial publication of reference; observe note in SI.4.3 that no currency year is explicitly stated in the reference.",2020.0 +hydrogen storage tank type 1,lifetime,20.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 hydrogen storage tank type 1 including compressor,FOM,1.0794,%/year,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Fixed O&M,2015.0 hydrogen storage tank type 1 including compressor,investment,53.9217,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Specific investment,2015.0 hydrogen storage tank type 1 including compressor,lifetime,27.5,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Technical lifetime,2015.0 @@ -1049,8 +1005,8 @@ methanol-to-kerosene,hydrogen-input,0.0279,MWh_H2/MWh_kerosene,"Concawe (2022): methanol-to-kerosene,investment,288000.0,EUR/MW_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",,2020.0 methanol-to-kerosene,lifetime,30.0,years,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",, methanol-to-kerosene,methanol-input,1.0764,MWh_MeOH/MWh_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 6.","Assuming LHV 11.94 kWh/kg for kerosene, 5.54 kWh/kg for methanol, 33.3 kWh/kg for hydrogen.", -methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker,2015.0 -methanol-to-olefins/aromatics,VOM,31.7466,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 +methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker, +methanol-to-olefins/aromatics,VOM,31.7466,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 methanol-to-olefins/aromatics,carbondioxide-output,0.6107,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Sections 4.5 (for ethylene and propylene) and 4.6 (for BTX)","Weighted average: 0.4 t_MeOH/t_ethylene+propylene for 21.7 Mt of ethylene and 17 Mt of propylene, 1.13 t_CO2/t_BTX for 15.7 Mt of BTX. The report also references process emissions of 0.55 t_MeOH/t_ethylene+propylene elsewhere. ", methanol-to-olefins/aromatics,electricity-input,1.3889,MWh_el/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), page 69",5 GJ/t_HVC , methanol-to-olefins/aromatics,investment,2781006.4359,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -1077,7 +1033,7 @@ nuclear,investment,8594.1354,EUR/kW_e,"Lazard's levelized cost of energy analysi nuclear,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 offwind,FOM,2.3741,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Fixed O&M [EUR/MW_e/y, 2020]",2020.0 offwind,VOM,0.0212,EUR/MWhel,RES costs made up to fix curtailment order, from old pypsa cost assumptions,2015.0 -offwind,investment,1769.1171,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs subtracted from investment costs",2020.0 +offwind,investment,1769.1171,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs substracted from investment costs",2020.0 offwind,lifetime,30.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",21 Offshore turbines: Technical lifetime [years],2020.0 offwind-ac-connection-submarine,investment,2841.3251,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 offwind-ac-connection-underground,investment,1420.1334,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 @@ -1106,12 +1062,18 @@ organic rankine cycle,FOM,2.0,%/year,"Aghahosseini, Breyer 2020: From hot rock t organic rankine cycle,electricity-input,0.12,MWh_el/MWh_th,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551; Breede et al. 2015: Overcoming challenges in the classification of deep geothermal potential, https://eprints.gla.ac.uk/169585/","Heat-input, Electricity-output. This is a rough estimate, depends on input temperature, implies ~150 C.",2020.0 organic rankine cycle,investment,1376.0,EUR/kW_el,Tartiere and Astolfi 2017: A world overview of the organic Rankine cycle market,"Low rollout complicates the estimation, compounded by a dependence both on plant size and temperature, converted from 1500 USD/kW using currency conversion 1.09 USD = 1 EUR.",2020.0 organic rankine cycle,lifetime,30.0,years,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551",,2020.0 +perennials gbr,FOM,0.0,%year,Own assumption,,2015.0 +perennials gbr,VOM,43.2317,EUR/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,"includes purchase of perennial crops and sales of proteine concentrate, table 8.1 wages, maintenance and auxiliary costs",2015.0 +perennials gbr,biogas-output,0.1947,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,electricity-input,0.0733,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,investment,1371168.1394,EUR/tDM/h,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,includes cost for biogas plant without upgrading,2015.0 +perennials gbr,lifetime,25.0,years,Own assumption,,2015.0 ror,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,investment,3412.2266,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 ror,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 seawater RO desalination,electricity-input,0.003,MWHh_el/t_H2O,"Caldera et al. (2016): Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",Desalination using SWRO. Assume medium salinity of 35 Practical Salinity Units (PSUs) = 35 kg/m^3., -seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2015.0 +seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, seawater desalination,electricity-input,3.0348,kWh/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",, seawater desalination,investment,39056.5182,EUR/(m^3-H2O/h),"Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402), Table 4.",,2015.0 seawater desalination,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, @@ -1137,7 +1099,7 @@ solar-utility,lifetime,37.5,years,"Danish Energy Agency, technology_data_for_el_ solar-utility single-axis tracking,FOM,2.0365,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Fixed O&M [2020-EUR/MW_e/y],2020.0 solar-utility single-axis tracking,investment,552.4113,EUR/kW_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Nominal investment [2020-MEUR/MW_e],2020.0 solar-utility single-axis tracking,lifetime,37.5,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Technical lifetime [years],2020.0 -solid biomass,CO2 intensity,0.3667,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, +solid biomass,CO2 intensity,0.3757,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, solid biomass,fuel,13.6489,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOWOOW1 (secondary forest residue wood chips), ENS_Ref for 2040",,2010.0 solid biomass boiler steam,FOM,5.7564,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Fixed O&M,2019.0 solid biomass boiler steam,VOM,2.8216,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Variable O&M,2019.0 @@ -1149,7 +1111,7 @@ solid biomass boiler steam CC,VOM,2.8216,EUR/MWh,"Danish Energy Agency, technolo solid biomass boiler steam CC,efficiency,0.89,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1e Steam boiler Wood: Total efficiency, net, annual average",2019.0 solid biomass boiler steam CC,investment,608.7773,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Nominal investment,2019.0 solid biomass boiler steam CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Technical lifetime,2019.0 -solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, solid biomass to hydrogen,efficiency,0.56,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,investment,3972.2994,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 diff --git a/outputs/costs_2030.csv b/outputs/costs_2030.csv index a55b4764..18b3298e 100644 --- a/outputs/costs_2030.csv +++ b/outputs/costs_2030.csv @@ -1,8 +1,4 @@ technology,parameter,value,unit,source,further description,currency_year -Alkaline electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,efficiency,0.69,p.u.,ICCT IRA e-fuels assumptions ,, -Alkaline electrolyzer,investment,832.9863,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Ammonia cracker,FOM,4.3,%/year,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 7.","Estimated based on Labour cost rate, Maintenance cost rate, Insurance rate, Admin. cost rate and Chemical & other consumables cost rate.",2015.0 Ammonia cracker,ammonia-input,1.46,MWh_NH3/MWh_H2,"ENGIE et al (2020): Ammonia to Green Hydrogen Feasibility Study (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/880826/HS420_-_Ecuity_-_Ammonia_to_Green_Hydrogen.pdf), Fig. 10.",Assuming a integrated 200t/d cracking and purification facility. Electricity demand (316 MWh per 2186 MWh_LHV H2 output) is assumed to also be ammonia LHV input which seems a fair assumption as the facility has options for a higher degree of integration according to the report)., Ammonia cracker,investment,1123945.3807,EUR/MW_H2,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 6.","Calculated. For a small (200 t_NH3/d input) facility. Base cost for facility: 51 MEUR at capacity 20 000m^3_NH3/h = 339 t_NH3/d input. Cost scaling exponent 0.67. Ammonia density 0.7069 kg/m^3. Conversion efficiency of cracker: 0.685. Ammonia LHV: 5.167 MWh/t_NH3.; and @@ -45,25 +41,18 @@ Battery electric (passenger cars),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL Battery electric (trucks),FOM,15.0,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),investment,136400.0,EUR/LKW,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 -BioSNG,C in fuel,0.3402,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,C stored,0.6598,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,CO2 stored,0.2419,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C in fuel,0.3321,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C stored,0.6679,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,CO2 stored,0.2509,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BioSNG,FOM,1.6375,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Fixed O&M",2020.0 BioSNG,VOM,1.8078,EUR/MWh_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Variable O&M",2020.0 BioSNG,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, BioSNG,efficiency,0.63,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Bio SNG Output",2020.0 BioSNG,investment,1701.44,EUR/kW_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Specific investment",2020.0 BioSNG,lifetime,25.0,years,TODO,"84 Gasif. CFB, Bio-SNG: Technical lifetime",2020.0 -Biomass gasification,efficiency,0.4083,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification,investment,1467.7693,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,FOM,0.02,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,efficiency,0.328,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,investment,3015.5325,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -BtL,C in fuel,0.2688,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,C stored,0.7312,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,CO2 stored,0.2681,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C in fuel,0.2624,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C stored,0.7376,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,CO2 stored,0.2771,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BtL,FOM,2.6667,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Fixed O&M",2020.0 BtL,VOM,1.1299,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Variable O&M",2020.0 BtL,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -109,11 +98,17 @@ CO2 liquefaction,lifetime,25.0,years,"Guesstimate, based on CH4 liquefaction.",, CO2 pipeline,FOM,0.9,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 pipeline,investment,2116.4433,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch onshore pipeline.,2015.0 CO2 pipeline,lifetime,50.0,years,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 +CO2 storage cylinders, FOM,1.0,%/year,AU Foulum,,2020.0 +CO2 storage cylinders, investment,77000.0, EUR/t_CO2,AU Foulum,,2020.0 +CO2 storage cylinders, lifetime,25.0,years,AU Foulum,,2020.0 CO2 storage tank,FOM,1.0,%/year,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,investment,2584.3462,EUR/t_CO2,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, Table 3.","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,lifetime,25.0,years,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 submarine pipeline,FOM,0.5,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 submarine pipeline,investment,4232.8865,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch offshore pipeline.,2015.0 +CO2_industrial_compressor, FOM,4.0,%/year,AU Foulum,,2020.0 +CO2_industrial_compressor, investment,1516000.0, EUR/t/h_CO2,AU Foulum,,2020.0 +CO2_industrial_compressor, lifetime,25.0,years,AU Foulum,,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,FOM,1.6,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,investment,448894.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 @@ -126,29 +121,6 @@ Charging infrastructure fuel cell vehicles trucks,lifetime,30.0,years,PATHS TO A Charging infrastructure slow (purely) battery electric vehicles passenger cars,FOM,1.8,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,investment,1005.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 -Coal gasification,FOM,0.06,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,efficiency,0.6357,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification,investment,399.2305,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,FOM,0.07,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,efficiency,0.609,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,investment,649.5969,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 90%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal-95%-CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -Coal-95%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-95%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,capture_rate,0.99,per unit,"NREL, NREL ATB 2024",, -Coal-99%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,efficiency,0.5,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,capture_rate,0.9,per unit,"NREL, NREL ATB 2024",, -Coal-IGCC-90%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Compressed-Air-Adiabatic-bicharger,FOM,0.9265,%/year,"Viswanathan_2022, p.64 (p.86) Figure 4.14","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 Compressed-Air-Adiabatic-bicharger,efficiency,0.7211,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.52^0.5']}",2020.0 Compressed-Air-Adiabatic-bicharger,investment,946180.9426,EUR/MW,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['Turbine Compressor BOP EPC Management']}",2020.0 @@ -249,15 +221,15 @@ FT fuel transport ship,FOM,5.0,%/year,"Assume comparable tanker as for LOHC tran FT fuel transport ship,capacity,75000.0,t_FTfuel,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,investment,35000000.0,EUR,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,lifetime,15.0,years,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 -Fischer-Tropsch,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +Fischer-Tropsch,FOM,3.0,%/year,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.",,2017.0 Fischer-Tropsch,VOM,4.4663,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",102 Hydrogen to Jet: Variable O&M,2020.0 Fischer-Tropsch,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -Fischer-Tropsch,carbondioxide-input,0.32,t_CO2/MWh_FT,ICCT IRA e-fuels assumptions ,"Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", -Fischer-Tropsch,efficiency,0.7,per unit,ICCT IRA e-fuels assumptions ,, -Fischer-Tropsch,electricity-input,0.04,MWh_el/MWh_FT,ICCT IRA e-fuels assumptions ,"0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,hydrogen-input,1.43,MWh_H2/MWh_FT,ICCT IRA e-fuels assumptions ,"0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,investment,1509724.4026,USD/MW_FT,ICCT IRA e-fuels assumptions ,"Well developed technology, no significant learning expected.",2022.0 -Fischer-Tropsch,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,,2020.0 +Fischer-Tropsch,carbondioxide-input,0.326,t_CO2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", +Fischer-Tropsch,efficiency,0.799,per unit,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.2.",,2017.0 +Fischer-Tropsch,electricity-input,0.007,MWh_el/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,hydrogen-input,1.421,MWh_H2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,investment,703726.4462,EUR/MW_FT,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected.",2017.0 +Fischer-Tropsch,lifetime,20.0,years,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.",,2017.0 Gasnetz,FOM,2.5,%,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,investment,28.0,EUR/kWGas,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,lifetime,30.0,years,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 @@ -265,7 +237,7 @@ General liquid hydrocarbon storage (crude),FOM,6.25,%/year,"Stelter and Nishida General liquid hydrocarbon storage (crude),investment,137.8999,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed 20% lower than for product storage. Crude or middle distillate tanks are usually larger compared to product storage due to lower requirements on safety and different construction method. Reference size used here: 80 000 – 120 000 m^3 .,2012.0 General liquid hydrocarbon storage (crude),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 General liquid hydrocarbon storage (product),FOM,6.25,%/year,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , figure 7 and pg. 12 .",Assuming ca. 10 EUR/m^3/a (center value between stand alone and addon facility).,2012.0 -General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 - 60 000 m^3 .,2012.0 +General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 – 60 000 m^3 .,2012.0 General liquid hydrocarbon storage (product),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 Gravity-Brick-bicharger,FOM,1.5,%/year,"Viswanathan_2022, p.76 (p.98) Sentence 1 in 4.7.2 Operating Costs","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['1.5 percent of capital cost']}",2020.0 Gravity-Brick-bicharger,efficiency,0.9274,per unit,"Viswanathan_2022, p.77 (p.99) Table 4.36","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.86^0.5']}",2020.0 @@ -341,15 +313,13 @@ HVDC underground,investment,1008.2934,EUR/MW/km,Härtel et al. (2017): https://d HVDC underground,lifetime,40.0,years,Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .,"Based on estimated costs for a NA-EU connector (bidirectional,4 GW, 3000km length and ca. 3000m depth). Costs in return based on existing/currently under construction undersea cables. (same as for HVDC submarine)",2018.0 Haber-Bosch,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 Haber-Bosch,VOM,0.0225,EUR/MWh_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Variable O&M,2015.0 +Haber-Bosch,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +Haber-Bosch,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 Haber-Bosch,electricity-input,0.2473,MWh_el/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), table 11.",Assume 5 GJ/t_NH3 for compressors and NH3 LHV = 5.16666 MWh/t_NH3., Haber-Bosch,hydrogen-input,1.1484,MWh_H2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.","178 kg_H2 per t_NH3, LHV for both assumed.", Haber-Bosch,investment,1460.0135,EUR/kW_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 Haber-Bosch,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 Haber-Bosch,nitrogen-input,0.1597,t_N2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.",".33 MWh electricity are required for ASU per t_NH3, considering 0.4 MWh are required per t_N2 and LHV of NH3 of 5.1666 Mwh.", -Heavy oil partial oxidation,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,efficiency,0.734,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Heavy oil partial oxidation,investment,491.0535,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, HighT-Molten-Salt-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 HighT-Molten-Salt-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 HighT-Molten-Salt-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -466,18 +436,6 @@ Methanol steam reforming,FOM,4.0,%/year,"Niermann et al. (2021): Liquid Organic Methanol steam reforming,investment,18016.8665,EUR/MW_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.","For high temperature steam reforming plant with a capacity of 200 MW_H2 output (6t/h). Reference plant of 1 MW (30kg_H2/h) costs 150kEUR, scale factor of 0.6 assumed.",2020.0 Methanol steam reforming,lifetime,20.0,years,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",,2020.0 Methanol steam reforming,methanol-input,1.201,MWh_MeOH/MWh_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",Assuming per 1 t_H2 (with LHV 33.3333 MWh/t): 4.5 MWh_th and 3.2 MWh_el are required. We assume electricity can be substituted / provided with 1:1 as heat energy., -NG 2-on-1 Combined Cycle (F-Frame),efficiency,0.573,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame),lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,efficiency,0.527,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,capture_rate,0.97,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,efficiency,0.525,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, NH3 (l) storage tank incl. liquefaction,FOM,2.0,%/year,"Guesstimate, based on H2 (l) storage tank.",,2010.0 NH3 (l) storage tank incl. liquefaction,investment,166.8201,EUR/MWh_NH3,"Calculated based on Morgan E. 2013: doi:10.7275/11KT-3F59 , Fig. 55, Fig 58.","Based on estimated for a double-wall liquid ammonia tank (~ambient pressure, -33°C), inner tank from stainless steel, outer tank from concrete including installations for liquefaction/condensation, boil-off gas recovery and safety installations; the necessary installations make only a small fraction of the total cost. The total cost are driven by material and working time on the tanks. While the costs do not scale strictly linearly, we here assume they do (good approximation c.f. ref. Fig 55.) and take the costs for a 9 kt NH3 (l) tank = 8 M$2010, which is smaller 4-5x smaller than the largest deployed tanks today. @@ -488,14 +446,6 @@ NH3 (l) transport ship,FOM,4.0,%/year,"Cihlar et al 2020 based on IEA 2019, Tabl NH3 (l) transport ship,capacity,53000.0,t_NH3,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,investment,81164200.0,EUR,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,lifetime,20.0,years,"Guess estimated based on H2 (l) tanker, but more mature technology",,2019.0 -Natural gas steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,efficiency,0.7623,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming,investment,180.0632,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,efficiency,0.637,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,investment,323.8999,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Ni-Zn-bicharger,FOM,2.1198,%/year,"Viswanathan_2022, p.51-52 in section 4.4.2","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Guesstimate 30% assumed of power components every 10 years ']}",2020.0 Ni-Zn-bicharger,efficiency,0.9,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['((0.75-0.87)/2)^0.5 mean value of range efficiency is not RTE but single way AC-store conversion']}",2020.0 Ni-Zn-bicharger,investment,81553.4846,EUR/MW,"Viswanathan_2022, p.59 (p.81) same as Li-LFP","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Power Equipment']}",2020.0 @@ -508,10 +458,6 @@ OCGT,VOM,4.762,EUR/MWh,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx OCGT,efficiency,0.41,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","52 OCGT - Natural gas: Electricity efficiency, annual average",2015.0 OCGT,investment,460.5804,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Specific investment,2015.0 OCGT,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Technical lifetime,2015.0 -PEM electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,efficiency,0.68,p.u.,ICCT IRA e-fuels assumptions ,, -PEM electrolyzer,investment,996.7357,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, PHS,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 @@ -534,19 +480,15 @@ Pumped-Storage-Hydro-bicharger,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90 Pumped-Storage-Hydro-store,FOM,0.43,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['derived']}",2020.0 Pumped-Storage-Hydro-store,investment,57074.0625,EUR/MWh,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['Reservoir Construction & Infrastructure']}",2020.0 Pumped-Storage-Hydro-store,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 -SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 +SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", SMR,efficiency,0.76,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR,investment,522201.0492,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 -SMR CC,capture_rate,0.9,per unit,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates between 54%-90%, +SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", +SMR CC,capture_rate,0.9,EUR/MW_CH4,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates betwen 54%-90%, SMR CC,efficiency,0.69,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR CC,investment,605753.2171,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR CC,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SOEC,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,efficiency,0.84,p.u.,ICCT IRA e-fuels assumptions ,, -SOEC,investment,1135.5667,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Sand-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 Sand-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 Sand-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -558,10 +500,6 @@ Sand-discharger,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier Sand-store,FOM,0.3308,%/year,"Viswanathan_2022, p 104 (p.126)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['not provided calculated as for hydrogen']}",2020.0 Sand-store,investment,6700.8517,EUR/MWh,"Viswanathan_2022, p.100 (p.122)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['SB and BOS 0.85 of 2021 value']}",2020.0 Sand-store,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['NULL']}",2020.0 -Solid biomass steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,efficiency,0.712,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Solid biomass steam reforming,investment,590.7702,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Steam methane reforming,FOM,3.0,%/year,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 Steam methane reforming,investment,497454.611,EUR/MW_H2,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW). Currency conversion 1.17 USD = 1 EUR.,2015.0 Steam methane reforming,lifetime,30.0,years,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 @@ -604,6 +542,8 @@ Zn-Br-Nonflow-store,FOM,0.2244,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrie Zn-Br-Nonflow-store,investment,239220.5823,EUR/MWh,"Viswanathan_2022, p.59 (p.81) Table 4.14","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['DC storage block']}",2020.0 Zn-Br-Nonflow-store,lifetime,15.0,years,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['NULL']}",2020.0 air separation unit,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 +air separation unit,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +air separation unit,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 air separation unit,electricity-input,0.25,MWh_el/t_N2,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), p.288.","For consistency reasons use value from Danish Energy Agency. DEA also reports range of values (0.2-0.4 MWh/t_N2) on pg. 288. Other efficienices reported are even higher, e.g. 0.11 Mwh/t_N2 from Morgan (2013): Techno-Economic Feasibility Study of Ammonia Plants Powered by Offshore Wind .", air separation unit,investment,820676.5784,EUR/t_N2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 air separation unit,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 @@ -618,12 +558,13 @@ battery inverter,lifetime,10.0,years,"Danish Energy Agency, technology_data_cata battery storage,investment,150.2675,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Energy storage expansion cost investment,2015.0 battery storage,lifetime,25.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Technical lifetime,2015.0 biochar pyrolysis,FOM,3.4167,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Fixed O&M",2020.0 -biochar pyrolysis,VOM,823.497,EUR/MWh_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 -biochar pyrolysis,efficiency-biochar,0.404,MWh_biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency biochar",2020.0 -biochar pyrolysis,efficiency-heat,0.4848,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency heat",2020.0 -biochar pyrolysis,investment,154405.68,EUR/kW_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 +biochar pyrolysis,VOM,47.6777,EUR/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 +biochar pyrolysis,biomass input,7.6748,MWh_biomass/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Biomass Input",2020.0 +biochar pyrolysis,electricity input,0.3184,MWh_e/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: El-Input",2020.0 +biochar pyrolysis,heat output,3.7859,MWh_th/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: H-Output",2020.0 +biochar pyrolysis,investment,8939565.2069,EUR/t_CO2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 biochar pyrolysis,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Technical lifetime",2020.0 -biochar pyrolysis,yield-biochar,0.0582,ton biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 +biochar pyrolysis,yield-biochar,0.0597,t_biochar/MWh_biomass,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 biodiesel crops,fuel,137.6508,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIORPS1 (rape seed), ENS_BaU_GFTM",,2010.0 bioethanol crops,fuel,82.4367,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOCRP11 (Bioethanol barley, wheat, grain maize, oats, other cereals and rye), ENS_BaU_GFTM",,2010.0 biogas,CO2 stored,0.0868,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, @@ -640,10 +581,17 @@ biogas CC,efficiency,1.0,per unit,Assuming input biomass is already given in bio biogas CC,investment,955.1865,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Specific investment",2020.0 biogas CC,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Technical lifetime",2020.0 biogas manure,fuel,19.8676,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOGAS1 (manure), ENS_BaU_GFTM",,2010.0 -biogas plus hydrogen,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Fixed O&M,2020.0 -biogas plus hydrogen,VOM,3.8282,EUR/MWh_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 -biogas plus hydrogen,investment,803.9304,EUR/kW_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 +biogas plus hydrogen,Biogas Input,1.1522,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Biogas Consumption,",2020.0 +biogas plus hydrogen,CO2 Input,0.1235,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: CO2 Input,",2020.0 +biogas plus hydrogen,Methane Output,1.9348,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Methane Output,",2020.0 +biogas plus hydrogen,VOM,7.4068,EUR/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 +biogas plus hydrogen,electricity input,0.0217,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: El-Input,",2020.0 +biogas plus hydrogen,heat output,0.2174,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: H-Output,",2020.0 +biogas plus hydrogen,hydrogen input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Hydrogen Consumption,",2020.0 +biogas plus hydrogen,investment,1555.4306,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 biogas plus hydrogen,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Technical lifetime,2020.0 +biogas storage, investment,14.45, EUR/kWh,AU Foulum,,2020.0 +biogas storage, lifetime,25.0,years,AU Foulum,,2020.0 biogas upgrading,FOM,17.0397,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Fixed O&M ",2020.0 biogas upgrading,VOM,3.6704,EUR/MWh output,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Variable O&M",2020.0 biogas upgrading,investment,170.2068,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: investment (upgrading, methane redution and grid injection)",2020.0 @@ -688,9 +636,9 @@ biomass boiler,efficiency,0.86,per unit,"Danish Energy Agency, technologydatafor biomass boiler,investment,687.1015,EUR/kW_th,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Specific investment",2015.0 biomass boiler,lifetime,20.0,years,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Technical lifetime",2015.0 biomass boiler,pelletizing cost,9.0,EUR/MWh_pellets,Assumption based on doi:10.1016/j.rser.2019.109506,,2019.0 -biomass-to-methanol,C in fuel,0.4129,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,C stored,0.5871,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,CO2 stored,0.2153,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C in fuel,0.403,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C stored,0.597,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,CO2 stored,0.2243,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, biomass-to-methanol,FOM,1.3333,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Fixed O&M,2020.0 biomass-to-methanol,VOM,14.4653,EUR/MWh_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Variable O&M,2020.0 biomass-to-methanol,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -699,6 +647,15 @@ biomass-to-methanol,efficiency-electricity,0.02,MWh_e/MWh_th,"Danish Energy Agen biomass-to-methanol,efficiency-heat,0.22,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","97 Methanol from biomass gasif.: District heat Output,",2020.0 biomass-to-methanol,investment,3106.3291,EUR/kW_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Specific investment,2020.0 biomass-to-methanol,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Technical lifetime,2020.0 +biomethanation,Biogas Input,1.1444,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Biogas Consumption,",2020.0 +biomethanation,CO2 Input,0.165,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: CO2 Input,",2020.0 +biomethanation,FOM,5.3333,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Fixed O&M ,2020.0 +biomethanation,Hydrogen Input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Hydrogen Input,",2020.0 +biomethanation,Methane Output,1.9673,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Methane Output,",2020.0 +biomethanation,electricity input,0.0417,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: El-Input,",2020.0 +biomethanation,heat output,0.1667,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: H-Output,",2020.0 +biomethanation,investment,1234.375,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Specific investment ,2020.0 +biomethanation,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Technical lifetime,2020.0 cement capture,FOM,3.0,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,capture_rate,0.9,per unit,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,compression-electricity-input,0.085,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 @@ -785,7 +742,7 @@ central solid biomass CHP CC,c_b,0.3506,50°C/100°C,"Danish Energy Agency, tech central solid biomass CHP CC,c_v,1.0,50°C/100°C,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Cv coefficient",2015.0 central solid biomass CHP CC,efficiency,0.2699,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Electricity efficiency, net, annual average",2015.0 central solid biomass CHP CC,efficiency-heat,0.8245,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Heat efficiency, net, annual average",2015.0 -central solid biomass CHP CC,investment,5207.5282,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 +central solid biomass CHP CC,investment,5248.2854,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 central solid biomass CHP CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Technical lifetime",2015.0 central solid biomass CHP powerboost CC,FOM,2.8661,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Fixed O&M",2015.0 central solid biomass CHP powerboost CC,VOM,4.8512,EUR/MWh_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Variable O&M ",2015.0 @@ -812,23 +769,23 @@ central water-sourced heat pump,VOM,1.6826,EUR/MWh,"Danish Energy Agency, techno central water-sourced heat pump,efficiency,3.82,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Total efficiency , net, annual average",2015.0 central water-sourced heat pump,investment,1058.2216,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Specific investment",2015.0 central water-sourced heat pump,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Technical lifetime",2015.0 -clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 +clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, clean water tank storage,investment,69.1286,EUR/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 clean water tank storage,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, coal,CO2 intensity,0.3361,tCO2/MWh_th,Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018,, coal,FOM,1.31,%/year,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (39.5+91.25) USD/kW_e/a /2 / (1.09 USD/EUR) / investment cost * 100.",2023.0 coal,VOM,3.2612,EUR/MWh_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (3+5.5)USD/MWh_e/2 / (1.09 USD/EUR).",2023.0 -coal,efficiency,0.356,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 +coal,efficiency,0.33,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 coal,fuel,9.5542,EUR/MWh_th,"DIW (2013): Current and propsective costs of electricity generation until 2050, http://hdl.handle.net/10419/80348 , pg. 80 text below figure 10, accessed: 2023-12-14.","Based on IEA 2011 data, 99 USD/t.",2010.0 coal,investment,3827.1629,EUR/kW_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Higher costs include coal plants with CCS, but since using here for calculating the average nevertheless. Calculated based on average of listed range, i.e. (3200+6775) USD/kW_e/2 / (1.09 USD/EUR).",2023.0 coal,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 -csp-tower,FOM,1.1,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,2020.0 +csp-tower,FOM,1.1,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,1.0 csp-tower,investment,108.37,"EUR/kW_th,dp",ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower,lifetime,30.0,years,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),-,2020.0 -csp-tower TES,FOM,1.1,%/year,see solar-tower.,-,2020.0 +csp-tower TES,FOM,1.1,%/year,see solar-tower.,-,1.0 csp-tower TES,investment,14.52,EUR/kWh_th,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower TES,lifetime,30.0,years,see solar-tower.,-,2020.0 -csp-tower power block,FOM,1.1,%/year,see solar-tower.,-,2020.0 +csp-tower power block,FOM,1.1,%/year,see solar-tower.,-,1.0 csp-tower power block,investment,759.17,EUR/kW_e,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower power block,lifetime,30.0,years,see solar-tower.,-,2020.0 decentral CHP,FOM,3.0,%/year,HP, from old pypsa cost assumptions,2015.0 @@ -874,19 +831,18 @@ decentral water tank storage,energy to power ratio,0.15,h,"Danish Energy Agency, decentral water tank storage,investment,433.8709,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Specific investment,2015.0 decentral water tank storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Technical lifetime,2015.0 digestible biomass,fuel,17.0611,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOAGRW1, ENS_Ref for 2040",,2010.0 -digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, digestible biomass to hydrogen,efficiency,0.39,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,investment,3707.4795,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 -direct air capture,FOM,1.3,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,FOM,4.95,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-electricity-input,0.15,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-heat-output,0.2,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,electricity-input,0.24,MWh_el/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 -direct air capture,heat-input,1.17,MWh_th/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 +direct air capture,electricity-input,0.4,MWh_el/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","0.4 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 0.182 MWh based on Breyer et al (2019). Should already include electricity for water scrubbing and compression (high quality CO2 output).",2020.0 +direct air capture,heat-input,1.6,MWh_th/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","Thermal energy demand. Provided via air-sourced heat pumps. 1.6 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 1.102 MWh based on Breyer et al (2019).",2020.0 direct air capture,heat-output,1.0,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,investment,11034260.0394,USD/t_CO2/h,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,investment,6000000.0,EUR/(tCO2/h),"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,lifetime,20.0,years,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,years,30.0,years,ICCT IRA e-fuels assumptions ,, direct firing gas,FOM,1.1818,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Fixed O&M,2019.0 direct firing gas,VOM,0.2794,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Variable O&M,2019.0 direct firing gas,efficiency,1.0,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","312.a Direct firing Natural Gas: Total efficiency, net, annual average",2019.0 @@ -927,8 +883,8 @@ electric boiler steam,VOM,0.8811,EUR/MWh,"Danish Energy Agency, technology_data_ electric boiler steam,efficiency,0.99,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","310.1 Electric boiler steam : Total efficiency, net, annual average",2019.0 electric boiler steam,investment,70.49,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Nominal investment,2019.0 electric boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Technical lifetime,2019.0 -electric steam cracker,FOM,3.0,%/year,Guesstimate,,2015.0 -electric steam cracker,VOM,190.4799,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 +electric steam cracker,FOM,3.0,%/year,Guesstimate,, +electric steam cracker,VOM,190.4799,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 electric steam cracker,carbondioxide-output,0.55,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), ",The report also references another source with 0.76 t_CO2/t_HVC, electric steam cracker,electricity-input,2.7,MWh_el/t_HVC,"Lechtenböhmer et al. (2016): 10.1016/j.energy.2016.07.110, Section 4.3, page 6.",Assuming electrified processing., electric steam cracker,investment,11124025.7434,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -940,14 +896,14 @@ electricity distribution grid,lifetime,40.0,years,TODO, from old pypsa cost assu electricity grid connection,FOM,2.0,%/year,TODO, from old pypsa cost assumptions,2015.0 electricity grid connection,investment,148.151,EUR/kW,DEA, from old pypsa cost assumptions,2015.0 electricity grid connection,lifetime,40.0,years,TODO, from old pypsa cost assumptions,2015.0 -electrobiofuels,C in fuel,0.9269,per unit,Stoichiometric calculation,, -electrobiofuels,FOM,2.6667,%/year,combination of BtL and electrofuels,,2015.0 -electrobiofuels,VOM,3.6212,EUR/MWh_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,C in fuel,0.9262,per unit,Stoichiometric calculation,, +electrobiofuels,FOM,2.6667,%/year,combination of BtL and electrofuels,, +electrobiofuels,VOM,4.2565,EUR/MWh_th,combination of BtL and electrofuels,,2017.0 electrobiofuels,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -electrobiofuels,efficiency-biomass,1.3217,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-hydrogen,1.0637,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-tot,0.5894,per unit,Stoichiometric calculation,, -electrobiofuels,investment,996146.9119,EUR/kW_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,efficiency-biomass,1.3531,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-hydrogen,1.2036,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-tot,0.637,per unit,Stoichiometric calculation,, +electrobiofuels,investment,470280.0038,EUR/kW_th,combination of BtL and electrofuels,,2017.0 electrolysis,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Fixed O&M ,2020.0 electrolysis,efficiency,0.6217,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Hydrogen Output,2020.0 electrolysis,efficiency-heat,0.2228,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: - hereof recoverable for district heating,2020.0 @@ -971,7 +927,7 @@ gas boiler steam,VOM,1.007,EUR/MWh,"Danish Energy Agency, technology_data_for_in gas boiler steam,efficiency,0.93,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1c Steam boiler Gas: Total efficiency, net, annual average",2019.0 gas boiler steam,investment,45.7727,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Nominal investment,2019.0 gas boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Technical lifetime,2019.0 -gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenance, salt cavern (units converted)",2015.0 +gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenace, salt cavern (units converted)",2015.0 gas storage,investment,0.0348,EUR/kWh,Danish Energy Agency,"150 Underground Storage of Gas, Establishment of one cavern (units converted)",2015.0 gas storage,lifetime,100.0,years,TODO no source,"estimation: most underground storage are already build, they do have a long lifetime",2015.0 gas storage charger,investment,15.1737,EUR/kW,Danish Energy Agency,"150 Underground Storage of Gas, Process equipment (units converted)",2015.0 @@ -995,14 +951,14 @@ hydro,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pyp hydro,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 hydro,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 hydro,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 -hydrogen storage compressor,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg., -hydrogen storage compressor,investment,2.0291,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage compressor,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage tank type 1,investment,15.0133,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage tank type 1,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-, +hydrogen storage compressor,FOM,4.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg.,2020.0 +hydrogen storage compressor,investment,87.69,EUR/kW_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.","2923 EUR/kg_H2. For a 206 kg/h compressor. Base CAPEX 40 528 EUR/kW_el with scale factor 0.4603. kg_H2 converted to MWh using LHV. Pressure range: 30 bar in, 250 bar out.",2020.0 +hydrogen storage compressor,lifetime,15.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage tank type 1,FOM,2.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,investment,13.5,EUR/kWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.","450 EUR/kg_H2 converted with LHV to MWh. For a type 1 hydrogen storage tank (steel, 15-250 bar). Currency year assumed 2020 for initial publication of reference; observe note in SI.4.3 that no currency year is explicitly stated in the reference.",2020.0 +hydrogen storage tank type 1,lifetime,20.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 hydrogen storage tank type 1 including compressor,FOM,1.1133,%/year,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Fixed O&M,2015.0 hydrogen storage tank type 1 including compressor,investment,47.5247,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Specific investment,2015.0 hydrogen storage tank type 1 including compressor,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Technical lifetime,2015.0 @@ -1049,8 +1005,8 @@ methanol-to-kerosene,hydrogen-input,0.0279,MWh_H2/MWh_kerosene,"Concawe (2022): methanol-to-kerosene,investment,269000.0,EUR/MW_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",,2020.0 methanol-to-kerosene,lifetime,30.0,years,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",, methanol-to-kerosene,methanol-input,1.0764,MWh_MeOH/MWh_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 6.","Assuming LHV 11.94 kWh/kg for kerosene, 5.54 kWh/kg for methanol, 33.3 kWh/kg for hydrogen.", -methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker,2015.0 -methanol-to-olefins/aromatics,VOM,31.7466,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 +methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker, +methanol-to-olefins/aromatics,VOM,31.7466,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 methanol-to-olefins/aromatics,carbondioxide-output,0.6107,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Sections 4.5 (for ethylene and propylene) and 4.6 (for BTX)","Weighted average: 0.4 t_MeOH/t_ethylene+propylene for 21.7 Mt of ethylene and 17 Mt of propylene, 1.13 t_CO2/t_BTX for 15.7 Mt of BTX. The report also references process emissions of 0.55 t_MeOH/t_ethylene+propylene elsewhere. ", methanol-to-olefins/aromatics,electricity-input,1.3889,MWh_el/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), page 69",5 GJ/t_HVC , methanol-to-olefins/aromatics,investment,2781006.4359,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -1077,7 +1033,7 @@ nuclear,investment,8594.1354,EUR/kW_e,"Lazard's levelized cost of energy analysi nuclear,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 offwind,FOM,2.3185,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Fixed O&M [EUR/MW_e/y, 2020]",2020.0 offwind,VOM,0.0212,EUR/MWhel,RES costs made up to fix curtailment order, from old pypsa cost assumptions,2015.0 -offwind,investment,1682.1226,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs subtracted from investment costs",2020.0 +offwind,investment,1682.1226,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs substracted from investment costs",2020.0 offwind,lifetime,30.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",21 Offshore turbines: Technical lifetime [years],2020.0 offwind-ac-connection-submarine,investment,2841.3251,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 offwind-ac-connection-underground,investment,1420.1334,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 @@ -1106,12 +1062,18 @@ organic rankine cycle,FOM,2.0,%/year,"Aghahosseini, Breyer 2020: From hot rock t organic rankine cycle,electricity-input,0.12,MWh_el/MWh_th,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551; Breede et al. 2015: Overcoming challenges in the classification of deep geothermal potential, https://eprints.gla.ac.uk/169585/","Heat-input, Electricity-output. This is a rough estimate, depends on input temperature, implies ~150 C.",2020.0 organic rankine cycle,investment,1376.0,EUR/kW_el,Tartiere and Astolfi 2017: A world overview of the organic Rankine cycle market,"Low rollout complicates the estimation, compounded by a dependence both on plant size and temperature, converted from 1500 USD/kW using currency conversion 1.09 USD = 1 EUR.",2020.0 organic rankine cycle,lifetime,30.0,years,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551",,2020.0 +perennials gbr,FOM,0.0,%year,Own assumption,,2015.0 +perennials gbr,VOM,43.2317,EUR/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,"includes purchase of perennial crops and sales of proteine concentrate, table 8.1 wages, maintenance and auxiliary costs",2015.0 +perennials gbr,biogas-output,0.1947,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,electricity-input,0.0733,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,investment,1371168.1394,EUR/tDM/h,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,includes cost for biogas plant without upgrading,2015.0 +perennials gbr,lifetime,25.0,years,Own assumption,,2015.0 ror,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,investment,3412.2266,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 ror,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 seawater RO desalination,electricity-input,0.003,MWHh_el/t_H2O,"Caldera et al. (2016): Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",Desalination using SWRO. Assume medium salinity of 35 Practical Salinity Units (PSUs) = 35 kg/m^3., -seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2015.0 +seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, seawater desalination,electricity-input,3.0348,kWh/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",, seawater desalination,investment,34796.4978,EUR/(m^3-H2O/h),"Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402), Table 4.",,2015.0 seawater desalination,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, @@ -1137,7 +1099,7 @@ solar-utility,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_ solar-utility single-axis tracking,FOM,2.2884,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Fixed O&M [2020-EUR/MW_e/y],2020.0 solar-utility single-axis tracking,investment,454.4703,EUR/kW_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Nominal investment [2020-MEUR/MW_e],2020.0 solar-utility single-axis tracking,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Technical lifetime [years],2020.0 -solid biomass,CO2 intensity,0.3667,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, +solid biomass,CO2 intensity,0.3757,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, solid biomass,fuel,13.6489,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOWOOW1 (secondary forest residue wood chips), ENS_Ref for 2040",,2010.0 solid biomass boiler steam,FOM,6.0754,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Fixed O&M,2019.0 solid biomass boiler steam,VOM,2.8448,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Variable O&M,2019.0 @@ -1149,7 +1111,7 @@ solid biomass boiler steam CC,VOM,2.8448,EUR/MWh,"Danish Energy Agency, technolo solid biomass boiler steam CC,efficiency,0.89,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1e Steam boiler Wood: Total efficiency, net, annual average",2019.0 solid biomass boiler steam CC,investment,595.0455,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Nominal investment,2019.0 solid biomass boiler steam CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Technical lifetime,2019.0 -solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, solid biomass to hydrogen,efficiency,0.56,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,investment,3707.4795,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 diff --git a/outputs/costs_2035.csv b/outputs/costs_2035.csv index d375edb0..1d8f0a2c 100644 --- a/outputs/costs_2035.csv +++ b/outputs/costs_2035.csv @@ -1,8 +1,4 @@ technology,parameter,value,unit,source,further description,currency_year -Alkaline electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,efficiency,0.715,p.u.,ICCT IRA e-fuels assumptions ,, -Alkaline electrolyzer,investment,756.8962,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Ammonia cracker,FOM,4.3,%/year,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 7.","Estimated based on Labour cost rate, Maintenance cost rate, Insurance rate, Admin. cost rate and Chemical & other consumables cost rate.",2015.0 Ammonia cracker,ammonia-input,1.46,MWh_NH3/MWh_H2,"ENGIE et al (2020): Ammonia to Green Hydrogen Feasibility Study (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/880826/HS420_-_Ecuity_-_Ammonia_to_Green_Hydrogen.pdf), Fig. 10.",Assuming a integrated 200t/d cracking and purification facility. Electricity demand (316 MWh per 2186 MWh_LHV H2 output) is assumed to also be ammonia LHV input which seems a fair assumption as the facility has options for a higher degree of integration according to the report)., Ammonia cracker,investment,982536.4099,EUR/MW_H2,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 6.","Calculated. For a small (200 t_NH3/d input) facility. Base cost for facility: 51 MEUR at capacity 20 000m^3_NH3/h = 339 t_NH3/d input. Cost scaling exponent 0.67. Ammonia density 0.7069 kg/m^3. Conversion efficiency of cracker: 0.685. Ammonia LHV: 5.167 MWh/t_NH3.; and @@ -45,25 +41,18 @@ Battery electric (passenger cars),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL Battery electric (trucks),FOM,16.0,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),investment,134700.0,EUR/LKW,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 -BioSNG,C in fuel,0.3496,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,C stored,0.6504,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,CO2 stored,0.2385,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C in fuel,0.3413,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C stored,0.6587,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,CO2 stored,0.2474,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BioSNG,FOM,1.6302,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Fixed O&M",2020.0 BioSNG,VOM,1.7812,EUR/MWh_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Variable O&M",2020.0 BioSNG,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, BioSNG,efficiency,0.6475,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Bio SNG Output",2020.0 BioSNG,investment,1674.855,EUR/kW_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Specific investment",2020.0 BioSNG,lifetime,25.0,years,TODO,"84 Gasif. CFB, Bio-SNG: Technical lifetime",2020.0 -Biomass gasification,efficiency,0.4375,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification,investment,1467.7693,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,FOM,0.02,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,efficiency,0.421,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,investment,3015.5325,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -BtL,C in fuel,0.2805,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,C stored,0.7195,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,CO2 stored,0.2638,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C in fuel,0.2738,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C stored,0.7262,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,CO2 stored,0.2728,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BtL,FOM,2.7484,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Fixed O&M",2020.0 BtL,VOM,1.1305,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Variable O&M",2020.0 BtL,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -109,11 +98,17 @@ CO2 liquefaction,lifetime,25.0,years,"Guesstimate, based on CH4 liquefaction.",, CO2 pipeline,FOM,0.9,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 pipeline,investment,2116.4433,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch onshore pipeline.,2015.0 CO2 pipeline,lifetime,50.0,years,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 +CO2 storage cylinders, FOM,1.0,%/year,AU Foulum,,2020.0 +CO2 storage cylinders, investment,77000.0, EUR/t_CO2,AU Foulum,,2020.0 +CO2 storage cylinders, lifetime,25.0,years,AU Foulum,,2020.0 CO2 storage tank,FOM,1.0,%/year,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,investment,2584.3462,EUR/t_CO2,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, Table 3.","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,lifetime,25.0,years,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 submarine pipeline,FOM,0.5,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 submarine pipeline,investment,4232.8865,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch offshore pipeline.,2015.0 +CO2_industrial_compressor, FOM,4.0,%/year,AU Foulum,,2020.0 +CO2_industrial_compressor, investment,1516000.0, EUR/t/h_CO2,AU Foulum,,2020.0 +CO2_industrial_compressor, lifetime,25.0,years,AU Foulum,,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,FOM,1.6,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,investment,448894.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 @@ -126,29 +121,6 @@ Charging infrastructure fuel cell vehicles trucks,lifetime,30.0,years,PATHS TO A Charging infrastructure slow (purely) battery electric vehicles passenger cars,FOM,1.8,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,investment,1005.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 -Coal gasification,FOM,0.06,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,efficiency,0.6735,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification,investment,399.2305,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,FOM,0.07,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,efficiency,0.609,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,investment,649.5969,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 90%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal-95%-CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -Coal-95%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-95%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,capture_rate,0.99,per unit,"NREL, NREL ATB 2024",, -Coal-99%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,efficiency,0.5,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,capture_rate,0.9,per unit,"NREL, NREL ATB 2024",, -Coal-IGCC-90%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Compressed-Air-Adiabatic-bicharger,FOM,0.9265,%/year,"Viswanathan_2022, p.64 (p.86) Figure 4.14","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 Compressed-Air-Adiabatic-bicharger,efficiency,0.7211,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.52^0.5']}",2020.0 Compressed-Air-Adiabatic-bicharger,investment,946180.9426,EUR/MW,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['Turbine Compressor BOP EPC Management']}",2020.0 @@ -249,15 +221,15 @@ FT fuel transport ship,FOM,5.0,%/year,"Assume comparable tanker as for LOHC tran FT fuel transport ship,capacity,75000.0,t_FTfuel,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,investment,35000000.0,EUR,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,lifetime,15.0,years,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 -Fischer-Tropsch,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +Fischer-Tropsch,FOM,3.0,%/year,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.",,2017.0 Fischer-Tropsch,VOM,3.9346,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",102 Hydrogen to Jet: Variable O&M,2020.0 Fischer-Tropsch,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -Fischer-Tropsch,carbondioxide-input,0.32,t_CO2/MWh_FT,ICCT IRA e-fuels assumptions ,"Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", -Fischer-Tropsch,efficiency,0.7,per unit,ICCT IRA e-fuels assumptions ,, -Fischer-Tropsch,electricity-input,0.04,MWh_el/MWh_FT,ICCT IRA e-fuels assumptions ,"0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,hydrogen-input,1.43,MWh_H2/MWh_FT,ICCT IRA e-fuels assumptions ,"0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,investment,1509724.4026,USD/MW_FT,ICCT IRA e-fuels assumptions ,"Well developed technology, no significant learning expected.",2022.0 -Fischer-Tropsch,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,,2020.0 +Fischer-Tropsch,carbondioxide-input,0.3135,t_CO2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", +Fischer-Tropsch,efficiency,0.799,per unit,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.2.",,2017.0 +Fischer-Tropsch,electricity-input,0.007,MWh_el/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,hydrogen-input,1.392,MWh_H2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,investment,657729.5552,EUR/MW_FT,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected.",2017.0 +Fischer-Tropsch,lifetime,20.0,years,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.",,2017.0 Gasnetz,FOM,2.5,%,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,investment,28.0,EUR/kWGas,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,lifetime,30.0,years,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 @@ -265,7 +237,7 @@ General liquid hydrocarbon storage (crude),FOM,6.25,%/year,"Stelter and Nishida General liquid hydrocarbon storage (crude),investment,137.8999,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed 20% lower than for product storage. Crude or middle distillate tanks are usually larger compared to product storage due to lower requirements on safety and different construction method. Reference size used here: 80 000 – 120 000 m^3 .,2012.0 General liquid hydrocarbon storage (crude),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 General liquid hydrocarbon storage (product),FOM,6.25,%/year,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , figure 7 and pg. 12 .",Assuming ca. 10 EUR/m^3/a (center value between stand alone and addon facility).,2012.0 -General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 - 60 000 m^3 .,2012.0 +General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 – 60 000 m^3 .,2012.0 General liquid hydrocarbon storage (product),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 Gravity-Brick-bicharger,FOM,1.5,%/year,"Viswanathan_2022, p.76 (p.98) Sentence 1 in 4.7.2 Operating Costs","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['1.5 percent of capital cost']}",2020.0 Gravity-Brick-bicharger,efficiency,0.9274,per unit,"Viswanathan_2022, p.77 (p.99) Table 4.36","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.86^0.5']}",2020.0 @@ -341,15 +313,13 @@ HVDC underground,investment,1008.2934,EUR/MW/km,Härtel et al. (2017): https://d HVDC underground,lifetime,40.0,years,Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .,"Based on estimated costs for a NA-EU connector (bidirectional,4 GW, 3000km length and ca. 3000m depth). Costs in return based on existing/currently under construction undersea cables. (same as for HVDC submarine)",2018.0 Haber-Bosch,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 Haber-Bosch,VOM,0.0225,EUR/MWh_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Variable O&M,2015.0 +Haber-Bosch,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +Haber-Bosch,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 Haber-Bosch,electricity-input,0.2473,MWh_el/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), table 11.",Assume 5 GJ/t_NH3 for compressors and NH3 LHV = 5.16666 MWh/t_NH3., Haber-Bosch,hydrogen-input,1.1484,MWh_H2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.","178 kg_H2 per t_NH3, LHV for both assumed.", Haber-Bosch,investment,1327.0808,EUR/kW_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 Haber-Bosch,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 Haber-Bosch,nitrogen-input,0.1597,t_N2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.",".33 MWh electricity are required for ASU per t_NH3, considering 0.4 MWh are required per t_N2 and LHV of NH3 of 5.1666 Mwh.", -Heavy oil partial oxidation,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,efficiency,0.734,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Heavy oil partial oxidation,investment,491.0535,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, HighT-Molten-Salt-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 HighT-Molten-Salt-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 HighT-Molten-Salt-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -466,18 +436,6 @@ Methanol steam reforming,FOM,4.0,%/year,"Niermann et al. (2021): Liquid Organic Methanol steam reforming,investment,18016.8665,EUR/MW_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.","For high temperature steam reforming plant with a capacity of 200 MW_H2 output (6t/h). Reference plant of 1 MW (30kg_H2/h) costs 150kEUR, scale factor of 0.6 assumed.",2020.0 Methanol steam reforming,lifetime,20.0,years,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",,2020.0 Methanol steam reforming,methanol-input,1.201,MWh_MeOH/MWh_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",Assuming per 1 t_H2 (with LHV 33.3333 MWh/t): 4.5 MWh_th and 3.2 MWh_el are required. We assume electricity can be substituted / provided with 1:1 as heat energy., -NG 2-on-1 Combined Cycle (F-Frame),efficiency,0.573,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame),lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,efficiency,0.527,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,capture_rate,0.97,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,efficiency,0.525,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, NH3 (l) storage tank incl. liquefaction,FOM,2.0,%/year,"Guesstimate, based on H2 (l) storage tank.",,2010.0 NH3 (l) storage tank incl. liquefaction,investment,166.8201,EUR/MWh_NH3,"Calculated based on Morgan E. 2013: doi:10.7275/11KT-3F59 , Fig. 55, Fig 58.","Based on estimated for a double-wall liquid ammonia tank (~ambient pressure, -33°C), inner tank from stainless steel, outer tank from concrete including installations for liquefaction/condensation, boil-off gas recovery and safety installations; the necessary installations make only a small fraction of the total cost. The total cost are driven by material and working time on the tanks. While the costs do not scale strictly linearly, we here assume they do (good approximation c.f. ref. Fig 55.) and take the costs for a 9 kt NH3 (l) tank = 8 M$2010, which is smaller 4-5x smaller than the largest deployed tanks today. @@ -488,14 +446,6 @@ NH3 (l) transport ship,FOM,4.0,%/year,"Cihlar et al 2020 based on IEA 2019, Tabl NH3 (l) transport ship,capacity,53000.0,t_NH3,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,investment,81164200.0,EUR,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,lifetime,20.0,years,"Guess estimated based on H2 (l) tanker, but more mature technology",,2019.0 -Natural gas steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,efficiency,0.7685,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming,investment,180.0632,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,efficiency,0.6515,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,investment,323.8999,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Ni-Zn-bicharger,FOM,2.1198,%/year,"Viswanathan_2022, p.51-52 in section 4.4.2","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Guesstimate 30% assumed of power components every 10 years ']}",2020.0 Ni-Zn-bicharger,efficiency,0.9,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['((0.75-0.87)/2)^0.5 mean value of range efficiency is not RTE but single way AC-store conversion']}",2020.0 Ni-Zn-bicharger,investment,81553.4846,EUR/MW,"Viswanathan_2022, p.59 (p.81) same as Li-LFP","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Power Equipment']}",2020.0 @@ -508,10 +458,6 @@ OCGT,VOM,4.762,EUR/MWh,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx OCGT,efficiency,0.415,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","52 OCGT - Natural gas: Electricity efficiency, annual average",2015.0 OCGT,investment,454.3898,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Specific investment,2015.0 OCGT,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Technical lifetime,2015.0 -PEM electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,efficiency,0.695,p.u.,ICCT IRA e-fuels assumptions ,, -PEM electrolyzer,investment,905.5166,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, PHS,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 @@ -534,19 +480,15 @@ Pumped-Storage-Hydro-bicharger,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90 Pumped-Storage-Hydro-store,FOM,0.43,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['derived']}",2020.0 Pumped-Storage-Hydro-store,investment,57074.0625,EUR/MWh,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['Reservoir Construction & Infrastructure']}",2020.0 Pumped-Storage-Hydro-store,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 -SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 +SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", SMR,efficiency,0.76,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR,investment,522201.0492,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 -SMR CC,capture_rate,0.9,per unit,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates between 54%-90%, +SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", +SMR CC,capture_rate,0.9,EUR/MW_CH4,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates betwen 54%-90%, SMR CC,efficiency,0.69,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR CC,investment,605753.2171,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR CC,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SOEC,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,efficiency,0.855,p.u.,ICCT IRA e-fuels assumptions ,, -SOEC,investment,1031.4435,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Sand-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 Sand-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 Sand-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -558,10 +500,6 @@ Sand-discharger,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier Sand-store,FOM,0.3308,%/year,"Viswanathan_2022, p 104 (p.126)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['not provided calculated as for hydrogen']}",2020.0 Sand-store,investment,6700.8517,EUR/MWh,"Viswanathan_2022, p.100 (p.122)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['SB and BOS 0.85 of 2021 value']}",2020.0 Sand-store,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['NULL']}",2020.0 -Solid biomass steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,efficiency,0.712,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Solid biomass steam reforming,investment,590.7702,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Steam methane reforming,FOM,3.0,%/year,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 Steam methane reforming,investment,497454.611,EUR/MW_H2,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW). Currency conversion 1.17 USD = 1 EUR.,2015.0 Steam methane reforming,lifetime,30.0,years,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 @@ -604,6 +542,8 @@ Zn-Br-Nonflow-store,FOM,0.2244,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrie Zn-Br-Nonflow-store,investment,239220.5823,EUR/MWh,"Viswanathan_2022, p.59 (p.81) Table 4.14","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['DC storage block']}",2020.0 Zn-Br-Nonflow-store,lifetime,15.0,years,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['NULL']}",2020.0 air separation unit,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 +air separation unit,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +air separation unit,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 air separation unit,electricity-input,0.25,MWh_el/t_N2,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), p.288.","For consistency reasons use value from Danish Energy Agency. DEA also reports range of values (0.2-0.4 MWh/t_N2) on pg. 288. Other efficienices reported are even higher, e.g. 0.11 Mwh/t_N2 from Morgan (2013): Techno-Economic Feasibility Study of Ammonia Plants Powered by Offshore Wind .", air separation unit,investment,745954.8206,EUR/t_N2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 air separation unit,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 @@ -618,12 +558,13 @@ battery inverter,lifetime,10.0,years,"Danish Energy Agency, technology_data_cata battery storage,investment,124.8702,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Energy storage expansion cost investment,2015.0 battery storage,lifetime,27.5,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Technical lifetime,2015.0 biochar pyrolysis,FOM,3.3913,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Fixed O&M",2020.0 -biochar pyrolysis,VOM,823.497,EUR/MWh_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 -biochar pyrolysis,efficiency-biochar,0.404,MWh_biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency biochar",2020.0 -biochar pyrolysis,efficiency-heat,0.4848,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency heat",2020.0 -biochar pyrolysis,investment,147972.11,EUR/kW_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 +biochar pyrolysis,VOM,47.6777,EUR/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 +biochar pyrolysis,biomass input,7.6748,MWh_biomass/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Biomass Input",2020.0 +biochar pyrolysis,electricity input,0.3184,MWh_e/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: El-Input",2020.0 +biochar pyrolysis,heat output,3.7859,MWh_th/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: H-Output",2020.0 +biochar pyrolysis,investment,8567083.3233,EUR/t_CO2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 biochar pyrolysis,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Technical lifetime",2020.0 -biochar pyrolysis,yield-biochar,0.0582,ton biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 +biochar pyrolysis,yield-biochar,0.0597,t_biochar/MWh_biomass,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 biodiesel crops,fuel,137.5968,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIORPS1 (rape seed), ENS_BaU_GFTM",,2010.0 bioethanol crops,fuel,84.2795,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOCRP11 (Bioethanol barley, wheat, grain maize, oats, other cereals and rye), ENS_BaU_GFTM",,2010.0 biogas,CO2 stored,0.0868,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, @@ -640,10 +581,17 @@ biogas CC,efficiency,1.0,per unit,Assuming input biomass is already given in bio biogas CC,investment,938.7177,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Specific investment",2020.0 biogas CC,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Technical lifetime",2020.0 biogas manure,fuel,19.8729,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOGAS1 (manure), ENS_BaU_GFTM",,2010.0 -biogas plus hydrogen,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Fixed O&M,2020.0 -biogas plus hydrogen,VOM,3.4454,EUR/MWh_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 -biogas plus hydrogen,investment,723.5374,EUR/kW_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 +biogas plus hydrogen,Biogas Input,1.1522,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Biogas Consumption,",2020.0 +biogas plus hydrogen,CO2 Input,0.1235,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: CO2 Input,",2020.0 +biogas plus hydrogen,Methane Output,1.9348,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Methane Output,",2020.0 +biogas plus hydrogen,VOM,6.6661,EUR/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 +biogas plus hydrogen,electricity input,0.0217,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: El-Input,",2020.0 +biogas plus hydrogen,heat output,0.2174,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: H-Output,",2020.0 +biogas plus hydrogen,hydrogen input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Hydrogen Consumption,",2020.0 +biogas plus hydrogen,investment,1399.8875,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 biogas plus hydrogen,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Technical lifetime,2020.0 +biogas storage, investment,14.45, EUR/kWh,AU Foulum,,2020.0 +biogas storage, lifetime,25.0,years,AU Foulum,,2020.0 biogas upgrading,FOM,17.3842,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Fixed O&M ",2020.0 biogas upgrading,VOM,3.373,EUR/MWh output,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Variable O&M",2020.0 biogas upgrading,investment,153.313,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: investment (upgrading, methane redution and grid injection)",2020.0 @@ -688,9 +636,9 @@ biomass boiler,efficiency,0.865,per unit,"Danish Energy Agency, technologydatafo biomass boiler,investment,670.7159,EUR/kW_th,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Specific investment",2015.0 biomass boiler,lifetime,20.0,years,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Technical lifetime",2015.0 biomass boiler,pelletizing cost,9.0,EUR/MWh_pellets,Assumption based on doi:10.1016/j.rser.2019.109506,,2019.0 -biomass-to-methanol,C in fuel,0.4197,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,C stored,0.5803,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,CO2 stored,0.2128,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C in fuel,0.4096,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C stored,0.5904,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,CO2 stored,0.2218,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, biomass-to-methanol,FOM,1.5331,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Fixed O&M,2020.0 biomass-to-methanol,VOM,14.4653,EUR/MWh_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Variable O&M,2020.0 biomass-to-methanol,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -699,6 +647,15 @@ biomass-to-methanol,efficiency-electricity,0.02,MWh_e/MWh_th,"Danish Energy Agen biomass-to-methanol,efficiency-heat,0.22,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","97 Methanol from biomass gasif.: District heat Output,",2020.0 biomass-to-methanol,investment,2681.013,EUR/kW_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Specific investment,2020.0 biomass-to-methanol,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Technical lifetime,2020.0 +biomethanation,Biogas Input,1.1444,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Biogas Consumption,",2020.0 +biomethanation,CO2 Input,0.165,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: CO2 Input,",2020.0 +biomethanation,FOM,5.9259,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Fixed O&M ,2020.0 +biomethanation,Hydrogen Input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Hydrogen Input,",2020.0 +biomethanation,Methane Output,1.9673,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Methane Output,",2020.0 +biomethanation,electricity input,0.0417,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: El-Input,",2020.0 +biomethanation,heat output,0.1667,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: H-Output,",2020.0 +biomethanation,investment,1110.9375,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Specific investment ,2020.0 +biomethanation,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Technical lifetime,2020.0 cement capture,FOM,3.0,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,capture_rate,0.925,per unit,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,compression-electricity-input,0.08,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 @@ -785,7 +742,7 @@ central solid biomass CHP CC,c_b,0.3485,50°C/100°C,"Danish Energy Agency, tech central solid biomass CHP CC,c_v,1.0,50°C/100°C,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Cv coefficient",2015.0 central solid biomass CHP CC,efficiency,0.2687,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Electricity efficiency, net, annual average",2015.0 central solid biomass CHP CC,efficiency-heat,0.8257,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Heat efficiency, net, annual average",2015.0 -central solid biomass CHP CC,investment,5061.4763,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 +central solid biomass CHP CC,investment,5099.9089,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 central solid biomass CHP CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Technical lifetime",2015.0 central solid biomass CHP powerboost CC,FOM,2.8627,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Fixed O&M",2015.0 central solid biomass CHP powerboost CC,VOM,4.8732,EUR/MWh_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Variable O&M ",2015.0 @@ -812,23 +769,23 @@ central water-sourced heat pump,VOM,1.5768,EUR/MWh,"Danish Energy Agency, techno central water-sourced heat pump,efficiency,3.84,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Total efficiency , net, annual average",2015.0 central water-sourced heat pump,investment,1058.2216,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Specific investment",2015.0 central water-sourced heat pump,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Technical lifetime",2015.0 -clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 +clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, clean water tank storage,investment,69.1286,EUR/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 clean water tank storage,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, coal,CO2 intensity,0.3361,tCO2/MWh_th,Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018,, coal,FOM,1.31,%/year,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (39.5+91.25) USD/kW_e/a /2 / (1.09 USD/EUR) / investment cost * 100.",2023.0 coal,VOM,3.2612,EUR/MWh_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (3+5.5)USD/MWh_e/2 / (1.09 USD/EUR).",2023.0 -coal,efficiency,0.356,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 +coal,efficiency,0.33,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 coal,fuel,9.5542,EUR/MWh_th,"DIW (2013): Current and propsective costs of electricity generation until 2050, http://hdl.handle.net/10419/80348 , pg. 80 text below figure 10, accessed: 2023-12-14.","Based on IEA 2011 data, 99 USD/t.",2010.0 coal,investment,3827.1629,EUR/kW_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Higher costs include coal plants with CCS, but since using here for calculating the average nevertheless. Calculated based on average of listed range, i.e. (3200+6775) USD/kW_e/2 / (1.09 USD/EUR).",2023.0 coal,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 -csp-tower,FOM,1.2,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,2020.0 +csp-tower,FOM,1.2,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,1.0 csp-tower,investment,104.17,"EUR/kW_th,dp",ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower,lifetime,30.0,years,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),-,2020.0 -csp-tower TES,FOM,1.2,%/year,see solar-tower.,-,2020.0 +csp-tower TES,FOM,1.2,%/year,see solar-tower.,-,1.0 csp-tower TES,investment,13.955,EUR/kWh_th,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower TES,lifetime,30.0,years,see solar-tower.,-,2020.0 -csp-tower power block,FOM,1.2,%/year,see solar-tower.,-,2020.0 +csp-tower power block,FOM,1.2,%/year,see solar-tower.,-,1.0 csp-tower power block,investment,729.755,EUR/kW_e,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower power block,lifetime,30.0,years,see solar-tower.,-,2020.0 decentral CHP,FOM,3.0,%/year,HP, from old pypsa cost assumptions,2015.0 @@ -874,19 +831,18 @@ decentral water tank storage,energy to power ratio,0.15,h,"Danish Energy Agency, decentral water tank storage,investment,433.8709,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Specific investment,2015.0 decentral water tank storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Technical lifetime,2015.0 digestible biomass,fuel,17.0611,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOAGRW1, ENS_Ref for 2040",,2010.0 -digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, digestible biomass to hydrogen,efficiency,0.39,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,investment,3442.6595,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 -direct air capture,FOM,1.3,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,FOM,4.95,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-electricity-input,0.15,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-heat-output,0.2,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,electricity-input,0.24,MWh_el/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 -direct air capture,heat-input,1.17,MWh_th/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 +direct air capture,electricity-input,0.4,MWh_el/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","0.4 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 0.182 MWh based on Breyer et al (2019). Should already include electricity for water scrubbing and compression (high quality CO2 output).",2020.0 +direct air capture,heat-input,1.6,MWh_th/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","Thermal energy demand. Provided via air-sourced heat pumps. 1.6 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 1.102 MWh based on Breyer et al (2019).",2020.0 direct air capture,heat-output,0.875,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,investment,11034260.0394,USD/t_CO2/h,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,investment,5500000.0,EUR/(tCO2/h),"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,lifetime,20.0,years,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,years,30.0,years,ICCT IRA e-fuels assumptions ,, direct firing gas,FOM,1.1667,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Fixed O&M,2019.0 direct firing gas,VOM,0.2807,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Variable O&M,2019.0 direct firing gas,efficiency,1.0,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","312.a Direct firing Natural Gas: Total efficiency, net, annual average",2019.0 @@ -927,8 +883,8 @@ electric boiler steam,VOM,0.8333,EUR/MWh,"Danish Energy Agency, technology_data_ electric boiler steam,efficiency,0.99,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","310.1 Electric boiler steam : Total efficiency, net, annual average",2019.0 electric boiler steam,investment,70.49,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Nominal investment,2019.0 electric boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Technical lifetime,2019.0 -electric steam cracker,FOM,3.0,%/year,Guesstimate,,2015.0 -electric steam cracker,VOM,190.4799,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 +electric steam cracker,FOM,3.0,%/year,Guesstimate,, +electric steam cracker,VOM,190.4799,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 electric steam cracker,carbondioxide-output,0.55,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), ",The report also references another source with 0.76 t_CO2/t_HVC, electric steam cracker,electricity-input,2.7,MWh_el/t_HVC,"Lechtenböhmer et al. (2016): 10.1016/j.energy.2016.07.110, Section 4.3, page 6.",Assuming electrified processing., electric steam cracker,investment,11124025.7434,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -940,14 +896,14 @@ electricity distribution grid,lifetime,40.0,years,TODO, from old pypsa cost assu electricity grid connection,FOM,2.0,%/year,TODO, from old pypsa cost assumptions,2015.0 electricity grid connection,investment,148.151,EUR/kW,DEA, from old pypsa cost assumptions,2015.0 electricity grid connection,lifetime,40.0,years,TODO, from old pypsa cost assumptions,2015.0 -electrobiofuels,C in fuel,0.9281,per unit,Stoichiometric calculation,, -electrobiofuels,FOM,2.7484,%/year,combination of BtL and electrofuels,,2015.0 -electrobiofuels,VOM,3.2735,EUR/MWh_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,C in fuel,0.9274,per unit,Stoichiometric calculation,, +electrobiofuels,FOM,2.7484,%/year,combination of BtL and electrofuels,, +electrobiofuels,VOM,3.8482,EUR/MWh_th,combination of BtL and electrofuels,,2017.0 electrobiofuels,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -electrobiofuels,efficiency-biomass,1.3233,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-hydrogen,1.081,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-tot,0.595,per unit,Stoichiometric calculation,, -electrobiofuels,investment,980042.9098,EUR/kW_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,efficiency-biomass,1.3548,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-hydrogen,1.2225,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-tot,0.6426,per unit,Stoichiometric calculation,, +electrobiofuels,investment,432732.1103,EUR/kW_th,combination of BtL and electrofuels,,2017.0 electrolysis,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Fixed O&M ,2020.0 electrolysis,efficiency,0.6374,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Hydrogen Output,2020.0 electrolysis,efficiency-heat,0.2039,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: - hereof recoverable for district heating,2020.0 @@ -971,7 +927,7 @@ gas boiler steam,VOM,1.007,EUR/MWh,"Danish Energy Agency, technology_data_for_in gas boiler steam,efficiency,0.93,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1c Steam boiler Gas: Total efficiency, net, annual average",2019.0 gas boiler steam,investment,45.7727,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Nominal investment,2019.0 gas boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Technical lifetime,2019.0 -gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenance, salt cavern (units converted)",2015.0 +gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenace, salt cavern (units converted)",2015.0 gas storage,investment,0.0348,EUR/kWh,Danish Energy Agency,"150 Underground Storage of Gas, Establishment of one cavern (units converted)",2015.0 gas storage,lifetime,100.0,years,TODO no source,"estimation: most underground storage are already build, they do have a long lifetime",2015.0 gas storage charger,investment,15.1737,EUR/kW,Danish Energy Agency,"150 Underground Storage of Gas, Process equipment (units converted)",2015.0 @@ -995,14 +951,14 @@ hydro,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pyp hydro,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 hydro,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 hydro,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 -hydrogen storage compressor,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg., -hydrogen storage compressor,investment,2.0291,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage compressor,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage tank type 1,investment,15.0133,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage tank type 1,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-, +hydrogen storage compressor,FOM,4.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg.,2020.0 +hydrogen storage compressor,investment,87.69,EUR/kW_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.","2923 EUR/kg_H2. For a 206 kg/h compressor. Base CAPEX 40 528 EUR/kW_el with scale factor 0.4603. kg_H2 converted to MWh using LHV. Pressure range: 30 bar in, 250 bar out.",2020.0 +hydrogen storage compressor,lifetime,15.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage tank type 1,FOM,2.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,investment,13.5,EUR/kWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.","450 EUR/kg_H2 converted with LHV to MWh. For a type 1 hydrogen storage tank (steel, 15-250 bar). Currency year assumed 2020 for initial publication of reference; observe note in SI.4.3 that no currency year is explicitly stated in the reference.",2020.0 +hydrogen storage tank type 1,lifetime,20.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 hydrogen storage tank type 1 including compressor,FOM,1.3897,%/year,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Fixed O&M,2015.0 hydrogen storage tank type 1 including compressor,investment,38.075,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Specific investment,2015.0 hydrogen storage tank type 1 including compressor,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Technical lifetime,2015.0 @@ -1049,8 +1005,8 @@ methanol-to-kerosene,hydrogen-input,0.0279,MWh_H2/MWh_kerosene,"Concawe (2022): methanol-to-kerosene,investment,251750.0,EUR/MW_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",,2020.0 methanol-to-kerosene,lifetime,30.0,years,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",, methanol-to-kerosene,methanol-input,1.0764,MWh_MeOH/MWh_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 6.","Assuming LHV 11.94 kWh/kg for kerosene, 5.54 kWh/kg for methanol, 33.3 kWh/kg for hydrogen.", -methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker,2015.0 -methanol-to-olefins/aromatics,VOM,31.7466,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 +methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker, +methanol-to-olefins/aromatics,VOM,31.7466,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 methanol-to-olefins/aromatics,carbondioxide-output,0.6107,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Sections 4.5 (for ethylene and propylene) and 4.6 (for BTX)","Weighted average: 0.4 t_MeOH/t_ethylene+propylene for 21.7 Mt of ethylene and 17 Mt of propylene, 1.13 t_CO2/t_BTX for 15.7 Mt of BTX. The report also references process emissions of 0.55 t_MeOH/t_ethylene+propylene elsewhere. ", methanol-to-olefins/aromatics,electricity-input,1.3889,MWh_el/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), page 69",5 GJ/t_HVC , methanol-to-olefins/aromatics,investment,2781006.4359,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -1077,7 +1033,7 @@ nuclear,investment,8594.1354,EUR/kW_e,"Lazard's levelized cost of energy analysi nuclear,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 offwind,FOM,2.25,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Fixed O&M [EUR/MW_e/y, 2020]",2020.0 offwind,VOM,0.0212,EUR/MWhel,RES costs made up to fix curtailment order, from old pypsa cost assumptions,2015.0 -offwind,investment,1622.2443,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs subtracted from investment costs",2020.0 +offwind,investment,1622.2443,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs substracted from investment costs",2020.0 offwind,lifetime,30.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",21 Offshore turbines: Technical lifetime [years],2020.0 offwind-ac-connection-submarine,investment,2841.3251,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 offwind-ac-connection-underground,investment,1420.1334,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 @@ -1106,12 +1062,18 @@ organic rankine cycle,FOM,2.0,%/year,"Aghahosseini, Breyer 2020: From hot rock t organic rankine cycle,electricity-input,0.12,MWh_el/MWh_th,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551; Breede et al. 2015: Overcoming challenges in the classification of deep geothermal potential, https://eprints.gla.ac.uk/169585/","Heat-input, Electricity-output. This is a rough estimate, depends on input temperature, implies ~150 C.",2020.0 organic rankine cycle,investment,1376.0,EUR/kW_el,Tartiere and Astolfi 2017: A world overview of the organic Rankine cycle market,"Low rollout complicates the estimation, compounded by a dependence both on plant size and temperature, converted from 1500 USD/kW using currency conversion 1.09 USD = 1 EUR.",2020.0 organic rankine cycle,lifetime,30.0,years,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551",,2020.0 +perennials gbr,FOM,0.0,%year,Own assumption,,2015.0 +perennials gbr,VOM,43.2317,EUR/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,"includes purchase of perennial crops and sales of proteine concentrate, table 8.1 wages, maintenance and auxiliary costs",2015.0 +perennials gbr,biogas-output,0.1947,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,electricity-input,0.0733,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,investment,1371168.1394,EUR/tDM/h,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,includes cost for biogas plant without upgrading,2015.0 +perennials gbr,lifetime,25.0,years,Own assumption,,2015.0 ror,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,investment,3412.2266,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 ror,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 seawater RO desalination,electricity-input,0.003,MWHh_el/t_H2O,"Caldera et al. (2016): Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",Desalination using SWRO. Assume medium salinity of 35 Practical Salinity Units (PSUs) = 35 kg/m^3., -seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2015.0 +seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, seawater desalination,electricity-input,3.0348,kWh/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",, seawater desalination,investment,31312.5066,EUR/(m^3-H2O/h),"Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402), Table 4.",,2015.0 seawater desalination,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, @@ -1137,7 +1099,7 @@ solar-utility,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_ solar-utility single-axis tracking,FOM,2.3606,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Fixed O&M [2020-EUR/MW_e/y],2020.0 solar-utility single-axis tracking,investment,419.3908,EUR/kW_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Nominal investment [2020-MEUR/MW_e],2020.0 solar-utility single-axis tracking,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Technical lifetime [years],2020.0 -solid biomass,CO2 intensity,0.3667,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, +solid biomass,CO2 intensity,0.3757,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, solid biomass,fuel,13.6489,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOWOOW1 (secondary forest residue wood chips), ENS_Ref for 2040",,2010.0 solid biomass boiler steam,FOM,6.1236,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Fixed O&M,2019.0 solid biomass boiler steam,VOM,2.8564,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Variable O&M,2019.0 @@ -1149,7 +1111,7 @@ solid biomass boiler steam CC,VOM,2.8564,EUR/MWh,"Danish Energy Agency, technolo solid biomass boiler steam CC,efficiency,0.89,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1e Steam boiler Wood: Total efficiency, net, annual average",2019.0 solid biomass boiler steam CC,investment,581.3136,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Nominal investment,2019.0 solid biomass boiler steam CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Technical lifetime,2019.0 -solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, solid biomass to hydrogen,efficiency,0.56,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,investment,3442.6595,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 diff --git a/outputs/costs_2040.csv b/outputs/costs_2040.csv index 0cb131c0..4379a6c4 100644 --- a/outputs/costs_2040.csv +++ b/outputs/costs_2040.csv @@ -1,8 +1,4 @@ technology,parameter,value,unit,source,further description,currency_year -Alkaline electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,efficiency,0.74,p.u.,ICCT IRA e-fuels assumptions ,, -Alkaline electrolyzer,investment,680.8061,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Ammonia cracker,FOM,4.3,%/year,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 7.","Estimated based on Labour cost rate, Maintenance cost rate, Insurance rate, Admin. cost rate and Chemical & other consumables cost rate.",2015.0 Ammonia cracker,ammonia-input,1.46,MWh_NH3/MWh_H2,"ENGIE et al (2020): Ammonia to Green Hydrogen Feasibility Study (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/880826/HS420_-_Ecuity_-_Ammonia_to_Green_Hydrogen.pdf), Fig. 10.",Assuming a integrated 200t/d cracking and purification facility. Electricity demand (316 MWh per 2186 MWh_LHV H2 output) is assumed to also be ammonia LHV input which seems a fair assumption as the facility has options for a higher degree of integration according to the report)., Ammonia cracker,investment,841127.4391,EUR/MW_H2,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 6.","Calculated. For a small (200 t_NH3/d input) facility. Base cost for facility: 51 MEUR at capacity 20 000m^3_NH3/h = 339 t_NH3/d input. Cost scaling exponent 0.67. Ammonia density 0.7069 kg/m^3. Conversion efficiency of cracker: 0.685. Ammonia LHV: 5.167 MWh/t_NH3.; and @@ -45,25 +41,18 @@ Battery electric (passenger cars),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL Battery electric (trucks),FOM,16.0,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),investment,133000.0,EUR/LKW,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 -BioSNG,C in fuel,0.3591,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,C stored,0.6409,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,CO2 stored,0.235,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C in fuel,0.3505,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C stored,0.6495,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,CO2 stored,0.244,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BioSNG,FOM,1.6226,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Fixed O&M",2020.0 BioSNG,VOM,1.7546,EUR/MWh_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Variable O&M",2020.0 BioSNG,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, BioSNG,efficiency,0.665,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Bio SNG Output",2020.0 BioSNG,investment,1648.27,EUR/kW_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Specific investment",2020.0 BioSNG,lifetime,25.0,years,TODO,"84 Gasif. CFB, Bio-SNG: Technical lifetime",2020.0 -Biomass gasification,efficiency,0.4667,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification,investment,1467.7693,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,FOM,0.02,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,efficiency,0.514,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,investment,3015.5325,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -BtL,C in fuel,0.2922,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,C stored,0.7078,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,CO2 stored,0.2595,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C in fuel,0.2852,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C stored,0.7148,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,CO2 stored,0.2685,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BtL,FOM,2.8364,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Fixed O&M",2020.0 BtL,VOM,1.1311,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Variable O&M",2020.0 BtL,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -109,11 +98,17 @@ CO2 liquefaction,lifetime,25.0,years,"Guesstimate, based on CH4 liquefaction.",, CO2 pipeline,FOM,0.9,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 pipeline,investment,2116.4433,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch onshore pipeline.,2015.0 CO2 pipeline,lifetime,50.0,years,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 +CO2 storage cylinders, FOM,1.0,%/year,AU Foulum,,2020.0 +CO2 storage cylinders, investment,77000.0, EUR/t_CO2,AU Foulum,,2020.0 +CO2 storage cylinders, lifetime,25.0,years,AU Foulum,,2020.0 CO2 storage tank,FOM,1.0,%/year,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,investment,2584.3462,EUR/t_CO2,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, Table 3.","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,lifetime,25.0,years,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 submarine pipeline,FOM,0.5,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 submarine pipeline,investment,4232.8865,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch offshore pipeline.,2015.0 +CO2_industrial_compressor, FOM,4.0,%/year,AU Foulum,,2020.0 +CO2_industrial_compressor, investment,1516000.0, EUR/t/h_CO2,AU Foulum,,2020.0 +CO2_industrial_compressor, lifetime,25.0,years,AU Foulum,,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,FOM,1.6,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,investment,448894.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 @@ -126,29 +121,6 @@ Charging infrastructure fuel cell vehicles trucks,lifetime,30.0,years,PATHS TO A Charging infrastructure slow (purely) battery electric vehicles passenger cars,FOM,1.8,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,investment,1005.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 -Coal gasification,FOM,0.06,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,efficiency,0.7113,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification,investment,399.2305,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,FOM,0.07,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,efficiency,0.609,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,investment,649.5969,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 90%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal-95%-CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -Coal-95%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-95%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,capture_rate,0.99,per unit,"NREL, NREL ATB 2024",, -Coal-99%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,efficiency,0.5,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,capture_rate,0.9,per unit,"NREL, NREL ATB 2024",, -Coal-IGCC-90%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Compressed-Air-Adiabatic-bicharger,FOM,0.9265,%/year,"Viswanathan_2022, p.64 (p.86) Figure 4.14","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 Compressed-Air-Adiabatic-bicharger,efficiency,0.7211,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.52^0.5']}",2020.0 Compressed-Air-Adiabatic-bicharger,investment,946180.9426,EUR/MW,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['Turbine Compressor BOP EPC Management']}",2020.0 @@ -249,15 +221,15 @@ FT fuel transport ship,FOM,5.0,%/year,"Assume comparable tanker as for LOHC tran FT fuel transport ship,capacity,75000.0,t_FTfuel,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,investment,35000000.0,EUR,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,lifetime,15.0,years,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 -Fischer-Tropsch,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +Fischer-Tropsch,FOM,3.0,%/year,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.",,2017.0 Fischer-Tropsch,VOM,3.4029,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",102 Hydrogen to Jet: Variable O&M,2020.0 Fischer-Tropsch,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -Fischer-Tropsch,carbondioxide-input,0.32,t_CO2/MWh_FT,ICCT IRA e-fuels assumptions ,"Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", -Fischer-Tropsch,efficiency,0.7,per unit,ICCT IRA e-fuels assumptions ,, -Fischer-Tropsch,electricity-input,0.04,MWh_el/MWh_FT,ICCT IRA e-fuels assumptions ,"0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,hydrogen-input,1.43,MWh_H2/MWh_FT,ICCT IRA e-fuels assumptions ,"0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,investment,1509724.4026,USD/MW_FT,ICCT IRA e-fuels assumptions ,"Well developed technology, no significant learning expected.",2022.0 -Fischer-Tropsch,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,,2020.0 +Fischer-Tropsch,carbondioxide-input,0.301,t_CO2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", +Fischer-Tropsch,efficiency,0.799,per unit,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.2.",,2017.0 +Fischer-Tropsch,electricity-input,0.007,MWh_el/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,hydrogen-input,1.363,MWh_H2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,investment,611732.6641,EUR/MW_FT,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected.",2017.0 +Fischer-Tropsch,lifetime,20.0,years,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.",,2017.0 Gasnetz,FOM,2.5,%,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,investment,28.0,EUR/kWGas,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,lifetime,30.0,years,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 @@ -265,7 +237,7 @@ General liquid hydrocarbon storage (crude),FOM,6.25,%/year,"Stelter and Nishida General liquid hydrocarbon storage (crude),investment,137.8999,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed 20% lower than for product storage. Crude or middle distillate tanks are usually larger compared to product storage due to lower requirements on safety and different construction method. Reference size used here: 80 000 – 120 000 m^3 .,2012.0 General liquid hydrocarbon storage (crude),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 General liquid hydrocarbon storage (product),FOM,6.25,%/year,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , figure 7 and pg. 12 .",Assuming ca. 10 EUR/m^3/a (center value between stand alone and addon facility).,2012.0 -General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 - 60 000 m^3 .,2012.0 +General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 – 60 000 m^3 .,2012.0 General liquid hydrocarbon storage (product),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 Gravity-Brick-bicharger,FOM,1.5,%/year,"Viswanathan_2022, p.76 (p.98) Sentence 1 in 4.7.2 Operating Costs","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['1.5 percent of capital cost']}",2020.0 Gravity-Brick-bicharger,efficiency,0.9274,per unit,"Viswanathan_2022, p.77 (p.99) Table 4.36","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.86^0.5']}",2020.0 @@ -341,15 +313,13 @@ HVDC underground,investment,1008.2934,EUR/MW/km,Härtel et al. (2017): https://d HVDC underground,lifetime,40.0,years,Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .,"Based on estimated costs for a NA-EU connector (bidirectional,4 GW, 3000km length and ca. 3000m depth). Costs in return based on existing/currently under construction undersea cables. (same as for HVDC submarine)",2018.0 Haber-Bosch,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 Haber-Bosch,VOM,0.0225,EUR/MWh_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Variable O&M,2015.0 +Haber-Bosch,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +Haber-Bosch,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 Haber-Bosch,electricity-input,0.2473,MWh_el/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), table 11.",Assume 5 GJ/t_NH3 for compressors and NH3 LHV = 5.16666 MWh/t_NH3., Haber-Bosch,hydrogen-input,1.1484,MWh_H2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.","178 kg_H2 per t_NH3, LHV for both assumed.", Haber-Bosch,investment,1194.148,EUR/kW_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 Haber-Bosch,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 Haber-Bosch,nitrogen-input,0.1597,t_N2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.",".33 MWh electricity are required for ASU per t_NH3, considering 0.4 MWh are required per t_N2 and LHV of NH3 of 5.1666 Mwh.", -Heavy oil partial oxidation,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,efficiency,0.734,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Heavy oil partial oxidation,investment,491.0535,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, HighT-Molten-Salt-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 HighT-Molten-Salt-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 HighT-Molten-Salt-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -466,18 +436,6 @@ Methanol steam reforming,FOM,4.0,%/year,"Niermann et al. (2021): Liquid Organic Methanol steam reforming,investment,18016.8665,EUR/MW_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.","For high temperature steam reforming plant with a capacity of 200 MW_H2 output (6t/h). Reference plant of 1 MW (30kg_H2/h) costs 150kEUR, scale factor of 0.6 assumed.",2020.0 Methanol steam reforming,lifetime,20.0,years,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",,2020.0 Methanol steam reforming,methanol-input,1.201,MWh_MeOH/MWh_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",Assuming per 1 t_H2 (with LHV 33.3333 MWh/t): 4.5 MWh_th and 3.2 MWh_el are required. We assume electricity can be substituted / provided with 1:1 as heat energy., -NG 2-on-1 Combined Cycle (F-Frame),efficiency,0.573,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame),lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,efficiency,0.527,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,capture_rate,0.97,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,efficiency,0.525,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, NH3 (l) storage tank incl. liquefaction,FOM,2.0,%/year,"Guesstimate, based on H2 (l) storage tank.",,2010.0 NH3 (l) storage tank incl. liquefaction,investment,166.8201,EUR/MWh_NH3,"Calculated based on Morgan E. 2013: doi:10.7275/11KT-3F59 , Fig. 55, Fig 58.","Based on estimated for a double-wall liquid ammonia tank (~ambient pressure, -33°C), inner tank from stainless steel, outer tank from concrete including installations for liquefaction/condensation, boil-off gas recovery and safety installations; the necessary installations make only a small fraction of the total cost. The total cost are driven by material and working time on the tanks. While the costs do not scale strictly linearly, we here assume they do (good approximation c.f. ref. Fig 55.) and take the costs for a 9 kt NH3 (l) tank = 8 M$2010, which is smaller 4-5x smaller than the largest deployed tanks today. @@ -488,14 +446,6 @@ NH3 (l) transport ship,FOM,4.0,%/year,"Cihlar et al 2020 based on IEA 2019, Tabl NH3 (l) transport ship,capacity,53000.0,t_NH3,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,investment,81164200.0,EUR,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,lifetime,20.0,years,"Guess estimated based on H2 (l) tanker, but more mature technology",,2019.0 -Natural gas steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,efficiency,0.7747,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming,investment,180.0632,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,efficiency,0.666,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,investment,323.8999,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Ni-Zn-bicharger,FOM,2.1198,%/year,"Viswanathan_2022, p.51-52 in section 4.4.2","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Guesstimate 30% assumed of power components every 10 years ']}",2020.0 Ni-Zn-bicharger,efficiency,0.9,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['((0.75-0.87)/2)^0.5 mean value of range efficiency is not RTE but single way AC-store conversion']}",2020.0 Ni-Zn-bicharger,investment,81553.4846,EUR/MW,"Viswanathan_2022, p.59 (p.81) same as Li-LFP","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Power Equipment']}",2020.0 @@ -508,10 +458,6 @@ OCGT,VOM,4.762,EUR/MWh,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx OCGT,efficiency,0.42,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","52 OCGT - Natural gas: Electricity efficiency, annual average",2015.0 OCGT,investment,448.1992,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Specific investment,2015.0 OCGT,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Technical lifetime,2015.0 -PEM electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,efficiency,0.71,p.u.,ICCT IRA e-fuels assumptions ,, -PEM electrolyzer,investment,814.2975,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, PHS,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 @@ -534,19 +480,15 @@ Pumped-Storage-Hydro-bicharger,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90 Pumped-Storage-Hydro-store,FOM,0.43,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['derived']}",2020.0 Pumped-Storage-Hydro-store,investment,57074.0625,EUR/MWh,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['Reservoir Construction & Infrastructure']}",2020.0 Pumped-Storage-Hydro-store,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 -SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 +SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", SMR,efficiency,0.76,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR,investment,522201.0492,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 -SMR CC,capture_rate,0.9,per unit,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates between 54%-90%, +SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", +SMR CC,capture_rate,0.9,EUR/MW_CH4,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates betwen 54%-90%, SMR CC,efficiency,0.69,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR CC,investment,605753.2171,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR CC,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SOEC,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,efficiency,0.87,p.u.,ICCT IRA e-fuels assumptions ,, -SOEC,investment,927.3202,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Sand-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 Sand-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 Sand-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -558,10 +500,6 @@ Sand-discharger,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier Sand-store,FOM,0.3308,%/year,"Viswanathan_2022, p 104 (p.126)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['not provided calculated as for hydrogen']}",2020.0 Sand-store,investment,6700.8517,EUR/MWh,"Viswanathan_2022, p.100 (p.122)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['SB and BOS 0.85 of 2021 value']}",2020.0 Sand-store,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['NULL']}",2020.0 -Solid biomass steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,efficiency,0.712,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Solid biomass steam reforming,investment,590.7702,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Steam methane reforming,FOM,3.0,%/year,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 Steam methane reforming,investment,497454.611,EUR/MW_H2,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW). Currency conversion 1.17 USD = 1 EUR.,2015.0 Steam methane reforming,lifetime,30.0,years,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 @@ -604,6 +542,8 @@ Zn-Br-Nonflow-store,FOM,0.2244,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrie Zn-Br-Nonflow-store,investment,239220.5823,EUR/MWh,"Viswanathan_2022, p.59 (p.81) Table 4.14","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['DC storage block']}",2020.0 Zn-Br-Nonflow-store,lifetime,15.0,years,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['NULL']}",2020.0 air separation unit,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 +air separation unit,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +air separation unit,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 air separation unit,electricity-input,0.25,MWh_el/t_N2,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), p.288.","For consistency reasons use value from Danish Energy Agency. DEA also reports range of values (0.2-0.4 MWh/t_N2) on pg. 288. Other efficienices reported are even higher, e.g. 0.11 Mwh/t_N2 from Morgan (2013): Techno-Economic Feasibility Study of Ammonia Plants Powered by Offshore Wind .", air separation unit,investment,671233.0629,EUR/t_N2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 air separation unit,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 @@ -618,12 +558,13 @@ battery inverter,lifetime,10.0,years,"Danish Energy Agency, technology_data_cata battery storage,investment,99.4728,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Energy storage expansion cost investment,2015.0 battery storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Technical lifetime,2015.0 biochar pyrolysis,FOM,3.3636,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Fixed O&M",2020.0 -biochar pyrolysis,VOM,823.497,EUR/MWh_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 -biochar pyrolysis,efficiency-biochar,0.404,MWh_biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency biochar",2020.0 -biochar pyrolysis,efficiency-heat,0.4848,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency heat",2020.0 -biochar pyrolysis,investment,141538.54,EUR/kW_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 +biochar pyrolysis,VOM,47.6777,EUR/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 +biochar pyrolysis,biomass input,7.6748,MWh_biomass/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Biomass Input",2020.0 +biochar pyrolysis,electricity input,0.3184,MWh_e/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: El-Input",2020.0 +biochar pyrolysis,heat output,3.7859,MWh_th/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: H-Output",2020.0 +biochar pyrolysis,investment,8194601.4397,EUR/t_CO2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 biochar pyrolysis,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Technical lifetime",2020.0 -biochar pyrolysis,yield-biochar,0.0582,ton biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 +biochar pyrolysis,yield-biochar,0.0597,t_biochar/MWh_biomass,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 biodiesel crops,fuel,137.5427,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIORPS1 (rape seed), ENS_BaU_GFTM",,2010.0 bioethanol crops,fuel,86.1222,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOCRP11 (Bioethanol barley, wheat, grain maize, oats, other cereals and rye), ENS_BaU_GFTM",,2010.0 biogas,CO2 stored,0.0868,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, @@ -640,10 +581,17 @@ biogas CC,efficiency,1.0,per unit,Assuming input biomass is already given in bio biogas CC,investment,922.249,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Specific investment",2020.0 biogas CC,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Technical lifetime",2020.0 biogas manure,fuel,19.8782,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOGAS1 (manure), ENS_BaU_GFTM",,2010.0 -biogas plus hydrogen,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Fixed O&M,2020.0 -biogas plus hydrogen,VOM,3.0626,EUR/MWh_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 -biogas plus hydrogen,investment,643.1443,EUR/kW_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 +biogas plus hydrogen,Biogas Input,1.1522,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Biogas Consumption,",2020.0 +biogas plus hydrogen,CO2 Input,0.1235,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: CO2 Input,",2020.0 +biogas plus hydrogen,Methane Output,1.9348,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Methane Output,",2020.0 +biogas plus hydrogen,VOM,5.9254,EUR/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 +biogas plus hydrogen,electricity input,0.0217,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: El-Input,",2020.0 +biogas plus hydrogen,heat output,0.2174,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: H-Output,",2020.0 +biogas plus hydrogen,hydrogen input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Hydrogen Consumption,",2020.0 +biogas plus hydrogen,investment,1244.3444,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 biogas plus hydrogen,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Technical lifetime,2020.0 +biogas storage, investment,14.45, EUR/kWh,AU Foulum,,2020.0 +biogas storage, lifetime,25.0,years,AU Foulum,,2020.0 biogas upgrading,FOM,17.8139,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Fixed O&M ",2020.0 biogas upgrading,VOM,3.0755,EUR/MWh output,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Variable O&M",2020.0 biogas upgrading,investment,136.4191,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: investment (upgrading, methane redution and grid injection)",2020.0 @@ -688,9 +636,9 @@ biomass boiler,efficiency,0.87,per unit,"Danish Energy Agency, technologydatafor biomass boiler,investment,654.3303,EUR/kW_th,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Specific investment",2015.0 biomass boiler,lifetime,20.0,years,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Technical lifetime",2015.0 biomass boiler,pelletizing cost,9.0,EUR/MWh_pellets,Assumption based on doi:10.1016/j.rser.2019.109506,,2019.0 -biomass-to-methanol,C in fuel,0.4265,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,C stored,0.5735,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,CO2 stored,0.2103,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C in fuel,0.4163,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C stored,0.5837,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,CO2 stored,0.2193,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, biomass-to-methanol,FOM,1.8083,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Fixed O&M,2020.0 biomass-to-methanol,VOM,14.4653,EUR/MWh_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Variable O&M,2020.0 biomass-to-methanol,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -699,6 +647,15 @@ biomass-to-methanol,efficiency-electricity,0.02,MWh_e/MWh_th,"Danish Energy Agen biomass-to-methanol,efficiency-heat,0.22,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","97 Methanol from biomass gasif.: District heat Output,",2020.0 biomass-to-methanol,investment,2255.697,EUR/kW_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Specific investment,2020.0 biomass-to-methanol,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Technical lifetime,2020.0 +biomethanation,Biogas Input,1.1444,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Biogas Consumption,",2020.0 +biomethanation,CO2 Input,0.165,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: CO2 Input,",2020.0 +biomethanation,FOM,6.6667,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Fixed O&M ,2020.0 +biomethanation,Hydrogen Input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Hydrogen Input,",2020.0 +biomethanation,Methane Output,1.9673,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Methane Output,",2020.0 +biomethanation,electricity input,0.0417,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: El-Input,",2020.0 +biomethanation,heat output,0.1667,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: H-Output,",2020.0 +biomethanation,investment,987.5,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Specific investment ,2020.0 +biomethanation,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Technical lifetime,2020.0 cement capture,FOM,3.0,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,capture_rate,0.95,per unit,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,compression-electricity-input,0.075,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 @@ -785,7 +742,7 @@ central solid biomass CHP CC,c_b,0.3465,50°C/100°C,"Danish Energy Agency, tech central solid biomass CHP CC,c_v,1.0,50°C/100°C,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Cv coefficient",2015.0 central solid biomass CHP CC,efficiency,0.2675,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Electricity efficiency, net, annual average",2015.0 central solid biomass CHP CC,efficiency-heat,0.8269,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Heat efficiency, net, annual average",2015.0 -central solid biomass CHP CC,investment,4917.5537,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 +central solid biomass CHP CC,investment,4953.7139,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 central solid biomass CHP CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Technical lifetime",2015.0 central solid biomass CHP powerboost CC,FOM,2.8591,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Fixed O&M",2015.0 central solid biomass CHP powerboost CC,VOM,4.8953,EUR/MWh_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Variable O&M ",2015.0 @@ -812,23 +769,23 @@ central water-sourced heat pump,VOM,1.4709,EUR/MWh,"Danish Energy Agency, techno central water-sourced heat pump,efficiency,3.86,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Total efficiency , net, annual average",2015.0 central water-sourced heat pump,investment,1058.2216,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Specific investment",2015.0 central water-sourced heat pump,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Technical lifetime",2015.0 -clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 +clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, clean water tank storage,investment,69.1286,EUR/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 clean water tank storage,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, coal,CO2 intensity,0.3361,tCO2/MWh_th,Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018,, coal,FOM,1.31,%/year,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (39.5+91.25) USD/kW_e/a /2 / (1.09 USD/EUR) / investment cost * 100.",2023.0 coal,VOM,3.2612,EUR/MWh_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (3+5.5)USD/MWh_e/2 / (1.09 USD/EUR).",2023.0 -coal,efficiency,0.356,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 +coal,efficiency,0.33,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 coal,fuel,9.5542,EUR/MWh_th,"DIW (2013): Current and propsective costs of electricity generation until 2050, http://hdl.handle.net/10419/80348 , pg. 80 text below figure 10, accessed: 2023-12-14.","Based on IEA 2011 data, 99 USD/t.",2010.0 coal,investment,3827.1629,EUR/kW_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Higher costs include coal plants with CCS, but since using here for calculating the average nevertheless. Calculated based on average of listed range, i.e. (3200+6775) USD/kW_e/2 / (1.09 USD/EUR).",2023.0 coal,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 -csp-tower,FOM,1.3,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,2020.0 +csp-tower,FOM,1.3,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,1.0 csp-tower,investment,99.97,"EUR/kW_th,dp",ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower,lifetime,30.0,years,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),-,2020.0 -csp-tower TES,FOM,1.3,%/year,see solar-tower.,-,2020.0 +csp-tower TES,FOM,1.3,%/year,see solar-tower.,-,1.0 csp-tower TES,investment,13.39,EUR/kWh_th,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower TES,lifetime,30.0,years,see solar-tower.,-,2020.0 -csp-tower power block,FOM,1.3,%/year,see solar-tower.,-,2020.0 +csp-tower power block,FOM,1.3,%/year,see solar-tower.,-,1.0 csp-tower power block,investment,700.34,EUR/kW_e,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower power block,lifetime,30.0,years,see solar-tower.,-,2020.0 decentral CHP,FOM,3.0,%/year,HP, from old pypsa cost assumptions,2015.0 @@ -874,19 +831,18 @@ decentral water tank storage,energy to power ratio,0.15,h,"Danish Energy Agency, decentral water tank storage,investment,433.8709,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Specific investment,2015.0 decentral water tank storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Technical lifetime,2015.0 digestible biomass,fuel,17.0611,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOAGRW1, ENS_Ref for 2040",,2010.0 -digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, digestible biomass to hydrogen,efficiency,0.39,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,investment,3177.8395,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 -direct air capture,FOM,1.3,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,FOM,4.95,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-electricity-input,0.15,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-heat-output,0.2,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,electricity-input,0.24,MWh_el/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 -direct air capture,heat-input,1.17,MWh_th/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 +direct air capture,electricity-input,0.4,MWh_el/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","0.4 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 0.182 MWh based on Breyer et al (2019). Should already include electricity for water scrubbing and compression (high quality CO2 output).",2020.0 +direct air capture,heat-input,1.6,MWh_th/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","Thermal energy demand. Provided via air-sourced heat pumps. 1.6 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 1.102 MWh based on Breyer et al (2019).",2020.0 direct air capture,heat-output,0.75,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,investment,11034260.0394,USD/t_CO2/h,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,investment,5000000.0,EUR/(tCO2/h),"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,lifetime,20.0,years,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,years,30.0,years,ICCT IRA e-fuels assumptions ,, direct firing gas,FOM,1.1515,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Fixed O&M,2019.0 direct firing gas,VOM,0.282,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Variable O&M,2019.0 direct firing gas,efficiency,1.0,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","312.a Direct firing Natural Gas: Total efficiency, net, annual average",2019.0 @@ -927,8 +883,8 @@ electric boiler steam,VOM,0.7855,EUR/MWh,"Danish Energy Agency, technology_data_ electric boiler steam,efficiency,0.99,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","310.1 Electric boiler steam : Total efficiency, net, annual average",2019.0 electric boiler steam,investment,70.49,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Nominal investment,2019.0 electric boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Technical lifetime,2019.0 -electric steam cracker,FOM,3.0,%/year,Guesstimate,,2015.0 -electric steam cracker,VOM,190.4799,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 +electric steam cracker,FOM,3.0,%/year,Guesstimate,, +electric steam cracker,VOM,190.4799,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 electric steam cracker,carbondioxide-output,0.55,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), ",The report also references another source with 0.76 t_CO2/t_HVC, electric steam cracker,electricity-input,2.7,MWh_el/t_HVC,"Lechtenböhmer et al. (2016): 10.1016/j.energy.2016.07.110, Section 4.3, page 6.",Assuming electrified processing., electric steam cracker,investment,11124025.7434,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -940,14 +896,14 @@ electricity distribution grid,lifetime,40.0,years,TODO, from old pypsa cost assu electricity grid connection,FOM,2.0,%/year,TODO, from old pypsa cost assumptions,2015.0 electricity grid connection,investment,148.151,EUR/kW,DEA, from old pypsa cost assumptions,2015.0 electricity grid connection,lifetime,40.0,years,TODO, from old pypsa cost assumptions,2015.0 -electrobiofuels,C in fuel,0.9292,per unit,Stoichiometric calculation,, -electrobiofuels,FOM,2.8364,%/year,combination of BtL and electrofuels,,2015.0 -electrobiofuels,VOM,2.9357,EUR/MWh_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,C in fuel,0.9285,per unit,Stoichiometric calculation,, +electrobiofuels,FOM,2.8364,%/year,combination of BtL and electrofuels,, +electrobiofuels,VOM,3.4512,EUR/MWh_th,combination of BtL and electrofuels,,2017.0 electrobiofuels,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -electrobiofuels,efficiency-biomass,1.325,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-hydrogen,1.0989,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-tot,0.6007,per unit,Stoichiometric calculation,, -electrobiofuels,investment,963938.9077,EUR/kW_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,efficiency-biomass,1.3565,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-hydrogen,1.242,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-tot,0.6484,per unit,Stoichiometric calculation,, +electrobiofuels,investment,396128.7941,EUR/kW_th,combination of BtL and electrofuels,,2017.0 electrolysis,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Fixed O&M ,2020.0 electrolysis,efficiency,0.6532,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Hydrogen Output,2020.0 electrolysis,efficiency-heat,0.1849,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: - hereof recoverable for district heating,2020.0 @@ -971,7 +927,7 @@ gas boiler steam,VOM,1.007,EUR/MWh,"Danish Energy Agency, technology_data_for_in gas boiler steam,efficiency,0.93,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1c Steam boiler Gas: Total efficiency, net, annual average",2019.0 gas boiler steam,investment,45.7727,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Nominal investment,2019.0 gas boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Technical lifetime,2019.0 -gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenance, salt cavern (units converted)",2015.0 +gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenace, salt cavern (units converted)",2015.0 gas storage,investment,0.0348,EUR/kWh,Danish Energy Agency,"150 Underground Storage of Gas, Establishment of one cavern (units converted)",2015.0 gas storage,lifetime,100.0,years,TODO no source,"estimation: most underground storage are already build, they do have a long lifetime",2015.0 gas storage charger,investment,15.1737,EUR/kW,Danish Energy Agency,"150 Underground Storage of Gas, Process equipment (units converted)",2015.0 @@ -995,14 +951,14 @@ hydro,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pyp hydro,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 hydro,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 hydro,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 -hydrogen storage compressor,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg., -hydrogen storage compressor,investment,2.0291,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage compressor,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage tank type 1,investment,15.0133,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage tank type 1,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-, +hydrogen storage compressor,FOM,4.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg.,2020.0 +hydrogen storage compressor,investment,87.69,EUR/kW_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.","2923 EUR/kg_H2. For a 206 kg/h compressor. Base CAPEX 40 528 EUR/kW_el with scale factor 0.4603. kg_H2 converted to MWh using LHV. Pressure range: 30 bar in, 250 bar out.",2020.0 +hydrogen storage compressor,lifetime,15.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage tank type 1,FOM,2.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,investment,13.5,EUR/kWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.","450 EUR/kg_H2 converted with LHV to MWh. For a type 1 hydrogen storage tank (steel, 15-250 bar). Currency year assumed 2020 for initial publication of reference; observe note in SI.4.3 that no currency year is explicitly stated in the reference.",2020.0 +hydrogen storage tank type 1,lifetime,20.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 hydrogen storage tank type 1 including compressor,FOM,1.8484,%/year,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Fixed O&M,2015.0 hydrogen storage tank type 1 including compressor,investment,28.6253,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Specific investment,2015.0 hydrogen storage tank type 1 including compressor,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Technical lifetime,2015.0 @@ -1049,8 +1005,8 @@ methanol-to-kerosene,hydrogen-input,0.0279,MWh_H2/MWh_kerosene,"Concawe (2022): methanol-to-kerosene,investment,234500.0,EUR/MW_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",,2020.0 methanol-to-kerosene,lifetime,30.0,years,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",, methanol-to-kerosene,methanol-input,1.0764,MWh_MeOH/MWh_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 6.","Assuming LHV 11.94 kWh/kg for kerosene, 5.54 kWh/kg for methanol, 33.3 kWh/kg for hydrogen.", -methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker,2015.0 -methanol-to-olefins/aromatics,VOM,31.7466,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 +methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker, +methanol-to-olefins/aromatics,VOM,31.7466,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 methanol-to-olefins/aromatics,carbondioxide-output,0.6107,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Sections 4.5 (for ethylene and propylene) and 4.6 (for BTX)","Weighted average: 0.4 t_MeOH/t_ethylene+propylene for 21.7 Mt of ethylene and 17 Mt of propylene, 1.13 t_CO2/t_BTX for 15.7 Mt of BTX. The report also references process emissions of 0.55 t_MeOH/t_ethylene+propylene elsewhere. ", methanol-to-olefins/aromatics,electricity-input,1.3889,MWh_el/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), page 69",5 GJ/t_HVC , methanol-to-olefins/aromatics,investment,2781006.4359,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -1077,7 +1033,7 @@ nuclear,investment,8594.1354,EUR/kW_e,"Lazard's levelized cost of energy analysi nuclear,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 offwind,FOM,2.1762,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Fixed O&M [EUR/MW_e/y, 2020]",2020.0 offwind,VOM,0.0212,EUR/MWhel,RES costs made up to fix curtailment order, from old pypsa cost assumptions,2015.0 -offwind,investment,1562.3661,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs subtracted from investment costs",2020.0 +offwind,investment,1562.3661,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs substracted from investment costs",2020.0 offwind,lifetime,30.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",21 Offshore turbines: Technical lifetime [years],2020.0 offwind-ac-connection-submarine,investment,2841.3251,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 offwind-ac-connection-underground,investment,1420.1334,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 @@ -1106,12 +1062,18 @@ organic rankine cycle,FOM,2.0,%/year,"Aghahosseini, Breyer 2020: From hot rock t organic rankine cycle,electricity-input,0.12,MWh_el/MWh_th,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551; Breede et al. 2015: Overcoming challenges in the classification of deep geothermal potential, https://eprints.gla.ac.uk/169585/","Heat-input, Electricity-output. This is a rough estimate, depends on input temperature, implies ~150 C.",2020.0 organic rankine cycle,investment,1376.0,EUR/kW_el,Tartiere and Astolfi 2017: A world overview of the organic Rankine cycle market,"Low rollout complicates the estimation, compounded by a dependence both on plant size and temperature, converted from 1500 USD/kW using currency conversion 1.09 USD = 1 EUR.",2020.0 organic rankine cycle,lifetime,30.0,years,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551",,2020.0 +perennials gbr,FOM,0.0,%year,Own assumption,,2015.0 +perennials gbr,VOM,43.2317,EUR/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,"includes purchase of perennial crops and sales of proteine concentrate, table 8.1 wages, maintenance and auxiliary costs",2015.0 +perennials gbr,biogas-output,0.1947,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,electricity-input,0.0733,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,investment,1371168.1394,EUR/tDM/h,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,includes cost for biogas plant without upgrading,2015.0 +perennials gbr,lifetime,25.0,years,Own assumption,,2015.0 ror,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,investment,3412.2266,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 ror,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 seawater RO desalination,electricity-input,0.003,MWHh_el/t_H2O,"Caldera et al. (2016): Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",Desalination using SWRO. Assume medium salinity of 35 Practical Salinity Units (PSUs) = 35 kg/m^3., -seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2015.0 +seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, seawater desalination,electricity-input,3.0348,kWh/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",, seawater desalination,investment,27828.5154,EUR/(m^3-H2O/h),"Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402), Table 4.",,2015.0 seawater desalination,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, @@ -1137,7 +1099,7 @@ solar-utility,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_ solar-utility single-axis tracking,FOM,2.4459,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Fixed O&M [2020-EUR/MW_e/y],2020.0 solar-utility single-axis tracking,investment,384.3112,EUR/kW_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Nominal investment [2020-MEUR/MW_e],2020.0 solar-utility single-axis tracking,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Technical lifetime [years],2020.0 -solid biomass,CO2 intensity,0.3667,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, +solid biomass,CO2 intensity,0.3757,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, solid biomass,fuel,13.6489,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOWOOW1 (secondary forest residue wood chips), ENS_Ref for 2040",,2010.0 solid biomass boiler steam,FOM,6.1742,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Fixed O&M,2019.0 solid biomass boiler steam,VOM,2.8679,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Variable O&M,2019.0 @@ -1149,7 +1111,7 @@ solid biomass boiler steam CC,VOM,2.8679,EUR/MWh,"Danish Energy Agency, technolo solid biomass boiler steam CC,efficiency,0.89,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1e Steam boiler Wood: Total efficiency, net, annual average",2019.0 solid biomass boiler steam CC,investment,567.5818,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Nominal investment,2019.0 solid biomass boiler steam CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Technical lifetime,2019.0 -solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, solid biomass to hydrogen,efficiency,0.56,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,investment,3177.8395,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 diff --git a/outputs/costs_2045.csv b/outputs/costs_2045.csv index 81c0ff4a..729226bb 100644 --- a/outputs/costs_2045.csv +++ b/outputs/costs_2045.csv @@ -1,8 +1,4 @@ technology,parameter,value,unit,source,further description,currency_year -Alkaline electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,efficiency,0.76,p.u.,ICCT IRA e-fuels assumptions ,, -Alkaline electrolyzer,investment,618.5101,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Ammonia cracker,FOM,4.3,%/year,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 7.","Estimated based on Labour cost rate, Maintenance cost rate, Insurance rate, Admin. cost rate and Chemical & other consumables cost rate.",2015.0 Ammonia cracker,ammonia-input,1.46,MWh_NH3/MWh_H2,"ENGIE et al (2020): Ammonia to Green Hydrogen Feasibility Study (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/880826/HS420_-_Ecuity_-_Ammonia_to_Green_Hydrogen.pdf), Fig. 10.",Assuming a integrated 200t/d cracking and purification facility. Electricity demand (316 MWh per 2186 MWh_LHV H2 output) is assumed to also be ammonia LHV input which seems a fair assumption as the facility has options for a higher degree of integration according to the report)., Ammonia cracker,investment,699718.4683,EUR/MW_H2,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 6.","Calculated. For a small (200 t_NH3/d input) facility. Base cost for facility: 51 MEUR at capacity 20 000m^3_NH3/h = 339 t_NH3/d input. Cost scaling exponent 0.67. Ammonia density 0.7069 kg/m^3. Conversion efficiency of cracker: 0.685. Ammonia LHV: 5.167 MWh/t_NH3.; and @@ -45,25 +41,18 @@ Battery electric (passenger cars),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL Battery electric (trucks),FOM,16.0,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),investment,131200.0,EUR/LKW,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 -BioSNG,C in fuel,0.3686,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,C stored,0.6314,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,CO2 stored,0.2315,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C in fuel,0.3597,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C stored,0.6403,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,CO2 stored,0.2405,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BioSNG,FOM,1.6148,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Fixed O&M",2020.0 BioSNG,VOM,1.728,EUR/MWh_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Variable O&M",2020.0 BioSNG,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, BioSNG,efficiency,0.6825,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Bio SNG Output",2020.0 BioSNG,investment,1621.685,EUR/kW_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Specific investment",2020.0 BioSNG,lifetime,25.0,years,TODO,"84 Gasif. CFB, Bio-SNG: Technical lifetime",2020.0 -Biomass gasification,efficiency,0.4958,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification,investment,1467.7693,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,FOM,0.02,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,efficiency,0.514,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,investment,3015.5325,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -BtL,C in fuel,0.3039,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,C stored,0.6961,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,CO2 stored,0.2552,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C in fuel,0.2966,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C stored,0.7034,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,CO2 stored,0.2642,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BtL,FOM,2.9164,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Fixed O&M",2020.0 BtL,VOM,1.1305,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Variable O&M",2020.0 BtL,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -109,11 +98,17 @@ CO2 liquefaction,lifetime,25.0,years,"Guesstimate, based on CH4 liquefaction.",, CO2 pipeline,FOM,0.9,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 pipeline,investment,2116.4433,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch onshore pipeline.,2015.0 CO2 pipeline,lifetime,50.0,years,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 +CO2 storage cylinders, FOM,1.0,%/year,AU Foulum,,2020.0 +CO2 storage cylinders, investment,77000.0, EUR/t_CO2,AU Foulum,,2020.0 +CO2 storage cylinders, lifetime,25.0,years,AU Foulum,,2020.0 CO2 storage tank,FOM,1.0,%/year,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,investment,2584.3462,EUR/t_CO2,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, Table 3.","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,lifetime,25.0,years,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 submarine pipeline,FOM,0.5,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 submarine pipeline,investment,4232.8865,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch offshore pipeline.,2015.0 +CO2_industrial_compressor, FOM,4.0,%/year,AU Foulum,,2020.0 +CO2_industrial_compressor, investment,1516000.0, EUR/t/h_CO2,AU Foulum,,2020.0 +CO2_industrial_compressor, lifetime,25.0,years,AU Foulum,,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,FOM,1.6,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,investment,448894.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 @@ -126,29 +121,6 @@ Charging infrastructure fuel cell vehicles trucks,lifetime,30.0,years,PATHS TO A Charging infrastructure slow (purely) battery electric vehicles passenger cars,FOM,1.8,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,investment,1005.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 -Coal gasification,FOM,0.06,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,efficiency,0.7492,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification,investment,399.2305,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,FOM,0.07,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,efficiency,0.609,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,investment,649.5969,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 90%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal-95%-CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -Coal-95%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-95%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,capture_rate,0.99,per unit,"NREL, NREL ATB 2024",, -Coal-99%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,efficiency,0.5,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,capture_rate,0.9,per unit,"NREL, NREL ATB 2024",, -Coal-IGCC-90%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Compressed-Air-Adiabatic-bicharger,FOM,0.9265,%/year,"Viswanathan_2022, p.64 (p.86) Figure 4.14","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 Compressed-Air-Adiabatic-bicharger,efficiency,0.7211,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.52^0.5']}",2020.0 Compressed-Air-Adiabatic-bicharger,investment,946180.9426,EUR/MW,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['Turbine Compressor BOP EPC Management']}",2020.0 @@ -249,15 +221,15 @@ FT fuel transport ship,FOM,5.0,%/year,"Assume comparable tanker as for LOHC tran FT fuel transport ship,capacity,75000.0,t_FTfuel,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,investment,35000000.0,EUR,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,lifetime,15.0,years,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 -Fischer-Tropsch,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +Fischer-Tropsch,FOM,3.0,%/year,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.",,2017.0 Fischer-Tropsch,VOM,2.818,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",102 Hydrogen to Jet: Variable O&M,2020.0 Fischer-Tropsch,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -Fischer-Tropsch,carbondioxide-input,0.32,t_CO2/MWh_FT,ICCT IRA e-fuels assumptions ,"Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", -Fischer-Tropsch,efficiency,0.7,per unit,ICCT IRA e-fuels assumptions ,, -Fischer-Tropsch,electricity-input,0.04,MWh_el/MWh_FT,ICCT IRA e-fuels assumptions ,"0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,hydrogen-input,1.43,MWh_H2/MWh_FT,ICCT IRA e-fuels assumptions ,"0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,investment,1509724.4026,USD/MW_FT,ICCT IRA e-fuels assumptions ,"Well developed technology, no significant learning expected.",2022.0 -Fischer-Tropsch,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,,2020.0 +Fischer-Tropsch,carbondioxide-input,0.2885,t_CO2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", +Fischer-Tropsch,efficiency,0.799,per unit,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.2.",,2017.0 +Fischer-Tropsch,electricity-input,0.007,MWh_el/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,hydrogen-input,1.345,MWh_H2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,investment,565735.7731,EUR/MW_FT,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected.",2017.0 +Fischer-Tropsch,lifetime,20.0,years,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.",,2017.0 Gasnetz,FOM,2.5,%,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,investment,28.0,EUR/kWGas,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,lifetime,30.0,years,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 @@ -265,7 +237,7 @@ General liquid hydrocarbon storage (crude),FOM,6.25,%/year,"Stelter and Nishida General liquid hydrocarbon storage (crude),investment,137.8999,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed 20% lower than for product storage. Crude or middle distillate tanks are usually larger compared to product storage due to lower requirements on safety and different construction method. Reference size used here: 80 000 – 120 000 m^3 .,2012.0 General liquid hydrocarbon storage (crude),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 General liquid hydrocarbon storage (product),FOM,6.25,%/year,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , figure 7 and pg. 12 .",Assuming ca. 10 EUR/m^3/a (center value between stand alone and addon facility).,2012.0 -General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 - 60 000 m^3 .,2012.0 +General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 – 60 000 m^3 .,2012.0 General liquid hydrocarbon storage (product),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 Gravity-Brick-bicharger,FOM,1.5,%/year,"Viswanathan_2022, p.76 (p.98) Sentence 1 in 4.7.2 Operating Costs","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['1.5 percent of capital cost']}",2020.0 Gravity-Brick-bicharger,efficiency,0.9274,per unit,"Viswanathan_2022, p.77 (p.99) Table 4.36","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.86^0.5']}",2020.0 @@ -341,15 +313,13 @@ HVDC underground,investment,1008.2934,EUR/MW/km,Härtel et al. (2017): https://d HVDC underground,lifetime,40.0,years,Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .,"Based on estimated costs for a NA-EU connector (bidirectional,4 GW, 3000km length and ca. 3000m depth). Costs in return based on existing/currently under construction undersea cables. (same as for HVDC submarine)",2018.0 Haber-Bosch,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 Haber-Bosch,VOM,0.0225,EUR/MWh_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Variable O&M,2015.0 +Haber-Bosch,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +Haber-Bosch,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 Haber-Bosch,electricity-input,0.2473,MWh_el/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), table 11.",Assume 5 GJ/t_NH3 for compressors and NH3 LHV = 5.16666 MWh/t_NH3., Haber-Bosch,hydrogen-input,1.1484,MWh_H2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.","178 kg_H2 per t_NH3, LHV for both assumed.", Haber-Bosch,investment,1054.8211,EUR/kW_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 Haber-Bosch,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 Haber-Bosch,nitrogen-input,0.1597,t_N2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.",".33 MWh electricity are required for ASU per t_NH3, considering 0.4 MWh are required per t_N2 and LHV of NH3 of 5.1666 Mwh.", -Heavy oil partial oxidation,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,efficiency,0.734,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Heavy oil partial oxidation,investment,491.0535,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, HighT-Molten-Salt-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 HighT-Molten-Salt-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 HighT-Molten-Salt-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -466,18 +436,6 @@ Methanol steam reforming,FOM,4.0,%/year,"Niermann et al. (2021): Liquid Organic Methanol steam reforming,investment,18016.8665,EUR/MW_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.","For high temperature steam reforming plant with a capacity of 200 MW_H2 output (6t/h). Reference plant of 1 MW (30kg_H2/h) costs 150kEUR, scale factor of 0.6 assumed.",2020.0 Methanol steam reforming,lifetime,20.0,years,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",,2020.0 Methanol steam reforming,methanol-input,1.201,MWh_MeOH/MWh_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",Assuming per 1 t_H2 (with LHV 33.3333 MWh/t): 4.5 MWh_th and 3.2 MWh_el are required. We assume electricity can be substituted / provided with 1:1 as heat energy., -NG 2-on-1 Combined Cycle (F-Frame),efficiency,0.573,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame),lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,efficiency,0.527,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,capture_rate,0.97,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,efficiency,0.525,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, NH3 (l) storage tank incl. liquefaction,FOM,2.0,%/year,"Guesstimate, based on H2 (l) storage tank.",,2010.0 NH3 (l) storage tank incl. liquefaction,investment,166.8201,EUR/MWh_NH3,"Calculated based on Morgan E. 2013: doi:10.7275/11KT-3F59 , Fig. 55, Fig 58.","Based on estimated for a double-wall liquid ammonia tank (~ambient pressure, -33°C), inner tank from stainless steel, outer tank from concrete including installations for liquefaction/condensation, boil-off gas recovery and safety installations; the necessary installations make only a small fraction of the total cost. The total cost are driven by material and working time on the tanks. While the costs do not scale strictly linearly, we here assume they do (good approximation c.f. ref. Fig 55.) and take the costs for a 9 kt NH3 (l) tank = 8 M$2010, which is smaller 4-5x smaller than the largest deployed tanks today. @@ -488,14 +446,6 @@ NH3 (l) transport ship,FOM,4.0,%/year,"Cihlar et al 2020 based on IEA 2019, Tabl NH3 (l) transport ship,capacity,53000.0,t_NH3,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,investment,81164200.0,EUR,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,lifetime,20.0,years,"Guess estimated based on H2 (l) tanker, but more mature technology",,2019.0 -Natural gas steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,efficiency,0.7808,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming,investment,180.0632,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,efficiency,0.6805,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,investment,323.8999,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Ni-Zn-bicharger,FOM,2.1198,%/year,"Viswanathan_2022, p.51-52 in section 4.4.2","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Guesstimate 30% assumed of power components every 10 years ']}",2020.0 Ni-Zn-bicharger,efficiency,0.9,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['((0.75-0.87)/2)^0.5 mean value of range efficiency is not RTE but single way AC-store conversion']}",2020.0 Ni-Zn-bicharger,investment,81553.4846,EUR/MW,"Viswanathan_2022, p.59 (p.81) same as Li-LFP","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Power Equipment']}",2020.0 @@ -508,10 +458,6 @@ OCGT,VOM,4.762,EUR/MWh,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx OCGT,efficiency,0.425,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","52 OCGT - Natural gas: Electricity efficiency, annual average",2015.0 OCGT,investment,442.0086,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Specific investment,2015.0 OCGT,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Technical lifetime,2015.0 -PEM electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,efficiency,0.72,p.u.,ICCT IRA e-fuels assumptions ,, -PEM electrolyzer,investment,739.9873,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, PHS,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 @@ -534,19 +480,15 @@ Pumped-Storage-Hydro-bicharger,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90 Pumped-Storage-Hydro-store,FOM,0.43,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['derived']}",2020.0 Pumped-Storage-Hydro-store,investment,57074.0625,EUR/MWh,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['Reservoir Construction & Infrastructure']}",2020.0 Pumped-Storage-Hydro-store,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 -SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 +SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", SMR,efficiency,0.76,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR,investment,522201.0492,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 -SMR CC,capture_rate,0.9,per unit,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates between 54%-90%, +SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", +SMR CC,capture_rate,0.9,EUR/MW_CH4,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates betwen 54%-90%, SMR CC,efficiency,0.69,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR CC,investment,605753.2171,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR CC,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SOEC,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,efficiency,0.885,p.u.,ICCT IRA e-fuels assumptions ,, -SOEC,investment,842.7756,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Sand-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 Sand-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 Sand-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -558,10 +500,6 @@ Sand-discharger,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier Sand-store,FOM,0.3308,%/year,"Viswanathan_2022, p 104 (p.126)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['not provided calculated as for hydrogen']}",2020.0 Sand-store,investment,6700.8517,EUR/MWh,"Viswanathan_2022, p.100 (p.122)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['SB and BOS 0.85 of 2021 value']}",2020.0 Sand-store,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['NULL']}",2020.0 -Solid biomass steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,efficiency,0.712,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Solid biomass steam reforming,investment,590.7702,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Steam methane reforming,FOM,3.0,%/year,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 Steam methane reforming,investment,497454.611,EUR/MW_H2,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW). Currency conversion 1.17 USD = 1 EUR.,2015.0 Steam methane reforming,lifetime,30.0,years,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 @@ -604,6 +542,8 @@ Zn-Br-Nonflow-store,FOM,0.2244,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrie Zn-Br-Nonflow-store,investment,239220.5823,EUR/MWh,"Viswanathan_2022, p.59 (p.81) Table 4.14","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['DC storage block']}",2020.0 Zn-Br-Nonflow-store,lifetime,15.0,years,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['NULL']}",2020.0 air separation unit,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 +air separation unit,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +air separation unit,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 air separation unit,electricity-input,0.25,MWh_el/t_N2,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), p.288.","For consistency reasons use value from Danish Energy Agency. DEA also reports range of values (0.2-0.4 MWh/t_N2) on pg. 288. Other efficienices reported are even higher, e.g. 0.11 Mwh/t_N2 from Morgan (2013): Techno-Economic Feasibility Study of Ammonia Plants Powered by Offshore Wind .", air separation unit,investment,592917.0978,EUR/t_N2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 air separation unit,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 @@ -618,12 +558,13 @@ battery inverter,lifetime,10.0,years,"Danish Energy Agency, technology_data_cata battery storage,investment,89.4197,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Energy storage expansion cost investment,2015.0 battery storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Technical lifetime,2015.0 biochar pyrolysis,FOM,3.381,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Fixed O&M",2020.0 -biochar pyrolysis,VOM,823.497,EUR/MWh_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 -biochar pyrolysis,efficiency-biochar,0.404,MWh_biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency biochar",2020.0 -biochar pyrolysis,efficiency-heat,0.4848,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency heat",2020.0 -biochar pyrolysis,investment,135104.97,EUR/kW_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 +biochar pyrolysis,VOM,47.6777,EUR/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 +biochar pyrolysis,biomass input,7.6748,MWh_biomass/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Biomass Input",2020.0 +biochar pyrolysis,electricity input,0.3184,MWh_e/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: El-Input",2020.0 +biochar pyrolysis,heat output,3.7859,MWh_th/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: H-Output",2020.0 +biochar pyrolysis,investment,7822119.5561,EUR/t_CO2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 biochar pyrolysis,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Technical lifetime",2020.0 -biochar pyrolysis,yield-biochar,0.0582,ton biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 +biochar pyrolysis,yield-biochar,0.0597,t_biochar/MWh_biomass,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 biodiesel crops,fuel,134.6872,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIORPS1 (rape seed), ENS_BaU_GFTM",,2010.0 bioethanol crops,fuel,87.9862,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOCRP11 (Bioethanol barley, wheat, grain maize, oats, other cereals and rye), ENS_BaU_GFTM",,2010.0 biogas,CO2 stored,0.0868,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, @@ -640,10 +581,17 @@ biogas CC,efficiency,1.0,per unit,Assuming input biomass is already given in bio biogas CC,investment,894.8011,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Specific investment",2020.0 biogas CC,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Technical lifetime",2020.0 biogas manure,fuel,19.9144,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOGAS1 (manure), ENS_BaU_GFTM",,2010.0 -biogas plus hydrogen,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Fixed O&M,2020.0 -biogas plus hydrogen,VOM,2.6798,EUR/MWh_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 -biogas plus hydrogen,investment,562.7513,EUR/kW_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 +biogas plus hydrogen,Biogas Input,1.1522,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Biogas Consumption,",2020.0 +biogas plus hydrogen,CO2 Input,0.1235,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: CO2 Input,",2020.0 +biogas plus hydrogen,Methane Output,1.9348,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Methane Output,",2020.0 +biogas plus hydrogen,VOM,5.1848,EUR/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 +biogas plus hydrogen,electricity input,0.0217,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: El-Input,",2020.0 +biogas plus hydrogen,heat output,0.2174,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: H-Output,",2020.0 +biogas plus hydrogen,hydrogen input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Hydrogen Consumption,",2020.0 +biogas plus hydrogen,investment,1088.8014,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 biogas plus hydrogen,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Technical lifetime,2020.0 +biogas storage, investment,14.45, EUR/kWh,AU Foulum,,2020.0 +biogas storage, lifetime,25.0,years,AU Foulum,,2020.0 biogas upgrading,FOM,17.4434,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Fixed O&M ",2020.0 biogas upgrading,VOM,2.8874,EUR/MWh output,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Variable O&M",2020.0 biogas upgrading,investment,130.7968,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: investment (upgrading, methane redution and grid injection)",2020.0 @@ -688,9 +636,9 @@ biomass boiler,efficiency,0.875,per unit,"Danish Energy Agency, technologydatafo biomass boiler,investment,637.9448,EUR/kW_th,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Specific investment",2015.0 biomass boiler,lifetime,20.0,years,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Technical lifetime",2015.0 biomass boiler,pelletizing cost,9.0,EUR/MWh_pellets,Assumption based on doi:10.1016/j.rser.2019.109506,,2019.0 -biomass-to-methanol,C in fuel,0.4332,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,C stored,0.5668,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,CO2 stored,0.2078,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C in fuel,0.4229,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C stored,0.5771,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,CO2 stored,0.2168,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, biomass-to-methanol,FOM,2.1583,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Fixed O&M,2020.0 biomass-to-methanol,VOM,14.4653,EUR/MWh_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Variable O&M,2020.0 biomass-to-methanol,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -699,6 +647,15 @@ biomass-to-methanol,efficiency-electricity,0.02,MWh_e/MWh_th,"Danish Energy Agen biomass-to-methanol,efficiency-heat,0.22,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","97 Methanol from biomass gasif.: District heat Output,",2020.0 biomass-to-methanol,investment,1904.4308,EUR/kW_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Specific investment,2020.0 biomass-to-methanol,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Technical lifetime,2020.0 +biomethanation,Biogas Input,1.1444,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Biogas Consumption,",2020.0 +biomethanation,CO2 Input,0.165,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: CO2 Input,",2020.0 +biomethanation,FOM,6.6667,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Fixed O&M ,2020.0 +biomethanation,Hydrogen Input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Hydrogen Input,",2020.0 +biomethanation,Methane Output,1.9673,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Methane Output,",2020.0 +biomethanation,electricity input,0.0417,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: El-Input,",2020.0 +biomethanation,heat output,0.1667,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: H-Output,",2020.0 +biomethanation,investment,987.5,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Specific investment ,2020.0 +biomethanation,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Technical lifetime,2020.0 cement capture,FOM,3.0,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,capture_rate,0.95,per unit,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,compression-electricity-input,0.075,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 @@ -785,7 +742,7 @@ central solid biomass CHP CC,c_b,0.3444,50°C/100°C,"Danish Energy Agency, tech central solid biomass CHP CC,c_v,1.0,50°C/100°C,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Cv coefficient",2015.0 central solid biomass CHP CC,efficiency,0.2664,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Electricity efficiency, net, annual average",2015.0 central solid biomass CHP CC,efficiency-heat,0.8282,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Heat efficiency, net, annual average",2015.0 -central solid biomass CHP CC,investment,4836.5672,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 +central solid biomass CHP CC,investment,4871.9975,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 central solid biomass CHP CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Technical lifetime",2015.0 central solid biomass CHP powerboost CC,FOM,2.8555,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Fixed O&M",2015.0 central solid biomass CHP powerboost CC,VOM,4.9173,EUR/MWh_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Variable O&M ",2015.0 @@ -812,23 +769,23 @@ central water-sourced heat pump,VOM,1.4709,EUR/MWh,"Danish Energy Agency, techno central water-sourced heat pump,efficiency,3.86,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Total efficiency , net, annual average",2015.0 central water-sourced heat pump,investment,1058.2216,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Specific investment",2015.0 central water-sourced heat pump,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Technical lifetime",2015.0 -clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 +clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, clean water tank storage,investment,69.1286,EUR/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 clean water tank storage,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, coal,CO2 intensity,0.3361,tCO2/MWh_th,Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018,, coal,FOM,1.31,%/year,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (39.5+91.25) USD/kW_e/a /2 / (1.09 USD/EUR) / investment cost * 100.",2023.0 coal,VOM,3.2612,EUR/MWh_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (3+5.5)USD/MWh_e/2 / (1.09 USD/EUR).",2023.0 -coal,efficiency,0.356,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 +coal,efficiency,0.33,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 coal,fuel,9.5542,EUR/MWh_th,"DIW (2013): Current and propsective costs of electricity generation until 2050, http://hdl.handle.net/10419/80348 , pg. 80 text below figure 10, accessed: 2023-12-14.","Based on IEA 2011 data, 99 USD/t.",2010.0 coal,investment,3827.1629,EUR/kW_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Higher costs include coal plants with CCS, but since using here for calculating the average nevertheless. Calculated based on average of listed range, i.e. (3200+6775) USD/kW_e/2 / (1.09 USD/EUR).",2023.0 coal,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 -csp-tower,FOM,1.35,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,2020.0 +csp-tower,FOM,1.35,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,1.0 csp-tower,investment,99.675,"EUR/kW_th,dp",ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower,lifetime,30.0,years,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),-,2020.0 -csp-tower TES,FOM,1.35,%/year,see solar-tower.,-,2020.0 +csp-tower TES,FOM,1.35,%/year,see solar-tower.,-,1.0 csp-tower TES,investment,13.355,EUR/kWh_th,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower TES,lifetime,30.0,years,see solar-tower.,-,2020.0 -csp-tower power block,FOM,1.35,%/year,see solar-tower.,-,2020.0 +csp-tower power block,FOM,1.35,%/year,see solar-tower.,-,1.0 csp-tower power block,investment,698.27,EUR/kW_e,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower power block,lifetime,30.0,years,see solar-tower.,-,2020.0 decentral CHP,FOM,3.0,%/year,HP, from old pypsa cost assumptions,2015.0 @@ -874,19 +831,18 @@ decentral water tank storage,energy to power ratio,0.15,h,"Danish Energy Agency, decentral water tank storage,investment,433.8709,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Specific investment,2015.0 decentral water tank storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Technical lifetime,2015.0 digestible biomass,fuel,17.0611,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOAGRW1, ENS_Ref for 2040",,2010.0 -digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, digestible biomass to hydrogen,efficiency,0.39,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,investment,2913.0196,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 -direct air capture,FOM,1.3,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,FOM,4.95,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-electricity-input,0.15,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-heat-output,0.2,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,electricity-input,0.24,MWh_el/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 -direct air capture,heat-input,1.17,MWh_th/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 +direct air capture,electricity-input,0.4,MWh_el/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","0.4 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 0.182 MWh based on Breyer et al (2019). Should already include electricity for water scrubbing and compression (high quality CO2 output).",2020.0 +direct air capture,heat-input,1.6,MWh_th/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","Thermal energy demand. Provided via air-sourced heat pumps. 1.6 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 1.102 MWh based on Breyer et al (2019).",2020.0 direct air capture,heat-output,0.75,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,investment,11034260.0394,USD/t_CO2/h,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,investment,4500000.0,EUR/(tCO2/h),"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,lifetime,20.0,years,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,years,30.0,years,ICCT IRA e-fuels assumptions ,, direct firing gas,FOM,1.0909,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Fixed O&M,2019.0 direct firing gas,VOM,0.282,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Variable O&M,2019.0 direct firing gas,efficiency,1.0,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","312.a Direct firing Natural Gas: Total efficiency, net, annual average",2019.0 @@ -927,8 +883,8 @@ electric boiler steam,VOM,0.7855,EUR/MWh,"Danish Energy Agency, technology_data_ electric boiler steam,efficiency,0.99,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","310.1 Electric boiler steam : Total efficiency, net, annual average",2019.0 electric boiler steam,investment,70.49,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Nominal investment,2019.0 electric boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Technical lifetime,2019.0 -electric steam cracker,FOM,3.0,%/year,Guesstimate,,2015.0 -electric steam cracker,VOM,190.4799,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 +electric steam cracker,FOM,3.0,%/year,Guesstimate,, +electric steam cracker,VOM,190.4799,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 electric steam cracker,carbondioxide-output,0.55,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), ",The report also references another source with 0.76 t_CO2/t_HVC, electric steam cracker,electricity-input,2.7,MWh_el/t_HVC,"Lechtenböhmer et al. (2016): 10.1016/j.energy.2016.07.110, Section 4.3, page 6.",Assuming electrified processing., electric steam cracker,investment,11124025.7434,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -940,14 +896,14 @@ electricity distribution grid,lifetime,40.0,years,TODO, from old pypsa cost assu electricity grid connection,FOM,2.0,%/year,TODO, from old pypsa cost assumptions,2015.0 electricity grid connection,investment,148.151,EUR/kW,DEA, from old pypsa cost assumptions,2015.0 electricity grid connection,lifetime,40.0,years,TODO, from old pypsa cost assumptions,2015.0 -electrobiofuels,C in fuel,0.9304,per unit,Stoichiometric calculation,, -electrobiofuels,FOM,2.9164,%/year,combination of BtL and electrofuels,,2015.0 -electrobiofuels,VOM,2.5772,EUR/MWh_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,C in fuel,0.9297,per unit,Stoichiometric calculation,, +electrobiofuels,FOM,2.9164,%/year,combination of BtL and electrofuels,, +electrobiofuels,VOM,3.0295,EUR/MWh_th,combination of BtL and electrofuels,,2017.0 electrobiofuels,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -electrobiofuels,efficiency-biomass,1.3267,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-hydrogen,1.1173,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-tot,0.6065,per unit,Stoichiometric calculation,, -electrobiofuels,investment,947834.9056,EUR/kW_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,efficiency-biomass,1.3581,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-hydrogen,1.2622,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-tot,0.6542,per unit,Stoichiometric calculation,, +electrobiofuels,investment,360470.0551,EUR/kW_th,combination of BtL and electrofuels,,2017.0 electrolysis,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Fixed O&M ,2020.0 electrolysis,efficiency,0.6763,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Hydrogen Output,2020.0 electrolysis,efficiency-heat,0.1571,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: - hereof recoverable for district heating,2020.0 @@ -971,7 +927,7 @@ gas boiler steam,VOM,1.007,EUR/MWh,"Danish Energy Agency, technology_data_for_in gas boiler steam,efficiency,0.935,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1c Steam boiler Gas: Total efficiency, net, annual average",2019.0 gas boiler steam,investment,45.7727,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Nominal investment,2019.0 gas boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Technical lifetime,2019.0 -gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenance, salt cavern (units converted)",2015.0 +gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenace, salt cavern (units converted)",2015.0 gas storage,investment,0.0348,EUR/kWh,Danish Energy Agency,"150 Underground Storage of Gas, Establishment of one cavern (units converted)",2015.0 gas storage,lifetime,100.0,years,TODO no source,"estimation: most underground storage are already build, they do have a long lifetime",2015.0 gas storage charger,investment,15.1737,EUR/kW,Danish Energy Agency,"150 Underground Storage of Gas, Process equipment (units converted)",2015.0 @@ -995,14 +951,14 @@ hydro,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pyp hydro,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 hydro,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 hydro,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 -hydrogen storage compressor,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg., -hydrogen storage compressor,investment,2.0291,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage compressor,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage tank type 1,investment,15.0133,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage tank type 1,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-, +hydrogen storage compressor,FOM,4.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg.,2020.0 +hydrogen storage compressor,investment,87.69,EUR/kW_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.","2923 EUR/kg_H2. For a 206 kg/h compressor. Base CAPEX 40 528 EUR/kW_el with scale factor 0.4603. kg_H2 converted to MWh using LHV. Pressure range: 30 bar in, 250 bar out.",2020.0 +hydrogen storage compressor,lifetime,15.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage tank type 1,FOM,2.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,investment,13.5,EUR/kWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.","450 EUR/kg_H2 converted with LHV to MWh. For a type 1 hydrogen storage tank (steel, 15-250 bar). Currency year assumed 2020 for initial publication of reference; observe note in SI.4.3 that no currency year is explicitly stated in the reference.",2020.0 +hydrogen storage tank type 1,lifetime,20.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 hydrogen storage tank type 1 including compressor,FOM,1.873,%/year,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Fixed O&M,2015.0 hydrogen storage tank type 1 including compressor,investment,25.424,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Specific investment,2015.0 hydrogen storage tank type 1 including compressor,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Technical lifetime,2015.0 @@ -1049,8 +1005,8 @@ methanol-to-kerosene,hydrogen-input,0.0279,MWh_H2/MWh_kerosene,"Concawe (2022): methanol-to-kerosene,investment,217250.0,EUR/MW_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",,2020.0 methanol-to-kerosene,lifetime,30.0,years,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",, methanol-to-kerosene,methanol-input,1.0764,MWh_MeOH/MWh_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 6.","Assuming LHV 11.94 kWh/kg for kerosene, 5.54 kWh/kg for methanol, 33.3 kWh/kg for hydrogen.", -methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker,2015.0 -methanol-to-olefins/aromatics,VOM,31.7466,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 +methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker, +methanol-to-olefins/aromatics,VOM,31.7466,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 methanol-to-olefins/aromatics,carbondioxide-output,0.6107,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Sections 4.5 (for ethylene and propylene) and 4.6 (for BTX)","Weighted average: 0.4 t_MeOH/t_ethylene+propylene for 21.7 Mt of ethylene and 17 Mt of propylene, 1.13 t_CO2/t_BTX for 15.7 Mt of BTX. The report also references process emissions of 0.55 t_MeOH/t_ethylene+propylene elsewhere. ", methanol-to-olefins/aromatics,electricity-input,1.3889,MWh_el/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), page 69",5 GJ/t_HVC , methanol-to-olefins/aromatics,investment,2781006.4359,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -1077,7 +1033,7 @@ nuclear,investment,8594.1354,EUR/kW_e,"Lazard's levelized cost of energy analysi nuclear,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 offwind,FOM,2.1709,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Fixed O&M [EUR/MW_e/y, 2020]",2020.0 offwind,VOM,0.0212,EUR/MWhel,RES costs made up to fix curtailment order, from old pypsa cost assumptions,2015.0 -offwind,investment,1543.1486,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs subtracted from investment costs",2020.0 +offwind,investment,1543.1486,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs substracted from investment costs",2020.0 offwind,lifetime,30.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",21 Offshore turbines: Technical lifetime [years],2020.0 offwind-ac-connection-submarine,investment,2841.3251,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 offwind-ac-connection-underground,investment,1420.1334,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 @@ -1106,12 +1062,18 @@ organic rankine cycle,FOM,2.0,%/year,"Aghahosseini, Breyer 2020: From hot rock t organic rankine cycle,electricity-input,0.12,MWh_el/MWh_th,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551; Breede et al. 2015: Overcoming challenges in the classification of deep geothermal potential, https://eprints.gla.ac.uk/169585/","Heat-input, Electricity-output. This is a rough estimate, depends on input temperature, implies ~150 C.",2020.0 organic rankine cycle,investment,1376.0,EUR/kW_el,Tartiere and Astolfi 2017: A world overview of the organic Rankine cycle market,"Low rollout complicates the estimation, compounded by a dependence both on plant size and temperature, converted from 1500 USD/kW using currency conversion 1.09 USD = 1 EUR.",2020.0 organic rankine cycle,lifetime,30.0,years,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551",,2020.0 +perennials gbr,FOM,0.0,%year,Own assumption,,2015.0 +perennials gbr,VOM,43.2317,EUR/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,"includes purchase of perennial crops and sales of proteine concentrate, table 8.1 wages, maintenance and auxiliary costs",2015.0 +perennials gbr,biogas-output,0.1947,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,electricity-input,0.0733,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,investment,1371168.1394,EUR/tDM/h,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,includes cost for biogas plant without upgrading,2015.0 +perennials gbr,lifetime,25.0,years,Own assumption,,2015.0 ror,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,investment,3412.2266,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 ror,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 seawater RO desalination,electricity-input,0.003,MWHh_el/t_H2O,"Caldera et al. (2016): Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",Desalination using SWRO. Assume medium salinity of 35 Practical Salinity Units (PSUs) = 35 kg/m^3., -seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2015.0 +seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, seawater desalination,electricity-input,3.0348,kWh/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",, seawater desalination,investment,25039.1517,EUR/(m^3-H2O/h),"Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402), Table 4.",,2015.0 seawater desalination,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, @@ -1137,7 +1099,7 @@ solar-utility,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_ solar-utility single-axis tracking,FOM,2.4972,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Fixed O&M [2020-EUR/MW_e/y],2020.0 solar-utility single-axis tracking,investment,368.412,EUR/kW_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Nominal investment [2020-MEUR/MW_e],2020.0 solar-utility single-axis tracking,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Technical lifetime [years],2020.0 -solid biomass,CO2 intensity,0.3667,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, +solid biomass,CO2 intensity,0.3757,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, solid biomass,fuel,13.6489,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOWOOW1 (secondary forest residue wood chips), ENS_Ref for 2040",,2010.0 solid biomass boiler steam,FOM,6.2273,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Fixed O&M,2019.0 solid biomass boiler steam,VOM,2.8679,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Variable O&M,2019.0 @@ -1149,7 +1111,7 @@ solid biomass boiler steam CC,VOM,2.8679,EUR/MWh,"Danish Energy Agency, technolo solid biomass boiler steam CC,efficiency,0.895,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1e Steam boiler Wood: Total efficiency, net, annual average",2019.0 solid biomass boiler steam CC,investment,553.85,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Nominal investment,2019.0 solid biomass boiler steam CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Technical lifetime,2019.0 -solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, solid biomass to hydrogen,efficiency,0.56,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,investment,2913.0196,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 diff --git a/outputs/costs_2050.csv b/outputs/costs_2050.csv index 6bca3f7a..f91e131d 100644 --- a/outputs/costs_2050.csv +++ b/outputs/costs_2050.csv @@ -1,8 +1,4 @@ technology,parameter,value,unit,source,further description,currency_year -Alkaline electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,efficiency,0.78,p.u.,ICCT IRA e-fuels assumptions ,, -Alkaline electrolyzer,investment,556.2141,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -Alkaline electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Ammonia cracker,FOM,4.3,%/year,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 7.","Estimated based on Labour cost rate, Maintenance cost rate, Insurance rate, Admin. cost rate and Chemical & other consumables cost rate.",2015.0 Ammonia cracker,ammonia-input,1.46,MWh_NH3/MWh_H2,"ENGIE et al (2020): Ammonia to Green Hydrogen Feasibility Study (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/880826/HS420_-_Ecuity_-_Ammonia_to_Green_Hydrogen.pdf), Fig. 10.",Assuming a integrated 200t/d cracking and purification facility. Electricity demand (316 MWh per 2186 MWh_LHV H2 output) is assumed to also be ammonia LHV input which seems a fair assumption as the facility has options for a higher degree of integration according to the report)., Ammonia cracker,investment,558309.4975,EUR/MW_H2,"Ishimoto et al. (2020): 10.1016/j.ijhydene.2020.09.017 , table 6.","Calculated. For a small (200 t_NH3/d input) facility. Base cost for facility: 51 MEUR at capacity 20 000m^3_NH3/h = 339 t_NH3/d input. Cost scaling exponent 0.67. Ammonia density 0.7069 kg/m^3. Conversion efficiency of cracker: 0.685. Ammonia LHV: 5.167 MWh/t_NH3.; and @@ -45,25 +41,18 @@ Battery electric (passenger cars),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL Battery electric (trucks),FOM,16.0,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),investment,129400.0,EUR/LKW,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 Battery electric (trucks),lifetime,15.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Battery electric (trucks),2020.0 -BioSNG,C in fuel,0.378,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,C stored,0.622,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BioSNG,CO2 stored,0.2281,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C in fuel,0.369,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,C stored,0.631,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BioSNG,CO2 stored,0.2371,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BioSNG,FOM,1.6067,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Fixed O&M",2020.0 BioSNG,VOM,1.7014,EUR/MWh_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Variable O&M",2020.0 BioSNG,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, BioSNG,efficiency,0.7,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Bio SNG Output",2020.0 BioSNG,investment,1595.1,EUR/kW_th,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","84 Gasif. CFB, Bio-SNG: Specific investment",2020.0 BioSNG,lifetime,25.0,years,TODO,"84 Gasif. CFB, Bio-SNG: Technical lifetime",2020.0 -Biomass gasification,efficiency,0.525,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification,investment,1467.7693,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,FOM,0.02,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,efficiency,0.514,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Biomass gasification CC,investment,3015.5325,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Biomass gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -BtL,C in fuel,0.3156,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,C stored,0.6844,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -BtL,CO2 stored,0.251,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C in fuel,0.308,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,C stored,0.692,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +BtL,CO2 stored,0.2599,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, BtL,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Fixed O&M",2020.0 BtL,VOM,1.1299,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","85 Gasif. Ent. Flow FT, liq fu : Variable O&M",2020.0 BtL,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -109,11 +98,17 @@ CO2 liquefaction,lifetime,25.0,years,"Guesstimate, based on CH4 liquefaction.",, CO2 pipeline,FOM,0.9,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 pipeline,investment,2116.4433,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch onshore pipeline.,2015.0 CO2 pipeline,lifetime,50.0,years,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 +CO2 storage cylinders, FOM,1.0,%/year,AU Foulum,,2020.0 +CO2 storage cylinders, investment,77000.0, EUR/t_CO2,AU Foulum,,2020.0 +CO2 storage cylinders, lifetime,25.0,years,AU Foulum,,2020.0 CO2 storage tank,FOM,1.0,%/year,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,investment,2584.3462,EUR/t_CO2,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, Table 3.","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 storage tank,lifetime,25.0,years,"Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .","Assuming a 3000m^3 pressurised steel cylinder tanks and a CO2 density of 1100 kg/m^3 (close to triple point at -56.6°C and 5.2 bar with max density of 1200kg/m^3 ). Lauri et al. report costs 3x higher per m^3 for steel tanks, which are consistent with other sources. The numbers reported are in rather difficult to pinpoint as systems can greatly vary.",2013.0 CO2 submarine pipeline,FOM,0.5,%/year,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",,2015.0 CO2 submarine pipeline,investment,4232.8865,EUR/(tCO2/h)/km,"Danish Energy Agency, Technology Data for Energy Transport (March 2021), Excel datasheet: 121 co2 pipeline.",Assuming the 120-500 t CO2/h range that is based on cost of a 12 inch offshore pipeline.,2015.0 +CO2_industrial_compressor, FOM,4.0,%/year,AU Foulum,,2020.0 +CO2_industrial_compressor, investment,1516000.0, EUR/t/h_CO2,AU Foulum,,2020.0 +CO2_industrial_compressor, lifetime,25.0,years,AU Foulum,,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,FOM,1.6,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,investment,448894.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure fast (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure fast (purely) battery electric vehicles passenger cars,2020.0 @@ -126,29 +121,6 @@ Charging infrastructure fuel cell vehicles trucks,lifetime,30.0,years,PATHS TO A Charging infrastructure slow (purely) battery electric vehicles passenger cars,FOM,1.8,%,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,investment,1005.0,EUR/Lades�ule,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 Charging infrastructure slow (purely) battery electric vehicles passenger cars,lifetime,30.0,years,PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html,Charging infrastructure slow (purely) battery electric vehicles passenger cars,2020.0 -Coal gasification,FOM,0.06,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,efficiency,0.787,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification,investment,399.2305,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,FOM,0.07,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,efficiency,0.609,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal gasification CC,investment,649.5969,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Coal gasification CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 90%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal integrated retrofit 95%-CCS,efficiency,0.386,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -Coal-95%-CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -Coal-95%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-95%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,capture_rate,0.99,per unit,"NREL, NREL ATB 2024",, -Coal-99%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-99%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,efficiency,0.5,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,capture_rate,0.9,per unit,"NREL, NREL ATB 2024",, -Coal-IGCC-90%-CCS,efficiency,0.403,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Coal-IGCC-90%-CCS,lifetime,40.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Compressed-Air-Adiabatic-bicharger,FOM,0.9265,%/year,"Viswanathan_2022, p.64 (p.86) Figure 4.14","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 Compressed-Air-Adiabatic-bicharger,efficiency,0.7211,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.52^0.5']}",2020.0 Compressed-Air-Adiabatic-bicharger,investment,946180.9426,EUR/MW,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'pair', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['Turbine Compressor BOP EPC Management']}",2020.0 @@ -249,15 +221,15 @@ FT fuel transport ship,FOM,5.0,%/year,"Assume comparable tanker as for LOHC tran FT fuel transport ship,capacity,75000.0,t_FTfuel,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,investment,35000000.0,EUR,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 FT fuel transport ship,lifetime,15.0,years,"Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .",,2020.0 -Fischer-Tropsch,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +Fischer-Tropsch,FOM,3.0,%/year,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.1.",,2017.0 Fischer-Tropsch,VOM,2.2331,EUR/MWh_FT,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",102 Hydrogen to Jet: Variable O&M,2020.0 Fischer-Tropsch,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -Fischer-Tropsch,carbondioxide-input,0.32,t_CO2/MWh_FT,ICCT IRA e-fuels assumptions ,"Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", -Fischer-Tropsch,efficiency,0.7,per unit,ICCT IRA e-fuels assumptions ,, -Fischer-Tropsch,electricity-input,0.04,MWh_el/MWh_FT,ICCT IRA e-fuels assumptions ,"0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,hydrogen-input,1.43,MWh_H2/MWh_FT,ICCT IRA e-fuels assumptions ,"0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", -Fischer-Tropsch,investment,1509724.4026,USD/MW_FT,ICCT IRA e-fuels assumptions ,"Well developed technology, no significant learning expected.",2022.0 -Fischer-Tropsch,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,,2020.0 +Fischer-Tropsch,carbondioxide-input,0.276,t_CO2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","Input per 1t FT liquid fuels output, carbon efficiency increases with years (4.3, 3.9, 3.6, 3.3 t_CO2/t_FT from 2020-2050 with LHV 11.95 MWh_th/t_FT).", +Fischer-Tropsch,efficiency,0.799,per unit,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), section 6.3.2.2.",,2017.0 +Fischer-Tropsch,electricity-input,0.007,MWh_el/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.005 MWh_el input per FT output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,hydrogen-input,1.327,MWh_H2/MWh_FT,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), Hydrogen to Jet Fuel, Table 10 / pg. 267.","0.995 MWh_H2 per output, output increasing from 2020 to 2050 (0.65, 0.7, 0.73, 0.75 MWh liquid FT output).", +Fischer-Tropsch,investment,519738.882,EUR/MW_FT,"Agora Energiewende (2018): The Future Cost of Electricity-Based Synthetic Fuels (https://www.agora-energiewende.de/en/publications/the-future-cost-of-electricity-based-synthetic-fuels-1/), table 8: “Reference scenario”.","Well developed technology, no significant learning expected.",2017.0 +Fischer-Tropsch,lifetime,20.0,years,"Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.",,2017.0 Gasnetz,FOM,2.5,%,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,investment,28.0,EUR/kWGas,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 Gasnetz,lifetime,30.0,years,"WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg",Gasnetz,2020.0 @@ -265,7 +237,7 @@ General liquid hydrocarbon storage (crude),FOM,6.25,%/year,"Stelter and Nishida General liquid hydrocarbon storage (crude),investment,137.8999,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed 20% lower than for product storage. Crude or middle distillate tanks are usually larger compared to product storage due to lower requirements on safety and different construction method. Reference size used here: 80 000 – 120 000 m^3 .,2012.0 General liquid hydrocarbon storage (crude),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 General liquid hydrocarbon storage (product),FOM,6.25,%/year,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , figure 7 and pg. 12 .",Assuming ca. 10 EUR/m^3/a (center value between stand alone and addon facility).,2012.0 -General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 - 60 000 m^3 .,2012.0 +General liquid hydrocarbon storage (product),investment,172.3748,EUR/m^3,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .",Assumed at the higher end for addon facilities/mid-range for stand-alone facilities. Product storage usually smaller due to higher requirements on safety and different construction method. Reference size used here: 40 000 – 60 000 m^3 .,2012.0 General liquid hydrocarbon storage (product),lifetime,30.0,years,"Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.",,2012.0 Gravity-Brick-bicharger,FOM,1.5,%/year,"Viswanathan_2022, p.76 (p.98) Sentence 1 in 4.7.2 Operating Costs","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['1.5 percent of capital cost']}",2020.0 Gravity-Brick-bicharger,efficiency,0.9274,per unit,"Viswanathan_2022, p.77 (p.99) Table 4.36","{'carrier': ['elec', 'gravity', 'elec'], 'technology_type': ['bicharger'], 'type': ['mechanical'], 'note': ['AC-AC efficiency at transformer level 0.86^0.5']}",2020.0 @@ -341,15 +313,13 @@ HVDC underground,investment,1008.2934,EUR/MW/km,Härtel et al. (2017): https://d HVDC underground,lifetime,40.0,years,Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .,"Based on estimated costs for a NA-EU connector (bidirectional,4 GW, 3000km length and ca. 3000m depth). Costs in return based on existing/currently under construction undersea cables. (same as for HVDC submarine)",2018.0 Haber-Bosch,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 Haber-Bosch,VOM,0.0225,EUR/MWh_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Variable O&M,2015.0 +Haber-Bosch,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +Haber-Bosch,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 Haber-Bosch,electricity-input,0.2473,MWh_el/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), table 11.",Assume 5 GJ/t_NH3 for compressors and NH3 LHV = 5.16666 MWh/t_NH3., Haber-Bosch,hydrogen-input,1.1484,MWh_H2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.","178 kg_H2 per t_NH3, LHV for both assumed.", Haber-Bosch,investment,915.4941,EUR/kW_NH3,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 Haber-Bosch,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 Haber-Bosch,nitrogen-input,0.1597,t_N2/MWh_NH3,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), pg. 57.",".33 MWh electricity are required for ASU per t_NH3, considering 0.4 MWh are required per t_N2 and LHV of NH3 of 5.1666 Mwh.", -Heavy oil partial oxidation,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,efficiency,0.734,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Heavy oil partial oxidation,investment,491.0535,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Heavy oil partial oxidation,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, HighT-Molten-Salt-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 HighT-Molten-Salt-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 HighT-Molten-Salt-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'salthight'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -466,18 +436,6 @@ Methanol steam reforming,FOM,4.0,%/year,"Niermann et al. (2021): Liquid Organic Methanol steam reforming,investment,18016.8665,EUR/MW_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.","For high temperature steam reforming plant with a capacity of 200 MW_H2 output (6t/h). Reference plant of 1 MW (30kg_H2/h) costs 150kEUR, scale factor of 0.6 assumed.",2020.0 Methanol steam reforming,lifetime,20.0,years,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",,2020.0 Methanol steam reforming,methanol-input,1.201,MWh_MeOH/MWh_H2,"Niermann et al. (2021): Liquid Organic Hydrogen Carriers and alternatives for international transport of renewable hydrogen (https://doi.org/10.1016/j.rser.2020.110171), table 4.",Assuming per 1 t_H2 (with LHV 33.3333 MWh/t): 4.5 MWh_th and 3.2 MWh_el are required. We assume electricity can be substituted / provided with 1:1 as heat energy., -NG 2-on-1 Combined Cycle (F-Frame),efficiency,0.573,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame),lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,capture_rate,0.95,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,efficiency,0.527,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 95% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,capture_rate,0.97,per unit,"NREL, NREL ATB 2024",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,efficiency,0.525,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG 2-on-1 Combined Cycle (F-Frame) 97% CCS,lifetime,30.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,capture_rate,0.9,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 90%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,capture_rate,0.95,per unit,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, -NG Combined Cycle F-Class integrated retrofit 95%-CCS,efficiency,0.536,p.u.,"NREL, “Cost and performance projections for coal- and natural gas-fired power plants""",, NH3 (l) storage tank incl. liquefaction,FOM,2.0,%/year,"Guesstimate, based on H2 (l) storage tank.",,2010.0 NH3 (l) storage tank incl. liquefaction,investment,166.8201,EUR/MWh_NH3,"Calculated based on Morgan E. 2013: doi:10.7275/11KT-3F59 , Fig. 55, Fig 58.","Based on estimated for a double-wall liquid ammonia tank (~ambient pressure, -33°C), inner tank from stainless steel, outer tank from concrete including installations for liquefaction/condensation, boil-off gas recovery and safety installations; the necessary installations make only a small fraction of the total cost. The total cost are driven by material and working time on the tanks. While the costs do not scale strictly linearly, we here assume they do (good approximation c.f. ref. Fig 55.) and take the costs for a 9 kt NH3 (l) tank = 8 M$2010, which is smaller 4-5x smaller than the largest deployed tanks today. @@ -488,14 +446,6 @@ NH3 (l) transport ship,FOM,4.0,%/year,"Cihlar et al 2020 based on IEA 2019, Tabl NH3 (l) transport ship,capacity,53000.0,t_NH3,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,investment,81164200.0,EUR,"Cihlar et al 2020 based on IEA 2019, Table 3-B",,2019.0 NH3 (l) transport ship,lifetime,20.0,years,"Guess estimated based on H2 (l) tanker, but more mature technology",,2019.0 -Natural gas steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,efficiency,0.787,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming,investment,180.0632,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,efficiency,0.695,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Natural gas steam reforming CC,investment,323.8999,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Natural gas steam reforming CC,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Ni-Zn-bicharger,FOM,2.1198,%/year,"Viswanathan_2022, p.51-52 in section 4.4.2","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Guesstimate 30% assumed of power components every 10 years ']}",2020.0 Ni-Zn-bicharger,efficiency,0.9,per unit,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['((0.75-0.87)/2)^0.5 mean value of range efficiency is not RTE but single way AC-store conversion']}",2020.0 Ni-Zn-bicharger,investment,81553.4846,EUR/MW,"Viswanathan_2022, p.59 (p.81) same as Li-LFP","{'carrier': ['elec', 'nizn', 'elec'], 'technology_type': ['bicharger'], 'type': ['electrochemical'], 'note': ['Power Equipment']}",2020.0 @@ -508,10 +458,6 @@ OCGT,VOM,4.762,EUR/MWh,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx OCGT,efficiency,0.43,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","52 OCGT - Natural gas: Electricity efficiency, annual average",2015.0 OCGT,investment,435.818,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Specific investment,2015.0 OCGT,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",52 OCGT - Natural gas: Technical lifetime,2015.0 -PEM electrolyzer,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,efficiency,0.73,p.u.,ICCT IRA e-fuels assumptions ,, -PEM electrolyzer,investment,665.6771,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -PEM electrolyzer,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, PHS,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 PHS,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 @@ -534,19 +480,15 @@ Pumped-Storage-Hydro-bicharger,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90 Pumped-Storage-Hydro-store,FOM,0.43,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['derived']}",2020.0 Pumped-Storage-Hydro-store,investment,57074.0625,EUR/MWh,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['Reservoir Construction & Infrastructure']}",2020.0 Pumped-Storage-Hydro-store,lifetime,60.0,years,"Viswanathan_2022, p.68 (p.90)","{'carrier': ['phs'], 'technology_type': ['store'], 'type': ['mechanical'], 'note': ['NULL']}",2020.0 -SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 +SMR,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", SMR,efficiency,0.76,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR,investment,522201.0492,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 -SMR CC,capture_rate,0.9,per unit,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates between 54%-90%, +SMR CC,FOM,5.0,%/year,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311", +SMR CC,capture_rate,0.9,EUR/MW_CH4,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",wide range: capture rates betwen 54%-90%, SMR CC,efficiency,0.69,per unit (in LHV),"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, SMR CC,investment,605753.2171,EUR/MW_CH4,Danish Energy Agency,"Technology data for renewable fuels, in pdf on table 3 p.311",2015.0 SMR CC,lifetime,30.0,years,"IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050",, -SOEC,FOM,0.04,%/year,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,efficiency,0.9,p.u.,ICCT IRA e-fuels assumptions ,, -SOEC,investment,758.2311,USD/kW,ICCT IRA e-fuels assumptions ,,2022.0 -SOEC,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,, Sand-charger,FOM,1.075,%/year,"Viswanathan_2022, NULL","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Guesstimate, 50% on charger']}",2020.0 Sand-charger,efficiency,0.99,per unit,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['RTE assume 99% for charge and other for discharge']}",2020.0 Sand-charger,investment,144192.2682,EUR/MW,"Georgiou_2018, Guesstimate that charge is 20% of capital costs of power components for sensible thermal storage","{'carrier': ['elec', 'sand'], 'technology_type': ['charger'], 'type': ['thermal'], 'note': ['Power Equipment Charge']}",2020.0 @@ -558,10 +500,6 @@ Sand-discharger,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier Sand-store,FOM,0.3308,%/year,"Viswanathan_2022, p 104 (p.126)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['not provided calculated as for hydrogen']}",2020.0 Sand-store,investment,6700.8517,EUR/MWh,"Viswanathan_2022, p.100 (p.122)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['SB and BOS 0.85 of 2021 value']}",2020.0 Sand-store,lifetime,35.0,years,"Viswanathan_2022, p.107 (p.129)","{'carrier': ['sand'], 'technology_type': ['store'], 'type': ['thermal'], 'note': ['NULL']}",2020.0 -Solid biomass steam reforming,FOM,0.05,%/year,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,efficiency,0.712,p.u.,"JRC, 01_JRC-EU-TIMES Full model ",, -Solid biomass steam reforming,investment,590.7702,EUR/kW,"JRC, 01_JRC-EU-TIMES Full model ",,2010.0 -Solid biomass steam reforming,lifetime,20.0,years,"JRC, 01_JRC-EU-TIMES Full model ",, Steam methane reforming,FOM,3.0,%/year,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 Steam methane reforming,investment,497454.611,EUR/MW_H2,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW). Currency conversion 1.17 USD = 1 EUR.,2015.0 Steam methane reforming,lifetime,30.0,years,"International Energy Agency (2015): Technology Roadmap Hydrogen and Fuel Cells , table 15.",Large scale SMR facility (150-300 MW).,2015.0 @@ -604,6 +542,8 @@ Zn-Br-Nonflow-store,FOM,0.2244,%/year,"Viswanathan_2022, 0.43 % of SB","{'carrie Zn-Br-Nonflow-store,investment,239220.5823,EUR/MWh,"Viswanathan_2022, p.59 (p.81) Table 4.14","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['DC storage block']}",2020.0 Zn-Br-Nonflow-store,lifetime,15.0,years,"Viswanathan_2022, p.59 (p.81)","{'carrier': ['znbr'], 'technology_type': ['store'], 'type': ['electrochemical'], 'note': ['NULL']}",2020.0 air separation unit,FOM,3.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Fixed O&M,2015.0 +air separation unit,efficiency,0.0005,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: Electricity Consumption,",2015.0 +air separation unit,efficiency-heat,0.0004,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","103 Hydrogen to Ammonia: District Heating Output,",2015.0 air separation unit,electricity-input,0.25,MWh_el/t_N2,"DEA (2022): Technology Data for Renewable Fuels (https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-renewable-fuels), p.288.","For consistency reasons use value from Danish Energy Agency. DEA also reports range of values (0.2-0.4 MWh/t_N2) on pg. 288. Other efficienices reported are even higher, e.g. 0.11 Mwh/t_N2 from Morgan (2013): Techno-Economic Feasibility Study of Ammonia Plants Powered by Offshore Wind .", air separation unit,investment,514601.1327,EUR/t_N2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Specific investment,2015.0 air separation unit,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",103 Hydrogen to Ammonia: Technical lifetime,2015.0 @@ -618,12 +558,13 @@ battery inverter,lifetime,10.0,years,"Danish Energy Agency, technology_data_cata battery storage,investment,79.3666,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Energy storage expansion cost investment,2015.0 battery storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",: Technical lifetime,2015.0 biochar pyrolysis,FOM,3.4,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Fixed O&M",2020.0 -biochar pyrolysis,VOM,823.497,EUR/MWh_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 -biochar pyrolysis,efficiency-biochar,0.404,MWh_biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency biochar",2020.0 -biochar pyrolysis,efficiency-heat,0.4848,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: efficiency heat",2020.0 -biochar pyrolysis,investment,128671.4,EUR/kW_biochar,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 +biochar pyrolysis,VOM,47.6777,EUR/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Variable O&M",2020.0 +biochar pyrolysis,biomass input,7.6748,MWh_biomass/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Biomass Input",2020.0 +biochar pyrolysis,electricity input,0.3184,MWh_e/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: El-Input",2020.0 +biochar pyrolysis,heat output,3.7859,MWh_th/t_CO2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: H-Output",2020.0 +biochar pyrolysis,investment,7449637.6724,EUR/t_CO2/h,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Specific investment",2020.0 biochar pyrolysis,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: Technical lifetime",2020.0 -biochar pyrolysis,yield-biochar,0.0582,ton biochar/MWh_feedstock,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 +biochar pyrolysis,yield-biochar,0.0597,t_biochar/MWh_biomass,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","105 Slow pyrolysis, Straw: yield biochar",2020.0 biodiesel crops,fuel,131.8317,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIORPS1 (rape seed), ENS_BaU_GFTM",,2010.0 bioethanol crops,fuel,89.8502,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOCRP11 (Bioethanol barley, wheat, grain maize, oats, other cereals and rye), ENS_BaU_GFTM",,2010.0 biogas,CO2 stored,0.0868,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, @@ -640,10 +581,17 @@ biogas CC,efficiency,1.0,per unit,Assuming input biomass is already given in bio biogas CC,investment,867.3532,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Specific investment",2020.0 biogas CC,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","81 Biogas, Basic plant, small: Technical lifetime",2020.0 biogas manure,fuel,19.9506,EUR/MWhth,"JRC ENSPRESO ca avg for MINBIOGAS1 (manure), ENS_BaU_GFTM",,2010.0 -biogas plus hydrogen,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Fixed O&M,2020.0 -biogas plus hydrogen,VOM,2.2969,EUR/MWh_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 -biogas plus hydrogen,investment,482.3582,EUR/kW_CH4,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 +biogas plus hydrogen,Biogas Input,1.1522,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Biogas Consumption,",2020.0 +biogas plus hydrogen,CO2 Input,0.1235,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: CO2 Input,",2020.0 +biogas plus hydrogen,Methane Output,1.9348,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Methane Output,",2020.0 +biogas plus hydrogen,VOM,4.4441,EUR/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Variable O&M,2020.0 +biogas plus hydrogen,electricity input,0.0217,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: El-Input,",2020.0 +biogas plus hydrogen,heat output,0.2174,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: H-Output,",2020.0 +biogas plus hydrogen,hydrogen input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","99 SNG from methan. of biogas: Hydrogen Consumption,",2020.0 +biogas plus hydrogen,investment,933.2583,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Specific investment,2020.0 biogas plus hydrogen,lifetime,25.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",99 SNG from methan. of biogas: Technical lifetime,2020.0 +biogas storage, investment,14.45, EUR/kWh,AU Foulum,,2020.0 +biogas storage, lifetime,25.0,years,AU Foulum,,2020.0 biogas upgrading,FOM,17.0397,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Fixed O&M ",2020.0 biogas upgrading,VOM,2.6993,EUR/MWh output,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: Variable O&M",2020.0 biogas upgrading,investment,125.1744,EUR/kW,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","82 Upgrading 3,000 Nm3 per h: investment (upgrading, methane redution and grid injection)",2020.0 @@ -688,9 +636,9 @@ biomass boiler,efficiency,0.88,per unit,"Danish Energy Agency, technologydatafor biomass boiler,investment,621.5592,EUR/kW_th,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Specific investment",2015.0 biomass boiler,lifetime,20.0,years,"Danish Energy Agency, technologydatafor_heating_installations_marts_2018.xlsx","204 Biomass boiler, automatic: Technical lifetime",2015.0 biomass boiler,pelletizing cost,9.0,EUR/MWh_pellets,Assumption based on doi:10.1016/j.rser.2019.109506,,2019.0 -biomass-to-methanol,C in fuel,0.44,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,C stored,0.56,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, -biomass-to-methanol,CO2 stored,0.2053,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C in fuel,0.4295,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,C stored,0.5705,per unit,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, +biomass-to-methanol,CO2 stored,0.2143,tCO2/MWh_th,"Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016",, biomass-to-methanol,FOM,2.6667,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Fixed O&M,2020.0 biomass-to-methanol,VOM,14.4653,EUR/MWh_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Variable O&M,2020.0 biomass-to-methanol,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, @@ -699,6 +647,15 @@ biomass-to-methanol,efficiency-electricity,0.02,MWh_e/MWh_th,"Danish Energy Agen biomass-to-methanol,efficiency-heat,0.22,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","97 Methanol from biomass gasif.: District heat Output,",2020.0 biomass-to-methanol,investment,1553.1646,EUR/kW_MeOH,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Specific investment,2020.0 biomass-to-methanol,lifetime,20.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",97 Methanol from biomass gasif.: Technical lifetime,2020.0 +biomethanation,Biogas Input,1.1444,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Biogas Consumption,",2020.0 +biomethanation,CO2 Input,0.165,t_CO2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: CO2 Input,",2020.0 +biomethanation,FOM,6.6667,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Fixed O&M ,2020.0 +biomethanation,Hydrogen Input,1.0,MWh_H2/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Hydrogen Input,",2020.0 +biomethanation,Methane Output,1.9673,MWh_CH4/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: Methane Output,",2020.0 +biomethanation,electricity input,0.0417,MWh_e/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: El-Input,",2020.0 +biomethanation,heat output,0.1667,MWh_th/MWh_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx","106 Biomethanation of biogas: H-Output,",2020.0 +biomethanation,investment,987.5,EUR/kW_H2,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Specific investment ,2020.0 +biomethanation,lifetime,30.0,years,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",106 Biomethanation of biogas: Technical lifetime,2020.0 cement capture,FOM,3.0,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,capture_rate,0.95,per unit,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 cement capture,compression-electricity-input,0.075,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",401.c Post comb - Cement kiln,2020.0 @@ -785,7 +742,7 @@ central solid biomass CHP CC,c_b,0.3423,50°C/100°C,"Danish Energy Agency, tech central solid biomass CHP CC,c_v,1.0,50°C/100°C,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Cv coefficient",2015.0 central solid biomass CHP CC,efficiency,0.2652,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Electricity efficiency, net, annual average",2015.0 central solid biomass CHP CC,efficiency-heat,0.8294,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Heat efficiency, net, annual average",2015.0 -central solid biomass CHP CC,investment,4755.697,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 +central solid biomass CHP CC,investment,4790.4001,EUR/kW_e,Combination of central solid biomass CHP CC and solid biomass boiler steam,,2015.0 central solid biomass CHP CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Technical lifetime",2015.0 central solid biomass CHP powerboost CC,FOM,2.8518,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Fixed O&M",2015.0 central solid biomass CHP powerboost CC,VOM,4.9394,EUR/MWh_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","09a Wood Chips, Large 50 degree: Variable O&M ",2015.0 @@ -812,23 +769,23 @@ central water-sourced heat pump,VOM,1.4709,EUR/MWh,"Danish Energy Agency, techno central water-sourced heat pump,efficiency,3.86,per unit,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Total efficiency , net, annual average",2015.0 central water-sourced heat pump,investment,1058.2216,EUR/kW,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Specific investment",2015.0 central water-sourced heat pump,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","40 Comp. hp, seawater 20 MW: Technical lifetime",2015.0 -clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 +clean water tank storage,FOM,2.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, clean water tank storage,investment,69.1286,EUR/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2013.0 clean water tank storage,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, coal,CO2 intensity,0.3361,tCO2/MWh_th,Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018,, coal,FOM,1.31,%/year,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (39.5+91.25) USD/kW_e/a /2 / (1.09 USD/EUR) / investment cost * 100.",2023.0 coal,VOM,3.2612,EUR/MWh_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. (3+5.5)USD/MWh_e/2 / (1.09 USD/EUR).",2023.0 -coal,efficiency,0.356,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 +coal,efficiency,0.33,p.u.,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Calculated based on average of listed range, i.e. 1 / ((8.75+12) MMbtu/MWh_th /2 / (3.4095 MMbtu/MWh_th)), rounded up.",2023.0 coal,fuel,9.5542,EUR/MWh_th,"DIW (2013): Current and propsective costs of electricity generation until 2050, http://hdl.handle.net/10419/80348 , pg. 80 text below figure 10, accessed: 2023-12-14.","Based on IEA 2011 data, 99 USD/t.",2010.0 coal,investment,3827.1629,EUR/kW_e,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.","Higher costs include coal plants with CCS, but since using here for calculating the average nevertheless. Calculated based on average of listed range, i.e. (3200+6775) USD/kW_e/2 / (1.09 USD/EUR).",2023.0 coal,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 -csp-tower,FOM,1.4,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,2020.0 +csp-tower,FOM,1.4,%/year,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),Ratio between CAPEX and FOM from ATB database for “moderate” scenario.,1.0 csp-tower,investment,99.38,"EUR/kW_th,dp",ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include solar field and solar tower as well as EPC cost for the default installation size (104 MWe plant). Total costs (223,708,924 USD) are divided by active area (heliostat reflective area, 1,269,054 m2) and multiplied by design point DNI (0.95 kW/m2) to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower,lifetime,30.0,years,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power),-,2020.0 -csp-tower TES,FOM,1.4,%/year,see solar-tower.,-,2020.0 +csp-tower TES,FOM,1.4,%/year,see solar-tower.,-,1.0 csp-tower TES,investment,13.32,EUR/kWh_th,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the TES incl. EPC cost for the default installation size (104 MWe plant, 2.791 MW_th TES). Total costs (69390776.7 USD) are divided by TES size to obtain EUR/kW_th. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower TES,lifetime,30.0,years,see solar-tower.,-,2020.0 -csp-tower power block,FOM,1.4,%/year,see solar-tower.,-,2020.0 +csp-tower power block,FOM,1.4,%/year,see solar-tower.,-,1.0 csp-tower power block,investment,696.2,EUR/kW_e,ATB CSP data (https://atb.nrel.gov/electricity/2021/concentrating_solar_power) and NREL SAM v2021.12.2 (https://sam.nrel.gov/).,"Based on NREL’s SAM (v2021.12.2) numbers for a CSP power plant, 2020 numbers. CAPEX degression (=learning) taken from ATB database (“moderate”) scenario. Costs include the power cycle incl. BOP and EPC cost for the default installation size (104 MWe plant). Total costs (135185685.5 USD) are divided by power block nameplate capacity size to obtain EUR/kW_e. Exchange rate: 1.16 USD to 1 EUR.",2020.0 csp-tower power block,lifetime,30.0,years,see solar-tower.,-,2020.0 decentral CHP,FOM,3.0,%/year,HP, from old pypsa cost assumptions,2015.0 @@ -874,19 +831,18 @@ decentral water tank storage,energy to power ratio,0.15,h,"Danish Energy Agency, decentral water tank storage,investment,433.8709,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Specific investment,2015.0 decentral water tank storage,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",142 Small scale hot water tank: Technical lifetime,2015.0 digestible biomass,fuel,17.0611,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOAGRW1, ENS_Ref for 2040",,2010.0 -digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +digestible biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, digestible biomass to hydrogen,efficiency,0.39,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, digestible biomass to hydrogen,investment,2648.1996,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 -direct air capture,FOM,1.3,%/year,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,FOM,4.95,%/year,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-electricity-input,0.15,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,compression-heat-output,0.2,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,electricity-input,0.24,MWh_el/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 -direct air capture,heat-input,1.17,MWh_th/t_CO2,ICCT IRA e-fuels assumptions ,,2020.0 +direct air capture,electricity-input,0.4,MWh_el/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","0.4 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 0.182 MWh based on Breyer et al (2019). Should already include electricity for water scrubbing and compression (high quality CO2 output).",2020.0 +direct air capture,heat-input,1.6,MWh_th/t_CO2,"Beuttler et al (2019): The Role of Direct Air Capture in Mitigation of Antropogenic Greenhouse Gas emissions (https://doi.org/10.3389/fclim.2019.00010), alternative: Breyer et al (2019).","Thermal energy demand. Provided via air-sourced heat pumps. 1.6 MWh based on Beuttler et al (2019) for Climeworks LT DAC, alternative value: 1.102 MWh based on Breyer et al (2019).",2020.0 direct air capture,heat-output,0.75,MWh/tCO2,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,investment,11034260.0394,USD/t_CO2/h,ICCT IRA e-fuels assumptions ,,2022.0 +direct air capture,investment,4000000.0,EUR/(tCO2/h),"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 direct air capture,lifetime,20.0,years,"Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx",403.a Direct air capture,2020.0 -direct air capture,years,30.0,years,ICCT IRA e-fuels assumptions ,, direct firing gas,FOM,1.0303,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Fixed O&M,2019.0 direct firing gas,VOM,0.282,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",312.a Direct firing Natural Gas: Variable O&M,2019.0 direct firing gas,efficiency,1.0,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","312.a Direct firing Natural Gas: Total efficiency, net, annual average",2019.0 @@ -927,8 +883,8 @@ electric boiler steam,VOM,0.7855,EUR/MWh,"Danish Energy Agency, technology_data_ electric boiler steam,efficiency,0.99,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","310.1 Electric boiler steam : Total efficiency, net, annual average",2019.0 electric boiler steam,investment,70.49,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Nominal investment,2019.0 electric boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",310.1 Electric boiler steam : Technical lifetime,2019.0 -electric steam cracker,FOM,3.0,%/year,Guesstimate,,2015.0 -electric steam cracker,VOM,190.4799,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 +electric steam cracker,FOM,3.0,%/year,Guesstimate,, +electric steam cracker,VOM,190.4799,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",,2015.0 electric steam cracker,carbondioxide-output,0.55,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), ",The report also references another source with 0.76 t_CO2/t_HVC, electric steam cracker,electricity-input,2.7,MWh_el/t_HVC,"Lechtenböhmer et al. (2016): 10.1016/j.energy.2016.07.110, Section 4.3, page 6.",Assuming electrified processing., electric steam cracker,investment,11124025.7434,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -940,14 +896,14 @@ electricity distribution grid,lifetime,40.0,years,TODO, from old pypsa cost assu electricity grid connection,FOM,2.0,%/year,TODO, from old pypsa cost assumptions,2015.0 electricity grid connection,investment,148.151,EUR/kW,DEA, from old pypsa cost assumptions,2015.0 electricity grid connection,lifetime,40.0,years,TODO, from old pypsa cost assumptions,2015.0 -electrobiofuels,C in fuel,0.9316,per unit,Stoichiometric calculation,, -electrobiofuels,FOM,3.0,%/year,combination of BtL and electrofuels,,2015.0 -electrobiofuels,VOM,2.2297,EUR/MWh_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,C in fuel,0.9308,per unit,Stoichiometric calculation,, +electrobiofuels,FOM,3.0,%/year,combination of BtL and electrofuels,, +electrobiofuels,VOM,2.6202,EUR/MWh_th,combination of BtL and electrofuels,,2017.0 electrobiofuels,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, -electrobiofuels,efficiency-biomass,1.3283,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-hydrogen,1.1364,per unit,Stoichiometric calculation,, -electrobiofuels,efficiency-tot,0.6125,per unit,Stoichiometric calculation,, -electrobiofuels,investment,931730.9035,EUR/kW_th,combination of BtL and electrofuels,,2022.0 +electrobiofuels,efficiency-biomass,1.3598,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-hydrogen,1.283,per unit,Stoichiometric calculation,, +electrobiofuels,efficiency-tot,0.6601,per unit,Stoichiometric calculation,, +electrobiofuels,investment,325755.8934,EUR/kW_th,combination of BtL and electrofuels,,2017.0 electrolysis,FOM,4.0,%/year,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Fixed O&M ,2020.0 electrolysis,efficiency,0.6994,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: Hydrogen Output,2020.0 electrolysis,efficiency-heat,0.1294,per unit,"Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx",86 AEC 100 MW: - hereof recoverable for district heating,2020.0 @@ -971,7 +927,7 @@ gas boiler steam,VOM,1.007,EUR/MWh,"Danish Energy Agency, technology_data_for_in gas boiler steam,efficiency,0.94,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1c Steam boiler Gas: Total efficiency, net, annual average",2019.0 gas boiler steam,investment,45.7727,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Nominal investment,2019.0 gas boiler steam,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1c Steam boiler Gas: Technical lifetime,2019.0 -gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenance, salt cavern (units converted)",2015.0 +gas storage,FOM,3.5919,%,Danish Energy Agency,"150 Underground Storage of Gas, Operation and Maintenace, salt cavern (units converted)",2015.0 gas storage,investment,0.0348,EUR/kWh,Danish Energy Agency,"150 Underground Storage of Gas, Establishment of one cavern (units converted)",2015.0 gas storage,lifetime,100.0,years,TODO no source,"estimation: most underground storage are already build, they do have a long lifetime",2015.0 gas storage charger,investment,15.1737,EUR/kW,Danish Energy Agency,"150 Underground Storage of Gas, Process equipment (units converted)",2015.0 @@ -995,14 +951,14 @@ hydro,FOM,1.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pyp hydro,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 hydro,investment,2274.8177,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 hydro,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 -hydrogen storage compressor,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg., -hydrogen storage compressor,investment,2.0291,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage compressor,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,FOM,4.0,%/year,ICCT IRA e-fuels assumptions ,-,2022.0 -hydrogen storage tank type 1,investment,15.0133,USD/kWh_H2,ICCT IRA e-fuels assumptions ,,2022.0 -hydrogen storage tank type 1,lifetime,30.0,years,ICCT IRA e-fuels assumptions ,-, -hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-, +hydrogen storage compressor,FOM,4.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage compressor,compression-electricity-input,0.05,MWh_el/MWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",1.707 kWh/kg.,2020.0 +hydrogen storage compressor,investment,87.69,EUR/kW_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.","2923 EUR/kg_H2. For a 206 kg/h compressor. Base CAPEX 40 528 EUR/kW_el with scale factor 0.4603. kg_H2 converted to MWh using LHV. Pressure range: 30 bar in, 250 bar out.",2020.0 +hydrogen storage compressor,lifetime,15.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.",-,2020.0 +hydrogen storage tank type 1,FOM,2.0,%/year,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,investment,13.5,EUR/kWh_H2,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.","450 EUR/kg_H2 converted with LHV to MWh. For a type 1 hydrogen storage tank (steel, 15-250 bar). Currency year assumed 2020 for initial publication of reference; observe note in SI.4.3 that no currency year is explicitly stated in the reference.",2020.0 +hydrogen storage tank type 1,lifetime,20.0,years,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 +hydrogen storage tank type 1,min_fill_level,6.0,%,"Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.",-,2020.0 hydrogen storage tank type 1 including compressor,FOM,1.9048,%/year,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Fixed O&M,2015.0 hydrogen storage tank type 1 including compressor,investment,22.2227,EUR/kWh,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Specific investment,2015.0 hydrogen storage tank type 1 including compressor,lifetime,30.0,years,"Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx",151a Hydrogen Storage - Tanks: Technical lifetime,2015.0 @@ -1049,8 +1005,8 @@ methanol-to-kerosene,hydrogen-input,0.0279,MWh_H2/MWh_kerosene,"Concawe (2022): methanol-to-kerosene,investment,200000.0,EUR/MW_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",,2020.0 methanol-to-kerosene,lifetime,30.0,years,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 94.",, methanol-to-kerosene,methanol-input,1.0764,MWh_MeOH/MWh_kerosene,"Concawe (2022): E-Fuels: A technoeconomic assessment of European domestic production and imports towards 2050 (https://www.concawe.eu/wp-content/uploads/Rpt_22-17.pdf), table 6.","Assuming LHV 11.94 kWh/kg for kerosene, 5.54 kWh/kg for methanol, 33.3 kWh/kg for hydrogen.", -methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker,2015.0 -methanol-to-olefins/aromatics,VOM,31.7466,EUR/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 +methanol-to-olefins/aromatics,FOM,3.0,%/year,Guesstimate,same as steam cracker, +methanol-to-olefins/aromatics,VOM,31.7466,€/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35", ,2015.0 methanol-to-olefins/aromatics,carbondioxide-output,0.6107,t_CO2/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Sections 4.5 (for ethylene and propylene) and 4.6 (for BTX)","Weighted average: 0.4 t_MeOH/t_ethylene+propylene for 21.7 Mt of ethylene and 17 Mt of propylene, 1.13 t_CO2/t_BTX for 15.7 Mt of BTX. The report also references process emissions of 0.55 t_MeOH/t_ethylene+propylene elsewhere. ", methanol-to-olefins/aromatics,electricity-input,1.3889,MWh_el/t_HVC,"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), page 69",5 GJ/t_HVC , methanol-to-olefins/aromatics,investment,2781006.4359,EUR/(t_HVC/h),"DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry (https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf), Table 35",Assuming CAPEX of 1200 €/t actually given in €/(t/a).,2015.0 @@ -1077,7 +1033,7 @@ nuclear,investment,8594.1354,EUR/kW_e,"Lazard's levelized cost of energy analysi nuclear,lifetime,40.0,years,"Lazard's levelized cost of energy analysis - version 16.0 (2023): https://www.lazard.com/media/typdgxmm/lazards-lcoeplus-april-2023.pdf , pg. 49 (Levelized Cost of Energy - Key Assumptions), accessed: 2023-12-14.",,2023.0 offwind,FOM,2.1655,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Fixed O&M [EUR/MW_e/y, 2020]",2020.0 offwind,VOM,0.0212,EUR/MWhel,RES costs made up to fix curtailment order, from old pypsa cost assumptions,2015.0 -offwind,investment,1523.9311,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs subtracted from investment costs",2020.0 +offwind,investment,1523.9311,"EUR/kW_e, 2020","Danish Energy Agency, technology_data_for_el_and_dh.xlsx","21 Offshore turbines: Nominal investment [MEUR/MW_e, 2020] grid connection costs substracted from investment costs",2020.0 offwind,lifetime,30.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",21 Offshore turbines: Technical lifetime [years],2020.0 offwind-ac-connection-submarine,investment,2841.3251,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 offwind-ac-connection-underground,investment,1420.1334,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data, from old pypsa cost assumptions,2015.0 @@ -1106,12 +1062,18 @@ organic rankine cycle,FOM,2.0,%/year,"Aghahosseini, Breyer 2020: From hot rock t organic rankine cycle,electricity-input,0.12,MWh_el/MWh_th,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551; Breede et al. 2015: Overcoming challenges in the classification of deep geothermal potential, https://eprints.gla.ac.uk/169585/","Heat-input, Electricity-output. This is a rough estimate, depends on input temperature, implies ~150 C.",2020.0 organic rankine cycle,investment,1376.0,EUR/kW_el,Tartiere and Astolfi 2017: A world overview of the organic Rankine cycle market,"Low rollout complicates the estimation, compounded by a dependence both on plant size and temperature, converted from 1500 USD/kW using currency conversion 1.09 USD = 1 EUR.",2020.0 organic rankine cycle,lifetime,30.0,years,"Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551",,2020.0 +perennials gbr,FOM,0.0,%year,Own assumption,,2015.0 +perennials gbr,VOM,43.2317,EUR/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,"includes purchase of perennial crops and sales of proteine concentrate, table 8.1 wages, maintenance and auxiliary costs",2015.0 +perennials gbr,biogas-output,0.1947,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,electricity-input,0.0733,MWh/tDM,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,table 2,2015.0 +perennials gbr,investment,1371168.1394,EUR/tDM/h,https://doi.org/10.1016/B978-0-323-95879-0.50147-8,includes cost for biogas plant without upgrading,2015.0 +perennials gbr,lifetime,25.0,years,Own assumption,,2015.0 ror,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2015.0 ror,investment,3412.2266,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348, from old pypsa cost assumptions,2010.0 ror,lifetime,80.0,years,IEA2010, from old pypsa cost assumptions,2015.0 seawater RO desalination,electricity-input,0.003,MWHh_el/t_H2O,"Caldera et al. (2016): Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",Desalination using SWRO. Assume medium salinity of 35 Practical Salinity Units (PSUs) = 35 kg/m^3., -seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",,2015.0 +seawater desalination,FOM,4.0,%/year,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, seawater desalination,electricity-input,3.0348,kWh/m^3-H2O,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Fig. 4.",, seawater desalination,investment,22249.7881,EUR/(m^3-H2O/h),"Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402), Table 4.",,2015.0 seawater desalination,lifetime,30.0,years,"Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004), Table 1.",, @@ -1137,7 +1099,7 @@ solar-utility,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_ solar-utility single-axis tracking,FOM,2.5531,%/year,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Fixed O&M [2020-EUR/MW_e/y],2020.0 solar-utility single-axis tracking,investment,352.5127,EUR/kW_e,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Nominal investment [2020-MEUR/MW_e],2020.0 solar-utility single-axis tracking,lifetime,40.0,years,"Danish Energy Agency, technology_data_for_el_and_dh.xlsx",22 Utility-scale PV tracker: Technical lifetime [years],2020.0 -solid biomass,CO2 intensity,0.3667,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, +solid biomass,CO2 intensity,0.3757,tCO2/MWh_th,Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass,, solid biomass,fuel,13.6489,EUR/MWh_th,"JRC ENSPRESO ca avg for MINBIOWOOW1 (secondary forest residue wood chips), ENS_Ref for 2040",,2010.0 solid biomass boiler steam,FOM,6.2831,%/year,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Fixed O&M,2019.0 solid biomass boiler steam,VOM,2.8679,EUR/MWh,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Variable O&M,2019.0 @@ -1149,7 +1111,7 @@ solid biomass boiler steam CC,VOM,2.8679,EUR/MWh,"Danish Energy Agency, technolo solid biomass boiler steam CC,efficiency,0.9,per unit,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx","311.1e Steam boiler Wood: Total efficiency, net, annual average",2019.0 solid biomass boiler steam CC,investment,540.1182,EUR/kW,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Nominal investment,2019.0 solid biomass boiler steam CC,lifetime,25.0,years,"Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx",311.1e Steam boiler Wood: Technical lifetime,2019.0 -solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 +solid biomass to hydrogen,FOM,4.25,%/year,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,capture rate,0.9,per unit,Assumption based on doi:10.1016/j.biombioe.2015.01.006,, solid biomass to hydrogen,efficiency,0.56,per unit,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",, solid biomass to hydrogen,investment,2648.1996,EUR/kW_th,"Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014",,2014.0 diff --git a/scripts/compile_cost_assumptions.py b/scripts/compile_cost_assumptions.py index 4790d65c..777ceb8b 100644 --- a/scripts/compile_cost_assumptions.py +++ b/scripts/compile_cost_assumptions.py @@ -1,10 +1,7 @@ -# SPDX-FileCopyrightText: Contributors to technology-data -# -# SPDX-License-Identifier: GPL-3.0-only - -# coding: utf-8 +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- """ -Script creates cost csv for chosen years from different source (source_dict). +Script creates cost csv for choosen years from different source (source_dict). The data is standardized for uniform: - cost years (depending on the rate of inflation ) - technology names @@ -28,254 +25,247 @@ @author: Marta, Lisa """ -import numpy as np import pandas as pd - +import numpy as np try: - pd.set_option("future.no_silent_downcasting", True) + pd.set_option('future.no_silent_downcasting', True) except Exception: pass # ---------- sources ------------------------------------------------------- source_dict = { - "DEA": "Danish Energy Agency", - # solar utility - "Vartiaien": "Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility‐scale PV levelised cost of electricity", - # solar rooftop - "ETIP": "European PV Technology and Innovation Platform", - # fuel cost - "zappa": "Is a 100% renewable European power system feasible by 2050?", - # co2 intensity - "co2": "Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018", - # gas pipeline costs - "ISE": "WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg", - # Water desalination costs - "Caldera2016": "Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004)", - "Caldera2017": "Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402)", - # home battery storage and inverter investment costs - "EWG": "Global Energy System based on 100% Renewable Energy, Energywatchgroup/LTU University, 2019", - "HyNOW": "Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014", - # efficiencies + lifetime SMR / SMR + CC - "IEA": "IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050", - # SMR capture rate - "Timmerberg": "Hydrogen and hydrogen-derived fuels through methane decomposition of natural gas – GHG emissions and costs Timmerberg et al. (2020), https://doi.org/10.1016/j.ecmx.2020.100043", - # geothermal (enhanced geothermal systems) - "Aghahosseini2020": "Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551", - # review of existing deep geothermal projects - "Breede2015": "Breede et al. 2015: Overcoming challenges in the classification of deep geothermal potential, https://eprints.gla.ac.uk/169585/", - # Study of deep geothermal systems in the Northern Upper Rhine Graben - "Frey2022": "Frey et al. 2022: Techno-Economic Assessment of Geothermal Resources in the Variscan Basement of the Northern Upper Rhine Graben", - # vehicles - "vehicles": "PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html", -} + 'DEA': 'Danish Energy Agency', + # solar utility + 'Vartiaien': 'Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility‐scale PV levelised cost of electricity', + # solar rooftop + 'ETIP': 'European PV Technology and Innovation Platform', + # fuel cost + 'zappa': 'Is a 100% renewable European power system feasible by 2050?', + # co2 intensity + "co2" :'Entwicklung der spezifischen Kohlendioxid-Emissionen des deutschen Strommix in den Jahren 1990 - 2018', + # gas pipeline costs + "ISE": "WEGE ZU EINEM KLIMANEUTRALEN ENERGIESYSEM, Anhang zur Studie, Fraunhofer-Institut für Solare Energiesysteme ISE, Freiburg", + # Water desalination costs + "Caldera2016": "Caldera et al 2016: Local cost of seawater RO desalination based on solar PV and windenergy: A global estimate. (https://doi.org/10.1016/j.desal.2016.02.004)", + "Caldera2017": "Caldera et al 2017: Learning Curve for Seawater Reverse Osmosis Desalination Plants: Capital Cost Trend of the Past, Present, and Future (https://doi.org/10.1002/2017WR021402)", + # home battery storage and inverter investment costs + "EWG": "Global Energy System based on 100% Renewable Energy, Energywatchgroup/LTU University, 2019", + "HyNOW" : "Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014", + # efficiencies + lifetime SMR / SMR + CC + "IEA": "IEA Global average levelised cost of hydrogen production by energy source and technology, 2019 and 2050 (2020), https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050", + # SMR capture rate + "Timmerberg": "Hydrogen and hydrogen-derived fuels through methane decomposition of natural gas – GHG emissions and costs Timmerberg et al. (2020), https://doi.org/10.1016/j.ecmx.2020.100043", + # geothermal (enhanced geothermal systems) + "Aghahosseini2020": "Aghahosseini, Breyer 2020: From hot rock to useful energy: A global estimate of enhanced geothermal systems potential, https://www.sciencedirect.com/science/article/pii/S0306261920312551", + # review of existing deep geothermal projects + "Breede2015": "Breede et al. 2015: Overcoming challenges in the classification of deep geothermal potential, https://eprints.gla.ac.uk/169585/", + # Study of deep geothermal systems in the Northern Upper Rhine Graben + "Frey2022": "Frey et al. 2022: Techno-Economic Assessment of Geothermal Resources in the Variscan Basement of the Northern Upper Rhine Graben", + # vehicles + "vehicles" : "PATHS TO A CLIMATE-NEUTRAL ENERGY SYSTEM The German energy transformation in its social context. https://www.ise.fraunhofer.de/en/publications/studies/paths-to-a-climate-neutral-energy-system.html" + } # [DEA-sheet-names] -sheet_names = { - "onwind": "20 Onshore turbines", - "offwind": "21 Offshore turbines", - "solar-utility": "22 Utility-scale PV", - "solar-utility single-axis tracking": "22 Utility-scale PV tracker", - "solar-rooftop residential": "22 Rooftop PV residential", - "solar-rooftop commercial": "22 Rooftop PV commercial", - "OCGT": "52 OCGT - Natural gas", - "CCGT": "05 Gas turb. CC, steam extract.", - "oil": "50 Diesel engine farm", - "biomass CHP": "09c Straw, Large, 40 degree", - "biomass EOP": "09c Straw, Large, 40 degree", - "biomass HOP": "09c Straw HOP", - "central coal CHP": "01 Coal CHP", - "central gas CHP": "04 Gas turb. simple cycle, L", - "central gas CHP CC": "04 Gas turb. simple cycle, L", - "central solid biomass CHP": "09a Wood Chips, Large 50 degree", - "central solid biomass CHP CC": "09a Wood Chips, Large 50 degree", - "central solid biomass CHP powerboost CC": "09a Wood Chips, Large 50 degree", - # 'solid biomass power': '09a Wood Chips extract. plant', - # 'solid biomass power CC': '09a Wood Chips extract. plant', - "central air-sourced heat pump": "40 Comp. hp, airsource 3 MW", - "central geothermal-sourced heat pump": "45.1.a Geothermal DH, 1200m, E", - "central geothermal heat source": "45.1.a Geothermal DH, 1200m, E", - "central excess-heat-sourced heat pump": "40 Comp. hp, excess heat 10 MW", - "central water-sourced heat pump": "40 Comp. hp, seawater 20 MW", - "central ground-sourced heat pump": "40 Absorption heat pump, DH", - "central resistive heater": "41 Electric Boilers", - "central gas boiler": "44 Natural Gas DH Only", - "decentral gas boiler": "202 Natural gas boiler", - "direct firing gas": "312.a Direct firing Natural Gas", - "direct firing gas CC": "312.a Direct firing Natural Gas", - "direct firing solid fuels": "312.b Direct firing Sold Fuels", - "direct firing solid fuels CC": "312.b Direct firing Sold Fuels", - "decentral ground-sourced heat pump": "207.7 Ground source existing", - "decentral air-sourced heat pump": "207.3 Air to water existing", - # 'decentral resistive heater': '216 Electric heating', - "central water pit storage": "140 PTES seasonal", - "central water tank storage": "141 Large hot water tank", - "decentral water tank storage": "142 Small scale hot water tank", - "fuel cell": "12 LT-PEMFC CHP", - "hydrogen storage underground": "151c Hydrogen Storage - Caverns", - "hydrogen storage tank type 1 including compressor": "151a Hydrogen Storage - Tanks", - "micro CHP": "219 LT-PEMFC mCHP - natural gas", - "biogas": "81 Biogas, Basic plant, small", - "biogas CC": "81 Biogas, Basic plant, small", - "biogas upgrading": "82 Upgrading 3,000 Nm3 per h", - "battery": "180 Lithium Ion Battery", - "industrial heat pump medium temperature": "302.a High temp. hp Up to 125 C", - "industrial heat pump high temperature": "302.b High temp. hp Up to 150", - "electric boiler steam": "310.1 Electric boiler steam ", - "gas boiler steam": "311.1c Steam boiler Gas", - "solid biomass boiler steam": "311.1e Steam boiler Wood", - "solid biomass boiler steam CC": "311.1e Steam boiler Wood", - "biomass boiler": "204 Biomass boiler, automatic", - "electrolysis": "86 AEC 100 MW", - "direct air capture": "403.a Direct air capture", - "biomass CHP capture": "401.a Post comb - small CHP", - "cement capture": "401.c Post comb - Cement kiln", - "BioSNG": "84 Gasif. CFB, Bio-SNG", - "BtL": "85 Gasif. Ent. Flow FT, liq fu ", - "biomass-to-methanol": "97 Methanol from biomass gasif.", - "biogas plus hydrogen": "99 SNG from methan. of biogas", - "methanolisation": "98 Methanol from hydrogen", - "Fischer-Tropsch": "102 Hydrogen to Jet", - "central hydrogen CHP": "12 LT-PEMFC CHP", - "Haber-Bosch": "103 Hydrogen to Ammonia", - "air separation unit": "103 Hydrogen to Ammonia", - "waste CHP": "08 WtE CHP, Large, 50 degree", - "waste CHP CC": "08 WtE CHP, Large, 50 degree", - # 'electricity distribution rural': '101 2 el distri Rural', - # 'electricity distribution urban': '101 4 el distri city', - # 'gas distribution rural': '102 7 gas Rural', - # 'gas distribution urban': '102 9 gas City', - # 'DH distribution rural': '103_12 DH_Distribu Rural', - # 'DH distribution urban': '103_14 DH_Distribu City', - # 'DH distribution low T': '103_16 DH_Distr New area LTDH', - # 'gas pipeline': '102 6 gas Main distri line', - # "DH main transmission": "103_11 DH transmission", - "biochar pyrolysis": "105 Slow pyrolysis, Straw", - #'biomethanation': '106 Biomethanation of biogas', - "electrolysis small": "86 AEC 10 MW", -} +sheet_names = {'onwind': '20 Onshore turbines', + 'offwind': '21 Offshore turbines', + 'solar-utility': '22 Utility-scale PV', + 'solar-utility single-axis tracking': '22 Utility-scale PV tracker', + 'solar-rooftop residential': '22 Rooftop PV residential', + 'solar-rooftop commercial': '22 Rooftop PV commercial', + 'OCGT': '52 OCGT - Natural gas', + 'CCGT': '05 Gas turb. CC, steam extract.', + 'oil': '50 Diesel engine farm', + 'biomass CHP': '09c Straw, Large, 40 degree', + 'biomass EOP': '09c Straw, Large, 40 degree', + 'biomass HOP': '09c Straw HOP', + 'central coal CHP': '01 Coal CHP', + 'central gas CHP': '04 Gas turb. simple cycle, L', + 'central gas CHP CC': '04 Gas turb. simple cycle, L', + 'central solid biomass CHP': '09a Wood Chips, Large 50 degree', + 'central solid biomass CHP CC': '09a Wood Chips, Large 50 degree', + 'central solid biomass CHP powerboost CC': '09a Wood Chips, Large 50 degree', + # 'solid biomass power': '09a Wood Chips extract. plant', + # 'solid biomass power CC': '09a Wood Chips extract. plant', + 'central air-sourced heat pump': '40 Comp. hp, airsource 3 MW', + 'central geothermal-sourced heat pump': '45.1.a Geothermal DH, 1200m, E', + 'central geothermal heat source': '45.1.a Geothermal DH, 1200m, E', + 'central excess-heat-sourced heat pump': '40 Comp. hp, excess heat 10 MW', + 'central water-sourced heat pump': '40 Comp. hp, seawater 20 MW', + 'central ground-sourced heat pump': '40 Absorption heat pump, DH', + 'central resistive heater': '41 Electric Boilers', + 'central gas boiler': '44 Natural Gas DH Only', + 'decentral gas boiler': '202 Natural gas boiler', + 'direct firing gas': '312.a Direct firing Natural Gas', + 'direct firing gas CC': '312.a Direct firing Natural Gas', + 'direct firing solid fuels': '312.b Direct firing Sold Fuels', + 'direct firing solid fuels CC': '312.b Direct firing Sold Fuels', + 'decentral ground-sourced heat pump': '207.7 Ground source existing', + 'decentral air-sourced heat pump': '207.3 Air to water existing', + # 'decentral resistive heater': '216 Electric heating', + 'central water pit storage': '140 PTES seasonal', + 'central water tank storage': '141 Large hot water tank', + 'decentral water tank storage': '142 Small scale hot water tank', + 'fuel cell': '12 LT-PEMFC CHP', + 'hydrogen storage underground': '151c Hydrogen Storage - Caverns', + 'hydrogen storage tank type 1 including compressor': '151a Hydrogen Storage - Tanks', + 'micro CHP': '219 LT-PEMFC mCHP - natural gas', + 'biogas' : '81 Biogas, Basic plant, small', + 'biogas CC' : '81 Biogas, Basic plant, small', + 'biogas upgrading': '82 Upgrading 3,000 Nm3 per h', + 'battery': '180 Lithium Ion Battery', + 'industrial heat pump medium temperature': '302.a High temp. hp Up to 125 C', + 'industrial heat pump high temperature': '302.b High temp. hp Up to 150', + 'electric boiler steam': '310.1 Electric boiler steam ', + 'gas boiler steam': '311.1c Steam boiler Gas', + 'solid biomass boiler steam': '311.1e Steam boiler Wood', + 'solid biomass boiler steam CC': '311.1e Steam boiler Wood', + 'biomass boiler': '204 Biomass boiler, automatic', + 'electrolysis': '86 AEC 100 MW', + 'direct air capture': '403.a Direct air capture', + 'biomass CHP capture': '401.a Post comb - small CHP', + 'cement capture': '401.c Post comb - Cement kiln', + 'BioSNG': '84 Gasif. CFB, Bio-SNG', + 'BtL': '85 Gasif. Ent. Flow FT, liq fu ', + 'biomass-to-methanol': '97 Methanol from biomass gasif.', + 'biogas plus hydrogen': '99 SNG from methan. of biogas', + 'methanolisation': '98 Methanol from hydrogen', + 'Fischer-Tropsch': '102 Hydrogen to Jet', + 'central hydrogen CHP': '12 LT-PEMFC CHP', + 'Haber-Bosch': '103 Hydrogen to Ammonia', + 'air separation unit': '103 Hydrogen to Ammonia', + 'waste CHP': '08 WtE CHP, Large, 50 degree', + 'waste CHP CC': '08 WtE CHP, Large, 50 degree', + # 'electricity distribution rural': '101 2 el distri Rural', + # 'electricity distribution urban': '101 4 el distri city', + # 'gas distribution rural': '102 7 gas Rural', + # 'gas distribution urban': '102 9 gas City', + # 'DH distribution rural': '103_12 DH_Distribu Rural', + # 'DH distribution urban': '103_14 DH_Distribu City', + # 'DH distribution low T': '103_16 DH_Distr New area LTDH', + # 'gas pipeline': '102 6 gas Main distri line', + # "DH main transmission": "103_11 DH transmission", + 'biochar pyrolysis': '105 Slow pyrolysis, Straw', + 'biomethanation': '106 Biomethanation of biogas', + 'electrolysis small': '86 AEC 10 MW', + } # [DEA-sheet-names] -uncrtnty_lookup = { - "onwind": "J:K", - "offwind": "J:K", - "solar-utility": "J:K", - "solar-utility single-axis tracking": "J:K", - "solar-rooftop residential": "J:K", - "solar-rooftop commercial": "J:K", - "OCGT": "I:J", - "CCGT": "I:J", - "oil": "I:J", - "biomass CHP": "I:J", - "biomass EOP": "I:J", - "biomass HOP": "I:J", - "central coal CHP": "", - "central gas CHP": "I:J", - "central gas CHP CC": "I:J", - "central hydrogen CHP": "I:J", - "central solid biomass CHP": "I:J", - "central solid biomass CHP CC": "I:J", - "central solid biomass CHP powerboost CC": "I:J", - # 'solid biomass power': 'J:K', - # 'solid biomass power CC': 'J:K', - "solar": "", - "central air-sourced heat pump": "J:K", - "central geothermal-sourced heat pump": "H:K", - "central geothermal heat source": "H:K", - "central excess-heat-sourced heat pump": "H:K", - "central water-sourced heat pump": "H:K", - "central ground-sourced heat pump": "I:J", - "central resistive heater": "I:J", - "central gas boiler": "I:J", - "decentral gas boiler": "I:J", - "direct firing gas": "H:I", - "direct firing gas CC": "H:I", - "direct firing solid fuels": "H:I", - "direct firing solid fuels CC": "H:I", - "decentral ground-sourced heat pump": "I:J", - "decentral air-sourced heat pump": "I:J", - "central water pit storage": "J:K", - "central water tank storage": "J:K", - "decentral water tank storage": "J:K", - "fuel cell": "I:J", - "hydrogen storage underground": "J:K", - "hydrogen storage tank type 1 including compressor": "J:K", - "micro CHP": "I:J", - "biogas": "I:J", - "biogas CC": "I:J", - "biogas upgrading": "I:J", - "electrolysis": "I:J", - "battery": "L,N", - "direct air capture": "I:J", - "cement capture": "I:J", - "biomass CHP capture": "I:J", - "BioSNG": "I:J", - "BtL": "J:K", - "biomass-to-methanol": "J:K", - "biogas plus hydrogen": "J:K", - "industrial heat pump medium temperature": "H:I", - "industrial heat pump high temperature": "H:I", - "electric boiler steam": "H:I", - "gas boiler steam": "H:I", - "solid biomass boiler steam": "H:I", - "solid biomass boiler steam CC": "H:I", - "biomass boiler": "I:J", - "Fischer-Tropsch": "I:J", - "Haber-Bosch": "I:J", - "air separation unit": "I:J", - "methanolisation": "J:K", - "waste CHP": "I:J", - "waste CHP CC": "I:J", - "biochar pyrolysis": "J:K", - "biomethanation": "J:K", - "electrolysis small": "I:J", -} +uncrtnty_lookup = {'onwind': 'J:K', + 'offwind': 'J:K', + 'solar-utility': 'J:K', + 'solar-utility single-axis tracking': 'J:K', + 'solar-rooftop residential': 'J:K', + 'solar-rooftop commercial': 'J:K', + 'OCGT': 'I:J', + 'CCGT': 'I:J', + 'oil': 'I:J', + 'biomass CHP': 'I:J', + 'biomass EOP': 'I:J', + 'biomass HOP': 'I:J', + 'central coal CHP': '', + 'central gas CHP': 'I:J', + 'central gas CHP CC': 'I:J', + 'central hydrogen CHP': 'I:J', + 'central solid biomass CHP': 'I:J', + 'central solid biomass CHP CC': 'I:J', + 'central solid biomass CHP powerboost CC': 'I:J', + # 'solid biomass power': 'J:K', + # 'solid biomass power CC': 'J:K', + 'solar': '', + 'central air-sourced heat pump': 'J:K', + 'central geothermal-sourced heat pump': 'H:K', + 'central geothermal heat source': 'H:K', + 'central excess-heat-sourced heat pump': 'H:K', + 'central water-sourced heat pump': 'H:K', + 'central ground-sourced heat pump': 'I:J', + 'central resistive heater': 'I:J', + 'central gas boiler': 'I:J', + 'decentral gas boiler': 'I:J', + 'direct firing gas': 'H:I', + 'direct firing gas CC': 'H:I', + 'direct firing solid fuels': 'H:I', + 'direct firing solid fuels CC': 'H:I', + 'decentral ground-sourced heat pump': 'I:J', + 'decentral air-sourced heat pump': 'I:J', + 'central water pit storage': 'J:K', + 'central water tank storage': 'J:K', + 'decentral water tank storage': 'J:K', + 'fuel cell': 'I:J', + 'hydrogen storage underground': 'J:K', + 'hydrogen storage tank type 1 including compressor': 'J:K', + 'micro CHP': 'I:J', + 'biogas': 'I:J', + 'biogas CC': 'I:J', + 'biogas upgrading': 'I:J', + 'electrolysis': 'I:J', + 'battery': 'L,N', + 'direct air capture': 'I:J', + 'cement capture': 'I:J', + 'biomass CHP capture': 'I:J', + 'BioSNG': 'I:J', + 'BtL': 'J:K', + 'biomass-to-methanol': 'J:K', + 'biogas plus hydrogen': 'J:K', + 'industrial heat pump medium temperature': 'H:I', + 'industrial heat pump high temperature': 'H:I', + 'electric boiler steam': 'H:I', + 'gas boiler steam': 'H:I', + 'solid biomass boiler steam': 'H:I', + 'solid biomass boiler steam CC': 'H:I', + 'biomass boiler': 'I:J', + 'Fischer-Tropsch': 'I:J', + 'Haber-Bosch': 'I:J', + 'air separation unit': 'I:J', + 'methanolisation': 'J:K', + 'waste CHP': 'I:J', + 'waste CHP CC': 'I:J', + 'biochar pyrolysis': 'J:K', + 'biomethanation': 'J:K', + 'electrolysis small': 'I:J', + } # since February 2022 DEA uses a new format for the technology data # all excel sheets of updated technologies have a different layout and are # given in EUR_2020 money (instead of EUR_2015) -cost_year_2020 = [ - "solar-utility", - "solar-utility single-axis tracking", - "solar-rooftop residential", - "solar-rooftop commercial", - "offwind", - "electrolysis", - "biogas", - "biogas CC", - "biogas upgrading", - "direct air capture", - "biomass CHP capture", - "cement capture", - "BioSNG", - "BtL", - "biomass-to-methanol", - "biogas plus hydrogen", - "methanolisation", - "Fischer-Tropsch", - "biochar pyrolysis", - "biomethanation", - "electrolysis small", -] - -cost_year_2019 = [ - "direct firing gas", - "direct firing gas CC", - "direct firing solid fuels", - "direct firing solid fuels CC", - "industrial heat pump medium temperature", - "industrial heat pump high temperature", - "electric boiler steam", - "gas boiler steam", - "solid biomass boiler steam", - "solid biomass boiler steam CC", -] +cost_year_2020 = ['solar-utility', + 'solar-utility single-axis tracking', + 'solar-rooftop residential', + 'solar-rooftop commercial', + 'offwind', + 'electrolysis', + 'biogas', + 'biogas CC', + 'biogas upgrading', + 'direct air capture', + 'biomass CHP capture', + 'cement capture', + 'BioSNG', + 'BtL', + 'biomass-to-methanol', + 'biogas plus hydrogen', + 'methanolisation', + 'Fischer-Tropsch', + 'biochar pyrolysis', + 'biomethanation', + 'electrolysis small', + ] + +cost_year_2019 = ['direct firing gas', + 'direct firing gas CC', + 'direct firing solid fuels', + 'direct firing solid fuels CC', + 'industrial heat pump medium temperature', + 'industrial heat pump high temperature', + 'electric boiler steam', + 'gas boiler steam', + 'solid biomass boiler steam', + 'solid biomass boiler steam CC', + ] # -------- FUNCTIONS --------------------------------------------------- - def get_excel_sheets(excel_files): - """ - " + """" read all excel sheets and return them as a dictionary (data_in) """ @@ -291,60 +281,54 @@ def get_excel_sheets(excel_files): def get_sheet_location(tech, sheet_names, data_in): """ - Looks up in which excel file technology is saved + looks up in which excel file technology is saved """ for key in data_in: if sheet_names[tech] in data_in[key]: return key print("******* warning *************") - print( - "tech ", - tech, - " with sheet name ", - sheet_names[tech], - " not found in excel sheets.", - ) + print("tech ", tech, " with sheet name ", sheet_names[tech], + " not found in excel sheets.") print("****************************") return None - # - def get_dea_maritime_data(fn, data): """ Get technology data for shipping from DEA. """ - sheet_names = [ - "Container feeder, diesel", - "Container feeder, methanol", - "Container feeder, ammonia", - "Container, diesel", - "Container, methanol", - "Container, ammonia", - "Tank&bulk, diesel", - "Tank&bulk, methanol", - "Tankbulk, ammonia", - ] - excel = pd.read_excel( - fn, sheet_name=sheet_names, index_col=[0, 1], usecols="A:F", na_values="N/A" - ) - - wished_index = [ - "Typical ship lifetime (years)", - "Upfront ship cost (mill. €)", - "Fixed O&M (€/year)", - "Variable O&M (€/nm)", - ] - + sheet_names = ['Container feeder, diesel', + 'Container feeder, methanol', + 'Container feeder, ammonia', + 'Container, diesel', + 'Container, methanol', + 'Container, ammonia', + 'Tank&bulk, diesel', + 'Tank&bulk, methanol', + 'Tankbulk, ammonia', + ] + excel = pd.read_excel(fn, + sheet_name=sheet_names, + index_col=[0,1], + usecols="A:F", + na_values="N/A") + + wished_index = ["Typical ship lifetime (years)", + "Upfront ship cost (mill. €)", + "Fixed O&M (€/year)", + "Variable O&M (€/nm)", + ] + + for sheet in excel.keys(): df = excel[sheet] - df = df.iloc[1:, :].set_axis(df.iloc[0], axis=1) - + df = df.iloc[1:,:].set_axis(df.iloc[0], axis=1) + assert "Typical operational speed" in df.index.get_level_values(1)[22] # in unit GJ/nm efficiency = df.iloc[22] - + df = df[df.index.get_level_values(1).isin(wished_index)] df = df.droplevel(level=0) df.loc["efficiency (GJ/nm)"] = efficiency @@ -352,171 +336,157 @@ def get_dea_maritime_data(fn, data): df = df.astype(float) df = df.interpolate(axis=1, limit_direction="both") df = df[years] - + # dropna df = df.dropna(how="all", axis=0) # add column for units - df["unit"] = df.rename( - index=lambda x: x[x.rfind("(") + 1 : x.rfind(")")] - ).index.values + df["unit"] = (df.rename(index=lambda x: + x[x.rfind("(")+1: x.rfind(")")]).index.values) df["unit"] = df.unit.str.replace("€", "EUR") # remove units from index - df.index = df.index.str.replace(r" \(.*\)", "", regex=True) - + df.index = df.index.str.replace(r" \(.*\)","", regex=True) + # convert million Euro -> Euro - df_i = df[df.unit == "mill. EUR"].index + df_i = df[df.unit == 'mill. EUR'].index df.loc[df_i, years] *= 1e6 df.loc[df_i, "unit"] = "EUR" - + # convert FOM in % of investment/year - if "Fixed O&M" in df.index: - df.loc["Fixed O&M", years] /= df.loc["Upfront ship cost", years] * 100 - df.loc["Fixed O&M", "unit"] = "%/year" - + if 'Fixed O&M' in df.index: + df.loc['Fixed O&M', years] /= (df.loc['Upfront ship cost', years] + * 100) + df.loc['Fixed O&M', "unit"] = "%/year" + # convert nm in km # 1 Nautical Mile (nm) = 1.852 Kilometers (km) - df_i = df[df.unit.str.contains("/nm")].index + df_i = df[df.unit.str.contains('/nm')].index df.loc[df_i, years] /= 1.852 df.loc[df_i, "unit"] = df.loc[df_i, "unit"].str.replace("/nm", "/km") - + # 1 GJ = 1/3600 * 1e9 Wh = 1/3600 * 1e3 MWh - df_i = df[df.unit.str.contains("GJ")].index - df.loc[df_i, years] *= 1e3 / 3600 + df_i = df[df.unit.str.contains('GJ')].index + df.loc[df_i, years] *= 1e3/3600 df.loc[df_i, "unit"] = df.loc[df_i, "unit"].str.replace("GJ", "MWh") - + # add source + cost year df["source"] = f"Danish Energy Agency, {fn}" # cost year is 2023 p.10 df["currency_year"] = 2023 # add sheet name - df["further description"] = sheet - + df['further description'] = sheet + # FOM, VOM,efficiency, lifetime, investment - rename = { - "Typical ship lifetime": "lifetime", - "Upfront ship cost": "investment", - "Fixed O&M": "FOM", - "Variable O&M": "VOM", - } - + rename = {'Typical ship lifetime': "lifetime", + 'Upfront ship cost': "investment", + 'Fixed O&M': "FOM", + 'Variable O&M': "VOM", + } + df = df.rename(index=rename) - + df = pd.concat([df], keys=[sheet], names=["technology", "parameter"]) - + data = pd.concat([data, df]) - + return data - - + + + def get_dea_vehicle_data(fn, data): """ Get heavy-duty vehicle data from DEA. """ - sheet_names = [ - "Diesel L1", - "Diesel L2", - "Diesel L3", - "Diesel B1", - "Diesel B2", - "BEV L1", - "BEV L2", - "BEV L3", - "BEV B1", - "BEV B2", - "FCV L1", - "FCV L2", - "FCV L3", - "FCV B1", - "FCV B2", - ] - excel = pd.read_excel( - fn, sheet_name=sheet_names, index_col=0, usecols="A:F", na_values="no data" - ) - - wished_index = [ - "Typical vehicle lifetime (years)", - "Upfront vehicle cost (€)", - "Fixed maintenance cost (€/year)", - "Variable maintenance cost (€/km)", - "Motor size (kW)", - ] - + sheet_names = ['Diesel L1', 'Diesel L2', 'Diesel L3', + 'Diesel B1', 'Diesel B2', + 'BEV L1', 'BEV L2', 'BEV L3', + 'BEV B1', 'BEV B2', + 'FCV L1', 'FCV L2', 'FCV L3', + 'FCV B1', 'FCV B2'] + excel = pd.read_excel(fn, + sheet_name=sheet_names, + index_col=0, + usecols="A:F", + na_values="no data") + + wished_index = ["Typical vehicle lifetime (years)", + "Upfront vehicle cost (€)", + "Fixed maintenance cost (€/year)", + "Variable maintenance cost (€/km)", + "Motor size (kW)", + ] + # clarify DEA names - types = { - "L1": "Truck Solo max 26 tons", - "L2": "Truck Trailer max 56 tons", - "L3": "Truck Semi-Trailer max 50 tons", - "B1": "Bus city", - "B2": "Coach", - } - + types = {"L1": "Truck Solo max 26 tons", + "L2": "Truck Trailer max 56 tons", + "L3": "Truck Semi-Trailer max 50 tons", + "B1": "Bus city", + "B2": "Coach"} + for sheet in excel.keys(): df = excel[sheet] tech = sheet.split()[0] + " " + types.get(sheet.split()[1], "") - df = df.iloc[1:, :].set_axis(df.iloc[0], axis=1) - # "Fuel energy - typical load (MJ/km)" + df = df.iloc[1:,:].set_axis(df.iloc[0], axis=1) + # "Fuel energy - typical load (MJ/km)" # represents efficiency for average weight vehicle carries during normal # operation, currently assuming mean between urban, regional and long haul - assert df.index[27] == "Fuel energy - typical load (MJ/km)" - efficiency = df.iloc[28:31].mean() + assert df.index[27] == 'Fuel energy - typical load (MJ/km)' + efficiency = df.iloc[28:31].mean() df = df[df.index.isin(wished_index)] df.loc["efficiency (MJ/km)"] = efficiency df = df.reindex(columns=pd.Index(years).union(df.columns)) df = df.interpolate(axis=1, limit_direction="both") df = df[years] - + # add column for units - df["unit"] = df.rename( - index=lambda x: x[x.rfind("(") + 1 : x.rfind(")")] - ).index.values + df["unit"] = (df.rename(index=lambda x: + x[x.rfind("(")+1: x.rfind(")")]).index.values) df["unit"] = df.unit.str.replace("€", "EUR") # remove units from index - df.index = df.index.str.replace(r" \(.*\)", "", regex=True) - + df.index = df.index.str.replace(r" \(.*\)","", regex=True) + # convert MJ in kWh -> 1 kWh = 3.6 MJ - df_i = df.index[df.unit == "MJ/km"] + df_i = df.index[df.unit=="MJ/km"] df.loc[df_i, years] /= 3.6 - df.loc[df_i, "unit"] = "kWh/km" - + df.loc[df_i, "unit"] = "kWh/km" + # convert FOM in % of investment/year - df.loc["Fixed maintenance cost", years] /= ( - df.loc["Upfront vehicle cost", years] * 100 - ) + df.loc["Fixed maintenance cost", years] /= (df.loc["Upfront vehicle cost", years] + * 100) df.loc["Fixed maintenance cost", "unit"] = "%/year" - + # clarify costs are per vehicle df.loc["Upfront vehicle cost", "unit"] += "/vehicle" - + # add source + cost year df["source"] = f"Danish Energy Agency, {fn}" # cost year is 2022 p.12 df["currency_year"] = 2022 # add sheet name - df["further description"] = sheet - + df['further description'] = sheet + # FOM, VOM,efficiency, lifetime, investment - rename = { - "Typical vehicle lifetime": "lifetime", - "Upfront vehicle cost": "investment", - "Fixed maintenance cost": "FOM", - "Variable maintenance cost": "VOM", - } - + rename = {'Typical vehicle lifetime': "lifetime", + 'Upfront vehicle cost': "investment", + 'Fixed maintenance cost': "FOM", + 'Variable maintenance cost': "VOM", + } + df = df.rename(index=rename) - - to_keep = ["Motor size", "lifetime", "FOM", "VOM", "efficiency", "investment"] + + to_keep = ['Motor size', 'lifetime', "FOM", "VOM", "efficiency", + "investment"] df = df[df.index.isin(to_keep)] - + df = pd.concat([df], keys=[tech], names=["technology", "parameter"]) - + data = pd.concat([data, df]) - + return data - + def get_data_DEA(tech, data_in, expectation=None): """ - Interpolate cost for a given technology from DEA database sheet + interpolate cost for a given technology from DEA database sheet uncertainty can be "optimist", "pessimist" or None|"" """ @@ -525,24 +495,14 @@ def get_data_DEA(tech, data_in, expectation=None): print("excel file not found for tech ", tech) return None - if tech == "battery": + if tech=="battery": usecols = "B:J" - elif tech in ["direct air capture", "cement capture", "biomass CHP capture"]: + elif tech in ['direct air capture', 'cement capture', 'biomass CHP capture']: usecols = "A:F" - elif tech in [ - "industrial heat pump medium temperature", - "industrial heat pump high temperature", - "electric boiler steam", - "gas boiler steam", - "solid biomass boiler steam", - "solid biomass boiler steam CC", - "direct firing gas", - "direct firing gas CC", - "direct firing solid fuels", - "direct firing solid fuels CC", - ]: + elif tech in ['industrial heat pump medium temperature', 'industrial heat pump high temperature', + 'electric boiler steam', "gas boiler steam", "solid biomass boiler steam", "solid biomass boiler steam CC", "direct firing gas", "direct firing gas CC", "direct firing solid fuels", "direct firing solid fuels CC"]: usecols = "A:E" - elif tech in ["Fischer-Tropsch", "Haber-Bosch", "air separation unit"]: + elif tech in ['Fischer-Tropsch', 'Haber-Bosch', 'air separation unit']: usecols = "B:F" elif tech in ["central water-sourced heat pump"]: usecols = "B,I,K" @@ -551,27 +511,23 @@ def get_data_DEA(tech, data_in, expectation=None): usecols += f",{uncrtnty_lookup[tech]}" - if ( - (tech in cost_year_2019) - or (tech in cost_year_2020) - or ("renewable_fuels" in excel_file) - ): + + if ((tech in cost_year_2019) or (tech in cost_year_2020) or ("renewable_fuels" in excel_file)): skiprows = [0] else: - skiprows = [0, 1] - - excel = pd.read_excel( - excel_file, - sheet_name=sheet_names[tech], - index_col=0, - usecols=usecols, - skiprows=skiprows, - na_values="N.A", - ) + skiprows = [0,1] + + excel = pd.read_excel(excel_file, + sheet_name=sheet_names[tech], + index_col=0, + usecols=usecols, + skiprows=skiprows, + na_values="N.A") # print(excel) excel.dropna(axis=1, how="all", inplace=True) + excel.index = excel.index.fillna(" ") excel.index = excel.index.astype(str) excel.dropna(axis=0, how="all", inplace=True) @@ -580,14 +536,9 @@ def get_data_DEA(tech, data_in, expectation=None): if tech in ["central water-sourced heat pump"]: # use only upper uncertainty range for systems without existing water intake # convert "Uncertainty (2025)"" to "2025", "Uncertainty (2050)"" to "2050" (and so on if more years are added) - this_years = ( - excel.loc[:, excel.iloc[1, :] == "Lower"] - .iloc[0, :] - .str.slice(-5, -1) - .astype(int) - ) + this_years = excel.loc[:,excel.iloc[1,:]=="Lower"].iloc[0,:].str.slice(-5,-1).astype(int) # get values in upper uncertainty range - excel = excel.loc[:, excel.iloc[1, :] == "Upper"] + excel = excel.loc[:,excel.iloc[1,:]=="Upper"] # rename columns to years constructed above excel.columns = this_years # add missing years @@ -606,11 +557,8 @@ def get_data_DEA(tech, data_in, expectation=None): # Extrapolation for missing values (not native in pandas) # Currently, this is only first column (2020), since DEA data is available for 2025 and 2050 if excel.iloc[:, 0].isnull().all(): - excel.iloc[:, 0] = excel.iloc[:, 1] + ( - excel.iloc[:, 1] - excel.iloc[:, 2] - ) / (excel.columns[2] - excel.columns[1]) * ( - excel.columns[1] - excel.columns[0] - ) + excel.iloc[:, 0] = excel.iloc[:, 1] + (excel.iloc[:, 1] - excel.iloc[:, 2]) / (excel.columns[2] - excel.columns[1]) * (excel.columns[1] - excel.columns[0]) + if 2020 not in excel.columns: selection = excel[excel.isin([2020])].dropna(how="all").index @@ -620,27 +568,16 @@ def get_data_DEA(tech, data_in, expectation=None): uncertainty_columns = ["2050-optimist", "2050-pessimist"] if uncrtnty_lookup[tech]: # hydrogen storage sheets have reverse order of lower/upper estimates - if tech in [ - "hydrogen storage tank type 1 including compressor", - "hydrogen storage cavern", - ]: + if tech in ["hydrogen storage tank type 1 including compressor", "hydrogen storage cavern"]: uncertainty_columns.reverse() - excel.rename( - columns={ - excel.columns[-2]: uncertainty_columns[0], - excel.columns[-1]: uncertainty_columns[1], - }, - inplace=True, - ) + excel.rename(columns={excel.columns[-2]: uncertainty_columns[0], + excel.columns[-1]: uncertainty_columns[1] + }, inplace=True) else: for col in uncertainty_columns: - excel.loc[:, col] = excel.loc[:, 2050] + excel.loc[:,col] = excel.loc[:,2050] - swap_patterns = [ - "technical life", - "efficiency", - "Hydrogen output, at LHV", - ] # cases where bigger is better + swap_patterns = ["technical life", "efficiency", "Hydrogen output, at LHV"] # cases where bigger is better swap = [any(term in idx.lower() for term in swap_patterns) for idx in excel.index] tmp = excel.loc[swap, "2050-pessimist"] excel.loc[swap, "2050-pessimist"] = excel.loc[swap, "2050-optimist"] @@ -649,65 +586,56 @@ def get_data_DEA(tech, data_in, expectation=None): if expectation: # drop duplicates excel = excel[~excel.index.duplicated()] - excel.loc[:, 2050] = excel.loc[:, f"2050-{expectation}"].combine_first( - excel.loc[:, 2050] - ) + excel.loc[:,2050] = excel.loc[:,f"2050-{expectation}"].combine_first(excel.loc[:,2050]) excel.drop(columns=uncertainty_columns, inplace=True) # fix for battery with different excel sheet format if tech == "battery": - excel.rename(columns={"Technology": 2040}, inplace=True) + excel.rename(columns={"Technology":2040}, inplace=True) if expectation: - excel = excel.loc[:, [2020, 2050]] - - parameters = [ - "efficiency", - "investment", - "Fixed O&M", - "Variable O&M", - "production capacity for one unit", - "Output capacity expansion cost", - "Hydrogen Output", - "Hydrogen (% total input_e (MWh / MWh))", - "Hydrogen [% total input_e", - " - hereof recoverable for district heating (%-points of heat loss)", - "Cb coefficient", - "Cv coefficient", - "Distribution network costs", - "Technical life", - "Energy storage expansion cost", - "Output capacity expansion cost (M€2015/MW)", - "Heat input", - "Heat input", - "Electricity input", - "Eletricity input", - "Heat out", - "capture rate", - "FT Liquids Output, MWh/MWh Total Input", - " - hereof recoverable for district heating [%-points of heat loss]", - " - hereof recoverable for district heating (%-points of heat loss)", - "Bio SNG Output [% of fuel input]", - "Methanol Output", - "District heat Output", - "Electricity Output", - "Total O&M", - "Biochar Output", # biochar pyrolysis - "Pyrolysis oil Output", # biochar pyrolysis - "Pyrolysis gas Output", # biochar pyrolysis - "Heat Output", # biochar pyrolysis - "Specific energy content [GJ/ton] biochar", # biochar pyrolysis - "Electricity Consumption", - "Feedstock Consumption", # biochar pyrolysis - "Methane Output", - "CO2 Consumption", - "Hydrogen Consumption", - " - of which is equipment excluding heat pump", - " - of which is heat pump including its installation", - "Input capacity", - "Output capacity", - "Energy storage capacity", - ] + excel = excel.loc[:,[2020,2050]] + + parameters = ["efficiency", "investment", "Fixed O&M", + "Variable O&M", "production capacity for one unit", + "Output capacity expansion cost", + "Hydrogen Output", + "Hydrogen (% total input_e (MWh / MWh))", + "Hydrogen [% total input_e", + " - hereof recoverable for district heating (%-points of heat loss)", + "Cb coefficient", + "Cv coefficient", + "Distribution network costs", "Technical life", + "Energy storage expansion cost", + 'Output capacity expansion cost (M€2015/MW)', + 'Heat input', 'Heat input', 'Electricity input', 'Eletricity input', 'Heat out', + 'capture rate', + "FT Liquids Output, MWh/MWh Total Input", + " - hereof recoverable for district heating [%-points of heat loss]", + " - hereof recoverable for district heating (%-points of heat loss)", + "Bio SNG Output [% of fuel input]", + "Methanol Output", + "District heat Output", + "Electricity Output", + "Total O&M", + "Biochar Output", # biochar pyrolysis + "Pyrolysis oil Output", # biochar pyrolysis + "Pyrolysis gas Output", # biochar pyrolysis + "Heat Output", # biochar pyrolysis + "Specific energy content [GJ/ton] biochar", # biochar pyrolysis + 'Electricity Consumption', + 'Feedstock Consumption', # biochar pyrolysis + 'Methane Output', + 'CO2 Consumption', + 'Hydrogen Consumption', + 'Biogas Consumption', + 'SNG Output', + 'District Heating Output', + ' - of which is equipment excluding heat pump', + ' - of which is heat pump including its installation', + 'Input capacity', + 'Output capacity', + 'Energy storage capacity'] df = pd.DataFrame() for para in parameters: @@ -715,320 +643,240 @@ def get_data_DEA(tech, data_in, expectation=None): attr = excel[[para in index for index in excel.index]] if len(attr) != 0: df = pd.concat([df, attr]) - df.index = df.index.str.replace("€", "EUR") + df.index = df.index.str.replace('€', 'EUR') df = df.reindex(columns=df.columns[df.columns.isin(years)]) - df = df[~df.index.duplicated(keep="first")] + df = df[~df.index.duplicated(keep='first')] # replace missing data df.replace("-", np.nan, inplace=True) # average data in format "lower_value-upper_value" - df = df.apply( - lambda row: row.apply( - lambda x: (float(x.split("-")[0]) + float(x.split("-")[1])) / 2 - if isinstance(x, str) and "-" in x - else x - ), - axis=1, - ) + df = df.apply(lambda row: row.apply(lambda x: (float(x.split("-")[0]) + + float(x.split("-")[1])) + / 2 if isinstance(x, str) and "-" in x else x), + axis=1) # remove symbols "~", ">", "<" and " " for sym in ["~", ">", "<", " "]: - df = df.apply( - lambda col: col.apply( - lambda x: x.replace(sym, "") if isinstance(x, str) else x - ) - ) + df = df.apply(lambda col: col.apply(lambda x: x.replace(sym, "") + if isinstance(x, str) else x)) + df = df.astype(float) - df = df.mask( - df.apply(pd.to_numeric, errors="coerce").isnull(), - df.astype(str).apply(lambda x: x.str.strip()), - ) + df = df.mask(df.apply(pd.to_numeric, errors='coerce').isnull(), df.astype(str).apply(lambda x: x.str.strip())) # print(df) ## Modify data loaded from DEA on a per-technology case - if (tech == "offwind") and snakemake.config["offwind_no_gridcosts"]: - df.loc["Nominal investment (*total) [MEUR/MW_e, 2020]"] -= excel.loc[ - "Nominal investment (installation: grid connection) [M€/MW_e, 2020]" - ] + if (tech == "offwind") and snakemake.config['offwind_no_gridcosts']: + df.loc['Nominal investment (*total) [MEUR/MW_e, 2020]'] -= excel.loc['Nominal investment (installation: grid connection) [M€/MW_e, 2020]'] # Exlucde indirect costs for centralised system with additional piping. - if tech.startswith("industrial heat pump"): - df = df.drop("Indirect investments cost (MEUR per MW)") + if tech.startswith('industrial heat pump'): + df = df.drop('Indirect investments cost (MEUR per MW)') - if tech == "biogas plus hydrogen": + if tech == 'biogas plus hydrogen': df.drop(df.loc[df.index.str.contains("GJ SNG")].index, inplace=True) - if tech == "BtL": + if tech == 'BtL': df.drop(df.loc[df.index.str.contains("1,000 t FT Liquids")].index, inplace=True) if tech == "biomass-to-methanol": df.drop(df.loc[df.index.str.contains("1,000 t Methanol")].index, inplace=True) - if tech == "methanolisation": + if tech == 'methanolisation': df.drop(df.loc[df.index.str.contains("1,000 t Methanol")].index, inplace=True) - if tech == "Fischer-Tropsch": + if tech == 'Fischer-Tropsch': df.drop(df.loc[df.index.str.contains("l FT Liquids")].index, inplace=True) - if tech == "biomass boiler": - df.drop( - df.loc[df.index.str.contains("Possible additional")].index, inplace=True - ) + if tech == 'biomass boiler': + df.drop(df.loc[df.index.str.contains("Possible additional")].index, inplace=True) df.drop(df.loc[df.index.str.contains("Total efficiency")].index, inplace=True) if tech == "Haber-Bosch": - df.drop( - df.loc[ - df.index.str.contains("Specific investment mark-up factor optional ASU") - ].index, - inplace=True, - ) - df.drop( - df.loc[ - df.index.str.contains( - "Specific investment (MEUR /TPD Ammonia output", regex=False - ) - ].index, - inplace=True, - ) - df.drop( - df.loc[ - df.index.str.contains("Fixed O&M (MEUR /TPD Ammonia", regex=False) - ].index, - inplace=True, - ) - df.drop( - df.loc[ - df.index.str.contains("Variable O&M (EUR /t Ammonia)", regex=False) - ].index, - inplace=True, - ) + df.drop(df.loc[df.index.str.contains("Specific investment mark-up factor optional ASU")].index, inplace=True) + df.drop(df.loc[df.index.str.contains("Specific investment (MEUR /TPD Ammonia output", regex=False)].index, inplace=True) + df.drop(df.loc[df.index.str.contains("Fixed O&M (MEUR /TPD Ammonia", regex=False)].index, inplace=True) + df.drop(df.loc[df.index.str.contains("Variable O&M (EUR /t Ammonia)", regex=False)].index, inplace=True) if tech == "air separation unit": - divisor = ( - (df.loc["Specific investment mark-up factor optional ASU"] - 1.0) - / excel.loc["N2 Consumption, [t/t] Ammonia"] - ).astype(float) - + divisor = ((df.loc["Specific investment mark-up factor optional ASU"] - 1.0) + / excel.loc["N2 Consumption, [t/t] Ammonia"]).astype(float) + # Calculate ASU cost separate to HB facility in terms of t N2 output - df.loc[ - [ - "Specific investment [MEUR /TPD Ammonia output]", - "Fixed O&M [kEUR /TPD Ammonia]", - "Variable O&M [EUR /t Ammonia]", - ] - ] *= divisor + df.loc[[ + "Specific investment [MEUR /TPD Ammonia output]", + "Fixed O&M [kEUR /TPD Ammonia]", + "Variable O&M [EUR /t Ammonia]" + ]] *= divisor # Convert output to hourly generation - df.loc[ - [ - "Specific investment [MEUR /TPD Ammonia output]", - "Fixed O&M [kEUR /TPD Ammonia]", - ] - ] *= 24 + df.loc[[ + "Specific investment [MEUR /TPD Ammonia output]", + "Fixed O&M [kEUR /TPD Ammonia]", + ]] *= 24 # Rename costs for correct units df.index = df.index.str.replace("MEUR /TPD Ammonia output", "MEUR/t_N2/h") df.index = df.index.str.replace("kEUR /TPD Ammonia", "kEUR/t_N2/h/year") df.index = df.index.str.replace("EUR /t Ammonia", "EUR/t_N2") - df.drop( - df.loc[ - df.index.str.contains("Specific investment mark-up factor optional ASU") - ].index, - inplace=True, - ) - df.drop( - df.loc[ - df.index.str.contains( - "Specific investment [MEUR /MW Ammonia output]", regex=False - ) - ].index, - inplace=True, - ) - df.drop( - df.loc[ - df.index.str.contains("Fixed O&M [kEUR/MW Ammonia/year]", regex=False) - ].index, - inplace=True, - ) - df.drop( - df.loc[ - df.index.str.contains("Variable O&M [EUR/MWh Ammonia]", regex=False) - ].index, - inplace=True, - ) - + df.drop(df.loc[df.index.str.contains("Specific investment mark-up factor optional ASU")].index, inplace=True) + df.drop(df.loc[df.index.str.contains("Specific investment [MEUR /MW Ammonia output]", regex=False)].index, inplace=True) + df.drop(df.loc[df.index.str.contains("Fixed O&M [kEUR/MW Ammonia/year]", regex=False)].index, inplace=True) + df.drop(df.loc[df.index.str.contains("Variable O&M [EUR/MWh Ammonia]", regex=False)].index, inplace=True) + if "solid biomass power" in tech: df.index = df.index.str.replace("EUR/MWeh", "EUR/MWh") if "biochar pyrolysis" in tech: - df = biochar_pyrolysis_harmonise_dea(df) + df = biochar_pyrolysis_dea(df) + + if "biomethanation" in tech: + df = biomethanation_dea(df) + + if "biogas plus hydrogen" in tech: + df = biogas_plus_hydrogen_dea(df) elif tech == "central geothermal-sourced heat pump": - df.loc["Nominal investment (MEUR per MW)"] = df.loc[ - " - of which is heat pump including its installation" - ] + df.loc["Nominal investment (MEUR per MW)"] = df.loc[" - of which is heat pump including its installation"] elif tech == "central geothermal heat source": - df.loc["Nominal investment (MEUR per MW)"] = df.loc[ - " - of which is equipment excluding heat pump" - ] + df.loc["Nominal investment (MEUR per MW)"] = df.loc[" - of which is equipment excluding heat pump"] df_final = pd.DataFrame(index=df.index, columns=years) # [RTD-interpolation-example] for index in df_final.index: - values = np.interp( - x=years, - xp=df.columns.values.astype(float), - fp=df.loc[index, :].values.astype(float), - ) + values = np.interp(x=years, xp=df.columns.values.astype(float), fp=df.loc[index, :].values.astype(float)) df_final.loc[index, :] = values # if year-specific data is missing and not fixed by interpolation fill forward with same values df_final = df_final.ffill(axis=1) - df_final["source"] = source_dict["DEA"] + ", " + excel_file.replace("inputs/", "") - if ( - tech in cost_year_2020 - and ("for_carbon_capture_transport_storage" not in excel_file) - and ("renewable_fuels" not in excel_file) - ): + df_final["source"] = source_dict["DEA"] + ", " + excel_file.replace("inputs/","") + if tech in cost_year_2020 and (not ("for_carbon_capture_transport_storage" in excel_file)) and (not ("renewable_fuels" in excel_file)): for attr in ["investment", "Fixed O&M"]: - to_drop = df[ - df.index.str.contains(attr) & ~df.index.str.contains(r"\(\*total\)") - ].index + to_drop = df[df.index.str.contains(attr) & + ~df.index.str.contains("\(\*total\)")].index df_final.drop(to_drop, inplace=True) - df_final["unit"] = df_final.rename( - index=lambda x: x[x.rfind("[") + 1 : x.rfind("]")] - ).index.values + df_final["unit"] = (df_final.rename(index=lambda x: + x[x.rfind("[")+1: x.rfind("]")]).index.values) else: - df_final.index = df_final.index.str.replace(r"\[", "(", regex=True).str.replace( - r"\]", ")", regex=True - ) - df_final["unit"] = df_final.rename( - index=lambda x: x[x.rfind("(") + 1 : x.rfind(")")] - ).index.values - df_final.index = df_final.index.str.replace(r" \(.*\)", "", regex=True) + df_final.index = df_final.index.str.replace("\[", "(", regex=True).str.replace("\]", ")", regex=True) + df_final["unit"] = (df_final.rename(index=lambda x: + x[x.rfind("(")+1: x.rfind(")")]).index.values) + df_final.index = df_final.index.str.replace(r" \(.*\)","", regex=True) - return df_final + return df_final def add_desalinsation_data(costs): """ - Add technology data for sea water desalination (SWRO) and water storage. + add technology data for sea water desalination (SWRO) and water storage. """ # Interpolate cost based on historic costs/cost projection to fitting year - cs = [2070, 1917, 1603, 1282, 1025] # in USD/(m^3/d) - ys = [2015, 2022, 2030, 2040, 2050] + cs = [2070,1917,1603,1282,1025] # in USD/(m^3/d) + ys = [2015,2022,2030,2040,2050] c = np.interp(year, ys, cs) - c *= 24 # in USD/(m^3/h) - c /= 1.17 # in EUR/(m^3/h) + c *= 24 # in USD/(m^3/h) + c /= 1.17 # in EUR/(m^3/h) tech = "seawater desalination" + costs.loc[(tech, 'investment'), 'value'] = c + costs.loc[(tech, 'investment'), 'unit'] = "EUR/(m^3-H2O/h)" + costs.loc[(tech, 'investment'), 'source'] = source_dict['Caldera2017'] + ", Table 4." + costs.loc[(tech, 'investment'), 'currency_year'] = 2015 + + costs.loc[(tech, 'FOM'), 'value'] = 4. + costs.loc[(tech, 'FOM'), 'unit'] = "%/year" + costs.loc[(tech, 'FOM'), 'source'] = source_dict['Caldera2016'] + ", Table 1." + + costs.loc[(tech, 'lifetime'), 'value'] = 30 + costs.loc[(tech, 'lifetime'), 'unit'] = "years" + costs.loc[(tech, 'lifetime'), 'source'] = source_dict['Caldera2016'] + ", Table 1." + + salinity = snakemake.config['desalination']['salinity'] + costs.loc[(tech, 'electricity-input'), 'value'] = (0.0003*salinity**2+0.0018*salinity+2.6043) + costs.loc[(tech, 'electricity-input'), 'unit'] = "kWh/m^3-H2O" + costs.loc[(tech, 'electricity-input'), 'source'] = source_dict['Caldera2016'] + ", Fig. 4." - costs.loc[(tech, "investment"), "value"] = c - costs.loc[(tech, "investment"), "unit"] = "EUR/(m^3-H2O/h)" - costs.loc[(tech, "investment"), "source"] = ( - source_dict["Caldera2017"] + ", Table 4." - ) - costs.loc[(tech, "investment"), "currency_year"] = 2015 + tech = "clean water tank storage" + costs.loc[(tech, 'investment'), 'value'] = 65 + costs.loc[(tech, 'investment'), 'unit'] = "EUR/m^3-H2O" + costs.loc[(tech, 'investment'), 'source'] = source_dict['Caldera2016'] + ", Table 1." + costs.loc[(tech, 'investment'), 'currency_year'] = 2013 - costs.loc[(tech, "FOM"), "value"] = 4.0 - costs.loc[(tech, "FOM"), "unit"] = "%/year" - costs.loc[(tech, "FOM"), "source"] = source_dict["Caldera2016"] + ", Table 1." - costs.loc[(tech, "FOM"), "currency_year"] = 2015 + costs.loc[(tech, 'FOM'), 'value'] = 2 + costs.loc[(tech, 'FOM'), 'unit'] = "%/year" + costs.loc[(tech, 'FOM'), 'source'] = source_dict['Caldera2016'] + ", Table 1." - costs.loc[(tech, "FOM"), "value"] = 4.0 - costs.loc[(tech, "FOM"), "unit"] = "%/year" - costs.loc[(tech, "FOM"), "source"] = source_dict["Caldera2016"] + ", Table 1." + costs.loc[(tech, 'lifetime'), 'value'] = 30 + costs.loc[(tech, 'lifetime'), 'unit'] = "years" + costs.loc[(tech, 'lifetime'), 'source'] = source_dict['Caldera2016'] + ", Table 1." - costs.loc[(tech, "lifetime"), "value"] = 30 - costs.loc[(tech, "lifetime"), "unit"] = "years" - costs.loc[(tech, "lifetime"), "source"] = source_dict["Caldera2016"] + ", Table 1." + return costs - salinity = snakemake.config["desalination"]["salinity"] - costs.loc[(tech, "electricity-input"), "value"] = ( - 0.0003 * salinity**2 + 0.0018 * salinity + 2.6043 - ) - costs.loc[(tech, "electricity-input"), "unit"] = "kWh/m^3-H2O" - costs.loc[(tech, "electricity-input"), "source"] = ( - source_dict["Caldera2016"] + ", Fig. 4." - ) +def biomass_properties(): + """ function that harmonises the properties of solid biomass properties with biomass potentials (JRC ENSPRESO) + NOTE: all energy contents are on Lower Heating Value (LHV)""" - tech = "clean water tank storage" - costs.loc[(tech, "investment"), "value"] = 65 - costs.loc[(tech, "investment"), "unit"] = "EUR/m^3-H2O" - costs.loc[(tech, "investment"), "source"] = ( - source_dict["Caldera2016"] + ", Table 1." - ) - costs.loc[(tech, "investment"), "currency_year"] = 2013 + idx_biomass = ['biomass_specific_energy_DM', 'biomass_carbon_content', 'biomass_moisture_content', + 'water_evap_heat', 'biomass_specific_energy', 'pyrolysis_feedstock_moisture_content', + 'pyrolysis_feedstock_specific_energy'] + cols_biomass = ['value', 'unit'] + units = ['GJ/t_DM', 'tC/t_biom_DM', 't_h2o/t_biom', 'GJ/t_h2o', 'GJ/t_biom', 't_h2o/t_pyrofeed', 'GJ/t_pyrofeed'] + solid_biomass_df = pd.DataFrame(index=idx_biomass, data=0, columns=cols_biomass) + solid_biomass_df = solid_biomass_df.astype({'value': 'float', 'unit': 'object'}) + solid_biomass_df.loc[:, 'unit'] = units - costs.loc[(tech, "FOM"), "value"] = 2 - costs.loc[(tech, "FOM"), "unit"] = "%/year" - costs.loc[(tech, "FOM"), "source"] = source_dict["Caldera2016"] + ", Table 1." - costs.loc[(tech, "FOM"), "currency_year"] = 2013 + solid_biomass_df.at['biomass_specific_energy_DM', 'value'] = 18 + solid_biomass_df.at['biomass_carbon_content', 'value'] = 0.5 + solid_biomass_df.at['biomass_moisture_content', 'value'] = 0.15 + solid_biomass_df.at['water_evap_heat', 'value'] = 2.44 + solid_biomass_df.at['pyrolysis_feedstock_moisture_content', 'value'] = 0.1 - costs.loc[(tech, "lifetime"), "value"] = 30 - costs.loc[(tech, "lifetime"), "unit"] = "years" - costs.loc[(tech, "lifetime"), "source"] = source_dict["Caldera2016"] + ", Table 1." + LHV_solid_biomass = solid_biomass_df.at['biomass_specific_energy_DM','value'] * (1-solid_biomass_df.at['biomass_moisture_content','value']) - solid_biomass_df.at['biomass_moisture_content','value'] * solid_biomass_df.at['water_evap_heat','value'] + LHV_pyrolysis_feedstock = solid_biomass_df.at['biomass_specific_energy_DM','value'] * (1-solid_biomass_df.at['pyrolysis_feedstock_moisture_content','value']) - solid_biomass_df.at['pyrolysis_feedstock_moisture_content','value'] * solid_biomass_df.at['water_evap_heat','value'] - return costs + solid_biomass_df.at['biomass_specific_energy', 'value'] = LHV_solid_biomass + solid_biomass_df.at['pyrolysis_feedstock_specific_energy', 'value'] = LHV_pyrolysis_feedstock + return solid_biomass_df def add_co2_intensity(costs): - """ - " + """" add CO2 intensity for the carriers """ TJ_to_MWh = 277.78 - costs.loc[("gas", "CO2 intensity"), "value"] = 55827 / 1e3 / TJ_to_MWh # Erdgas - costs.loc[("coal", "CO2 intensity"), "value"] = ( - 93369 / 1e3 / TJ_to_MWh - ) # Steinkohle - costs.loc[("lignite", "CO2 intensity"), "value"] = ( - 113031 / 1e3 / TJ_to_MWh - ) # Rohbraunkohle Rheinland - costs.loc[("oil", "CO2 intensity"), "value"] = ( - 74020 / 1e3 / TJ_to_MWh - ) # Heizöl, leicht - costs.loc[("methanol", "CO2 intensity"), "value"] = ( - 0.2482 # t_CO2/MWh_th, based on stochiometric composition. - ) - costs.loc[("solid biomass", "CO2 intensity"), "value"] = 0.3 - - oil_specific_energy = 44 # GJ/t - CO2_CH2_mass_ratio = 44 / 14 # kg/kg (1 mol per mol) - CO2_C_mass_ratio = 44 / 12 # kg/kg - methane_specific_energy = 50 # GJ/t - CO2_CH4_mass_ratio = 44 / 16 # kg/kg (1 mol per mol) - biomass_specific_energy = 18 # GJ/t LHV - biomass_carbon_content = 0.5 - costs.loc[("oil", "CO2 intensity"), "value"] = ( - (1 / oil_specific_energy) * 3.6 * CO2_CH2_mass_ratio - ) # tCO2/MWh - costs.loc[("gas", "CO2 intensity"), "value"] = ( - (1 / methane_specific_energy) * 3.6 * CO2_CH4_mass_ratio - ) # tCO2/MWh - costs.loc[("solid biomass", "CO2 intensity"), "value"] = ( - biomass_carbon_content * (1 / biomass_specific_energy) * 3.6 * CO2_C_mass_ratio - ) # tCO2/MWh - - costs.loc[("oil", "CO2 intensity"), "source"] = ( - "Stoichiometric calculation with 44 GJ/t diesel and -CH2- approximation of diesel" - ) - costs.loc[("gas", "CO2 intensity"), "source"] = ( - "Stoichiometric calculation with 50 GJ/t CH4" - ) - costs.loc[("solid biomass", "CO2 intensity"), "source"] = ( - "Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass" - ) - costs.loc[("coal", "CO2 intensity"), "source"] = source_dict["co2"] - costs.loc[("lignite", "CO2 intensity"), "source"] = source_dict["co2"] + costs.loc[('gas', 'CO2 intensity'), 'value'] = 55827 / 1e3 / TJ_to_MWh # Erdgas + costs.loc[('coal', 'CO2 intensity'), 'value'] = 93369 / 1e3 / TJ_to_MWh # Steinkohle + costs.loc[('lignite', 'CO2 intensity'), 'value'] = 113031 / 1e3 / TJ_to_MWh # Rohbraunkohle Rheinland + costs.loc[('oil', 'CO2 intensity'), 'value'] = 74020 / 1e3 / TJ_to_MWh # Heizöl, leicht + costs.loc[('methanol', 'CO2 intensity'), 'value'] = 0.2482 # t_CO2/MWh_th, based on stochiometric composition. + costs.loc[('solid biomass', 'CO2 intensity'), 'value'] = 0.3 + + oil_specific_energy = 44 #GJ/t + CO2_CH2_mass_ratio = 44/14 #kg/kg (1 mol per mol) + CO2_C_mass_ratio = 44/12 #kg/kg + methane_specific_energy = 50 #GJ/t + CO2_CH4_mass_ratio = 44/16 #kg/kg (1 mol per mol) + solid_biomass_df = biomass_properties() + biomass_specific_energy = solid_biomass_df.at['biomass_specific_energy','value'] # GJ/t_biom LHV + biomass_carbon_content = solid_biomass_df.at['biomass_carbon_content','value'] # tC/tbiomass_DM + biomass_moisture_content = solid_biomass_df.at['biomass_moisture_content','value'] # th2o/tbiom + costs.loc[('oil', 'CO2 intensity'), 'value'] = (1/oil_specific_energy) * 3.6 * CO2_CH2_mass_ratio #tCO2/MWh + costs.loc[('gas', 'CO2 intensity'), 'value'] = (1/methane_specific_energy) * 3.6 * CO2_CH4_mass_ratio #tCO2/MWh + costs.loc[('solid biomass', 'CO2 intensity'), 'value'] = biomass_carbon_content * (1 - biomass_moisture_content) * ( + 1 / biomass_specific_energy) * 3.6 * CO2_C_mass_ratio # tCO2/MWh + + costs.loc[('oil', 'CO2 intensity'), 'source'] = "Stoichiometric calculation with 44 GJ/t diesel and -CH2- approximation of diesel" + costs.loc[('gas', 'CO2 intensity'), 'source'] = "Stoichiometric calculation with 50 GJ/t CH4" + costs.loc[('solid biomass', 'CO2 intensity'), 'source'] = "Stoichiometric calculation with 18 GJ/t_DM LHV and 50% C-content for solid biomass" + costs.loc[('coal', 'CO2 intensity'), 'source'] = source_dict["co2"] + costs.loc[('lignite', 'CO2 intensity'), 'source'] = source_dict["co2"] + costs.loc[pd.IndexSlice[:, "CO2 intensity"], "unit"] = "tCO2/MWh_th" @@ -1036,196 +884,376 @@ def add_co2_intensity(costs): def add_solar_from_other(costs): - """ - " + """" add solar from other sources than DEA (since the lifetime assumed in DEA is very optimistic) """ # solar utility from Vartiaian 2019 - data = np.interp(x=years, xp=[2020, 2030, 2040, 2050], fp=[431, 275, 204, 164]) + data = np.interp(x=years, xp=[2020, 2030, 2040, 2050], + fp=[431, 275, 204, 164]) # the paper says 'In this report, all results are given in real 2019 # money.' - data = data / (1 + snakemake.config["rate_inflation"]) ** ( - 2019 - snakemake.config["eur_year"] - ) + data = data / (1 + snakemake.config['rate_inflation'])**(2019 - snakemake.config['eur_year']) solar_uti = pd.Series(data=data, index=years) # solar rooftop from ETIP 2019 data = np.interp(x=years, xp=[2020, 2030, 2050], fp=[1150, 800, 550]) # using 2016 money in page 10 - data = data / (1 + snakemake.config["rate_inflation"]) ** ( - 2016 - snakemake.config["eur_year"] - ) + data = data / (1 + snakemake.config['rate_inflation'])**(2016 - snakemake.config['eur_year']) solar_roof = pd.Series(data=data, index=years) # solar utility from Vartiaian 2019 - if snakemake.config["solar_utility_from_vartiaien"]: - costs.loc[("solar-utility", "investment"), "value"] = solar_uti[year] - costs.loc[("solar-utility", "investment"), "source"] = source_dict["Vartiaien"] - costs.loc[("solar-utility", "investment"), "currency_year"] = 2019 + if snakemake.config['solar_utility_from_vartiaien']: + costs.loc[('solar-utility', 'investment'), 'value'] = solar_uti[year] + costs.loc[('solar-utility', 'investment'), 'source'] = source_dict['Vartiaien'] + costs.loc[('solar-utility', 'investment'), 'currency_year'] = 2019 - costs.loc[("solar-utility", "lifetime"), "value"] = 30 - costs.loc[("solar-utility", "lifetime"), "source"] = source_dict["Vartiaien"] - costs.loc[("solar-utility", "lifetime"), "currency_year"] = 2019 + costs.loc[('solar-utility', 'lifetime'), 'value'] = 30 + costs.loc[('solar-utility', 'lifetime'), 'source'] = source_dict['Vartiaien'] + costs.loc[('solar-utility', 'lifetime'), 'currency_year'] = 2019 - if snakemake.config["solar_rooftop_from_etip"]: + if snakemake.config['solar_rooftop_from_etip']: # solar rooftop from ETIP 2019 - costs.loc[("solar-rooftop", "investment"), "value"] = solar_roof[year] - costs.loc[("solar-rooftop", "investment"), "source"] = source_dict["ETIP"] - costs.loc[("solar-rooftop", "investment"), "currency_year"] = 2019 + costs.loc[('solar-rooftop', 'investment'), 'value'] = solar_roof[year] + costs.loc[('solar-rooftop', 'investment'), 'source'] = source_dict['ETIP'] + costs.loc[('solar-rooftop', 'investment'), 'currency_year'] = 2019 - costs.loc[("solar-rooftop", "lifetime"), "value"] = 30 - costs.loc[("solar-rooftop", "lifetime"), "source"] = source_dict["ETIP"] - costs.loc[("solar-rooftop", "lifetime"), "currency_year"] = 2019 + costs.loc[('solar-rooftop', 'lifetime'), 'value'] = 30 + costs.loc[('solar-rooftop', 'lifetime'), 'source'] = source_dict['ETIP'] + costs.loc[('solar-rooftop', 'lifetime'), 'currency_year'] = 2019 # lifetime&efficiency for solar - costs.loc[("solar", "lifetime"), "value"] = costs.loc[ - (["solar-rooftop", "solar-utility"], "lifetime"), "value" - ].mean() - costs.loc[("solar", "lifetime"), "unit"] = "years" - costs.loc[("solar", "lifetime"), "currency_year"] = 2019 - costs.loc[("solar", "lifetime"), "source"] = "Assuming 50% rooftop, 50% utility" + costs.loc[('solar', 'lifetime'), 'value'] = costs.loc[( + ['solar-rooftop', 'solar-utility'], 'lifetime'), 'value'].mean() + costs.loc[('solar', 'lifetime'), 'unit'] = 'years' + costs.loc[('solar', 'lifetime'), 'currency_year'] = 2019 + costs.loc[('solar', 'lifetime'), + 'source'] = 'Assuming 50% rooftop, 50% utility' # costs.loc[('solar', 'efficiency'), 'value'] = 1 # costs.loc[('solar', 'efficiency'), 'unit'] = 'per unit' return costs - # [add-h2-from-other] def add_h2_from_other(costs): """ - Assume higher efficiency for electrolysis(0.8) and fuel cell(0.58) + assume higher efficiency for electrolysis(0.8) and fuel cell(0.58) """ - costs.loc[("electrolysis", "efficiency"), "value"] = 0.8 - costs.loc[("fuel cell", "efficiency"), "value"] = 0.58 - costs.loc[("electrolysis", "efficiency"), "source"] = "budischak2013" - costs.loc[("electrolysis", "efficiency"), "currency_year"] = 2013 - costs.loc[("fuel cell", "efficiency"), "source"] = "budischak2013" - costs.loc[("fuel cell", "efficiency"), "currency_year"] = 2013 + costs.loc[('electrolysis', 'efficiency'), 'value'] = 0.8 + costs.loc[('fuel cell', 'efficiency'), 'value'] = 0.58 + costs.loc[('electrolysis', 'efficiency'), 'source'] = 'budischak2013' + costs.loc[('electrolysis', 'efficiency'), 'currency_year'] = 2013 + costs.loc[('fuel cell', 'efficiency'), 'source'] = 'budischak2013' + costs.loc[('fuel cell', 'efficiency'), 'currency_year'] = 2013 return costs - # [unify-diw-inflation] def unify_diw(costs): - """ - " + """" add currency year for the DIW costs from 2010 """ - costs.loc[("PHS", "investment"), "currency_year"] = 2010 - costs.loc[("ror", "investment"), "currency_year"] = 2010 - costs.loc[("hydro", "investment"), "currency_year"] = 2010 + costs.loc[('PHS', 'investment'), 'currency_year'] = 2010 + costs.loc[('ror', 'investment'), 'currency_year'] = 2010 + costs.loc[('hydro', 'investment'), 'currency_year'] = 2010 return costs +def biomethanation_dea(df): + """This function does: + - import DEA data for biomethanation (4H2 + CO2 -> CH4 + 2H2O) + - recalculates cost and inputs per MW of H2 added (bus 0 is H2) + """ + + CO2_density = 1.98 / 1000 # kg/Nm3 + CH4_vol = 0.58 # biogas vol%, from DEA source for biomethanation + CO2_vol = 0.42 # biogas vol%, from DEA source for biomethanation + CH4_lhv = 35.8 / 3600 # MWh/Nm3 + CO2_biogas = CO2_vol / CH4_vol / CH4_lhv * CO2_density # t_CO2/MWh_biogas + + # Find index labels directly + idx = df.index[df.index.str.contains("Total Input")] + idx2 = df.index[df.index.str.contains("Hydrogen Consumption")] + idx3 = df.index[df.index.str.contains("CO2 Consumption")] + idx4 = df.index[df.index.str.contains("Methane Output")] + idx5 = df.index[df.index.str.contains("EUR")] + + # H2/CH4 ratio (MW/MW) + CH4_H2_ratio = df.loc[idx4].astype(float) / df.loc[idx2[0]].astype(float) + + # Adjust costs from €/MWh CH4 to €/MW_H2 + df.loc[idx5] = df.loc[idx5].astype(float).mul(CH4_H2_ratio.values.flatten(), axis=1) + df.index = [ + i.replace("MW", "MW_H2").replace("MWh", "MWh_H2") if i in idx5 else i + for i in df.index + ] + + # Normalize all inputs & outputs to MW of hydrogen + df.loc[idx] = df.loc[idx].astype(float) / df.loc[idx2[0]].astype(float) + + # Convert CO2 input from Nm3 to tons + df.loc[idx3[0]] = df.loc[idx3[0]].astype(float) * CO2_density # tCO2 / h / MW_H2 + + # Biogas input in MWh/MWh_H2 + df.loc['Biogas Consumption, [MWh_th/MWh_H2]'] = ( + df.loc[idx3[0]].astype(float) / CO2_biogas + ) + + # Add biogas back to methane output (correct total output) + df.loc[idx4] = df.loc[idx4].astype(float) + df.loc['Biogas Consumption, [MWh_th/MWh_H2]'] + + # change unit to H2 basis + df.index = df.index.str.replace(" Total Input", "_H2") + + # Rename indices and update units + replacements = { + "Hydrogen Consumption": "Hydrogen Input", + "CO2 Consumption": "CO2 Input", + "Electricity Consumption": "El-Input", + "Methane Output": "Methane Output", + "Heat Output": "H-Output", + } + + old_units = { + "Hydrogen Consumption": "MWh/", + "CO2 Consumption": "Nm3", + "Electricity Consumption": "MWh/", + "Methane Output": "MWh/", + "Heat Output": "MWh/", + } + + new_units = { + "Hydrogen Consumption": "MWh_H2/", + "CO2 Consumption": "t_CO2", + "Electricity Consumption": "MWh_e/", + "Methane Output": "MWh_CH4/", + "Heat Output": "MWh_th/", + } + + for old_label, new_label in replacements.items(): + matches = df.index[df.index.str.contains(old_label)] + if not matches.empty: + old_index = matches[0] + updated_index = old_index.replace(old_label, new_label) + updated_index = updated_index.replace(old_units[old_label], new_units[old_label]) + df.rename(index={old_index: updated_index}, inplace=True) + + return df + +def biogas_plus_hydrogen_dea(df): + # convert efficiencies from MW/MWTotal input to bigas input basis + # adjust names of efficiencies paramaters for further processing + + CO2_density = 1.98 / 1000 # kg/Nm3 + CH4_vol = 0.65 # biogas vol%, from DEA source for SNG from Methanation of Biogas + CO2_vol = 0.35 # biogas vol%, from DEA source for SNG from Methanation of Biogas + CH4_lhv = 35.8 / 3600 # MWh/Nm3 + CO2_biogas = CO2_vol / CH4_vol / CH4_lhv * CO2_density # t_CO2/MWh_biogas + + # use actual index names instead of boolean masks + idx = df.index[df.index.str.contains("Total Input")] + idx2 = df.index[df.index.str.contains("Hydrogen Consumption")] + idx3 = df.index[df.index.str.contains("Biogas Consumption")] + idx4 = df.index[df.index.str.contains("SNG Output")] + idx5 = df.index[df.index.str.contains("EUR")] + + # calculate H2/CH4 ratio (MW/MW) using the first match (assuming one match) + SNG_H2_ratio = df.loc[idx4].astype(float) / df.loc[idx2[0]].astype(float) + + # adjust cost basis from €/MWh CH4 to €/MW H2 + df.loc[idx5] = df.loc[idx5].astype(float).mul(SNG_H2_ratio.values.flatten(), axis=1) + df.index = [ + i.replace(" SNG", "_H2") if i in idx5 else i + for i in df.index + ] + + # normalize all inputs & outputs to MW of hydrogen + df.loc[idx] = df.loc[idx].astype(float) / df.loc[idx2[0]].astype(float) + + # Calculate CO2 input in t_CO2/MWh_H2 + df.loc['CO2 Input, [t_CO2/MWh_H2]'] = df.loc[idx3[0]].astype(float) * CO2_biogas + + # change unit to H2 basis + df.index = df.index.str.replace(" Total Input", "_H2") + + # Renaming for standardization + replacements = { + "Hydrogen Consumption": "Hydrogen Consumption", + "Biogas Consumption": "Biogas Consumption", + "Electricity Consumption": "El-Input", + "SNG Output": "Methane Output", + "District Heating Output": "H-Output", + } + + old_units = { + "Hydrogen Consumption": "MWh/", + "Biogas Consumption": "MWh/", + "Electricity Consumption": "MWh/", + "SNG Output": "MWh/", + "District Heating Output": "MWh/", + } + + new_units = { + "Hydrogen Consumption": "MWh_H2/", + "Biogas Consumption": "MWh_th/", + "Electricity Consumption": "MWh_e/", + "SNG Output": "MWh_CH4/", + "District Heating Output": "MWh_th/", + } + + for old_label, new_label in replacements.items(): + matches = df.index[df.index.str.contains(old_label)] + if not matches.empty: + old_index = matches[0] + updated_index = old_index.replace(old_label, new_label) + updated_index = updated_index.replace(old_units[old_label], new_units[old_label]) + df.rename(index={old_index: updated_index}, inplace=True) + + return df + + +def biochar_pyrolysis_dea (df): + """This function does: + 1) defined the properties of solid biomass in pypsa-eur: moisture, LHV dry and LHV moist + 2) defines the properties of the feedstock for pyrolysis (dried biomass) + 3) calculates the energy required for drying the biomass to feedstock + 4) imports the DEA data for biochar pyrolysis + 5) re-calculate the parameters from DEA per MWh of biomass in pypsa-eur. + 6) if not specified all values refer to DEA renewable fuels""" + + # definition of solid biomass in pypsa + solid_biomass_df = biomass_properties() + biomass_specific_energy = solid_biomass_df.at['biomass_specific_energy','value'] / 3.6 # MWh/t_biom LHV + biomass_carbon_content = solid_biomass_df.at['biomass_carbon_content','value'] # tC/tbiomass_DM + biomass_moisture_content = solid_biomass_df.at['biomass_moisture_content','value'] # th2o/tbiom + + # definition of feedstock for pyrolysis + pyrolysis_feedstock_moisture_content = solid_biomass_df.at['pyrolysis_feedstock_moisture_content','value'] # t H2O/ t feedstock + pyrolysis_feedstock_specific_energy = solid_biomass_df.at['pyrolysis_feedstock_specific_energy','value'] / 3.6 # LHV feedstock (MWh /t feedstock) + + # mass ratio between feedstock and solid biomass + pyrolysis_feedstock_biomass_mass_ratio = (pyrolysis_feedstock_moisture_content / (1 - pyrolysis_feedstock_moisture_content) + (1 - biomass_moisture_content)) # (t_feedstock / t_biomass) after drying + pyrolysis_feedstock_biomass_energy_ratio = pyrolysis_feedstock_biomass_mass_ratio * pyrolysis_feedstock_specific_energy / biomass_specific_energy # MWh feedstock / MWh biomass input to the process + + # Updated pre-treatment heat demand. DEA includes drying (13% - 10%) + heat_drying = 0.83 # MWh/tH2O removed + Delta_heat_drying = heat_drying * (biomass_moisture_content / (1 - biomass_moisture_content) - 0.13 / (1 - 0.13)) * ( + 1 - biomass_moisture_content) / biomass_specific_energy # (MWh heat/MWh LHV biomass) + + # DEA pyrolysis carbon balance + C_biochar_feedstock_ratio = 0.5 # (%) of carbon from original biomass contained in biochar - from DEA (for straw) + + # Assumption on biochar stability in soil beyond 100 years + biochar_100years = 0.7 # tC >100 years /tC application https://www.nature.com/articles/s41558-023-01604-9 -def biochar_pyrolysis_harmonise_dea(df): # data for 2020 not available if 2020 in df.columns: df.drop(columns=2020, inplace=True) # normalize biochar and total heat output to feedstock input idx = df.index.str.contains("Total Input") idx2 = df.index.str.contains("Feedstock Consumption") - df.loc[idx] = df.loc[idx].astype(float) / df.loc[idx2].values.astype(float) - df.index = df.index.str.replace("Total Input", "feedstock") + df.loc[idx] = df.loc[idx].astype(float) / df.loc[idx2].values.astype(float) * pyrolysis_feedstock_biomass_energy_ratio + df.index = df.index.str.replace("Total Input", "biomass") # all pyrolysis product except char are combusted for heat df_sum = pd.concat( - ( - df.iloc[df.index.str.contains("Pyrolysis oil Output")], - df.iloc[df.index.str.contains("Pyrolysis gas Output")], - df.iloc[df.index.str.contains("Heat Output")], - ), - axis=0, - ).sum(axis=0, skipna=False) - df.iloc[df.index.str.contains("Heat Output")] = df_sum * 100 - - to_drop = df[ - df.index.str.contains("Pyrolysis oil Output") - | df.index.str.contains("Pyrolysis gas Output") - | df.index.str.contains("Electricity Consumption") - | df.index.str.contains("Feedstock Consumption") - ].index + (df.iloc[df.index.str.contains("Pyrolysis oil Output")], + df.iloc[df.index.str.contains("Pyrolysis gas Output")], + df.iloc[df.index.str.contains("Heat Output")]), axis=0).sum(axis=0, skipna=False) + df.iloc[df.index.str.contains("Heat Output")] = df_sum # adjust for difference in drying heat demand + + # normalizing costs to biomass input + df_tot_out_DEA = pd.concat((df.iloc[df.index.str.contains("Biochar Output")], + df.iloc[df.index.str.contains("Heat Output")]), axis=0).sum(axis=0, skipna=False) + + + # remove additional heat for drying + df.iloc[df.index.str.contains( + "Heat Output")] = df_sum - Delta_heat_drying # adjust for difference in drying heat demand + + # Calcualte biochar yield (t biochar / MWh biomass) + df_div2 = df.iloc[df.index.str.contains("Specific energy content")].astype(float) / 3.6 + df.iloc[df.index.str.contains("Biochar Output")] = df.iloc[df.index.str.contains( + "Biochar Output")].astype(float) / df_div2.values.astype(float) + + df.rename( index={df.loc[df.index.str.contains("Biochar Output")].index.values[ + 0]: 'yield biochar [t_biochar/MWh_biomass]'}, inplace=True) + + # drop unnecessary indexes + to_drop = df[df.index.str.contains("Pyrolysis oil Output") | + df.index.str.contains("Pyrolysis gas Output") | + df.index.str.contains("Feedstock Consumption")].index df.drop(to_drop, inplace=True) - # normalizing costs to biochar output - df_divid = pd.concat( - ( - df.iloc[df.index.str.contains("Biochar Output")], - df.iloc[df.index.str.contains("Heat Output")], - ), - axis=0, - ).sum(axis=0, skipna=False) - biochar_totoutput = df.iloc[df.index.str.contains("Biochar Output")] / df_divid - idx3 = df.index.str.contains("EUR") - df.loc[idx3] = df.loc[idx3].values.astype(float) / biochar_totoutput.values.astype( - float - ) - df.index = df.index.str.replace(" output from pyrolysis process", "", regex=True) + # Calculated biochar Carbon content from: PyPSA-Eur solid biomass and DEA pyrolysis inputs + # Cw_biochar (tC_biochar/tbiochar) = (tC_feedstock/t_feedstock) * (tfeedstcok/GJfeedstock) * (GJ feedstock / t biochar) * (tCbiochar / tC feedstock) + biochar_carbon_content = biomass_carbon_content * (1 - pyrolysis_feedstock_moisture_content) / pyrolysis_feedstock_specific_energy / df.loc['yield biochar [t_biochar/MWh_biomass]',:] * C_biochar_feedstock_ratio # tC/tbiochar - # rename units - df.rename( - index={ - df.loc[df.index.str.contains("Specific investment")].index[0]: df.loc[ - df.index.str.contains("Specific investment") - ].index.str.replace("MW", "MW_biochar")[0], - df.loc[df.index.str.contains("Fixed O&M")].index[0]: df.loc[ - df.index.str.contains("Fixed O&M") - ].index.str.replace("MW", "MW_biochar")[0], - df.loc[df.index.str.contains("Variable O&M")].index[0]: df.loc[ - df.index.str.contains("Variable O&M") - ].index.str.replace("MWh", "MWh_biochar")[0], - }, - inplace=True, - ) + # Calculated CO2 sequestration in biochar per unit of biomass + # CO2seq_biomass = (tC/tbiochar) * (tbiochar/GJbiomass) * (tbiochar>100y /tbiochar) + df.loc['Biomass Input [MWh_biomass/t_CO2]',:] = 1 / (biochar_carbon_content * df.loc['yield biochar [t_biochar/MWh_biomass]', :] * biochar_100years * 44 / 12) # MWh_biomass/tCO2seq - df_div = ( - df.iloc[df.index.str.contains("Specific energy content")].astype(float) / 3.6 - ) - df.iloc[df.index.str.contains("Specific energy content")] = df.iloc[ - df.index.str.contains("Biochar Output") - ].astype(float) / df_div.values.astype(float) + # express all data per tonne of CO2 sequestred + df.loc[df.index.str.contains("Heat Output")] = df.loc[df.index.str.contains("Heat Output")].astype(float) * df.loc['Biomass Input [MWh_biomass/t_CO2]'].astype(float) + df.loc[df.index.str.contains("Electricity Consumption")] = df.loc[df.index.str.contains("Electricity Consumption")].astype(float) * df.loc['Biomass Input [MWh_biomass/t_CO2]'].astype(float) df.rename( index={ - df.loc[df.index.str.contains("Specific energy content")].index.values[ - 0 - ]: "yield biochar [ton biochar/MWh_feedstock]", - df.loc[df.index.str.contains("Biochar Output")].index.values[ - 0 - ]: "efficiency biochar [MWh_biochar/MWh_feedstock]", - df.loc[df.index.str.contains("Heat Output")].index.values[ - 0 - ]: "efficiency heat [% MWh_feedstock]", - }, - inplace=True, - ) + df.loc[df.index.str.contains("Heat Output")].index.values[ + 0]: 'H-Output [MWh_th/t_CO2]', + df.loc[df.index.str.contains("Electricity Consumption")].index.values[ + 0]: 'El-Input [MWh_e/t_CO2]'}, inplace=True) + + # adjust cost basis from €/MWh tot_output to €/tCO2 sequestred + idx3 = df.index.str.contains("EUR") + df.loc[idx3] = df.loc[idx3].values.astype(float) * df_tot_out_DEA.values.astype(float) # converto to €/MWhbiom + df.loc[idx3] = df.loc[idx3] * df.loc['Biomass Input [MWh_biomass/t_CO2]'].astype(float) # converto to € /t_CO2/h + df.index = df.index.str.replace(" output from pyrolysis process", "", regex=True) - # df = df.astype(float) - # df = df.mask(df.apply(pd.to_numeric, errors='coerce').isna(), df.astype(str).apply(lambda x: x.str.strip())) + # rename units + df.rename(index={df.loc[df.index.str.contains('Specific investment')].index[0]: + df.loc[df.index.str.contains("Specific investment")].index.str.replace( + "MW", "t_CO2/h")[0], + df.loc[df.index.str.contains('Fixed O&M')].index[0]: + df.loc[df.index.str.contains("Fixed O&M")].index.str.replace( + "MW", "t_CO2/h")[0], + df.loc[df.index.str.contains("Variable O&M")].index[0]: + df.loc[df.index.str.contains("Variable O&M")].index.str.replace( + "MWh", "t_CO2")[0]}, inplace=True) + + # print intermediate results (for publications) + print_flag = 0 + if print_flag == 1: + print(df.loc[df.index.str.contains('Specific investment')]) + print(df.loc[df.index.str.contains("Variable O&M")]) + print(df.loc[df.index.str.contains("Fixed O&M")]) + print('Cw biochar = ' + str(biochar_carbon_content)) + print(df.loc['Biomass Input [MWh_biomass/t_CO2]',:]) + print(str(df.loc['yield biochar [t_biochar/MWh_biomass]', :])) + print(df.loc[df.index.str.contains("H-Output")]) + print(df.loc[df.index.str.contains("El-Input")]) return df def get_data_from_DEA(data_in, expectation=None): """ - Saves technology data from DEA in dictionary d_by_tech + saves technology data from DEA in dictionary d_by_tech """ d_by_tech = {} for tech, dea_tech in sheet_names.items(): - print(f"{tech} in PyPSA corresponds to {dea_tech} in DEA database.") + print(f'{tech} in PyPSA corresponds to {dea_tech} in DEA database.') df = get_data_DEA(tech, data_in, expectation).fillna(0) d_by_tech[tech] = df return d_by_tech - def adjust_for_inflation(inflation_rate, costs, techs, ref_year, col): """ - Adjust the investment costs for the specified techs for inflation. + adjust the investment costs for the specified techs for inflation. techs: str or list One or more techs in costs index for which the inflation adjustment is done. @@ -1234,55 +1262,48 @@ def adjust_for_inflation(inflation_rate, costs, techs, ref_year, col): costs: pd.Dataframe Dataframe containing the costs data with multiindex on technology and one index key 'investment'. """ - + def get_factor(inflation_rate, ref_year, eur_year): - if (pd.isna(ref_year)) or (ref_year < 1900): - return np.nan - if ref_year == eur_year: - return 1 + if (pd.isna(ref_year)) or (ref_year<1900): return np.nan + if ref_year == eur_year: return 1 mean = inflation_rate.mean() - if ref_year < eur_year: - new_index = np.arange(ref_year + 1, eur_year + 1) - df = 1 + inflation_rate.reindex(new_index).fillna(mean) + if ref_year< eur_year: + new_index = np.arange(ref_year+1, eur_year+1) + df = 1 + inflation_rate.reindex(new_index).fillna(mean) return df.cumprod().loc[eur_year] else: - new_index = np.arange(eur_year + 1, ref_year + 1) + new_index = np.arange(eur_year+1, ref_year+1) df = 1 + inflation_rate.reindex(new_index).fillna(mean) - return 1 / df.cumprod().loc[ref_year] - - inflation = costs.currency_year.apply( - lambda x: get_factor(inflation_rate, x, snakemake.config["eur_year"]) - ) + return 1/df.cumprod().loc[ref_year] + + inflation = costs.currency_year.apply(lambda x: get_factor(inflation_rate, x, snakemake.config['eur_year'])) paras = ["investment", "VOM", "fuel"] - filter_i = costs.index.get_level_values(0).isin( - techs - ) & costs.index.get_level_values(1).isin(paras) - costs.loc[filter_i, col] = costs.loc[filter_i, col].mul( - inflation.loc[filter_i], axis=0 - ) + filter_i = costs.index.get_level_values(0).isin(techs) & costs.index.get_level_values(1).isin(paras) + costs.loc[filter_i, col] = costs.loc[filter_i, col].mul(inflation.loc[filter_i], axis=0) + return costs def clean_up_units(tech_data, value_column="", source=""): """ - Converts units of a pd.Dataframe tech_data to match: + converts units of a pd.Dataframe tech_data to match: power: Mega Watt (MW) energy: Mega-Watt-hour (MWh) currency: Euro (EUR) clarifies if MW_th or MW_e """ + from currency_converter import CurrencyConverter from datetime import date - - from currency_converter import ECB_URL, CurrencyConverter + from currency_converter import ECB_URL # Currency conversion REPLACEMENTS = [ - ("€", "EUR"), - ("$", "USD"), - ("₤", "GBP"), + ('€', 'EUR'), + ('$', 'USD'), + ('₤', 'GBP'), ] # Download the full history, this will be up to date. Current value is: # https://www.ecb.europa.eu/stats/eurofxref/eurofxref-hist.zip @@ -1291,9 +1312,7 @@ def clean_up_units(tech_data, value_column="", source=""): for old, new in REPLACEMENTS: tech_data.unit = tech_data.unit.str.replace(old, new, regex=False) - tech_data.loc[tech_data.unit.str.contains(new), value_column] *= c.convert( - 1, new, "EUR", date=date(2020, 1, 1) - ) + tech_data.loc[tech_data.unit.str.contains(new), value_column] *= c.convert(1, new, "EUR", date=date(2020, 1, 1)) tech_data.unit = tech_data.unit.str.replace(new, "EUR") tech_data.unit = tech_data.unit.str.replace(" per ", "/") @@ -1307,7 +1326,7 @@ def clean_up_units(tech_data, value_column="", source=""): tech_data.loc[tech_data.unit.str.contains("mio EUR"), value_column] *= 1e6 tech_data.unit = tech_data.unit.str.replace("mio EUR", "EUR") - + tech_data.loc[tech_data.unit.str.contains("mill. EUR"), value_column] *= 1e6 tech_data.unit = tech_data.unit.str.replace("mill. EUR", "EUR") @@ -1320,10 +1339,7 @@ def clean_up_units(tech_data, value_column="", source=""): tech_data.loc[tech_data.unit.str.contains("/kW"), value_column] *= 1e3 - tech_data.loc[ - tech_data.unit.str.contains("kW") & ~tech_data.unit.str.contains("/kW"), - value_column, - ] /= 1e3 + tech_data.loc[tech_data.unit.str.contains("kW") & ~tech_data.unit.str.contains("/kW"), value_column] /= 1e3 tech_data.unit = tech_data.unit.str.replace("kW", "MW") tech_data.loc[tech_data.unit.str.contains("/GWh"), value_column] /= 1e3 @@ -1341,9 +1357,7 @@ def clean_up_units(tech_data, value_column="", source=""): tech_data.unit = tech_data.unit.str.replace("EUR-2015", "EUR") tech_data.unit = tech_data.unit.str.replace("MWe", "MW_e") tech_data.unit = tech_data.unit.str.replace("EUR/MW of total input_e", "EUR/MW_e") - tech_data.unit = tech_data.unit.str.replace( - r"MWh/MWh\)", "MWh_H2/MWh_e", regex=True - ) + tech_data.unit = tech_data.unit.str.replace("MWh/MWh\)", "MWh_H2/MWh_e", regex=True) tech_data.unit = tech_data.unit.str.replace("MWth", "MW_th") tech_data.unit = tech_data.unit.str.replace("MWheat", "MW_th") tech_data.unit = tech_data.unit.str.replace("MWhth", "MWh_th") @@ -1361,94 +1375,62 @@ def clean_up_units(tech_data, value_column="", source=""): tech_data.unit = tech_data.unit.str.replace("MW SNG", "MW_CH4") tech_data.unit = tech_data.unit.str.replace("EUR/MWh of total input", "EUR/MWh_e") tech_data.unit = tech_data.unit.str.replace("EUR/MWeh", "EUR/MWh_e") - tech_data.unit = tech_data.unit.str.replace( - "% -points of heat loss", "MWh_th/MWh_el" - ) - tech_data.unit = tech_data.unit.str.replace( - "FT Liquids Output, MWh/MWh Total Input", "MWh_FT/MWh_H2" - ) + tech_data.unit = tech_data.unit.str.replace("% -points of heat loss", "MWh_th/MWh_el") + tech_data.unit = tech_data.unit.str.replace("FT Liquids Output, MWh/MWh Total Inpu", "MWh_FT/MWh_H2") # biomass-to-methanol-specific if isinstance(tech_data.index, pd.MultiIndex): - tech_data.loc[ - tech_data.index.get_level_values(1) == "Methanol Output,", "unit" - ] = "MWh_MeOH/MWh_th" - tech_data.loc[ - tech_data.index.get_level_values(1) == "District heat Output,", "unit" - ] = "MWh_th/MWh_th" - tech_data.loc[ - tech_data.index.get_level_values(1) == "Electricity Output,", "unit" - ] = "MWh_e/MWh_th" - + tech_data.loc[tech_data.index.get_level_values(1)=="Methanol Output,", "unit"] = "MWh_MeOH/MWh_th" + tech_data.loc[tech_data.index.get_level_values(1)=='District heat Output,', "unit"] = "MWh_th/MWh_th" + tech_data.loc[tech_data.index.get_level_values(1)=='Electricity Output,', "unit"] = "MWh_e/MWh_th" + # Ammonia-specific - tech_data.unit = tech_data.unit.str.replace( - "MW Ammonia output", "MW_NH3" - ) # specific investment - tech_data.unit = tech_data.unit.str.replace("MW Ammonia", "MW_NH3") # fom - tech_data.unit = tech_data.unit.str.replace("MWh Ammonia", "MWh_NH3") # vom - tech_data.loc[tech_data.unit == "EUR/MW/y", "unit"] = "EUR/MW/year" + tech_data.unit = tech_data.unit.str.replace("MW Ammonia output", "MW_NH3") #specific investment + tech_data.unit = tech_data.unit.str.replace("MW Ammonia", "MW_NH3") #fom + tech_data.unit = tech_data.unit.str.replace("MWh Ammonia", "MWh_NH3") #vom + tech_data.loc[tech_data.unit=='EUR/MW/y', "unit"] = 'EUR/MW/year' # convert per unit costs to MW cost_per_unit = tech_data.unit.str.contains("/unit") - tech_data.loc[cost_per_unit, value_column] = tech_data.loc[ - cost_per_unit, value_column - ].apply( - lambda x: ( - x - / tech_data.loc[(x.name[0], "Heat production capacity for one unit")][ - value_column - ] - ).iloc[0, :], - axis=1, - ) - tech_data.loc[cost_per_unit, "unit"] = tech_data.loc[ - cost_per_unit, "unit" - ].str.replace("/unit", "/MW_th") + tech_data.loc[cost_per_unit, value_column] = tech_data.loc[cost_per_unit, value_column].apply( + lambda x: (x / tech_data.loc[(x.name[0], + "Heat production capacity for one unit")][value_column]).iloc[0,:], + axis=1) + tech_data.loc[cost_per_unit, "unit"] = tech_data.loc[cost_per_unit, + "unit"].str.replace("/unit", "/MW_th") if source == "dea": # clarify MW -> MW_th # see on p.278 of docu: "However, the primary purpose of the heat pumps in the # technology catalogue is heating. In this chapter the unit MW is referring to # the heat output (also MJ/s) unless otherwise noted" - techs_mwth = [ - "central air-sourced heat pump", - "central geothermal-sourced heat pump", - "central gas boiler", - "central resistive heater", - "decentral air-sourced heat pump", - "decentral gas boiler", - "decentral ground-sourced heat pump", - ] - tech_data.loc[techs_mwth, "unit"] = tech_data.loc[techs_mwth, "unit"].replace( - { - "EUR/MW": "EUR/MW_th", - "EUR/MW/year": "EUR/MW_th/year", - "EUR/MWh": "EUR/MWh_th", - "MW": "MW_th", - } - ) + techs_mwth = ['central air-sourced heat pump', 'central geothermal-sourced heat pump', + 'central gas boiler', 'central resistive heater', 'decentral air-sourced heat pump', + 'decentral gas boiler', 'decentral ground-sourced heat pump' ] + tech_data.loc[techs_mwth, "unit"] = (tech_data.loc[techs_mwth, "unit"] + .replace({"EUR/MW": "EUR/MW_th", + "EUR/MW/year": "EUR/MW_th/year", + 'EUR/MWh':'EUR/MWh_th', + "MW": "MW_th"})) # clarify MW -> MW_e - techs_e = ["fuel cell"] - tech_data.loc[techs_e, "unit"] = tech_data.loc[techs_e, "unit"].replace( - { - "EUR/MW": "EUR/MW_e", - "EUR/MW/year": "EUR/MW_e/year", - "EUR/MWh": "EUR/MWh_e", - "MW": "MW_e", - } - ) + techs_e = ['fuel cell'] + tech_data.loc[techs_e, "unit"] = (tech_data.loc[techs_e, "unit"] + .replace({"EUR/MW": "EUR/MW_e", + "EUR/MW/year": "EUR/MW_e/year", + 'EUR/MWh':'EUR/MWh_e', + "MW": "MW_e"})) if "methanolisation" in tech_data.index: tech_data = tech_data.sort_index() - tech_data.loc[("methanolisation", "Variable O&M"), "unit"] = "EUR/MWh_MeOH" - - tech_data.unit = tech_data.unit.str.replace(r"\)", "") + tech_data.loc[('methanolisation', 'Variable O&M'), "unit"] = "EUR/MWh_MeOH" + + tech_data.unit = tech_data.unit.str.replace("\)", "") return tech_data def set_specify_assumptions(tech_data): """ - For following technologies more specific investment and efficiency + for following technologies more specific investment and efficiency assumptions are taken: - central resistive heater (investment costs for large > 10 MW @@ -1466,7 +1448,7 @@ def set_specify_assumptions(tech_data): # for central resistive heater there are investment costs for small (1-5MW) # and large (>10 MW) generators, assume the costs for large generators - to_drop = [("central resistive heater", "Nominal investment, 400/690 V; 1-5 MW")] + to_drop = [("central resistive heater", 'Nominal investment, 400/690 V; 1-5 MW')] # for decentral gas boilers total and heat efficiency given, the values are # the same, drop one of the rows to avoid duplicates @@ -1477,33 +1459,24 @@ def set_specify_assumptions(tech_data): # not connected yet those costs are added as an extra row since the # lifetime of the branchpipe is assumed to be 50 years (see comment K in # excel sheet) - boiler_connect = tech_data.loc[ - [ - ("decentral gas boiler", "Possible additional specific investment"), - ("decentral gas boiler", "Technical lifetime"), - ] - ] + boiler_connect = tech_data.loc[[("decentral gas boiler", + "Possible additional specific investment"), + ("decentral gas boiler", + "Technical lifetime")]] boiler_connect.loc[("decentral gas boiler", "Technical lifetime"), years] = 50 - boiler_connect.rename( - index={"decentral gas boiler": "decentral gas boiler connection"}, inplace=True - ) + boiler_connect.rename(index={"decentral gas boiler": + "decentral gas boiler connection"}, inplace=True) tech_data = pd.concat([tech_data, boiler_connect]) to_drop.append(("decentral gas boiler", "Possible additional specific investment")) # biogas upgrading investment costs should include grid injection costs index = tech_data.loc["biogas upgrading"].index.str.contains("investment") - name = "investment (upgrading, methane redution and grid injection)" - inv = ( - tech_data.loc["biogas upgrading"] - .loc[index] - .groupby(["unit", "source"]) - .sum() - .reset_index() - ) - new = pd.concat([tech_data.loc["biogas upgrading"].loc[~index], inv]).rename( - {0: name} - ) - new.index = pd.MultiIndex.from_product([["biogas upgrading"], new.index.to_list()]) + name = 'investment (upgrading, methane redution and grid injection)' + inv = tech_data.loc["biogas upgrading"].loc[index].groupby(["unit", "source"]).sum().reset_index() + new = pd.concat([tech_data.loc["biogas upgrading"].loc[~index], + inv]).rename({0:name}) + new.index = pd.MultiIndex.from_product([["biogas upgrading"], + new.index.to_list()]) tech_data.drop("biogas upgrading", level=0, inplace=True) tech_data = pd.concat([tech_data, new]) @@ -1514,13 +1487,13 @@ def set_specify_assumptions(tech_data): # in the DEA they do differ between heating the floor area or heating with # radiators, since most households heat with radiators and there # efficiencies are lower (conservative approach) those are assumed - # furthermore the total efficiency is assumed which includes auxiliary electricity + # furthermore the total efficiency is assumed which includes auxilary electricity # consumption - name = "Heat efficiency, annual average, net, radiators" + name = 'Heat efficiency, annual average, net, radiators' techs_radiator = tech_data.xs(name, level=1).index for tech in techs_radiator: df = tech_data.loc[tech] - df = df[(~df.index.str.contains("efficiency")) | (df.index == name)] + df = df[(~df.index.str.contains("efficiency")) | (df.index==name)] df.rename(index={name: name + ", existing one family house"}, inplace=True) df.index = pd.MultiIndex.from_product([[tech], df.index.to_list()]) tech_data.drop(tech, level=0, inplace=True) @@ -1533,64 +1506,44 @@ def set_specify_assumptions(tech_data): def set_round_trip_efficiency(tech_data): """ - Get round trip efficiency for hydrogen and battery storage + get round trip efficiency for hydrogen and battery storage assume for battery sqrt(DC efficiency) and split into inverter + storage rename investment rows for easier sorting """ # hydrogen storage - to_drop = [ - ("hydrogen storage tank type 1 including compressor", " - Charge efficiency") - ] - to_drop.append( - ("hydrogen storage tank type 1 including compressor", " - Discharge efficiency") - ) - to_drop.append(("hydrogen storage underground", " - Charge efficiency")) - to_drop.append(("hydrogen storage underground", " - Discharge efficiency")) - tech_data.loc[("hydrogen storage underground", "Round trip efficiency"), years] *= ( - 100 - ) - tech_data.loc[ - ("hydrogen storage tank type 1 including compressor", "Round trip efficiency"), - years, - ] *= 100 + to_drop = [("hydrogen storage tank type 1 including compressor", ' - Charge efficiency')] + to_drop.append(("hydrogen storage tank type 1 including compressor", ' - Discharge efficiency')) + to_drop.append(("hydrogen storage underground", ' - Charge efficiency')) + to_drop.append(("hydrogen storage underground", ' - Discharge efficiency')) + tech_data.loc[("hydrogen storage underground", "Round trip efficiency"), years] *= 100 + tech_data.loc[("hydrogen storage tank type 1 including compressor", "Round trip efficiency"), years] *= 100 + + # battery split into inverter and storage, assume for efficiency sqr(round trip DC) df = tech_data.loc["battery"] - inverter = df.loc[ - [ - "Round trip efficiency DC", - "Output capacity expansion cost", - "Technical lifetime", - "Fixed O&M", - ] - ] + inverter = df.loc[['Round trip efficiency DC', + 'Output capacity expansion cost', + 'Technical lifetime', 'Fixed O&M']] - inverter.rename( - index={ - "Output capacity expansion cost": "Output capacity expansion cost investment" - }, - inplace=True, - ) + inverter.rename(index ={'Output capacity expansion cost': + 'Output capacity expansion cost investment'}, + inplace=True) # Manual correction based on footnote. - inverter.loc["Technical lifetime", years] = 10.0 - inverter.loc["Technical lifetime", "source"] += ", Note K." - - inverter.index = pd.MultiIndex.from_product( - [["battery inverter"], inverter.index.to_list()] - ) - - storage = df.reindex(index=["Technical lifetime", "Energy storage expansion cost"]) - storage.rename( - index={ - "Energy storage expansion cost": "Energy storage expansion cost investment" - }, - inplace=True, - ) - storage.index = pd.MultiIndex.from_product( - [["battery storage"], storage.index.to_list()] - ) + inverter.loc['Technical lifetime', years] = 10. + inverter.loc['Technical lifetime', 'source'] += ', Note K.' + + inverter.index = pd.MultiIndex.from_product([["battery inverter"], + inverter.index.to_list()]) + + storage = df.reindex(index=['Technical lifetime', + 'Energy storage expansion cost']) + storage.rename(index={'Energy storage expansion cost': + 'Energy storage expansion cost investment'}, inplace=True) + storage.index = pd.MultiIndex.from_product([["battery storage"], + storage.index.to_list()]) tech_data.drop("battery", level=0, inplace=True) tech_data = pd.concat([tech_data, inverter, storage]) @@ -1599,7 +1552,7 @@ def set_round_trip_efficiency(tech_data): def order_data(tech_data): """ - Check if the units of different variables are conform + check if the units of different variables are conform -> print warning if not return a pd.Dataframe 'data' in pypsa tech data syntax (investment, FOM, VOM, efficiency) @@ -1612,83 +1565,68 @@ def order_data(tech_data): df = tech_data.loc[tech] # --- investment ---- - investment = df[ - ( - df.index.str.contains("investment") - | df.index.str.contains("Distribution network costs") - ) - & ( - (df.unit == "EUR/MW") - | (df.unit == "EUR/MW_e") - | (df.unit == "EUR/MW_th - heat output") - | (df.unit == "EUR/MW_th excluding drive energy") - | (df.unit == "EUR/MW_th") - | (df.unit == "EUR/MW_MeOH") - | (df.unit == "EUR/MW_FT/year") - | (df.unit == "EUR/MW_NH3") - | (df.unit == "EUR/MWhCapacity") - | (df.unit == "EUR/MWh") - | (df.unit == "EUR/MW_CH4") - | (df.unit == "EUR/MWh/year") - | (df.unit == "EUR/MW_e, 2020") - | (df.unit == "EUR/MW input") - | (df.unit == "EUR/MW-methanol") - | (df.unit == "EUR/t_N2/h") # air separation unit - | (df.unit == "EUR/MW_biochar") - ) - ].copy() + investment = df[(df.index.str.contains("investment") | + df.index.str.contains("Distribution network costs")) + & ((df.unit == "EUR/MW") | + (df.unit == "EUR/MW_e") | + (df.unit == "EUR/MW_th - heat output") | + (df.unit == "EUR/MW_th excluding drive energy") | + (df.unit == "EUR/MW_th") | + (df.unit == "EUR/MW_MeOH") | + (df.unit == "EUR/MW_FT/year") | + (df.unit == "EUR/MW_NH3") | + (df.unit == "EUR/MWhCapacity") | + (df.unit == "EUR/MWh") | + (df.unit == "EUR/MW_CH4") | + (df.unit == "EUR/MWh/year") | + (df.unit == "EUR/MW_e, 2020") | + (df.unit == "EUR/MW input") | + (df.unit == 'EUR/MW-methanol') | + (df.unit == "EUR/t_N2/h") | # air separation unit + (df.unit == 'EUR/t_CO2/h') | + (df.unit == 'EUR/MW_H2') | + (df.unit == 'EUR/MW_biomass')) + ].copy() if len(investment) != 1: switch = True - print( - "check investment: ", - tech, - " ", - df[df.index.str.contains("investment")].unit, - ) + print("check investment: ", tech, " ", + df[df.index.str.contains("investment")].unit) else: investment["parameter"] = "investment" clean_df[tech] = investment # ---- FOM ---------------- if len(investment): - fixed = df[ - ( - df.index.str.contains("Fixed O&M") - | df.index.str.contains("Total O&M") - ) - & ( - (df.unit == investment.unit.iloc[0] + "/year") - | (df.unit == "EUR/MW/km/year") - | (df.unit == "EUR/MW/year") - | (df.unit == "EUR/MW_e/y, 2020") - | (df.unit == "EUR/MW_e/y") - | (df.unit == "EUR/MW_FT/year") - | (df.unit == "EUR/MWh_FT") - | (df.unit == "EUR/MW_MeOH/year") - | (df.unit == "EUR/MW_CH4/year") - | (df.unit == "EUR/MW_biochar/year") - | (df.unit == "% of specific investment/year") - | (df.unit == investment.unit.str.split(" ").iloc[0][0] + "/year") - ) - ].copy() + fixed = df[(df.index.str.contains("Fixed O&M") | + df.index.str.contains("Total O&M")) & + ((df.unit == investment.unit.iloc[0] + "/year") | + (df.unit == "EUR/MW/km/year") | + (df.unit == "EUR/MW/year") | + (df.unit == "EUR/MW_e/y, 2020") | + (df.unit == "EUR/MW_e/y") | + (df.unit == "EUR/MW_FT/year") | + (df.unit == "EUR/MWh_FT") | + (df.unit == "EUR/MW_MeOH/year") | + (df.unit == "EUR/MW_CH4/year") | + (df.unit == 'EUR/MW_biomass/year') | + (df.unit == 'EUR/t_CO2/h/year') | + (df.unit == 'EUR/MW_H2/year') | + (df.unit == '% of specific investment/year') | + (df.unit == investment.unit.str.split(" ").iloc[0][0] + "/year"))].copy() if (len(fixed) != 1) and (len(df[df.index.str.contains("Fixed O&M")]) != 0): switch = True - print( - "check FOM: ", - tech, - " ", - df[df.index.str.contains("Fixed O&M")].unit, - ) + print("check FOM: ", tech, " ", + df[df.index.str.contains("Fixed O&M")].unit) if len(fixed) == 1: fixed["parameter"] = "fixed" clean_df[tech] = pd.concat([clean_df[tech], fixed]) fom = pd.DataFrame(columns=fixed.columns) - if not any(fixed.unit.str.contains("% of specific investment/year")): - investment[investment == 0] = float("nan") + if not any(fixed.unit.str.contains('% of specific investment/year')): + investment[investment==0] = float('nan') investment = investment.ffill(axis=1).fillna(0) - fom[years] = fixed[years] / investment[years].values * 100 + fom[years] = fixed[years]/investment[years].values*100 else: fom[years] = fixed[years] fom["parameter"] = "FOM" @@ -1697,112 +1635,112 @@ def order_data(tech_data): clean_df[tech] = pd.concat([clean_df[tech], fom]) # ---- VOM ----- - vom = df[ - df.index.str.contains("Variable O&M") - & ( - (df.unit == "EUR/MWh") - | (df.unit == "EUR/MWh_e") - | (df.unit == "EUR/MWh_th") - | (df.unit == "EUR/MWh_FT") - | (df.unit == "EUR/MWh_NH3") - | (df.unit == "EUR/MWh_MeOH") - | (df.unit == "EUR/MWh/year") - | (df.unit == "EUR/MWh/km") - | (df.unit == "EUR/MWh") - | (df.unit == "EUR/MWhoutput") - | (df.unit == "EUR/MWh_CH4") - | (df.unit == "EUR/MWh_biochar") - | (tech == "biogas upgrading") - ) - ].copy() + vom = df[df.index.str.contains("Variable O&M") & ((df.unit == "EUR/MWh") | + (df.unit == "EUR/MWh_e") | + (df.unit == "EUR/MWh_th") | + (df.unit == "EUR/MWh_FT") | + (df.unit == "EUR/MWh_NH3") | + (df.unit == "EUR/MWh_MeOH") | + (df.unit == "EUR/MWh/year") | + (df.unit == "EUR/MWh/km") | + (df.unit == "EUR/MWh") | + (df.unit == "EUR/MWhoutput") | + (df.unit == "EUR/MWh_CH4") | + (df.unit == 'EUR/MWh_biomass')| + (df.unit == 'EUR/t_CO2') | + (df.unit == 'EUR/MWh_H2') | + (tech == "biogas upgrading"))].copy() if len(vom) == 1: vom.loc[:, "parameter"] = "VOM" clean_df[tech] = pd.concat([clean_df[tech], vom]) - elif len(vom) != 1 and len(df[df.index.str.contains("Variable O&M")]) != 0: + elif len(vom)!=1 and len(df[df.index.str.contains("Variable O&M")])!=0: switch = True - print( - "check VOM: ", tech, " ", df[df.index.str.contains("Variable O&M")].unit - ) + print("check VOM: ", tech, " ", + df[df.index.str.contains("Variable O&M")].unit) # ----- lifetime -------- - lifetime = df[ - df.index.str.contains("Technical life") & (df.unit == "years") - ].copy() - if len(lifetime) != 1: - switch = True - print( - "check lifetime: ", - tech, - " ", - df[df.index.str.contains("Technical life")].unit, - ) + lifetime = df[df.index.str.contains("Technical life") & (df.unit=="years")].copy() + if len(lifetime)!=1: + switch = True + print("check lifetime: ", tech, " ", + df[df.index.str.contains("Technical life")].unit) else: lifetime["parameter"] = "lifetime" clean_df[tech] = pd.concat([clean_df[tech], lifetime]) + # ----- efficiencies ------ - efficiency = df[ - ( - (df.index.str.contains("efficiency")) - | (df.index.str.contains("Hydrogen output, at LHV")) - | (df.index.str.contains("Hydrogen Output")) - | (df.index.str.contains("FT Liquids Output, MWh/MWh Total Input")) - | (df.index.str.contains("Methanol Output")) - | (df.index.str.contains("District heat Output")) - | (df.index.str.contains("Electricity Output")) - | (df.index.str.contains("hereof recoverable for district heating")) - | (df.index.str.contains("Bio SNG")) - | (df.index.str.contains("biochar")) - | (df.index == ("Hydrogen")) - ) - & ( - (df.unit == "%") - | (df.unit == "% total size") - | (df.unit == "% of fuel input") - | (df.unit == "MWh_H2/MWh_e") - | (df.unit == "%-points of heat loss") - | (df.unit == "MWh_MeOH/MWh_th") - | (df.unit == "MWh_e/MWh_th") - | (df.unit == "MWh_th/MWh_th") - | (df.unit == "MWh/MWh Total Input") - | df.unit.str.contains("MWh_FT/MWh_H2") - | df.unit.str.contains("MWh_biochar/MWh_feedstock") - | df.unit.str.contains("ton biochar/MWh_feedstock") - | df.unit.str.contains("MWh_CH4/MWh_H2") - | df.unit.str.contains("% MWh_feedstock") - ) - ].copy() - - if tech == "Fischer-Tropsch": + efficiency = df[((df.index.str.contains("efficiency")) | + (df.index.str.contains("Hydrogen output, at LHV")) | + (df.index.str.contains("Hydrogen Output")) | + (df.index.str.contains("Hydrogen Consumption")) | + (df.index.str.contains("FT Liquids Output, MWh/MWh Total Input")) | + (df.index.str.contains("Methanol Output")) | + (df.index.str.contains("District heat Output")) | + (df.index.str.contains("District Heating Output")) | + (df.index.str.contains("Electricity Output")) | + (df.index.str.contains("Electricity Consumption")) | + (df.index.str.contains("Electricity Intput")) | + (df.index.str.contains("hereof recoverable for district heating")) | + (df.index.str.contains("Bio SNG")) | + (df.index.str.contains("biochar")) | + (df.index.str.contains("biomethanation")) | + (df.index.str.contains("H-Output")) | + (df.index.str.contains("Hydrogen Input")) | + (df.index.str.contains("CO2 Input")) | + (df.index.str.contains("SNG Output")) | + (df.index.str.contains("Biogas Consumption")) | + (df.index.str.contains("Methane Output")) | + (df.index.str.contains("Biomass Input")) | + (df.index.str.contains("El-Input")) | + (df.index == ("Hydrogen"))) + & ((df.unit == "%") | (df.unit == "% total size") | + (df.unit == "% of fuel input") | + (df.unit == "MWh_H2/MWh_e") | + (df.unit == "%-points of heat loss") | + (df.unit == "MWh_MeOH/MWh_th") | + (df.unit == "MWh_e/MWh_th") | + (df.unit == "MWh_th/MWh_th") | + (df.unit == 'MWh/MWh Total Input') | + df.unit.str.contains("MWh_FT/MWh_H2") | + df.unit.str.contains("MWh_biochar/MWh_biomass") | # efficiency biochar + df.unit.str.contains("t_biochar/MWh_biomass") | # yield biochar + df.unit.str.contains("MWh_th/t_CO2") | # Heat Output + df.unit.str.contains("MWh_biomass/t_CO2") | # Biomass Input + df.unit.str.contains("MWh_e/t_CO2") | # Electricity Input + df.unit.str.contains("t_CO2/MWh_H2") | # Electricity Input + df.unit.str.contains("MWh_e/MWh_H2") | # Electricity Input + df.unit.str.contains("MWh_CH4/MWh_H2") | + df.unit.str.contains("MWh/MWh_H2") | + df.unit.str.contains("MWh_th/MWh_H2") | + df.unit.str.contains("/MWh_H2") | + df.unit.str.contains("% MWh_biomass"))].copy() + + if tech == 'Fischer-Tropsch': efficiency[years] *= 100 + # take annual average instead of name plate efficiency, unless central air-sourced heat pump - if ( - any(efficiency.index.str.contains("annual average")) - and tech != "central air-sourced heat pump" - ): + if any(efficiency.index.str.contains("annual average")) and tech != "central air-sourced heat pump": efficiency = efficiency[efficiency.index.str.contains("annual average")] elif any(efficiency.index.str.contains("name plate")): efficiency = efficiency[efficiency.index.str.contains("name plate")] - # hydrogen electrolysiswith recoverable heat + # hydrogen electrolysis with recoverable heat heat_recovery_label = "hereof recoverable for district heating" with_heat_recovery = efficiency.index.str.contains(heat_recovery_label) if with_heat_recovery.any(): efficiency_heat = efficiency[with_heat_recovery].copy() efficiency_heat["parameter"] = "efficiency-heat" clean_df[tech] = pd.concat([clean_df[tech], efficiency_heat]) - efficiency_h2 = efficiency[ - efficiency.index.str.contains("Hydrogen Output") - ].copy() + efficiency_h2 = efficiency[efficiency.index.str.contains("Hydrogen Output")].copy() efficiency_h2["parameter"] = "efficiency" clean_df[tech] = pd.concat([clean_df[tech], efficiency_h2]) # check if electric and heat efficiencies are given - if any(["Electric" in ind for ind in efficiency.index]) and any( - ["Heat" in ind for ind in efficiency.index] - ): + if (any(["Electric" in ind for ind in efficiency.index]) and + any(["Heat" in ind for ind in efficiency.index])): efficiency_heat = efficiency[efficiency.index.str.contains("Heat")].copy() efficiency_heat["parameter"] = "efficiency-heat" clean_df[tech] = pd.concat([clean_df[tech], efficiency_heat]) @@ -1811,50 +1749,91 @@ def order_data(tech_data): clean_df[tech] = pd.concat([clean_df[tech], efficiency]) elif tech == "biomass-to-methanol": - efficiency_heat = efficiency[ - efficiency.index.str.contains("District heat") - ].copy() + efficiency_heat = efficiency[efficiency.index.str.contains("District heat")].copy() efficiency_heat["parameter"] = "efficiency-heat" - efficiency_heat.loc[:, years] *= 100 # in % + efficiency_heat.loc[:,years] *= 100 # in % clean_df[tech] = pd.concat([clean_df[tech], efficiency_heat]) - efficiency_elec = efficiency[ - efficiency.index.str.contains("Electric") - ].copy() + efficiency_elec = efficiency[efficiency.index.str.contains("Electric")].copy() efficiency_elec["parameter"] = "efficiency-electricity" clean_df[tech] = pd.concat([clean_df[tech], efficiency_elec]) - efficiency_meoh = efficiency[ - efficiency.index.str.contains("Methanol") - ].copy() + efficiency_meoh = efficiency[efficiency.index.str.contains("Methanol")].copy() efficiency_meoh["parameter"] = "efficiency" - efficiency_meoh.loc[:, years] *= 100 # in % + efficiency_meoh.loc[:,years] *= 100 # in % clean_df[tech] = pd.concat([clean_df[tech], efficiency_meoh]) elif tech == "biochar pyrolysis": - efficiency_biochar = efficiency[ - efficiency.index.str.contains("efficiency biochar") - ].copy() + efficiency_biochar = efficiency[efficiency.index.str.contains("efficiency biochar")].copy() efficiency_biochar["parameter"] = "efficiency-biochar" clean_df[tech] = pd.concat([clean_df[tech], efficiency_biochar]) - efficiency_biochar_mass = efficiency[ - efficiency.index.str.contains("yield biochar") - ].copy() + efficiency_biochar_mass = efficiency[efficiency.index.str.contains("yield biochar")].copy() efficiency_biochar_mass["parameter"] = "yield-biochar" clean_df[tech] = pd.concat([clean_df[tech], efficiency_biochar_mass]) - efficiency_heat = efficiency[ - efficiency.index.str.contains("efficiency heat") - ].copy() - efficiency_heat["parameter"] = "efficiency-heat" - clean_df[tech] = pd.concat([clean_df[tech], efficiency_heat]) + efficiency_heat_out = efficiency[efficiency.index.str.contains("H-Output")].copy() + efficiency_heat_out["parameter"] = "heat output" + clean_df[tech] = pd.concat([clean_df[tech], efficiency_heat_out]) + biomass_input = efficiency[efficiency.index.str.contains("Biomass Input")].copy() + biomass_input["parameter"] = "biomass input" + clean_df[tech] = pd.concat([clean_df[tech], biomass_input]) + electricity_input = efficiency[efficiency.index.str.contains("El-Input")].copy() + electricity_input["parameter"] = "electricity input" + clean_df[tech] = pd.concat([clean_df[tech], electricity_input]) + + elif tech == "biomethanation": + h2_input = efficiency[efficiency.index.str.contains("Hydrogen Input")].copy() + h2_input["parameter"] = "Hydrogen Input" + clean_df[tech] = pd.concat([clean_df[tech], h2_input]) + co2_input = efficiency[efficiency.index.str.contains("CO2 Input")].copy() + co2_input["parameter"] = "CO2 Input" + clean_df[tech] = pd.concat([clean_df[tech], co2_input]) + efficiency_heat_out = efficiency[efficiency.index.str.contains("H-Output")].copy() + efficiency_heat_out["parameter"] = "heat output" + clean_df[tech] = pd.concat([clean_df[tech], efficiency_heat_out]) + biomass_input = efficiency[efficiency.index.str.contains("Methane Output")].copy() + biomass_input["parameter"] = "Methane Output" + clean_df[tech] = pd.concat([clean_df[tech], biomass_input]) + electricity_input = efficiency[efficiency.index.str.contains("El-Input")].copy() + electricity_input["parameter"] = "electricity input" + clean_df[tech] = pd.concat([clean_df[tech], electricity_input]) + biogas_input = efficiency[efficiency.index.str.contains("Biogas Consumption")].copy() + biogas_input["parameter"] = "Biogas Input" + clean_df[tech] = pd.concat([clean_df[tech], biogas_input]) + + elif tech == "biogas plus hydrogen": + h2_input = efficiency[efficiency.index.str.contains("Hydrogen Consumption")].copy() + if not h2_input.empty: + h2_input["parameter"] = "hydrogen input" + clean_df[tech] = pd.concat([clean_df[tech], h2_input]) + + biogas_input = efficiency[efficiency.index.str.contains("Biogas Consumption")].copy() + if not biogas_input.empty: + biogas_input["parameter"] = "Biogas Input" + clean_df[tech] = pd.concat([clean_df[tech], biogas_input]) + + co2_input = efficiency[efficiency.index.str.contains("CO2 Input")].copy() + if not biogas_input.empty: + co2_input["parameter"] = "CO2 Input" + clean_df[tech] = pd.concat([clean_df[tech], co2_input]) + + sng_output = efficiency[efficiency.index.str.contains("Methane Output")].copy() + if not sng_output.empty: + sng_output["parameter"] = "Methane Output" + clean_df[tech] = pd.concat([clean_df[tech], sng_output]) + + heat_output = efficiency[efficiency.index.str.contains("H-Output")].copy() + if not heat_output.empty: + heat_output["parameter"] = "heat output" + clean_df[tech] = pd.concat([clean_df[tech], heat_output]) + + elec_input = efficiency[efficiency.index.str.contains("El-Input")].copy() + if not elec_input.empty: + elec_input["parameter"] = "electricity input" + clean_df[tech] = pd.concat([clean_df[tech], elec_input]) elif len(efficiency) != 1: switch = True if not any(efficiency.index.str.contains("Round trip")): - print( - "check efficiency: ", - tech, - " ", - df[df.index.str.contains("efficiency")].unit, - ) + print("check efficiency: ", tech, " ", + df[df.index.str.contains("efficiency")].unit) else: efficiency["parameter"] = "efficiency" clean_df[tech] = pd.concat([clean_df[tech], efficiency]) @@ -1875,161 +1854,87 @@ def order_data(tech_data): print("---------------------------------------") # concat data - data = ( - pd.concat(clean_df) - .reset_index() - .rename(columns={"level_0": "technology", "level_1": "further description"}) - .set_index(["technology", "parameter"]) - ) + data = (pd.concat(clean_df).reset_index().rename(columns={"level_0":"technology", + "level_1": "further description"}) + .set_index(["technology", "parameter"])) # add central water tank charger/ discharger - charger_tank = tech_data.loc[ - ("central water tank storage", " - Charge efficiency") - ].copy() + charger_tank = tech_data.loc[("central water tank storage", " - Charge efficiency")].copy() charger_tank["further description"] = "Charger efficiency" - charger_tank.rename( - index={" - Charge efficiency": "efficiency"}, level=1, inplace=True - ) - charger_tank.rename( - index={"central water tank storage": "central water tank charger"}, - level=0, - inplace=True, - ) + charger_tank.rename(index={" - Charge efficiency": "efficiency"}, + level=1, inplace=True) + charger_tank.rename(index={'central water tank storage': "central water tank charger"}, + level=0, inplace=True) data = pd.concat([data, charger_tank], sort=True) - charger_tank.rename( - index={"central water tank charger": "central water tank discharger"}, - level=0, - inplace=True, - ) + charger_tank.rename(index={"central water tank charger": "central water tank discharger"}, + level=0, inplace=True) charger_tank["further description"] = "Discharger efficiency" data = pd.concat([data, charger_tank], sort=True) # add decentral water tank charger/ discharger - charger_tank = tech_data.loc[ - ("decentral water tank storage", " - Charge efficiency") - ].copy() + charger_tank = tech_data.loc[("decentral water tank storage", " - Charge efficiency")].copy() charger_tank["further description"] = "Charger efficiency" - charger_tank.rename( - index={" - Charge efficiency": "efficiency"}, level=1, inplace=True - ) - charger_tank.rename( - index={"decentral water tank storage": "decentral water tank charger"}, - level=0, - inplace=True, - ) + charger_tank.rename(index={" - Charge efficiency": "efficiency"}, + level=1, inplace=True) + charger_tank.rename(index={'decentral water tank storage': "decentral water tank charger"}, + level=0, inplace=True) data = pd.concat([data, charger_tank], sort=True) - charger_tank.rename( - index={"decentral water tank charger": "decentral water tank discharger"}, - level=0, - inplace=True, - ) + charger_tank.rename(index={"decentral water tank charger": "decentral water tank discharger"}, + level=0, inplace=True) charger_tank["further description"] = "Discharger efficiency" data = pd.concat([data, charger_tank], sort=True) # add water pit charger/ discharger - charger_pit = tech_data.loc[ - ("central water pit storage", " - Charge efficiency") - ].copy() + charger_pit = tech_data.loc[("central water pit storage", " - Charge efficiency")].copy() charger_pit["further description"] = "Charger efficiency" - charger_pit.rename( - index={" - Charge efficiency": "efficiency"}, level=1, inplace=True - ) - charger_pit.rename( - index={"central water pit storage": "central water pit charger"}, - level=0, - inplace=True, - ) + charger_pit.rename(index={" - Charge efficiency": "efficiency"}, + level=1, inplace=True) + charger_pit.rename(index={'central water pit storage': "central water pit charger"}, + level=0, inplace=True) data = pd.concat([data, charger_pit], sort=True) - charger_pit.rename( - index={"central water pit charger": "central water pit discharger"}, - level=0, - inplace=True, - ) + charger_pit.rename(index={"central water pit charger": "central water pit discharger"}, + level=0, inplace=True) charger_pit["further description"] = "Discharger efficiency" data = pd.concat([data, charger_pit], sort=True) + # add energy to power ratio for central water tank storage - power_ratio_tank = ( - tech_data.loc[("central water tank storage", "Input capacity for one unit")] - .copy() - .squeeze() - ) - storage_capacity_tank = ( - tech_data.loc[ - ("central water tank storage", "Energy storage capacity for one unit") - ] - .copy() - .squeeze() - ) + power_ratio_tank = tech_data.loc[("central water tank storage", "Input capacity for one unit")].copy().squeeze() + storage_capacity_tank = tech_data.loc[("central water tank storage", "Energy storage capacity for one unit")].copy().squeeze() power_ratio_tank[years] = storage_capacity_tank[years].div(power_ratio_tank[years]) - power_ratio_tank["further description"] = ( - "Ratio between energy storage and input capacity" - ) + power_ratio_tank["further description"] = "Ratio between energy storage and input capacity" power_ratio_tank["unit"] = "h" power_ratio_tank = power_ratio_tank.to_frame().T - power_ratio_tank.rename( - index={"Input capacity for one unit": "energy to power ratio"}, - level=1, - inplace=True, - ) + power_ratio_tank.rename(index={"Input capacity for one unit": "energy to power ratio"}, + level=1, inplace=True) data = pd.concat([data, power_ratio_tank], sort=True) # add energy to power ratio for decentral water tank storage - power_ratio_tank = ( - tech_data.loc[("decentral water tank storage", "Input capacity for one unit")] - .copy() - .squeeze() - ) - storage_capacity_tank = ( - tech_data.loc[ - ("decentral water tank storage", "Energy storage capacity for one unit") - ] - .copy() - .squeeze() - ) + power_ratio_tank = tech_data.loc[("decentral water tank storage", "Input capacity for one unit")].copy().squeeze() + storage_capacity_tank = tech_data.loc[("decentral water tank storage", "Energy storage capacity for one unit")].copy().squeeze() power_ratio_tank[years] = storage_capacity_tank[years].div(power_ratio_tank[years]) - power_ratio_tank["further description"] = ( - "Ratio between energy storage and input capacity" - ) + power_ratio_tank["further description"] = "Ratio between energy storage and input capacity" power_ratio_tank["unit"] = "h" power_ratio_tank = power_ratio_tank.to_frame().T - power_ratio_tank.rename( - index={"Input capacity for one unit": "energy to power ratio"}, - level=1, - inplace=True, - ) + power_ratio_tank.rename(index={"Input capacity for one unit": "energy to power ratio"}, + level=1, inplace=True) data = pd.concat([data, power_ratio_tank], sort=True) # add energy to power ratio for water pit storage - power_ratio_pit = ( - tech_data.loc[("central water pit storage", "Input capacity for one unit")] - .copy() - .squeeze() - ) - storage_capacity_pit = ( - tech_data.loc[ - ("central water pit storage", "Energy storage capacity for one unit") - ] - .copy() - .squeeze() - ) + power_ratio_pit = tech_data.loc[("central water pit storage", "Input capacity for one unit")].copy().squeeze() + storage_capacity_pit = tech_data.loc[("central water pit storage", "Energy storage capacity for one unit")].copy().squeeze() power_ratio_pit[years] = storage_capacity_pit[years].div(power_ratio_pit[years]) - power_ratio_pit["further description"] = ( - "Ratio between energy storage and input capacity" - ) + power_ratio_pit["further description"] = "Ratio between energy storage and input capacity" power_ratio_pit["unit"] = "h" power_ratio_pit = power_ratio_pit.to_frame().T - power_ratio_pit.rename( - index={"Input capacity for one unit": "energy to power ratio"}, - level=1, - inplace=True, - ) + power_ratio_pit.rename(index={"Input capacity for one unit": "energy to power ratio"}, + level=1, inplace=True) data = pd.concat([data, power_ratio_pit], sort=True) return data @@ -2037,7 +1942,7 @@ def order_data(tech_data): def add_description(data): """ - Add as a column to the tech data the excel sheet name, + add as a column to the tech data the excel sheet name, add comment for offwind connection costs """ # add excel sheet names to data frame @@ -2049,210 +1954,249 @@ def add_description(data): data["further description"] = sheets + ": " + data["further description"] # add comment for offwind investment - if snakemake.config["offwind_no_gridcosts"]: - data.loc[("offwind", "investment"), "further description"] += ( - " grid connection costs subtracted from investment costs" - ) + if snakemake.config['offwind_no_gridcosts']: + data.loc[("offwind", "investment"), + "further description"] += " grid connection costs substracted from investment costs" return data def convert_units(data): """ - Convert investment and efficiency units to be align with old pypsa + convert investment and efficiency units to be align with old pypsa assumptions """ # convert efficiency from % -> per unit - data.loc[ - data.index.get_level_values(1).isin(["efficiency", "efficiency-heat"]), years - ] /= 100 - data.loc[ - data.index.get_level_values(1).isin(["efficiency", "efficiency-heat"]), "unit" - ] = "per unit" + data.loc[data.index.get_level_values(1).isin(["efficiency", "efficiency-heat"]) + , years] /= 100 + data.loc[data.index.get_level_values(1).isin(["efficiency", "efficiency-heat"]) + , "unit"] = "per unit" # convert MW -> kW - to_convert = data.index.get_level_values(1).isin( - ["fixed", "investment"] - ) & data.unit.str.contains("/MW") + to_convert = (data.index.get_level_values(1).isin(["fixed", "investment"]) & + data.unit.str.contains("/MW")) data.loc[to_convert, years] /= 1e3 - data.loc[to_convert, "unit"] = data.loc[to_convert, "unit"].str.replace( - "/MW", "/kW" - ) + data.loc[to_convert, "unit"] = (data.loc[to_convert, "unit"].str + .replace("/MW","/kW")) return data def add_gas_storage(data): """ - Add gas storage tech data, different methodolgy than other sheets and + add gas storage tech data, different methodolgy than other sheets and therefore added later """ - gas_storage = pd.read_excel( - snakemake.input.dea_storage, - sheet_name="150 Underground Storage of Gas", - index_col=1, - ) + gas_storage = pd.read_excel(snakemake.input.dea_storage, + sheet_name="150 Underground Storage of Gas", + index_col=1) gas_storage.dropna(axis=1, how="all", inplace=True) # establishment of one cavern ~ 100*1e6 Nm3 = 1.1 TWh - investment = gas_storage.loc["Total cost, 100 mio Nm3 active volume"].iloc[0] + investment = gas_storage.loc['Total cost, 100 mio Nm3 active volume'].iloc[0] # convert million EUR/1.1 TWh -> EUR/kWh - investment /= 1.1 * 1e3 + investment /= (1.1 * 1e3) data.loc[("gas storage", "investment"), years] = investment data.loc[("gas storage", "investment"), "source"] = source_dict["DEA"] - data.loc[("gas storage", "investment"), "further description"] = ( - "150 Underground Storage of Gas, Establishment of one cavern (units converted)" - ) + data.loc[("gas storage", "investment"), "further description"] = "150 Underground Storage of Gas, Establishment of one cavern (units converted)" data.loc[("gas storage", "investment"), "unit"] = "EUR/kWh" data.loc[("gas storage", "investment"), "currency_year"] = 2015 - + data.loc[("gas storage", "lifetime"), years] = 100 data.loc[("gas storage", "lifetime"), "source"] = "TODO no source" - data.loc[("gas storage", "lifetime"), "further description"] = ( - "estimation: most underground storage are already build, they do have a long lifetime" - ) + data.loc[("gas storage", "lifetime"), "further description"] = "estimation: most underground storage are already build, they do have a long lifetime" data.loc[("gas storage", "lifetime"), "unit"] = "years" + - # process equipment, injection (2200MW) withdrawal (6600MW) - # assuming half of investment costs for injection, half for withdrawal - investment_charge = ( - gas_storage.loc["Total investment cost"].iloc[0, 0] / 2 / 2200 * 1e3 - ) - investment_discharge = ( - gas_storage.loc["Total investment cost"].iloc[0, 0] / 2 / 6600 * 1e3 - ) + # process equipment, injection (2200MW) withdrawl (6600MW) + # assuming half of investment costs for injection, half for withdrawl + investment_charge = gas_storage.loc["Total investment cost"].iloc[0,0]/2/2200*1e3 + investment_discharge = gas_storage.loc["Total investment cost"].iloc[0,0]/2/6600*1e3 data.loc[("gas storage charger", "investment"), years] = investment_charge data.loc[("gas storage discharger", "investment"), years] = investment_discharge - + data.loc[("gas storage charger", "investment"), "source"] = source_dict["DEA"] - data.loc[("gas storage charger", "investment"), "further description"] = ( - "150 Underground Storage of Gas, Process equipment (units converted)" - ) + data.loc[("gas storage charger", "investment"), "further description"] = "150 Underground Storage of Gas, Process equipment (units converted)" data.loc[("gas storage charger", "investment"), "unit"] = "EUR/kW" data.loc[("gas storage charger", "investment"), "currency_year"] = 2015 + data.loc[("gas storage discharger", "investment"), "source"] = source_dict["DEA"] - data.loc[("gas storage discharger", "investment"), "further description"] = ( - "150 Underground Storage of Gas, Process equipment (units converted)" - ) + data.loc[("gas storage discharger", "investment"), "further description"] = "150 Underground Storage of Gas, Process equipment (units converted)" data.loc[("gas storage discharger", "investment"), "unit"] = "EUR/kW" data.loc[("gas storage charger", "investment"), "currency_year"] = 2015 # operation + maintenance 400-500 million m³ = 4.4-5.5 TWh - FOM = ( - gas_storage.loc["Total, incl. administration"].iloc[0] - / (5.5 * investment * 1e3) - * 100 - ) + FOM = gas_storage.loc["Total, incl. administration"].iloc[0] /(5.5*investment*1e3)*100 data.loc[("gas storage", "FOM"), years] = FOM data.loc[("gas storage", "FOM"), "source"] = source_dict["DEA"] - data.loc[("gas storage", "FOM"), "further description"] = ( - "150 Underground Storage of Gas, Operation and Maintenance, salt cavern (units converted)" - ) + data.loc[("gas storage", "FOM"), "further description"] = "150 Underground Storage of Gas, Operation and Maintenace, salt cavern (units converted)" data.loc[("gas storage", "FOM"), "unit"] = "%" return data - def add_carbon_capture(data, tech_data): - for tech in ["cement capture", "biomass CHP capture"]: - data.loc[(tech, "capture_rate"), years] = ( - tech_data.loc[(tech, "Ax) CO2 capture rate, net"), years].values[0] / 100 - ) - data.loc[(tech, "capture_rate"), "unit"] = "per unit" - for tech in ["direct air capture", "cement capture", "biomass CHP capture"]: - data.loc[(tech, "investment"), years] = ( - tech_data.loc[(tech, "Specific investment"), years].values[0] * 1e6 - ) - data.loc[(tech, "investment"), "unit"] = "EUR/(tCO2/h)" + for tech in ['cement capture', 'biomass CHP capture']: + data.loc[(tech,"capture_rate"), years] = tech_data.loc[(tech,'Ax) CO2 capture rate, net'), years].values[0]/100 + data.loc[(tech,"capture_rate"), 'unit'] = 'per unit' - data.loc[(tech, "FOM"), years] = ( - tech_data.loc[(tech, "Fixed O&M"), years].values[0] - / tech_data.loc[(tech, "Specific investment"), years].values[0] - * 100 - ) - data.loc[(tech, "FOM"), "unit"] = "%/year" - - name_list = [ - ("C2) Eletricity input ", "electricity-input"), - ("C1) Heat input ", "heat-input"), - ("C1) Heat out ", "heat-output"), - ( - "CO₂ compression and dehydration - Electricity input", - "compression-electricity-input", - ), - ("CO₂ compression and dehydration - Heat out", "compression-heat-output"), - ] + + for tech in ['direct air capture', 'cement capture', 'biomass CHP capture']: + + data.loc[(tech,"investment"), years] = tech_data.loc[(tech,'Specific investment'), years].values[0]*1e6 + data.loc[(tech,"investment"), 'unit'] = 'EUR/(tCO2/h)' + + data.loc[(tech,"FOM"), years] = tech_data.loc[(tech,'Fixed O&M'), years].values[0]/tech_data.loc[(tech,'Specific investment'), years].values[0]*100 + data.loc[(tech,"FOM"), 'unit'] = '%/year' + + name_list = [('C2) Eletricity input ',"electricity-input"), + ('C1) Heat input ',"heat-input"), + ('C1) Heat out ','heat-output'), + ('CO₂ compression and dehydration - Electricity input',"compression-electricity-input"), + ('CO₂ compression and dehydration - Heat out',"compression-heat-output")] for dea_name, our_name in name_list: - data.loc[(tech, our_name), years] = tech_data.loc[ - (tech, dea_name), years - ].values[0] - data.loc[(tech, our_name), "unit"] = "MWh/tCO2" + data.loc[(tech,our_name), years] = tech_data.loc[(tech,dea_name), years].values[0] + data.loc[(tech,our_name), 'unit'] = 'MWh/tCO2' + + data.loc[tech,'source'] = data.loc[(tech,'lifetime'),'source'] + data.loc[tech,'further description'] = sheet_names[tech] - data.loc[tech, "source"] = data.loc[(tech, "lifetime"), "source"] - data.loc[tech, "further description"] = sheet_names[tech] + return data + +def add_perennials_gbr(data): + """function that add perennials and green biorefining (GBR) including biogas production plant. + it considers purchase of raw materials (perennials) and sales of other products (proteins and biogas feedstock) in the VOM + + references: + R1 : https://doi.org/10.1016/B978-0-323-95879-0.50147-8 + R3: https://dcapub.au.dk/djfpublikation/djfpdf/DCArapport193.pdf + """ + """ general paramaters""" + LHV_ch4 = 50 / 3.6 # MWh/t + EUR_DKK = 7.46 # €/DKK + '''PERENNIALS AND GREEN BIOREFINING''' + + """GBR Cost estimation - Investment + OPEX. TENTATIVE + ref: R1 """ + + # MASS & ENERGY BALANCE + DM_perennials = 0.18 # dry matter content + biogas_ch4_vol = 0.348 # mass% CH4 in biogas + flh_y = 4200 # green crops harvest is only May-October + perennials_input_flow = 40 * DM_perennials # t_DM/h + perennials_input_annual = perennials_input_flow * flh_y # t_DM /y + protein_output_flow = 1.4 # t_DM/h + protein_output_annual = protein_output_flow * flh_y # t_protein_concentrate / y + biogas_output_flow = 0.29 * biogas_ch4_vol * LHV_ch4 # (t/tDM) * (%m CH4) + electricity_input_flow = 7.33/100 * perennials_input_flow + + # COSTS + # NOTE the biogas plat capacity was adjusted based assuming that the biogas plant can run the whole year around + capacity_ratio_biogas_gbr = flh_y / 8760 # we assume the feedstock from gbr can be stored + investment_biogas_adjusted = data.loc[('biogas','investment'), 2020] * biogas_output_flow / perennials_input_flow * (capacity_ratio_biogas_gbr - 1) # €/tDM biomass + FOM = 0 # (%investment) Own assumption + investment = 9.33 * 1e6 / (40 * DM_perennials) + investment_biogas_adjusted # t/tDM/h including biogas plant ref: R1 Table 4 + + # OPEX + protein_price = 535 # €/t ref: R1 + perennial_cost = 130 # €/tDM ref: R1 + other_VOM = (0.45 * 1e6)/ (40* DM_perennials * flh_y) # €/tDM ref: R1, Table 4: "labor and maintenance" + VOM = (perennial_cost - protein_price * protein_output_annual / perennials_input_annual + other_VOM) # EUR//tDM + + data.loc[("perennials gbr", "investment"), years] = investment + data.loc[("perennials gbr", "investment"), "source"] = 'https://doi.org/10.1016/B978-0-323-95879-0.50147-8' + data.loc[("perennials gbr", + "investment"), "further description"] = "includes cost for biogas plant without upgrading" + data.loc[("perennials gbr", "investment"), "unit"] = "EUR/tDM/h" + data.loc[("perennials gbr", "investment"), "currency_year"] = 2020 + + data.loc[("perennials gbr", "lifetime"), years] = 25 + data.loc[("perennials gbr", "lifetime"), "source"] = "Own assumption" + data.loc[("perennials gbr", + "lifetime"), "further description"] = "" + data.loc[("perennials gbr", "lifetime"), "unit"] = "years" + + data.loc[("perennials gbr", "FOM"), years] = FOM + data.loc[("perennials gbr", "FOM"), "source"] = "Own assumption" + data.loc[("perennials gbr", + "FOM"), "further description"] = "" + data.loc[("perennials gbr", "FOM"), "unit"] = "%year" + data.loc[("perennials gbr", "FOM"), "currency_year"] = 2020 + + data.loc[("perennials gbr", "VOM"), years] = VOM + data.loc[("perennials gbr", "VOM"), "source"] = "https://doi.org/10.1016/B978-0-323-95879-0.50147-8" + data.loc[("perennials gbr", + "VOM"), "further description"] = "includes purchase of perennial crops and sales of proteine concentrate, table 8.1 wages, maintenance and auxiliary costs" + data.loc[("perennials gbr", "VOM"), "unit"] = "EUR/tDM" + data.loc[("perennials gbr", "VOM"), "currency_year"] = 2020 + + data.loc[("perennials gbr", "biogas-output"), years] = biogas_output_flow / perennials_input_flow # MWh/tDM + data.loc[("perennials gbr", "biogas-output"), "source"] = "https://doi.org/10.1016/B978-0-323-95879-0.50147-8" + data.loc[("perennials gbr", + "biogas-output"), "further description"] = "table 2" + data.loc[("perennials gbr", "biogas-output"), "unit"] = "MWh/tDM" + + data.loc[("perennials gbr", "electricity-input"), years] = electricity_input_flow / perennials_input_flow + data.loc[("perennials gbr", "electricity-input"), "source"] = "https://doi.org/10.1016/B978-0-323-95879-0.50147-8" + data.loc[("perennials gbr", + "electricity-input"), "further description"] = "table 2" + data.loc[("perennials gbr", "electricity-input"), "unit"] = "MWh/tDM" return data def rename_pypsa_old(costs_pypsa): """ - Renames old technology names to new ones to compare + renames old technology names to new ones to compare converts units from water tanks to compare """ - to_drop = ["retrofitting I", "retrofitting II"] + to_drop = ['retrofitting I', 'retrofitting II'] costs_pypsa.drop(to_drop, level=0, inplace=True) # rename to new names - costs_pypsa.rename({"central CHP": "central gas CHP"}, inplace=True) - costs_pypsa.rename( - {"hydrogen underground storage": "hydrogen storage underground"}, inplace=True - ) + costs_pypsa.rename({'central CHP': 'central gas CHP'}, inplace=True) + costs_pypsa.rename({'hydrogen underground storage': 'hydrogen storage underground'}, + inplace=True) - # convert EUR/m^3 to EUR/kWh for 40 K diff and 1.17 kWh/m^3/K - costs_pypsa.loc[("decentral water tank storage", "investment"), "value"] /= ( - 1.17 * 40 - ) - costs_pypsa.loc[("decentral water tank storage", "investment"), "unit"] = "EUR/kWh" + #convert EUR/m^3 to EUR/kWh for 40 K diff and 1.17 kWh/m^3/K + costs_pypsa.loc[('decentral water tank storage','investment'), + 'value'] /= 1.17*40 + costs_pypsa.loc[('decentral water tank storage','investment'),'unit'] = 'EUR/kWh' return costs_pypsa - def add_manual_input(data): - df = pd.read_csv( - snakemake.input["manual_input"], quotechar='"', sep=",", keep_default_na=False - ) + + df = pd.read_csv(snakemake.input['manual_input'], quotechar='"',sep=',', keep_default_na=False) df = df.rename(columns={"further_description": "further description"}) + l = [] - for tech in df["technology"].unique(): - c0 = df[df["technology"] == tech] - for param in c0["parameter"].unique(): - c = df.query("technology == @tech and parameter == @param") - - s = pd.Series( - index=snakemake.config["years"], - data=np.interp(snakemake.config["years"], c["year"], c["value"]), - name=param, - ) - s["parameter"] = param - s["technology"] = tech + for tech in df['technology'].unique(): + c0 = df[df['technology'] == tech] + for param in c0['parameter'].unique(): + + c = df.query('technology == @tech and parameter == @param') + + s = pd.Series(index=snakemake.config['years'], + data=np.interp(snakemake.config['years'], c['year'], c['value']), + name=param) + s['parameter'] = param + s['technology'] = tech try: - s["currency_year"] = int(c["currency_year"].values[0]) + s["currency_year"] = int(c["currency_year"].values[0]) except ValueError: s["currency_year"] = np.nan - for col in ["unit", "source", "further description"]: + for col in ['unit','source','further description']: s[col] = "; and\n".join(c[col].unique().astype(str)) - s = s.rename( - {"further_description": "further description"} - ) # match column name between manual_input and original TD workflow + s = s.rename({"further_description":"further description"}) # match column name between manual_input and original TD workflow l.append(s) - new_df = pd.DataFrame(l).set_index(["technology", "parameter"]) + new_df = pd.DataFrame(l).set_index(['technology','parameter']) data.index.set_names(["technology", "parameter"], inplace=True) # overwrite DEA data with manual input data = new_df.combine_first(data) @@ -2262,88 +2206,69 @@ def add_manual_input(data): def rename_ISE(costs_ISE): """ - Rename ISE costs to fit to tech data + rename ISE costs to fit to tech data """ - costs_ISE.rename( - index={ - "Investition": "investment", - "Lebensdauer": "lifetime", - "M/O-Kosten": "FOM", - }, - columns={ - "Einheit": "unit", - "2020": 2020, - "2025": 2025, - "2030": 2030, - "2035": 2035, - "2040": 2040, - "2045": 2045, - "2050": 2050, - }, - inplace=True, - ) + costs_ISE.rename(index = {"Investition": "investment", + "Lebensdauer": "lifetime", + "M/O-Kosten": "FOM"}, + columns = {"Einheit": "unit", + "2020": 2020, + "2025": 2025, + "2030": 2030, + "2035": 2035, + "2040": 2040, + "2045": 2045, + "2050": 2050}, inplace=True) costs_ISE.index.names = ["technology", "parameter"] costs_ISE["unit"] = costs_ISE.unit.replace({"a": "years", "% Invest": "%"}) costs_ISE["source"] = source_dict["ISE"] - costs_ISE["further description"] = costs_ISE.reset_index()["technology"].values + costs_ISE['further description'] = costs_ISE.reset_index()["technology"].values # could not find specific currency year in report, assume year of publication - costs_ISE["currency_year"] = 2020 + costs_ISE['currency_year'] = 2020 return costs_ISE def rename_ISE_vehicles(costs_vehicles): """ - Rename ISE_vehicles costs to fit to tech data + rename ISE_vehicles costs to fit to tech data """ - costs_vehicles.rename( - index={ - "Investition": "investment", - "Lebensdauer": "lifetime", - "M/O-Kosten": "FOM", - "Wirkungsgrad*": "efficiency", - "PKW Batterie-Elektromotor": "Battery electric (passenger cars)", - "LKW Batterie-Elektromotor": "Battery electric (trucks)", - "LKW H2- Brennstoffzelle": "Hydrogen fuel cell (trucks)", - "PKW H2- Brennstoffzelle": "Hydrogen fuel cell (passenger cars)", - "LKW ICE- Fl�ssigtreibstoff": "Liquid fuels ICE (trucks)", - "PKW ICE- Fl�ssigtreibstoff": "Liquid fuels ICE (passenger cars)", - "LKW Ladeinfrastruktur Brennstoffzellen Fahrzeuge * LKW": "Charging infrastructure fuel cell vehicles trucks", - "PKW Ladeinfrastruktur Brennstoffzellen Fahrzeuge * PKW": "Charging infrastructure fuel cell vehicles passenger cars", - "PKW Ladeinfrastruktur schnell (reine) Batteriefahrzeuge*": "Charging infrastructure fast (purely) battery electric vehicles passenger cars", - "Ladeinfrastruktur langsam (reine) Batteriefahrzeuge*": "Charging infrastructure slow (purely) battery electric vehicles passenger cars", - }, - columns={ - "Einheit": "unit", - "2020": 2020, - "2025": 2025, - "2030": 2030, - "2035": 2035, - "2040": 2040, - "2045": 2045, - "2050": 2050, - }, - inplace=True, - ) + costs_vehicles.rename(index = {"Investition": "investment", + "Lebensdauer": "lifetime", + "M/O-Kosten": "FOM", + "Wirkungsgrad*" : "efficiency", + "PKW Batterie-Elektromotor" : "Battery electric (passenger cars)", + "LKW Batterie-Elektromotor" : "Battery electric (trucks)", + "LKW H2- Brennstoffzelle": "Hydrogen fuel cell (trucks)", + "PKW H2- Brennstoffzelle": "Hydrogen fuel cell (passenger cars)", + "LKW ICE- Fl�ssigtreibstoff": "Liquid fuels ICE (trucks)", + "PKW ICE- Fl�ssigtreibstoff": "Liquid fuels ICE (passenger cars)", + "LKW Ladeinfrastruktur Brennstoffzellen Fahrzeuge * LKW": "Charging infrastructure fuel cell vehicles trucks", + "PKW Ladeinfrastruktur Brennstoffzellen Fahrzeuge * PKW": "Charging infrastructure fuel cell vehicles passenger cars", + "PKW Ladeinfrastruktur schnell (reine) Batteriefahrzeuge*" : "Charging infrastructure fast (purely) battery electric vehicles passenger cars", + "Ladeinfrastruktur langsam (reine) Batteriefahrzeuge*" : "Charging infrastructure slow (purely) battery electric vehicles passenger cars"}, + columns = {"Einheit": "unit", + "2020": 2020, + "2025": 2025, + "2030": 2030, + "2035": 2035, + "2040": 2040, + "2045": 2045, + "2050": 2050}, inplace=True) costs_vehicles.index.names = ["technology", "parameter"] - costs_vehicles["unit"] = costs_vehicles.unit.replace( - {"a": "years", "% Invest": "%"} - ) + costs_vehicles["unit"] = costs_vehicles.unit.replace({"a": "years", "% Invest": "%"}) costs_vehicles["source"] = source_dict["vehicles"] # could not find specific currency year in report, assume year of publication costs_vehicles["currency_year"] = 2020 - costs_vehicles["further description"] = costs_vehicles.reset_index()[ - "technology" - ].values + costs_vehicles['further description'] = costs_vehicles.reset_index()["technology"].values return costs_vehicles - -def carbon_flow(costs, year): +def carbon_flow(costs,year): # NB: This requires some digits of accuracy; rounding to two digits creates carbon inbalances when scaling up - c_in_char = 0 # Carbon ending up in char: zero avoids inbalace -> assumed to be circulated back and eventually end up in one of the other output streams - medium_out = "" - CH4_specific_energy = 50 # GJ/t methane + c_in_char = 0 # Carbon ending up in char: zero avoids inbalace -> assumed to be circulated back and eventually end up in one of the other output streams + medium_out = '' + CH4_specific_energy = 50 #GJ/t methane btlcost_data = np.interp(x=years, xp=[2020, 2050], fp=[3500, 2000]) btl_cost = pd.Series(data=btlcost_data, index=years) @@ -2354,542 +2279,393 @@ def carbon_flow(costs, year): btleta_data = np.interp(x=years, xp=[2020, 2050], fp=[0.35, 0.45]) btl_eta = pd.Series(data=btleta_data, index=years) - # Adding pelletizing cost to biomass boiler - costs.loc[("biomass boiler", "pelletizing cost"), "value"] = 9 - costs.loc[("biomass boiler", "pelletizing cost"), "unit"] = "EUR/MWh_pellets" - costs.loc[("biomass boiler", "pelletizing cost"), "currency_year"] = 2019 - costs.loc[("biomass boiler", "pelletizing cost"), "source"] = ( - "Assumption based on doi:10.1016/j.rser.2019.109506" - ) + #Adding pelletizing cost to biomass boiler + costs.loc[('biomass boiler', 'pelletizing cost'), 'value'] = 9 + costs.loc[('biomass boiler', 'pelletizing cost'), 'unit'] = "EUR/MWh_pellets" + costs.loc[('biomass boiler', 'pelletizing cost'), 'currency_year'] = 2019 + costs.loc[('biomass boiler', 'pelletizing cost'), 'source'] = "Assumption based on doi:10.1016/j.rser.2019.109506" + - for tech in [ - "Fischer-Tropsch", - "methanolisation", - "BtL", - "biomass-to-methanol", - "BioSNG", - "biogas", - "biogas CC", - "digestible biomass to hydrogen", - "solid biomass to hydrogen", - "electrobiofuels", - ]: + for tech in ['Fischer-Tropsch', 'methanolisation', 'BtL', 'biomass-to-methanol', 'BioSNG', 'biogas', + 'biogas CC', 'digestible biomass to hydrogen', + 'solid biomass to hydrogen', 'electrobiofuels']: inv_cost = 0 eta = 0 lifetime = 0 FOM = 0 VOM = 0 currency_year = np.nan - source = "TODO" + source = 'TODO' co2_capture_rate = 0.90 - if (tech, "capture rate") not in costs.index: - costs.loc[(tech, "capture rate"), "value"] = co2_capture_rate - costs.loc[(tech, "capture rate"), "unit"] = "per unit" - costs.loc[(tech, "capture rate"), "source"] = ( - "Assumption based on doi:10.1016/j.biombioe.2015.01.006" - ) + if not (tech, "capture rate") in costs.index: + costs.loc[(tech, 'capture rate'), 'value'] = co2_capture_rate + costs.loc[(tech, 'capture rate'), 'unit'] = "per unit" + costs.loc[(tech, 'capture rate'), 'source'] = "Assumption based on doi:10.1016/j.biombioe.2015.01.006" + - if tech == "BtL": + if tech == 'BtL': inv_cost = btl_cost[year] - medium_out = "oil" + medium_out = 'oil' eta = btl_eta[year] source = "doi:10.1016/j.enpol.2017.05.013" currency_year = 2017 - if tech == "biomass-to-methanol": - medium_out = "methanol" + if tech == 'biomass-to-methanol': + medium_out = 'methanol' - elif tech == "BioSNG": - medium_out = "gas" + elif tech == 'BioSNG': + medium_out = 'gas' lifetime = 25 - elif tech in ["biogas", "biogas CC"]: + elif tech in ['biogas', 'biogas CC']: eta = 1 source = "Assuming input biomass is already given in biogas output" - AD_CO2_share = 0.4 # volumetric share in biogas (rest is CH4) + AD_CO2_share = 0.4 #volumetric share in biogas (rest is CH4) - elif tech == "biogas plus hydrogen": - # NB: this falls between power to gas and biogas and should be used with care, due to possible minor - # differences in resource use etc. which may tweak results in favour of one tech or another - eta = 1.6 - H2_in = 0.46 - - heat_out = 0.19 - source = "Calculated from data in Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx" - costs.loc[(tech, "hydrogen input"), "value"] = H2_in - costs.loc[(tech, "hydrogen input"), "unit"] = "MWh_H2/MWh_CH4" - costs.loc[(tech, "hydrogen input"), "source"] = source - - costs.loc[(tech, "heat output"), "value"] = heat_out - costs.loc[(tech, "heat output"), "unit"] = "MWh_th/MWh_CH4" - costs.loc[(tech, "heat output"), "source"] = source - currency_year = costs.loc[("biogas plus hydrogen", "VOM"), "currency_year"] - - # TODO: this needs to be refined based on e.g. stoichiometry: - AD_CO2_share = 0.1 # volumetric share in biogas (rest is CH4). - - elif tech == "digestible biomass to hydrogen": + + #elif tech == 'biogas plus hydrogen': + # #NB: this falls between power to gas and biogas and should be used with care, due to possible minor + # # differences in resource use etc. which may tweak results in favour of one tech or another + # eta = 1.6 + # H2_in = 0.46 + + # heat_out = 0.19 + # source = "Calculated from data in Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx" + # costs.loc[(tech, 'hydrogen input'), 'value'] = H2_in + # costs.loc[(tech, 'hydrogen input'), 'unit'] = "MWh_H2/MWh_CH4" + # costs.loc[(tech, 'hydrogen input'), 'source'] = source + + # costs.loc[(tech, 'heat output'), 'value'] = heat_out + # costs.loc[(tech, 'heat output'), 'unit'] = "MWh_th/MWh_CH4" + # costs.loc[(tech, 'heat output'), 'source'] = source + # currency_year = costs.loc[('biogas plus hydrogen', 'VOM'), "currency_year"] + + # #TODO: this needs to be refined based on e.g. stoichiometry: + # AD_CO2_share = 0.1 #volumetric share in biogas (rest is CH4). + + elif tech == 'digestible biomass to hydrogen': inv_cost = bmH2_cost[year] eta = 0.39 FOM = 4.25 currency_year = 2014 - costs.loc[(tech, "FOM"), "currency_year"] = 2014 - source = "Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014" # source_dict('HyNOW') + source = 'Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014' #source_dict('HyNOW') - elif tech == "solid biomass to hydrogen": + elif tech == 'solid biomass to hydrogen': inv_cost = bmH2_cost[year] eta = 0.56 FOM = 4.25 currency_year = 2014 - costs.loc[(tech, "FOM"), "currency_year"] = 2014 - source = "Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014" # source_dict('HyNOW') + source = 'Zech et.al. DBFZ Report Nr. 19. Hy-NOW - Evaluierung der Verfahren und Technologien für die Bereitstellung von Wasserstoff auf Basis von Biomasse, DBFZ, 2014' #source_dict('HyNOW') if eta > 0: - costs.loc[(tech, "efficiency"), "value"] = eta - costs.loc[(tech, "efficiency"), "unit"] = "per unit" - costs.loc[(tech, "efficiency"), "source"] = source - - if tech in ["BioSNG", "BtL", "biomass-to-methanol"]: - input_CO2_intensity = costs.loc[("solid biomass", "CO2 intensity"), "value"] - - costs.loc[(tech, "C in fuel"), "value"] = ( - costs.loc[(tech, "efficiency"), "value"] - * costs.loc[(medium_out, "CO2 intensity"), "value"] - / input_CO2_intensity - ) - costs.loc[(tech, "C stored"), "value"] = ( - 1 - costs.loc[(tech, "C in fuel"), "value"] - c_in_char - ) - costs.loc[(tech, "CO2 stored"), "value"] = ( - input_CO2_intensity * costs.loc[(tech, "C stored"), "value"] - ) - - costs.loc[(tech, "C in fuel"), "unit"] = "per unit" - costs.loc[(tech, "C stored"), "unit"] = "per unit" - costs.loc[(tech, "CO2 stored"), "unit"] = "tCO2/MWh_th" - - costs.loc[(tech, "C in fuel"), "source"] = ( - "Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016" - ) - costs.loc[(tech, "C stored"), "source"] = ( - "Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016" - ) - costs.loc[(tech, "CO2 stored"), "source"] = ( - "Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016" - ) - - elif tech in ["electrobiofuels"]: - input_CO2_intensity = costs.loc[("solid biomass", "CO2 intensity"), "value"] - oil_CO2_intensity = costs.loc[("oil", "CO2 intensity"), "value"] - - costs.loc[("electrobiofuels", "C in fuel"), "value"] = ( - costs.loc[("BtL", "C in fuel"), "value"] - + costs.loc[("BtL", "C stored"), "value"] - * costs.loc[("Fischer-Tropsch", "capture rate"), "value"] - ) - costs.loc[("electrobiofuels", "C in fuel"), "unit"] = "per unit" - costs.loc[("electrobiofuels", "C in fuel"), "source"] = ( - "Stoichiometric calculation" - ) - - costs.loc[("electrobiofuels", "efficiency-biomass"), "value"] = ( - costs.loc[("electrobiofuels", "C in fuel"), "value"] - * input_CO2_intensity - / oil_CO2_intensity - ) - costs.loc[("electrobiofuels", "efficiency-biomass"), "unit"] = "per unit" - costs.loc[("electrobiofuels", "efficiency-biomass"), "source"] = ( - "Stoichiometric calculation" - ) - - efuel_scale_factor = ( - costs.loc[("BtL", "C stored"), "value"] - * costs.loc[("Fischer-Tropsch", "capture rate"), "value"] - ) - - costs.loc[("electrobiofuels", "efficiency-hydrogen"), "value"] = ( - costs.loc[("Fischer-Tropsch", "efficiency"), "value"] - / efuel_scale_factor - ) - costs.loc[("electrobiofuels", "efficiency-hydrogen"), "unit"] = "per unit" - costs.loc[("electrobiofuels", "efficiency-hydrogen"), "source"] = ( - "Stoichiometric calculation" - ) - - costs.loc[("electrobiofuels", "efficiency-tot"), "value"] = 1 / ( - 1 / costs.loc[("electrobiofuels", "efficiency-hydrogen"), "value"] - + 1 / costs.loc[("electrobiofuels", "efficiency-biomass"), "value"] - ) - costs.loc[("electrobiofuels", "efficiency-tot"), "unit"] = "per unit" - costs.loc[("electrobiofuels", "efficiency-tot"), "source"] = ( - "Stoichiometric calculation" - ) - - costs.loc[("electrobiofuels", "efficiency-hydrogen"), "value"] = ( - costs.loc[("Fischer-Tropsch", "efficiency"), "value"] - / efuel_scale_factor - ) - costs.loc[("electrobiofuels", "efficiency-hydrogen"), "unit"] = "per unit" - costs.loc[("electrobiofuels", "efficiency-hydrogen"), "source"] = ( - "Stoichiometric calculation" - ) - - costs.loc[("electrobiofuels", "efficiency-tot"), "value"] = 1 / ( - 1 / costs.loc[("electrobiofuels", "efficiency-hydrogen"), "value"] - + 1 / costs.loc[("electrobiofuels", "efficiency-biomass"), "value"] - ) - costs.loc[("electrobiofuels", "efficiency-tot"), "unit"] = "per unit" - costs.loc[("electrobiofuels", "efficiency-tot"), "source"] = ( - "Stoichiometric calculation" - ) - - inv_cost = ( - btl_cost[year] - + costs.loc[("Fischer-Tropsch", "investment"), "value"] - * efuel_scale_factor - ) - VOM = ( - costs.loc[("BtL", "VOM"), "value"] - + costs.loc[("Fischer-Tropsch", "VOM"), "value"] * efuel_scale_factor - ) - FOM = costs.loc[("BtL", "FOM"), "value"] - medium_out = "oil" - currency_year = costs.loc[ - ("Fischer-Tropsch", "investment"), "currency_year" - ] - costs.loc[(tech, "FOM"), "currency_year"] = 2015 + costs.loc[(tech, 'efficiency'), 'value'] = eta + costs.loc[(tech, 'efficiency'), 'unit'] = "per unit" + costs.loc[(tech, 'efficiency'), 'source'] = source + + if tech in ['BioSNG', 'BtL', 'biomass-to-methanol']: + input_CO2_intensity = costs.loc[('solid biomass', 'CO2 intensity'), 'value'] + + costs.loc[(tech, 'C in fuel'), 'value'] = costs.loc[(tech, 'efficiency'), 'value'] \ + * costs.loc[(medium_out, 'CO2 intensity'), 'value'] \ + / input_CO2_intensity + costs.loc[(tech, 'C stored'), 'value'] = 1 - costs.loc[(tech, 'C in fuel'), 'value'] - c_in_char + costs.loc[(tech, 'CO2 stored'), 'value'] = input_CO2_intensity * costs.loc[(tech, 'C stored'), 'value'] + + costs.loc[(tech, 'C in fuel'), 'unit'] = "per unit" + costs.loc[(tech, 'C stored'), 'unit'] = "per unit" + costs.loc[(tech, 'CO2 stored'), 'unit'] = "tCO2/MWh_th" + + costs.loc[(tech, 'C in fuel'), 'source'] = "Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016" + costs.loc[(tech, 'C stored'), 'source'] = "Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016" + costs.loc[(tech, 'CO2 stored'), 'source'] = "Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016" + + elif tech in ['electrobiofuels']: + + input_CO2_intensity = costs.loc[('solid biomass', 'CO2 intensity'), 'value'] + oil_CO2_intensity = costs.loc[('oil', 'CO2 intensity'), 'value'] + + costs.loc[('electrobiofuels', 'C in fuel'), 'value'] = (costs.loc[('BtL', 'C in fuel'), 'value'] + + costs.loc[('BtL', 'C stored'), 'value'] + * costs.loc[('Fischer-Tropsch', 'capture rate'), 'value']) + costs.loc[('electrobiofuels', 'C in fuel'), 'unit'] = 'per unit' + costs.loc[('electrobiofuels', 'C in fuel'), 'source'] = 'Stoichiometric calculation' + + costs.loc[('electrobiofuels', 'efficiency-biomass'), 'value'] = costs.loc[('electrobiofuels', 'C in fuel'), 'value'] \ + * input_CO2_intensity / oil_CO2_intensity + costs.loc[('electrobiofuels', 'efficiency-biomass'), 'unit'] = 'per unit' + costs.loc[('electrobiofuels', 'efficiency-biomass'), 'source'] = 'Stoichiometric calculation' + + + efuel_scale_factor = costs.loc[('BtL', 'C stored'), 'value']* costs.loc[('Fischer-Tropsch', 'capture rate'), 'value'] + + costs.loc[('electrobiofuels', 'efficiency-hydrogen'), 'value'] = costs.loc[('Fischer-Tropsch', 'efficiency'), 'value']\ + / efuel_scale_factor + costs.loc[('electrobiofuels', 'efficiency-hydrogen'), 'unit'] = 'per unit' + costs.loc[('electrobiofuels', 'efficiency-hydrogen'), 'source'] = 'Stoichiometric calculation' + + costs.loc[('electrobiofuels', 'efficiency-tot'), 'value'] = (1 / + (1 / costs.loc[('electrobiofuels', 'efficiency-hydrogen'), 'value'] + + 1 / costs.loc[('electrobiofuels', 'efficiency-biomass'), 'value'])) + costs.loc[('electrobiofuels', 'efficiency-tot'), 'unit'] = 'per unit' + costs.loc[('electrobiofuels', 'efficiency-tot'), 'source'] = 'Stoichiometric calculation' + + inv_cost = btl_cost[year] + costs.loc[('Fischer-Tropsch', 'investment'), 'value'] * efuel_scale_factor + VOM = costs.loc[('BtL', 'VOM'), 'value'] + costs.loc[('Fischer-Tropsch', 'VOM'), 'value'] * efuel_scale_factor + FOM = costs.loc[('BtL', 'FOM'), 'value'] + medium_out = 'oil' + currency_year = costs.loc[('Fischer-Tropsch', 'investment'), "currency_year"] source = "combination of BtL and electrofuels" - elif tech in ["biogas", "biogas CC", "biogas plus hydrogen"]: - CH4_density = 0.657 # kg/Nm3 - CO2_density = 1.98 # kg/Nm3 - CH4_vol_energy_density = ( - CH4_specific_energy * CH4_density / (1000 * 3.6) - ) # MJ/Nm3 -> MWh/Nm3 + elif tech in ['biogas', 'biogas CC', 'biogas plus hydrogen']: + CH4_density = 0.657 #kg/Nm3 + CO2_density = 1.98 #kg/Nm3 + CH4_vol_energy_density = CH4_specific_energy * CH4_density / (1000 * 3.6) #MJ/Nm3 -> MWh/Nm3 CO2_weight_share = AD_CO2_share * CO2_density - costs.loc[(tech, "CO2 stored"), "value"] = ( - CO2_weight_share / CH4_vol_energy_density / 1000 - ) # tCO2/MWh,in (NB: assuming the input is already given in the biogas potential and cost - costs.loc[(tech, "CO2 stored"), "unit"] = "tCO2/MWh_th" - costs.loc[(tech, "CO2 stored"), "source"] = ( - "Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016" - ) + costs.loc[(tech, 'CO2 stored'), 'value'] = CO2_weight_share / CH4_vol_energy_density / 1000 #tCO2/MWh,in (NB: assuming the input is already given in the biogas potential and cost + costs.loc[(tech, 'CO2 stored'), 'unit'] = "tCO2/MWh_th" + costs.loc[(tech, 'CO2 stored'), 'source'] = "Stoichiometric calculation, doi:10.1016/j.apenergy.2022.120016" if inv_cost > 0: - costs.loc[(tech, "investment"), "value"] = inv_cost - costs.loc[(tech, "investment"), "unit"] = "EUR/kW_th" - costs.loc[(tech, "investment"), "source"] = source - costs.loc[(tech, "investment"), "currency_year"] = currency_year + costs.loc[(tech, 'investment'), 'value'] = inv_cost + costs.loc[(tech, 'investment'), 'unit'] = "EUR/kW_th" + costs.loc[(tech, 'investment'), 'source'] = source + costs.loc[(tech, 'investment'), 'currency_year'] = currency_year if lifetime > 0: - costs.loc[(tech, "lifetime"), "value"] = lifetime - costs.loc[(tech, "lifetime"), "unit"] = "years" - costs.loc[(tech, "lifetime"), "source"] = source + costs.loc[(tech, 'lifetime'), 'value'] = lifetime + costs.loc[(tech, 'lifetime'), 'unit'] = "years" + costs.loc[(tech, 'lifetime'), 'source'] = source if FOM > 0: - costs.loc[(tech, "FOM"), "value"] = FOM - costs.loc[(tech, "FOM"), "unit"] = "%/year" - costs.loc[(tech, "FOM"), "source"] = source + costs.loc[(tech, 'FOM'), 'value'] = FOM + costs.loc[(tech, 'FOM'), 'unit'] = "%/year" + costs.loc[(tech, 'FOM'), 'source'] = source if VOM > 0: - costs.loc[(tech, "VOM"), "value"] = VOM - costs.loc[(tech, "VOM"), "unit"] = "EUR/MWh_th" - costs.loc[(tech, "VOM"), "source"] = source - costs.loc[(tech, "VOM"), "currency_year"] = currency_year + costs.loc[(tech, 'VOM'), 'value'] = VOM + costs.loc[(tech, 'VOM'), 'unit'] = "EUR/MWh_th" + costs.loc[(tech, 'VOM'), 'source'] = source + costs.loc[(tech, 'VOM'), 'currency_year'] = currency_year return costs - def energy_penalty(costs): + # Energy penalty for biomass carbon capture # Need to take steam production for CC into account, assumed with the main feedstock, # e.g. the input biomass is used also for steam, and the efficiency for el and heat is scaled down accordingly - for tech in [ - "central solid biomass CHP CC", - "waste CHP CC", - "solid biomass boiler steam CC", - "direct firing solid fuels CC", - "direct firing gas CC", - "biogas CC", - ]: - if "powerboost" in tech: - boiler = "electric boiler steam" - feedstock = "solid biomass" - co2_capture = costs.loc[(feedstock, "CO2 intensity"), "value"] - elif "gas" in tech: - boiler = "gas boiler steam" - feedstock = "gas" - co2_capture = costs.loc[(feedstock, "CO2 intensity"), "value"] - elif "biogas" in tech: - boiler = "gas boiler steam" - co2_capture = costs.loc[(tech, "CO2 stored"), "value"] + for tech in ['central solid biomass CHP CC', 'waste CHP CC', 'solid biomass boiler steam CC', 'direct firing solid fuels CC', 'direct firing gas CC', 'biogas CC']: + + if 'powerboost' in tech: + boiler = 'electric boiler steam' + feedstock = 'solid biomass' + co2_capture = costs.loc[(feedstock, 'CO2 intensity'), 'value'] + elif 'gas' in tech: + boiler = 'gas boiler steam' + feedstock = 'gas' + co2_capture = costs.loc[(feedstock, 'CO2 intensity'), 'value'] + elif 'biogas' in tech: + boiler = 'gas boiler steam' + co2_capture = costs.loc[(tech, 'CO2 stored'), 'value'] else: - boiler = "solid biomass boiler steam" - feedstock = "solid biomass" - co2_capture = costs.loc[(feedstock, "CO2 intensity"), "value"] - - # Scaling biomass input to account for heat demand of carbon capture - scalingFactor = 1 / ( - 1 - + co2_capture - * costs.loc[("biomass CHP capture", "heat-input"), "value"] - / costs.loc[(boiler, "efficiency"), "value"] - ) + boiler = 'solid biomass boiler steam' + feedstock = 'solid biomass' + co2_capture = costs.loc[(feedstock, 'CO2 intensity'), 'value'] - eta_steam = (1 - scalingFactor) * costs.loc[(boiler, "efficiency"), "value"] - eta_old = costs.loc[(tech, "efficiency"), "value"] + #Scaling biomass input to account for heat demand of carbon capture + scalingFactor = 1 / (1 + co2_capture * costs.loc[ + ('biomass CHP capture', 'heat-input'), 'value'] + / costs.loc[(boiler, 'efficiency'), 'value']) - eta_main = costs.loc[(tech, "efficiency"), "value"] * scalingFactor + eta_steam = (1 - scalingFactor) * costs.loc[(boiler, 'efficiency'), 'value'] + eta_old = costs.loc[(tech, 'efficiency'), 'value'] + + temp = costs.loc[(tech, 'efficiency'), 'value'] + eta_main = costs.loc[(tech, 'efficiency'), 'value'] * scalingFactor # Adapting investment share of tech due to steam boiler addition. Investment per MW_el. - costs.loc[(tech, "investment"), "value"] = ( - costs.loc[(tech, "investment"), "value"] * eta_old / eta_main - + costs.loc[(boiler, "investment"), "value"] * eta_steam / eta_main - ) - costs.loc[(tech, "investment"), "source"] = ( - "Combination of " + tech + " and " + boiler - ) - costs.loc[(tech, "investment"), "further description"] = "" + costs.loc[(tech, 'investment'), 'value'] = costs.loc[(tech, 'investment'), 'value'] * eta_old / eta_main \ + + costs.loc[(boiler, 'investment'), 'value'] * eta_steam / eta_main + costs.loc[(tech, 'investment'), 'source'] = 'Combination of ' + tech + ' and ' + boiler + costs.loc[(tech, 'investment'), 'further description'] = '' - if costs.loc[(tech, "VOM"), "value"]: + if costs.loc[(tech, 'VOM'), 'value']: break else: - costs.loc[(tech, "VOM"), "value"] = 0.0 - - costs.loc[(tech, "VOM"), "value"] = ( - costs.loc[(tech, "VOM"), "value"] * eta_old / eta_main - + costs.loc[(boiler, "VOM"), "value"] * eta_steam / eta_main - ) - costs.loc[(tech, "VOM"), "source"] = "Combination of " + tech + " and " + boiler - costs.loc[(tech, "VOM"), "further description"] = "" - - costs.loc[(tech, "efficiency"), "value"] = eta_main - costs.loc[(tech, "efficiency"), "source"] = ( - "Combination of " + tech + " and " + boiler - ) - costs.loc[(tech, "efficiency"), "further description"] = "" - - if "CHP" in tech: - costs.loc[(tech, "efficiency-heat"), "value"] = costs.loc[ - (tech, "efficiency-heat"), "value" - ] * scalingFactor + costs.loc[ - ("solid biomass", "CO2 intensity"), "value" - ] * ( - costs.loc[("biomass CHP capture", "heat-output"), "value"] - + costs.loc[("biomass CHP capture", "compression-heat-output"), "value"] - ) - costs.loc[(tech, "efficiency-heat"), "source"] = ( - "Combination of " + tech + " and " + boiler - ) - costs.loc[(tech, "efficiency-heat"), "further description"] = "" - - if "biogas CC" in tech: - costs.loc[(tech, "VOM"), "value"] = 0 - costs.loc[(tech, "VOM"), "unit"] = "EUR/MWh" - - costs.loc[(tech, "VOM"), "value"] = ( - costs.loc[(tech, "VOM"), "value"] * eta_old / eta_main - + costs.loc[(boiler, "VOM"), "value"] * eta_steam / eta_main - ) - costs.loc[(tech, "VOM"), "source"] = "Combination of " + tech + " and " + boiler - costs.loc[(tech, "VOM"), "further description"] = "" + costs.loc[(tech, 'VOM'), 'value'] = 0. + + costs.loc[(tech, 'VOM'), 'value'] = costs.loc[(tech, 'VOM'), 'value'] * eta_old / eta_main \ + + costs.loc[(boiler, 'VOM'), 'value'] * eta_steam / eta_main + costs.loc[(tech, 'VOM'), 'source'] = 'Combination of ' + tech + ' and ' + boiler + costs.loc[(tech, 'VOM'), 'further description'] = '' + + costs.loc[(tech, 'efficiency'), 'value'] = eta_main + costs.loc[(tech, 'efficiency'), 'source'] = 'Combination of ' + tech + ' and ' + boiler + costs.loc[(tech, 'efficiency'), 'further description'] = '' + + if 'CHP' in tech: + costs.loc[(tech, 'efficiency-heat'), 'value'] = \ + costs.loc[(tech, 'efficiency-heat'), 'value'] * scalingFactor \ + + costs.loc[('solid biomass', 'CO2 intensity'), 'value'] * \ + (costs.loc[('biomass CHP capture', 'heat-output'), 'value'] + + costs.loc[('biomass CHP capture', 'compression-heat-output'), 'value']) + costs.loc[(tech, 'efficiency-heat'), 'source'] = 'Combination of ' + tech + ' and ' + boiler + costs.loc[(tech, 'efficiency-heat'), 'further description'] = '' + + if 'biogas CC' in tech: + costs.loc[(tech, 'VOM'), 'value'] = 0 + costs.loc[(tech, 'VOM'), 'unit'] = 'EUR/MWh' + + costs.loc[(tech, 'VOM'), 'value'] = costs.loc[(tech, 'VOM'), 'value'] * eta_old / eta_main \ + + costs.loc[(boiler, 'VOM'), 'value'] * eta_steam / eta_main + costs.loc[(tech, 'VOM'), 'source'] = 'Combination of ' + tech + ' and ' + boiler + costs.loc[(tech, 'VOM'), 'further description'] = '' return costs - def add_egs_data(data): """ Adds data of enhanced geothermal systems. Data taken from Aghahosseini, Breyer 2020: From hot rock to useful energy... - - """ - parameters = [ - "CO2 intensity", - "lifetime", - "efficiency residential heat", - "efficiency electricity", - "FOM", - ] + + """ + parameters = ["CO2 intensity", "lifetime", "efficiency residential heat", "efficiency electricity", "FOM"] techs = ["geothermal"] - multi_i = pd.MultiIndex.from_product( - [techs, parameters], names=["technology", "parameter"] - ) + multi_i = pd.MultiIndex.from_product([techs, parameters], names=["technology", "parameter"]) geoth_df = pd.DataFrame(index=multi_i, columns=data.columns) years = [col for col in data.columns if isinstance(col, int)] # lifetime - geoth_df.loc[("geothermal", "lifetime"), years] = 30 # years + geoth_df.loc[("geothermal", "lifetime"), years] = 30 #years geoth_df.loc[("geothermal", "lifetime"), "unit"] = "years" geoth_df.loc[("geothermal", "lifetime"), "source"] = source_dict["Aghahosseini2020"] # co2 emissions - geoth_df.loc[("geothermal", "CO2 intensity"), years] = 0.12 # tCO2/MWh_el + geoth_df.loc[("geothermal", "CO2 intensity"), years] = 0.12 # tCO2/MWh_el geoth_df.loc[("geothermal", "CO2 intensity"), "unit"] = "tCO2/MWh_el" - geoth_df.loc[("geothermal", "CO2 intensity"), "source"] = source_dict[ - "Aghahosseini2020" - ] - geoth_df.loc[("geothermal", "CO2 intensity"), "further description"] = ( - "Likely to be improved; Average of 85 percent of global egs power plant capacity" - ) + geoth_df.loc[("geothermal", "CO2 intensity"), "source"] = source_dict["Aghahosseini2020"] + geoth_df.loc[("geothermal", "CO2 intensity"), "further description"] = "Likely to be improved; Average of 85 percent of global egs power plant capacity" # efficiency for heat generation using organic rankine cycle geoth_df.loc[("geothermal", "efficiency residential heat"), years] = 0.8 geoth_df.loc[("geothermal", "efficiency residential heat"), "unit"] = "per unit" - geoth_df.loc[("geothermal", "efficiency residential heat"), "source"] = ( - "{}; {}".format(source_dict["Aghahosseini2020"], source_dict["Breede2015"]) - ) - geoth_df.loc[ - ("geothermal", "efficiency residential heat"), "further description" - ] = "This is a rough estimate, depends on local conditions" + geoth_df.loc[("geothermal", "efficiency residential heat"), "source"] = "{}; {}".format(source_dict["Aghahosseini2020"], source_dict["Breede2015"]) + geoth_df.loc[("geothermal", "efficiency residential heat"), "further description"] = "This is a rough estimate, depends on local conditions" # efficiency for electricity generation using organic rankine cycle geoth_df.loc[("geothermal", "efficiency electricity"), years] = 0.1 geoth_df.loc[("geothermal", "efficiency electricity"), "unit"] = "per unit" - geoth_df.loc[("geothermal", "efficiency electricity"), "source"] = "{}; {}".format( - source_dict["Aghahosseini2020"], source_dict["Breede2015"] - ) - geoth_df.loc[("geothermal", "efficiency electricity"), "further description"] = ( - "This is a rough estimate, depends on local conditions" - ) + geoth_df.loc[("geothermal", "efficiency electricity"), "source"] = "{}; {}".format(source_dict["Aghahosseini2020"], source_dict["Breede2015"]) + geoth_df.loc[("geothermal", "efficiency electricity"), "further description"] = "This is a rough estimate, depends on local conditions" # relative additional capital cost of using residual heat for district heating (25 percent) geoth_df.loc[("geothermal", "district heating cost"), years] = 0.25 geoth_df.loc[("geothermal", "district heating cost"), "unit"] = "%" - geoth_df.loc[("geothermal", "district heating cost"), "source"] = "{}".format( - source_dict["Frey2022"] - ) - geoth_df.loc[("geothermal", "district heating cost"), "further description"] = ( - "If capital cost of electric generation from EGS is 100%, district heating adds additional 25%" - ) + geoth_df.loc[("geothermal", "district heating cost"), "source"] = "{}".format(source_dict["Frey2022"]) + geoth_df.loc[("geothermal", "district heating cost"), "further description"] = "If capital cost of electric generation from EGS is 100%, district heating adds additional 25%" # fixed operational costs - geoth_df.loc[("geothermal", "FOM"), years] = 2.0 + geoth_df.loc[("geothermal", "FOM"), years] = 2. geoth_df.loc[("geothermal", "FOM"), "unit"] = "%/year" - geoth_df.loc[("geothermal", "FOM"), "source"] = source_dict["Aghahosseini2020"] - geoth_df.loc[("geothermal", "FOM"), "further description"] = ( - "Both for flash, binary and ORC plants. See Supplemental Material for details" - ) - - geoth_df = geoth_df.dropna(axis=1, how="all") - + geoth_df.loc[("geothermal", "FOM"), "source"] = source_dict["Aghahosseini2020"] + geoth_df.loc[("geothermal", "FOM"), "further description"] = "Both for flash, binary and ORC plants. See Supplemental Material for details" + + geoth_df = geoth_df.dropna(axis=1, how='all') + return pd.concat([data, geoth_df]) -def annuity(n, r=0.07): +def annuity(n,r=0.07): """ Calculate the annuity factor for an asset with lifetime n years and discount rate of r """ if isinstance(r, pd.Series): - return pd.Series(1 / n, index=r.index).where( - r == 0, r / (1.0 - 1.0 / (1.0 + r) ** n) - ) + return pd.Series(1/n, index=r.index).where(r == 0, r/(1. - 1./(1.+r)**n)) elif r > 0: - return r / (1.0 - 1.0 / (1.0 + r) ** n) + return r/(1. - 1./(1.+r)**n) else: - return 1 / n - + return 1/n def add_home_battery_costs(costs): """ - Adds investment costs for home battery storage and inverter. + adds investment costs for home battery storage and inverter. Since home battery costs are not part of the DEA cataloque, utility-scale costs are multiplied by a factor determined by data from the EWG study """ # get DEA assumptions for utility scale - home_battery = data.loc[["battery storage", "battery inverter"]].rename( - index=lambda x: "home " + x, level=0 - ) + home_battery = (data.loc[["battery storage", "battery inverter"]] + .rename(index=lambda x: "home " + x, level=0)) # get EWG cost assumptions - costs_ewg = pd.read_csv( - snakemake.input.EWG_costs, index_col=list(range(2)) - ).sort_index() + costs_ewg = pd.read_csv(snakemake.input.EWG_costs, + index_col=list(range(2))).sort_index() v = costs_ewg.unstack()[[str(year) for year in years]].swaplevel(axis=1) - def annuity(n, r=0.07): + def annuity(n,r=0.07): """ Calculate the annuity factor for an asset with lifetime n years and discount rate of r """ if isinstance(r, pd.Series): - return pd.Series(1 / n, index=r.index).where( - r == 0, r / (1.0 - 1.0 / (1.0 + r) ** n) - ) + return pd.Series(1/n, index=r.index).where(r == 0, r/(1. - 1./(1.+r)**n)) elif r > 0: - return r / (1.0 - 1.0 / (1.0 + r) ** n) + return r/(1. - 1./(1.+r)**n) else: - return 1 / n + return 1/n # annualise EWG cost assumptions - fixed = (annuity(v["lifetime"]) + v["FOM"] / 100.0) * v["investment"] + fixed = (annuity(v["lifetime"])+v["FOM"]/100.) * v["investment"] # battery storage index in EWG -------------- battery_store_i = [ - "Battery PV prosumer - commercial storage", - "Battery PV prosumer - industrial storage", - "Battery PV prosumer - residential storage", - "Battery storage", - ] + 'Battery PV prosumer - commercial storage', + 'Battery PV prosumer - industrial storage', + 'Battery PV prosumer - residential storage', + 'Battery storage'] battery_store_ewg = fixed.loc[battery_store_i].T def get_factor(df, cols, utility_col): - """Get factor by which costs are increasing for home installations""" - return ( - df[cols] - .div(df[utility_col], axis=0) - .mean(axis=1) - .rename(index=lambda x: float(x)) - ) + """get factor by which costs are increasing for home installations""" + return (df[cols].div(df[utility_col], axis=0).mean(axis=1) + .rename(index=lambda x: float(x))) # take mean of cost increase for commercial and residential storage compared to utility-scale - home_cols = [ - "Battery PV prosumer - commercial storage", - "Battery PV prosumer - residential storage", - ] + home_cols = ['Battery PV prosumer - commercial storage', + 'Battery PV prosumer - residential storage'] factor = get_factor(battery_store_ewg, home_cols, "Battery storage") - home_cost = ( - home_battery.loc[("home battery storage", "investment"), years] * factor - ).values + home_cost = (home_battery.loc[("home battery storage", "investment"), years] * factor).values home_battery.loc[("home battery storage", "investment"), years] = home_cost # battery inverter index in EWG ----------------------- battery_inverter_i = [ - "Battery PV prosumer - commercial interface", - "Battery PV prosumer - industrial interface PHES", - "Battery PV prosumer - residential interface", - "Battery interface", - ] + 'Battery PV prosumer - commercial interface', + 'Battery PV prosumer - industrial interface PHES', + 'Battery PV prosumer - residential interface', + 'Battery interface'] battery_inverter_ewg = fixed.loc[battery_inverter_i].T - home_cols = [ - "Battery PV prosumer - commercial interface", - "Battery PV prosumer - residential interface", - ] + home_cols = ['Battery PV prosumer - commercial interface', + 'Battery PV prosumer - residential interface'] factor = get_factor(battery_inverter_ewg, home_cols, "Battery interface") - home_cost = ( - home_battery.loc[("home battery inverter", "investment"), years] * factor - ).values + home_cost = (home_battery.loc[("home battery inverter", "investment"), years] * factor).values home_battery.loc[("home battery inverter", "investment"), years] = home_cost # adjust source - home_battery["source"] = home_battery["source"].apply( - lambda x: source_dict["EWG"] + ", " + x - ) + home_battery["source"] = home_battery["source"].apply(lambda x: source_dict["EWG"] + ", " + x) return pd.concat([costs, home_battery]) def add_SMR_data(data): - """ - Add steam methane reforming (SMR) technology data. + """Add steam methane reforming (SMR) technology data. investment cost : Currently no cost reduction for investment costs of SMR CC assumed. @@ -2912,14 +2688,12 @@ def add_SMR_data(data): """ parameters = ["FOM", "investment", "lifetime", "efficiency"] techs = ["SMR", "SMR CC"] - multi_i = pd.MultiIndex.from_product( - [techs, parameters], names=["technology", "parameter"] - ) + multi_i = pd.MultiIndex.from_product([techs, parameters], names=["technology", "parameter"]) SMR_df = pd.DataFrame(index=multi_i, columns=data.columns) # efficiencies per unit in LHV (stays constant 2019 to 2050) - SMR_df.loc[("SMR", "efficiency"), years] = 0.76 - SMR_df.loc[("SMR CC", "efficiency"), years] = 0.69 + SMR_df.loc[("SMR", "efficiency"), years] = 0.76 + SMR_df.loc[("SMR CC", "efficiency"), years] = 0.69 SMR_df.loc[(techs, "efficiency"), "source"] = source_dict["IEA"] SMR_df.loc[(techs, "efficiency"), "unit"] = "per unit (in LHV)" @@ -2932,72 +2706,53 @@ def add_SMR_data(data): SMR_df.loc[(techs, "FOM"), years] = 5 SMR_df.loc[(techs, "FOM"), "source"] = source_dict["DEA"] SMR_df.loc[(techs, "FOM"), "unit"] = "%/year" - SMR_df.loc[(techs, "FOM"), "currency_year"] = 2015 - SMR_df.loc[(techs, "FOM"), "further description"] = ( - "Technology data for renewable fuels, in pdf on table 3 p.311" - ) + SMR_df.loc[(techs, "FOM"), "further description"] = "Technology data for renewable fuels, in pdf on table 3 p.311" # investment # investment given in unit EUR/kg H_2/h -> convert to EUR/MW_CH4 # lower heating value (LHV) of H2 - LHV_H2 = 33.33 # unit kWh/kg - SMR = 12500 / LHV_H2 * 1e3 * 1 / SMR_df.loc[("SMR", "efficiency"), years] - SMR_CCS = 14500 / LHV_H2 * 1e3 * 1 / SMR_df.loc[("SMR", "efficiency"), years] + LHV_H2 = 33.33 # unit kWh/kg + SMR = 12500 / LHV_H2 * 1e3 * 1/SMR_df.loc[("SMR", "efficiency"), years] + SMR_CCS = 14500 / LHV_H2 * 1e3 * 1/SMR_df.loc[("SMR", "efficiency"), years] SMR_df.loc[("SMR", "investment"), years] = SMR SMR_df.loc[("SMR CC", "investment"), years] = SMR_CCS SMR_df.loc[(techs, "investment"), "source"] = source_dict["DEA"] SMR_df.loc[(techs, "investment"), "unit"] = "EUR/MW_CH4" SMR_df.loc[(techs, "investment"), "currency_year"] = 2015 - SMR_df.loc[(techs, "investment"), "further description"] = ( - "Technology data for renewable fuels, in pdf on table 3 p.311" - ) + SMR_df.loc[(techs, "investment"), "further description"] = "Technology data for renewable fuels, in pdf on table 3 p.311" # carbon capture rate SMR_df.loc[("SMR CC", "capture_rate"), years] = 0.9 SMR_df.loc[("SMR CC", "capture_rate"), "source"] = source_dict["IEA"] - SMR_df.loc[("SMR CC", "capture_rate"), "unit"] = "per unit" - SMR_df.loc[("SMR CC", "capture_rate"), "further description"] = ( - "wide range: capture rates between 54%-90%" - ) - - SMR_df = SMR_df.dropna(axis=1, how="all") - + SMR_df.loc[("SMR CC", "capture_rate"), "unit"] = "EUR/MW_CH4" + SMR_df.loc[("SMR CC", "capture_rate"), "further description"] = "wide range: capture rates betwen 54%-90%" + + SMR_df = SMR_df.dropna(axis=1, how='all') + return pd.concat([data, SMR_df]) def add_mean_solar_rooftop(data): # take mean of rooftop commercial and residential - rooftop = ( - data.loc[data.index.get_level_values(0).str.contains("solar-rooftop")][years] - .astype(float) - .groupby(level=1) - .mean() - ) + rooftop = (data.loc[data.index.get_level_values(0) + .str.contains("solar-rooftop")][years] + .astype(float).groupby(level=1).mean()) for col in data.columns[~data.columns.isin(years)]: rooftop[col] = data.loc["solar-rooftop residential"][col] # set multi index rooftop = pd.concat([rooftop], keys=["solar-rooftop"]) rooftop["source"] = "Calculated. See 'further description'." - rooftop["further description"] = ( - "Mixed investment costs based on average of 50% 'solar-rooftop commercial' and 50% 'solar-rooftop residential'" - ) + rooftop["further description"] = "Mixed investment costs based on average of 50% 'solar-rooftop commercial' and 50% 'solar-rooftop residential'" # add to data rooftop.index.names = data.index.names data = pd.concat([data, rooftop]) # add solar assuming 50% utility and 50% rooftop - solar = ( - (data.loc[["solar-rooftop", "solar-utility"]][years]) - .astype(float) - .groupby(level=1) - .mean() - ) + solar = (data.loc[["solar-rooftop", "solar-utility"]][years]).astype(float).groupby(level=1).mean() for col in data.columns[~data.columns.isin(years)]: - solar[col] = data.loc["solar-rooftop residential"][col] + solar[col] = data.loc["solar-rooftop residential"][col] solar["source"] = "Calculated. See 'further description'." - solar["further description"] = ( - "Mixed investment costs based on average of 50% 'solar-rooftop' and 50% 'solar-utility'" - ) + solar["further description"] = "Mixed investment costs based on average of 50% 'solar-rooftop' and 50% 'solar-utility'" # set multi index solar = pd.concat([solar], keys=["solar"]) solar.index.names = data.index.names @@ -3005,9 +2760,8 @@ def add_mean_solar_rooftop(data): def add_energy_storage_database(costs, data_year): - """ - Add energy storage database compiled - + """Add energy storage database compiled + Learning rate drop. For example, the nominal DC SB learning rate for RFBs is set at 4.5%, 1.5% for lead-acid batteries, compared to 10% for Li-ion batteries, corresponding to cost drops of 17%, 6%, and 35%, respectively. For the rest of the categories for battery-based systems, the learning @@ -3039,117 +2793,79 @@ def add_energy_storage_database(costs, data_year): "reference": str, "ref_size_MW": float, "EP_ratio_h": float, - }, + }, ) df = df.drop(columns=["ref_size_MW", "EP_ratio_h"]) df = df.fillna(df.dtypes.replace({"float64": 0.0, "O": "NULL"})) - df.loc[:, "unit"] = df.unit.str.replace("NULL", "per unit") + df.loc[:,"unit"] = df.unit.str.replace("NULL", "per unit") - # b) Change data to PyPSA format (aggregation of components, units, currency, etc.) + # b) Change data to PyPSA format (aggregation of components, units, currency, etc.) df = clean_up_units(df, "value") # base clean up # rewrite technology to be charger, store, discharger, bidirectional-charger - df.loc[:, "carrier"] = df.carrier.str.replace("NULL", "") - df.loc[:, "carrier"] = df["carrier"].apply(lambda x: x.split("-")) + df.loc[:,"carrier"] = df.carrier.str.replace("NULL", "") + df.loc[:,"carrier"] = df["carrier"].apply(lambda x: x.split('-')) carrier_list_len = df["carrier"].apply(lambda x: len(x)) carrier_str_len = df["carrier"].apply(lambda x: len(x[0])) - carrier_first_item = df["carrier"].apply(lambda x: x[0]) - carrier_last_item = df["carrier"].apply(lambda x: x[-1]) - bicharger_filter = carrier_list_len == 3 + carrier_first_item = df["carrier"].apply(lambda x: x[0]) + carrier_last_item = df["carrier"].apply(lambda x: x[-1]) + bicharger_filter = (carrier_list_len == 3) charger_filter = (carrier_list_len == 2) & (carrier_first_item == "elec") discharger_filter = (carrier_list_len == 2) & (carrier_last_item == "elec") store_filter = (carrier_list_len == 1) & (carrier_str_len > 0) - reference_filter = (carrier_list_len == 1) & ( - carrier_first_item == "reference_value" - ) + reference_filter = (carrier_list_len == 1) & (carrier_first_item == "reference_value") df = df[~reference_filter] # remove reference values - df.loc[bicharger_filter, "technology_type"] = "bicharger" - df.loc[charger_filter, "technology_type"] = "charger" - df.loc[discharger_filter, "technology_type"] = "discharger" - df.loc[store_filter, "technology_type"] = "store" - df.loc[df.unit == "EUR/MWh-year", "technology_type"] = "store" - # Some investment inputs need to be distributed between charger and discharger + df.loc[bicharger_filter,"technology_type"] = "bicharger" + df.loc[charger_filter,"technology_type"] = "charger" + df.loc[discharger_filter,"technology_type"] = "discharger" + df.loc[store_filter,"technology_type"] = "store" + df.loc[df.unit=="EUR/MWh-year", "technology_type"] = "store" + # Some investment inputs need to be distributed between charger and discharger for tech in df.technology.unique(): - nan_filter = ( - (df.technology == tech) - & (carrier_str_len == 0) - & (df.parameter == "investment") - ) - store_filter = nan_filter & (df.unit == "EUR/MWh") + nan_filter = (df.technology==tech) & (carrier_str_len==0) & (df.parameter=="investment") + store_filter = nan_filter & (df.unit=="EUR/MWh") if not df.loc[store_filter].empty: - df.loc[store_filter, "technology_type"] = ( - "store" # value will be aggregated later in the groupby - ) + df.loc[store_filter, "technology_type"] = "store" # value will be aggregated later in the groupby # charger and discharger with 50% distribution e.g. in case of Hydrogen - power_filter = nan_filter & (df.unit == "EUR/MW") + power_filter = nan_filter & (df.unit=="EUR/MW") if not df.loc[power_filter].empty: - agg = ( - df.loc[power_filter] - .groupby(["technology", "year"]) - .sum(numeric_only=True) - ) - charger_investment_filter = ( - charger_filter - & (df.technology == tech) - & (df.parameter == "investment") - ) - discharger_investment_filter = ( - discharger_filter - & (df.technology == tech) - & (df.parameter == "investment") - ) - df.loc[charger_investment_filter & df.year == 2021, "value"] += ( - agg.loc[(tech, 2021)] / 2 - ) - df.loc[charger_investment_filter & df.year == 2030, "value"] += ( - agg.loc[(tech, 2030)] / 2 - ) - df.loc[discharger_investment_filter & df.year == 2021, "value"] += ( - agg.loc[(tech, 2021)] / 2 - ) - df.loc[discharger_investment_filter & df.year == 2030, "value"] += ( - agg.loc[(tech, 2030)] / 2 - ) - df.loc[:, "technology"] = df["technology"] + "-" + df["technology_type"] + agg = df.loc[power_filter].groupby(["technology", "year"]).sum(numeric_only=True) + charger_investment_filter = charger_filter & (df.technology==tech) & (df.parameter=="investment") + discharger_investment_filter = discharger_filter & (df.technology==tech) & (df.parameter=="investment") + df.loc[charger_investment_filter & df.year==2021, "value"] += agg.loc[(tech, 2021)]/2 + df.loc[charger_investment_filter & df.year==2030, "value"] += agg.loc[(tech, 2030)]/2 + df.loc[discharger_investment_filter & df.year==2021, "value"] += agg.loc[(tech, 2021)]/2 + df.loc[discharger_investment_filter & df.year==2030, "value"] += agg.loc[(tech, 2030)]/2 + df.loc[:,"technology"] = df["technology"] + "-" + df["technology_type"] # aggregate technology_type and unit - df = ( - df.groupby(["technology", "unit", "year"]) - .agg( - { - "technology": "first", - "year": "first", - "parameter": "first", - "value": "sum", - "unit": "first", - "type": "first", - "carrier": "first", - "technology_type": "first", - "source": "first", - "note": "first", - "reference": "first", - } - ) - .reset_index(drop=True) - ) + df = df.groupby(["technology", "unit", "year"]).agg({ + 'technology': 'first', + 'year': 'first', + 'parameter': 'first', + 'value': 'sum', + 'unit': 'first', + 'type': 'first', + 'carrier': 'first', + 'technology_type': 'first', + 'source': 'first', + 'note': 'first', + 'reference': 'first', + }).reset_index(drop=True) # calculate %/year FOM on aggregated values for tech in df.technology.unique(): for year in df.year.unique(): df_tech = df.loc[(df.technology == tech) & (df.year == year)].copy() - a = df_tech.loc[df_tech.unit == "EUR/MW-year", "value"].values - b = df_tech.loc[df_tech.unit == "EUR/MW", "value"].values - df.loc[df_tech.loc[df_tech.unit == "EUR/MW-year"].index, "value"] = ( - a / b * 100 - ) # EUR/MW-year / EUR/MW = %/year - c = df_tech.loc[df_tech.unit == "EUR/MWh-year", "value"].values - d = df_tech.loc[df_tech.unit == "EUR/MWh", "value"].values - df.loc[df_tech.loc[df_tech.unit == "EUR/MWh-year"].index, "value"] = ( - c / d * 100 - ) # EUR/MWh-year / EUR/MWh = %/year - - df.loc[:, "unit"] = df.unit.str.replace("EUR/MW-year", "%/year") - df.loc[:, "unit"] = df.unit.str.replace("EUR/MWh-year", "%/year") + a = df_tech.loc[df_tech.unit=="EUR/MW-year", "value"].values + b = df_tech.loc[df_tech.unit=="EUR/MW", "value"].values + df.loc[df_tech.loc[df_tech.unit=="EUR/MW-year"].index, "value"] = a / b * 100 # EUR/MW-year / EUR/MW = %/year + c = df_tech.loc[df_tech.unit=="EUR/MWh-year", "value"].values + d = df_tech.loc[df_tech.unit=="EUR/MWh", "value"].values + df.loc[df_tech.loc[df_tech.unit=="EUR/MWh-year"].index, "value"] = c / d * 100 # EUR/MWh-year / EUR/MWh = %/year + + df.loc[:,"unit"] = df.unit.str.replace("EUR/MW-year", "%/year") + df.loc[:,"unit"] = df.unit.str.replace("EUR/MWh-year", "%/year") # c) Linear Inter/Extrapolation # data available for 2021 and 2030, but value for given "year" passed by function needs to be calculated @@ -3159,27 +2875,25 @@ def add_energy_storage_database(costs, data_year): y = df.loc[filter, "value"] if y.empty: continue # nothing to interpolate - elif y.iloc[0] == y.iloc[1] or param == "efficiency" or param == "lifetime": + elif y.iloc[0]==y.iloc[1] or param=="efficiency" or param=="lifetime": ynew = y.iloc[1] # assume new value is the same as 2030 - elif y.iloc[0] != y.iloc[1]: - x = df.loc[filter, "year"] # both values 2021+2030 - first_segment_diff = y.iloc[0] - y.iloc[1] + elif y.iloc[0]!=y.iloc[1]: + x = df.loc[filter, "year"] # both values 2021+2030 + first_segment_diff = y.iloc[0]-y.iloc[1] endp_first_segment = y.iloc[1] - + # Below we create linear segments between 2021-2030 - # While the first segment is known, the others are defined by the initial segments with a accumulating quadratic decreasing gradient + # While the first segment is known, the others are defined by the initial segments with a accumulating quadratic descreasing gradient other_segments_points = [2034, 2039, 2044, 2049, 2054, 2059] - - def geometric_series( - nominator, denominator=1, number_of_terms=1, start=1 - ): + + def geometric_series(nominator, denominator=1, number_of_terms=1, start=1): """ A geometric series is a series with a constant ratio between successive terms. When moving to infinity the geometric series converges to a limit. https://en.wikipedia.org/wiki/Series_(mathematics) Example: - ------- + -------- nominator = 1 denominator = 2 number_of_terms = 3 @@ -3188,85 +2902,37 @@ def geometric_series( If moving to infinity the result converges to 2 """ - return sum( - [ - nominator / denominator**i - for i in range(start, start + number_of_terms) - ] - ) - - if tech == "Hydrogen-discharger" or tech == "Pumped-Heat-store": - x1 = pd.concat( - [x, pd.DataFrame(other_segments_points)], ignore_index=True - ) + return sum([nominator/denominator**i for i in range(start, start+number_of_terms)]) + + if tech=="Hydrogen-discharger" or tech=="Pumped-Heat-store": + x1 = pd.concat([x,pd.DataFrame(other_segments_points)], ignore_index=True) y1 = y factor = 5 - for i in range( - len(other_segments_points) - ): # -1 because of segments - cost_at_year = endp_first_segment - geometric_series( - nominator=first_segment_diff, - denominator=factor, - number_of_terms=i + 1, - ) - y1 = pd.concat( - [y1, pd.DataFrame([cost_at_year])], ignore_index=True - ) - f = interpolate.interp1d( - x1.squeeze(), - y1.squeeze(), - kind="linear", - fill_value="extrapolate", - ) - elif tech == "Hydrogen-charger": - x2 = pd.concat( - [x, pd.DataFrame(other_segments_points)], ignore_index=True - ) + for i in range(len(other_segments_points)): # -1 because of segments + cost_at_year = endp_first_segment - geometric_series(nominator=first_segment_diff, denominator=factor, number_of_terms=i+1) + y1 = pd.concat([y1, pd.DataFrame([cost_at_year])], ignore_index=True) + f = interpolate.interp1d(x1.squeeze(), y1.squeeze(), kind='linear', fill_value="extrapolate") + elif tech=="Hydrogen-charger": + x2 = pd.concat([x,pd.DataFrame(other_segments_points)], ignore_index=True) y2 = y factor = 6.5 for i in range(len(other_segments_points)): - cost_at_year = endp_first_segment - geometric_series( - nominator=first_segment_diff, - denominator=factor, - number_of_terms=i + 1, - ) - y2 = pd.concat( - [y2, pd.DataFrame([cost_at_year])], ignore_index=True - ) - f = interpolate.interp1d( - x2.squeeze(), - y2.squeeze(), - kind="linear", - fill_value="extrapolate", - ) + cost_at_year = endp_first_segment - geometric_series(nominator=first_segment_diff, denominator=factor, number_of_terms=i+1) + y2 = pd.concat([y2, pd.DataFrame([cost_at_year])], ignore_index=True) + f = interpolate.interp1d(x2.squeeze(), y2.squeeze(), kind='linear', fill_value="extrapolate") else: - x3 = pd.concat( - [x, pd.DataFrame(other_segments_points)], ignore_index=True - ) + x3 = pd.concat([x,pd.DataFrame(other_segments_points)], ignore_index=True) y3 = y factor = 2 for i in range(len(other_segments_points)): - cost_at_year = endp_first_segment - geometric_series( - nominator=first_segment_diff, - denominator=factor, - number_of_terms=i + 1, - ) - y3 = pd.concat( - [y3, pd.DataFrame([cost_at_year])], ignore_index=True - ) - f = interpolate.interp1d( - x3.squeeze(), - y3.squeeze(), - kind="linear", - fill_value="extrapolate", - ) - - option = snakemake.config["energy_storage_database"][ - "pnnl_energy_storage" - ] - if option.get("approx_beyond_2030") == ["geometric_series"]: + cost_at_year = endp_first_segment - geometric_series(nominator=first_segment_diff, denominator=factor, number_of_terms=i+1) + y3 = pd.concat([y3, pd.DataFrame([cost_at_year])], ignore_index=True) + f = interpolate.interp1d(x3.squeeze(), y3.squeeze(), kind='linear', fill_value="extrapolate") + + option = snakemake.config['energy_storage_database']['pnnl_energy_storage'] + if option.get('approx_beyond_2030') == ["geometric_series"]: ynew = f(data_year) - if option.get("approx_beyond_2030") == ["same_as_2030"]: + if option.get('approx_beyond_2030') == ["same_as_2030"]: if data_year <= 2030: # apply linear interpolation ynew = f(data_year) @@ -3274,93 +2940,84 @@ def geometric_series( # apply same value as 2030 ynew = y.iloc[1] # assume new value is the same as 2030 - df_new = pd.DataFrame( - [ - { - "technology": tech, - "year": data_year, - "parameter": param, - "value": ynew, - "unit": df.loc[filter, "unit"].unique().item(), - "source": df.loc[filter, "source"].unique().item(), - "carrier": df.loc[filter, "carrier"].iloc[1], - "technology_type": df.loc[filter, "technology_type"] - .unique() - .item(), - "type": df.loc[filter, "type"].unique().item(), - "note": df.loc[filter, "note"].iloc[1], - "reference": df.loc[filter, "reference"].iloc[1], - } - ] - ) - # not concat if df year is 2021 or 2030 (otherwise duplicate) + df_new = pd.DataFrame([{ + "technology": tech, + "year": data_year, + "parameter": param, + "value": ynew, + "unit": df.loc[filter, "unit"].unique().item(), + "source": df.loc[filter, "source"].unique().item(), + 'carrier': df.loc[filter, "carrier"].iloc[1], + 'technology_type': df.loc[filter, "technology_type"].unique().item(), + 'type': df.loc[filter, "type"].unique().item(), + 'note': df.loc[filter, "note"].iloc[1], + 'reference': df.loc[filter, "reference"].iloc[1], + }]) + # not concat if df year is 2021 or 2030 (otherwhise duplicate) if data_year == 2021 or data_year == 2030: continue else: df = pd.concat([df, df_new], ignore_index=True) # d) Combine metadata and add to cost database - df.loc[:, "source"] = df["source"] + ", " + df["reference"] + df.loc[:,"source"] = df["source"] + ", " + df["reference"] for i in df.index: - df.loc[i, "further description"] = str( + df.loc[i,"further description"] = str( { - "carrier": df.loc[i, "carrier"], - "technology_type": [df.loc[i, "technology_type"]], - "type": [df.loc[i, "type"]], - "note": [df.loc[i, "note"]], + "carrier": df.loc[i,"carrier"], + "technology_type": [df.loc[i,"technology_type"]], + "type": [df.loc[i,"type"]], + "note": [df.loc[i,"note"]], } ) # keep only relevant columns - df = df.loc[ - df.year == data_year, - ["technology", "parameter", "value", "unit", "source", "further description"], - ] + df = df.loc[df.year == data_year,["technology", "parameter", "value", "unit", "source", "further description"]] tech = df.technology.unique() - df = df.set_index(["technology", "parameter"]) + df = df.set_index(['technology', 'parameter']) return pd.concat([costs, df]), tech def prepare_inflation_rate(fn): - """ - Read in annual inflation rate from Eurostat + """read in annual inflation rate from Eurostat https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/dataflow/ESTAT/prc_hicp_aind/1.0?references=descendants&detail=referencepartial&format=sdmx_2.1_generic&compressed=true """ - inflation_rate = pd.read_excel(fn, sheet_name="Sheet 1", index_col=0, header=[8]) - inflation_rate = ( - inflation_rate.loc["European Union - 27 countries (from 2020)"].dropna() - ).loc["2001"::] + inflation_rate = pd.read_excel(fn, + sheet_name="Sheet 1", index_col=0, + header=[8]) + inflation_rate = (inflation_rate.loc["European Union - 27 countries (from 2020)"] + .dropna()).loc["2001"::] inflation_rate.rename(index=lambda x: int(x), inplace=True) inflation_rate = inflation_rate.astype(float) - + inflation_rate /= 100 - + return inflation_rate - - + # %% ************************************************************************* # ---------- MAIN ------------------------------------------------------------ if __name__ == "__main__": - if "snakemake" not in globals(): - from _helpers import mock_snakemake - - # os.chdir(os.path.join(os.getcwd(), "scripts")) + if 'snakemake' not in globals(): + import os + from scripts._helpers import mock_snakemake + #os.chdir(os.path.join(os.getcwd(), "scripts")) snakemake = mock_snakemake("compile_cost_assumptions") - years = snakemake.config["years"] + years = snakemake.config['years'] inflation_rate = prepare_inflation_rate(snakemake.input.inflation_rate) - + # p.77 Figure 51 share of vehicle-km driven by truck # (1) DEA data # (a)-------- get data from DEA excel sheets ---------------------------------- # read excel sheet names of all excel files - excel_files = [v for k, v in snakemake.input.items() if "dea" in k] + excel_files = [v for k,v in snakemake.input.items() if "dea" in k] data_in = get_excel_sheets(excel_files) # create dictionary with raw data from DEA sheets d_by_tech = get_data_from_DEA(data_in, expectation=snakemake.config["expectation"]) # concat into pd.Dataframe tech_data = pd.concat(d_by_tech).sort_index() + # clean up units tech_data = clean_up_units(tech_data, years, source="dea") @@ -3374,12 +3031,13 @@ def prepare_inflation_rate(fn): tech_data = set_round_trip_efficiency(tech_data) # drop all rows which only contains zeros - tech_data = tech_data.loc[(tech_data[years] != 0).sum(axis=1) != 0] + tech_data = tech_data.loc[(tech_data[years]!=0).sum(axis=1)!=0] # (c) ----- get tech data in pypsa syntax ----------------------------------- # make categories: investment, FOM, VOM, efficiency, c_b, c_v data = order_data(tech_data) - # add Excel sheet names and further description + + # add excel sheet names and further description data = add_description(data) # convert efficiency from %-> per unit and investment from MW->kW to compare data = convert_units(data) @@ -3387,6 +3045,9 @@ def prepare_inflation_rate(fn): data = add_gas_storage(data) # add carbon capture data = add_carbon_capture(data, tech_data) + # add perennials and green biorefining + data = add_perennials_gbr(data) + # adjust for inflation for x in data.index.get_level_values("technology"): @@ -3396,45 +3057,41 @@ def prepare_inflation_rate(fn): data.at[x, "currency_year"] = 2019 else: data.at[x, "currency_year"] = 2015 - - # add heavy-duty assumptions, cost year is 2022 - data = get_dea_vehicle_data(snakemake.input.dea_vehicles, data) + + # add heavy duty assumptions, cost year is 2022 + data = get_dea_vehicle_data(snakemake.input.dea_vehicles, data) # add shipping data data = get_dea_maritime_data(snakemake.input.dea_ship, data) # %% (2) -- get data from other sources which need formatting ----------------- # (a) ---------- get old pypsa costs --------------------------------------- - costs_pypsa = pd.read_csv( - snakemake.input.pypsa_costs, index_col=[0, 2] - ).sort_index() + costs_pypsa = pd.read_csv(snakemake.input.pypsa_costs, + index_col=[0,2]).sort_index() # rename some techs and convert units costs_pypsa = rename_pypsa_old(costs_pypsa) # (b1) ------- add vehicle costs from Fraunhofer vehicle study ------------------------ - costs_vehicles = pd.read_csv( - snakemake.input.fraunhofer_vehicles_costs, - engine="python", - index_col=[0, 1], - encoding="ISO-8859-1", - ) + costs_vehicles = pd.read_csv(snakemake.input.fraunhofer_vehicles_costs, + engine="python", + index_col=[0,1], + encoding="ISO-8859-1") # rename + reorder to fit to other data costs_vehicles = rename_ISE_vehicles(costs_vehicles) - if "NT" in costs_vehicles.index: - costs_vehicles.drop(["NT"], axis=0, inplace=True, level=0) + if 'NT' in costs_vehicles.index: + costs_vehicles.drop(['NT'], axis=0, inplace=True, level=0) costs_vehicles = convert_units(costs_vehicles) # add costs for vehicles data = pd.concat([data, costs_vehicles], sort=True) + # (b) ------- add costs from Fraunhofer ISE study -------------------------- - costs_ISE = pd.read_csv( - snakemake.input.fraunhofer_costs, - engine="python", - index_col=[0, 1], - encoding="ISO-8859-1", - ) + costs_ISE = pd.read_csv(snakemake.input.fraunhofer_costs, + engine="python", + index_col=[0,1], + encoding = "ISO-8859-1") # rename + reorder to fit to other data - costs_ISE = rename_ISE(costs_ISE) + costs_ISE = rename_ISE(costs_ISE) # add costs for gas pipelines data = pd.concat([data, costs_ISE.loc[["Gasnetz"]]], sort=True) @@ -3448,45 +3105,35 @@ def prepare_inflation_rate(fn): # add solar rooftop costs by taking the mean of commercial and residential data = add_mean_solar_rooftop(data) + data.index.names = ["technology", "parameter"] # %% (3) ------ add additional sources and save cost as csv ------------------ # [RTD-target-multiindex-df] for year in years: - costs = data[ - [year, "unit", "source", "further description", "currency_year"] - ].rename(columns={year: "value"}) + costs = (data[[year, "unit", "source", "further description", + "currency_year"]] + .rename(columns={year: "value"})) costs["value"] = costs["value"].astype(float) # biomass is differentiated by biomass CHP and HOP - costs.loc[("solid biomass", "fuel"), "value"] = 12 - costs.loc[("solid biomass", "fuel"), "unit"] = "EUR/MWh_th" - costs.loc[("solid biomass", "fuel"), "source"] = ( - "JRC ENSPRESO ca avg for MINBIOWOOW1 (secondary forest residue wood chips), ENS_Ref for 2040" - ) - costs.loc[("solid biomass", "fuel"), "currency_year"] = 2010 - - costs.loc[("digestible biomass", "fuel"), "value"] = 15 - costs.loc[("digestible biomass", "fuel"), "unit"] = "EUR/MWh_th" - costs.loc[("digestible biomass", "fuel"), "source"] = ( - "JRC ENSPRESO ca avg for MINBIOAGRW1, ENS_Ref for 2040" - ) - costs.loc[("digestible biomass", "fuel"), "currency_year"] = 2010 - + costs.loc[('solid biomass', 'fuel'), 'value'] = 12 + costs.loc[('solid biomass', 'fuel'), 'unit'] = 'EUR/MWh_th' + costs.loc[('solid biomass', 'fuel'), 'source'] = "JRC ENSPRESO ca avg for MINBIOWOOW1 (secondary forest residue wood chips), ENS_Ref for 2040" + costs.loc[('solid biomass', 'fuel'), 'currency_year'] = 2010 + + costs.loc[('digestible biomass', 'fuel'), 'value'] = 15 + costs.loc[('digestible biomass', 'fuel'), 'unit'] = 'EUR/MWh_th' + costs.loc[('digestible biomass', 'fuel'), 'source'] = "JRC ENSPRESO ca avg for MINBIOAGRW1, ENS_Ref for 2040" + costs.loc[('digestible biomass', 'fuel'), 'currency_year'] = 2010 + # add solar data from other source than DEA - if any( - [ - snakemake.config["solar_utility_from_vartiaien"], - snakemake.config["solar_rooftop_from_etip"], - ] - ): + if any([snakemake.config['solar_utility_from_vartiaien'], snakemake.config['solar_rooftop_from_etip']]): costs = add_solar_from_other(costs) # add desalination and clean water tank storage costs = add_desalinsation_data(costs) # add energy storage database - if snakemake.config["energy_storage_database"]["pnnl_energy_storage"].get( - "add_data", True - ): + if snakemake.config['energy_storage_database']['pnnl_energy_storage'].get("add_data", True): costs, tech = add_energy_storage_database(costs, year) costs.loc[tech, "currency_year"] = 2020 @@ -3498,7 +3145,7 @@ def prepare_inflation_rate(fn): costs = add_co2_intensity(costs) # carbon balances - costs = carbon_flow(costs, year) + costs = carbon_flow(costs,year) # energy penalty of carbon capture costs = energy_penalty(costs) @@ -3508,7 +3155,7 @@ def prepare_inflation_rate(fn): # missing technologies missing = costs_pypsa.index.levels[0].difference(costs.index.levels[0]) - if len(missing) & (year == years[0]): + if (len(missing) & (year==years[0])): print("************************************************************") print("warning, in new cost assumptions the following components: ") for i in range(len(missing)): @@ -3517,27 +3164,26 @@ def prepare_inflation_rate(fn): print("************************************************************") to_add = costs_pypsa.loc[missing].drop("year", axis=1) - to_add.loc[:, "further description"] = " from old pypsa cost assumptions" + to_add.loc[:,"further description"] = " from old pypsa cost assumptions" # TODO check currency year from old pypsa cost assumptions to_add["currency_year"] = 2015 costs_tot = pd.concat([costs, to_add], sort=False) # single components missing comp_missing = costs_pypsa.index.difference(costs_tot.index) - if year == years[0]: - print( - "single parameters of technologies are missing, using old PyPSA assumptions: " - ) + if (year==years[0]): + print("single parameters of technologies are missing, using old PyPSA assumptions: ") print(comp_missing) print("old c_v and c_b values are assumed where given") to_add = costs_pypsa.loc[comp_missing].drop("year", axis=1) to_add.loc[:, "further description"] = " from old pypsa cost assumptions" # more data on geothermal is added downstream, so old assumptions are redundant - to_add = to_add.drop("geothermal") + to_add = to_add.drop("geothermal") # TODO check currency year from old pypsa cost assumptions to_add["currency_year"] = 2015 costs_tot = pd.concat([costs_tot, to_add], sort=False) + # unify the cost from DIW2010 costs_tot = unify_diw(costs_tot) costs_tot.drop("fixed", level=1, inplace=True) @@ -3545,13 +3191,11 @@ def prepare_inflation_rate(fn): # adjust for inflation techs = costs_tot.index.get_level_values(0).unique() costs_tot["currency_year"] = costs_tot.currency_year.astype(float) - costs_tot = adjust_for_inflation( - inflation_rate, costs_tot, techs, costs_tot.currency_year, ["value"] - ) + costs_tot = adjust_for_inflation(inflation_rate, costs_tot, techs, + costs_tot.currency_year, ["value"]) # format and sort costs_tot.sort_index(inplace=True) - costs_tot.loc[:, "value"] = round( - costs_tot.value.astype(float), snakemake.config.get("ndigits", 2) - ) - costs_tot.to_csv([v for v in snakemake.output if str(year) in v][0]) + costs_tot.loc[:,'value'] = round(costs_tot.value.astype(float), + snakemake.config.get("ndigits", 2)) + costs_tot.to_csv([v for v in snakemake.output if str(year) in v][0]) \ No newline at end of file