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‎docs/release_notes.rst

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@@ -5,6 +5,12 @@ Release Notes
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Upcoming Release
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================
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* Updated to latest release of DEA renewable fuels (released January 2024). With the following changes
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* The following technologies have updated assumptions: ['BioSNG', 'BtL', 'Fischer-Tropsch', 'Haber-Bosch', 'air separation unit', 'biogas', 'biogas CC', 'biogas plus hydrogen', 'biogas upgrading', 'biomass-to-methanol', 'electrobiofuels', 'electrolysis', 'methanolisation']
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* biogas upgrading and biogas plant are differentiated in new data set between small and large plant, we assume small plant here
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* methanol from power changed to methanol from hydrogen, VOM are zero in new data set
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* CAREFUL: biogas upgrading units changed for VOM and investment from per input to per output units
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* Add floating wind cost data
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* Add biomass-to-methanol route from DEA.
@@ -23,7 +29,7 @@ Upcoming Release
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* Updated source for 'fuel' costs of 'gas', 'uranium', 'coal', and 'lignite' to DIW (2013) data.
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* Updated hydrogen pipeline costs based on most recent `EHB report <https://ehb.eu/files/downloads/EHB-2023-20-Nov-FINAL-design.pdf>`_.
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* Updated hydrogen pipeline costs based on most recent `EHB report <https://ehb.eu/files/downloads/EHB-2023-20-Nov-FINAL-design.pdf>`_.
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Technology-Data 0.6.2 (7 August 2023)
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=====================================

‎outputs/costs_2020.csv

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‎outputs/costs_2025.csv

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‎outputs/costs_2030.csv

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‎outputs/costs_2035.csv

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‎outputs/costs_2040.csv

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‎outputs/costs_2045.csv

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‎outputs/costs_2050.csv

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‎scripts/compile_cost_assumptions.py

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@@ -413,26 +413,28 @@ def get_data_DEA(tech, data_in, expectation=None):
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if tech == "air separation unit":
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# Calculate ASU cost separate to HB facility in terms of t N2 output
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# To add the cost of an ASU a multiplication factor of 1.06-1.09
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# should be applied to the total Specific Investment
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# Calculate ASU cost separate to HB facility in terms of t N2 output
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df.loc[[
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"Specific investment [MEUR /MW Ammonia output]",
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"Fixed O&M [kEUR/MW Ammonia/year]",
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"Variable O&M [EUR/MWh Ammonia]"
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]] *= df.loc["Specific investment mark-up factor optional ASU"]
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# / excel.loc["N2 Consumption, [t/t] Ammonia"]
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"Specific investment [MEUR /TPD Ammonia output]",
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"Fixed O&M [kEUR /TPD Ammonia]",
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"Variable O&M [EUR /t Ammonia]"
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]] *= (df.loc["Specific investment mark-up factor optional ASU"] - 1.) / excel.loc["N2 Consumption, [t/t] Ammonia"]
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# Convert output to hourly generation
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df.loc[[
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"Specific investment [MEUR /TPD Ammonia output]",
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"Fixed O&M [kEUR /TPD Ammonia]",
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]] *= 24
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# Rename costs for correct units
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# df.index = df.index.str.replace("MEUR /MW Ammonia output", "MEUR/MW_N2/h")
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# df.index = df.index.str.replace("kEUR/MW Ammonia/year", "kEUR/MW_N2/h/year")
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# df.index = df.index.str.replace("EUR/MWh Ammonia", "EUR/MWh_N2")
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df.index = df.index.str.replace("MEUR /TPD Ammonia output", "MEUR/t_N2/h")
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df.index = df.index.str.replace("kEUR /TPD Ammonia", "kEUR/t_N2/h/year")
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df.index = df.index.str.replace("EUR /t Ammonia", "EUR/t_N2")
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df.drop(df.loc[df.index.str.contains("Specific investment mark-up factor optional ASU")].index, inplace=True)
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# df.drop(df.loc[df.index.str.contains("Specific investment [MEUR /MW Ammonia output]", regex=False)].index, inplace=True)
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# df.drop(df.loc[df.index.str.contains("Fixed O&M [kEUR/MW Ammonia/year]", regex=False)].index, inplace=True)
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# df.drop(df.loc[df.index.str.contains("Variable O&M [EUR/MWh Ammonia]", regex=False)].index, inplace=True)
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df.drop(df.loc[df.index.str.contains("Specific investment [MEUR /MW Ammonia output]", regex=False)].index, inplace=True)
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df.drop(df.loc[df.index.str.contains("Fixed O&M [kEUR/MW Ammonia/year]", regex=False)].index, inplace=True)
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df.drop(df.loc[df.index.str.contains("Variable O&M [EUR/MWh Ammonia]", regex=False)].index, inplace=True)
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if "solid biomass power" in tech:
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df.index = df.index.str.replace("EUR/MWeh", "EUR/MWh")
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@@ -749,9 +751,11 @@ def clean_up_units(tech_data, value_column="", source=""):
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tech_data.unit = tech_data.unit.str.replace("FT Liquids Output, MWh/MWh Total Inpu", "MWh_FT/MWh_H2")
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# biomass-to-methanol-specific
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tech_data.unit = tech_data.unit.str.replace("Methanol Output, MWh/MWh Total Inpu", "MWh_MeOH/MWh_th")
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tech_data.unit = tech_data.unit.str.replace("District heat Output, MWh/MWh Total Inpu", "MWh_th/MWh_th")
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tech_data.unit = tech_data.unit.str.replace("Electricity Output, MWh/MWh Total Inpu", "MWh_e/MWh_th")
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if isinstance(tech_data.index, pd.MultiIndex):
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tech_data.loc[tech_data.index.get_level_values(1)=="Methanol Output,", "unit"] = "MWh_MeOH/MWh_th"
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tech_data.loc[tech_data.index.get_level_values(1)=='District heat Output,', "unit"] = "MWh_th/MWh_th"
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tech_data.loc[tech_data.index.get_level_values(1)=='Electricity Output,', "unit"] = "MWh_e/MWh_th"
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# Ammonia-specific
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tech_data.unit = tech_data.unit.str.replace("MW Ammonia output", "MW_NH3") #specific investment
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tech_data.unit = tech_data.unit.str.replace("MW Ammonia", "MW_NH3") #fom
@@ -1915,14 +1919,11 @@ def add_energy_storage_database(costs, data_year):
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agg = df.loc[power_filter].groupby(["technology", "year"]).sum(numeric_only=True)
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charger_investment_filter = charger_filter & (df.technology==tech) & (df.parameter=="investment")
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discharger_investment_filter = discharger_filter & (df.technology==tech) & (df.parameter=="investment")
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for a in [2021, 2030]:
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df_year = (df.year == a)
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df.loc[charger_investment_filter & df_year, "value"] += agg.loc[(tech, a)]/2
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df.loc[discharger_investment_filter & df_year, "value"] += agg.loc[(tech, a)]/2
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index = df.loc[df["technology_type"]!="nan"].index
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df.technology_type.replace("nan", np.nan, inplace=True)
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df.loc[index,"technology"] = df.loc[index, "technology"] + "-" + df.loc[index, "technology_type"]
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df.loc[charger_investment_filter & df.year==2021, "value"] += agg.loc[(tech, 2021)]/2
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df.loc[charger_investment_filter & df.year==2030, "value"] += agg.loc[(tech, 2030)]/2
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df.loc[discharger_investment_filter & df.year==2021, "value"] += agg.loc[(tech, 2021)]/2
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df.loc[discharger_investment_filter & df.year==2030, "value"] += agg.loc[(tech, 2030)]/2
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df.loc[:,"technology"] = df["technology"] + "-" + df["technology_type"]
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# aggregate technology_type and unit
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df = df.groupby(["technology", "unit", "year"]).agg({

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