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‎07TrtU/260321_conversion_space_time.csv‎

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‎07TrtU/260321_equilibrium_space_time.csv‎

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# METADATA
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# Author: Marvin Meck
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# ORCiD: https://orcid.org/0000-0003-2930-8220
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# Date: 2026-03-21 07:16:19.232772
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# Description: Equilibrium conversion Xeq at different temperatures.
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# determined by evaluating the reaction quotient and comparing it with
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# the equilibrium constants (see rICClx)
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#
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# References:
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# -----------
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# Xu, J. and Froment, G.F. (1989) "Methane steam reforming, methanation
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# and water-gas shift: I. Intrinsic kinetics," AIChE Journal, 35(1),
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# pp. 88–96. Available at: https://doi.org/10.1002/aic.690350109.
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# END
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# W/F_CH4,0,eq(g(cat h)/mol),Xeq
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1.515515515515515554e+00,1.488353995840428247e-02
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1.409409409409409397e+00,2.104365841019001718e-02
18+
1.289289289289289098e+00,2.874392204407285115e-02
19+
1.169169169169169020e+00,3.805935031124003942e-02
20+
1.045045045045045029e+00,4.896654490134736554e-02
21+
9.449449449449448535e-01,6.152037562693705997e-02
22+
8.308308308308308687e-01,7.549653712464790556e-02
23+
7.447447447447447244e-01,9.101398290736731345e-02
24+
6.706706706706707655e-01,1.079436983021685725e-01
25+
6.026026026026025662e-01,1.261979012207802009e-01
26+
5.465465465465465522e-01,1.457719301241250653e-01
27+
4.964964964964964644e-01,1.665986276610431360e-01
28+
4.664664664664664451e-01,1.888003752055742446e-01
29+
4.224224224224223945e-01,2.119892506370085195e-01
30+
3.903903903903904182e-01,2.364524876160146172e-01
31+
3.723723723723723622e-01,2.621833076574783261e-01
32+
3.443443443443443552e-01,2.888827785133487591e-01
33+
3.283283283283283116e-01,3.167807886248179550e-01
34+
3.103103103103102556e-01,3.456422859193975516e-01
35+
3.043043043043042739e-01,3.756903928150286398e-01
36+
2.862862862862862734e-01,4.063053092039267122e-01
37+
2.802802802802802362e-01,4.379801961432472912e-01
38+
2.662662662662662605e-01,4.700549987218463621e-01
39+
2.602602602602602233e-01,5.028438234002566842e-01
40+
2.582582582582582664e-01,5.360547664936309298e-01
41+
2.502502502502502724e-01,5.692227756576758724e-01
42+
2.402402402402402382e-01,6.022011446439385907e-01
43+
2.362362362362362134e-01,6.350424956702579449e-01
44+
2.342342342342342287e-01,6.673596469940239473e-01
45+
2.322322322322322163e-01,6.988225607181360877e-01
46+
2.302302302302302039e-01,7.291706883175498310e-01
47+
2.282282282282282471e-01,7.581548924172547554e-01
48+
2.202202202202202252e-01,7.853880942394465547e-01
49+
2.122122122122122034e-01,8.108443469227576417e-01
50+
2.082082082082082064e-01,8.345025791782376690e-01
51+
2.042042042042042094e-01,8.561320562367765730e-01
52+
2.002002002002001846e-01,8.756816273924895588e-01
53+
1.961961961961961876e-01,8.931595901517481728e-01
54+
1.941941941941941752e-01,9.086818295988773331e-01
55+
1.901901901901901781e-01,9.222497602394740257e-01
56+
1.821821821821821563e-01,9.339807384359924969e-01
57+
1.761761761761761746e-01,9.441636813732690037e-01
58+
1.701701701701701652e-01,9.528435853092760421e-01
59+
1.661661661661661682e-01,9.602582327285910102e-01
60+
1.601601601601601588e-01,9.664980193002927766e-01
61+
1.561561561561561617e-01,9.718008449107383706e-01
62+
1.521521521521521370e-01,9.762644677028124995e-01
63+
1.461461461461461275e-01,9.799806032853417559e-01
64+
1.421421421421421305e-01,9.831332941487731514e-01
65+
1.381381381381381335e-01,9.857808265790471980e-01
66+
1.321321321321321240e-01,9.879708946798094749e-01
67+
1.281281281281281270e-01,9.898408869658926745e-01
68+
1.241241241241241161e-01,9.914104723894272553e-01
69+
1.181181181181181067e-01,9.926984150914252014e-01
70+
1.141141141141141235e-01,9.938079557863563895e-01
71+
1.161161161161161082e-01,9.947423470892796660e-01
72+
1.301301301301301117e-01,9.954618626022490036e-01
73+
1.481481481481481399e-01,9.961402355082438209e-01
74+
1.621621621621621712e-01,9.966740487910576984e-01
75+
1.701701701701701652e-01,9.971387401416127449e-01
76+
2.722722722722722422e-01,9.975502658452376492e-01
77+
1.721721721721721776e-01,9.978563924351631087e-01
78+
2.202202202202202252e-01,9.981708004654571420e-01
79+
2.922922922922922551e-01,9.983937119303822882e-01
80+
2.582582582582582664e-01,9.986150766227075426e-01

‎0dTLt9/260321_demand_maximization_markup_solution_summary.csv‎

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# METADATA
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# Author: Marvin Meck
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# ORCiD: https://orcid.org/0000-0003-2930-8220
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# Date: 2026-03-22 05:18:24.285076
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# Description: Solution summary for the demand maximization problem with constant markups.
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# For parameter values and solver configurations see: dO4twu
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# END
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RUN_ID,1,2,3,4,5,6
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Normalized total contribution (Euro),2455.127088769162,3492.8139769634363,4404.981151163283,5193.822833624791,5862.8004345098225,5865.269939389257
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Market share (percent),52.74515425410681,50.06210119400715,47.38028217872614,44.714620465888906,42.079750393495694,42.069170344112806
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Price (Euro),13964.090863823776,16279.57893336879,18594.1533000879,20907.884273906635,23220.988637391576,23230.347679488263
12+
Variable costs (Euro),9309.39390921585,9302.616533353596,9297.076650043948,9292.39301062517,9288.395454956632,9288.380458805843
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Contribution margin (Euro),4654.696954607925,6976.962400015194,9297.076650043951,11615.491263281465,13932.593182434945,13941.96722068242
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Markup,1.5,1.7499999999999998,2.0000000000000004,2.2500000000000004,2.4999999999999996,2.50101164379682
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Sample mean energy cost savings (percent),9.433225936295354,9.407127129444664,9.3825628107666,9.359076085834708,9.33671615539481,9.336628453981698
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Sample mean CO2 savings (percent),12.844075506337585,12.815699901039599,12.78899264614066,12.763456989518469,12.73914619464756,12.73905106493252
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Thermal power (kW),12.5,12.5,12.5,12.5,12.5,12.5
18+
Electrical power (kW),1.0000000002539304,0.999999998010518,0.9999999951420868,0.9999999912461363,0.9999998409436683,0.9999999845222366
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Thermal efficiency,0.8237935944721946,0.8234712282076453,0.8231680460402411,0.8228783727702459,0.8226028028818508,0.8226017059121074
20+
Electrical efficiency,0.06590348757451046,0.06587769812554913,0.06585344336330898,0.06583026924535046,0.06580821376333333,0.06580813545440582
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Fuel cell efficiency,0.21763873754484503,0.21761985085451216,0.21763855061607906,0.21767289296499173,0.21761332382964044,0.21761738708927322
22+
Fuel cell voltage (V),0.32272754243023377,0.3226995361330804,0.3227272652412465,0.3227781900995272,0.32268985747605455,0.322695882716829
23+
Current density (A/m2),1.3519087420826663,1.3520260601564407,1.3519099032319022,1.3516965773554925,1.3520666047552916,1.3520413643526596
24+
Power density (W/m2),0.43629818592228753,0.4362981898146831,0.4362981859225899,0.43629818991493485,0.4362981870833194,0.4362981899475751
25+
Total hydrogen consumption (mol/h),57.806300774722665,57.81131653582573,57.80635012879571,57.79722776814184,57.81304146475388,57.81197013093196
26+
Total hydrogen production (mol/h),210.98153684127277,210.9727403293999,210.9645103480608,210.9566851864707,210.94927454072908,210.94924541590356
27+
Methane feed (mol/h),61.29115088254111,61.31514467696054,61.337727743136746,61.35932012028188,61.379875339566695,61.37995719189647
28+
Steam-to-carbon ratio (H2O/CH4),2.2905636256107327,2.3372187103460056,2.3810973459297524,2.4230208899933308,2.4629045552577264,2.4630620814526165
29+
Reformer temperature (K),1199.9990787499173,1199.9991717623986,1199.9992415343984,1199.9992938346923,1199.993535052894,1199.9993332132078
30+
Space-time reformer (g(cat)*s/mol),0.010240330840819541,0.010419348119545686,0.01058502851949125,0.010740946344353892,0.010887540898014735,0.010887733809912663
31+
Methane conversion,0.8999994137305385,0.8999994139130363,0.899999414092351,0.8999994142717654,0.8999994225942967,0.8999994144506682
32+
Selectivity reforming (H2/CO),3.0000001464522907,3.000000146394972,3.0000001463409984,3.0000001462895827,3.000000146240591,3.000000146240399
33+
Mole fraction CO shift in,0.17119666422139107,0.1695938875536154,0.16811448059992232,0.16672563788447875,0.16542614882595047,0.165421058732514
34+
Mole fraction H2 shift in,0.5087816874885387,0.5087816874885387,0.5087816874885387,0.5087816874885387,0.5087816874885387,0.5087816874885387
35+
Shift temperature (K),523.1500009978552,523.1500007891538,523.1500014471724,523.1500014994209,523.1499859243736,523.1500014202361
36+
Space-time shift (g(cat)*s/mol),13.246351372217868,12.344954043127997,11.620834598928514,11.017791986053442,10.50988797878994,10.507989827557878
37+
Carbon monoxide conversion,0.824762288755199,0.8231061805200323,0.8215495213520989,0.8200630150682718,0.8186495476066799,0.8186439626330437
38+
Mole fraction CO shift out,0.030000111610901284,0.030000110529815438,0.030000109530704217,0.030000108591752214,0.030000106927270828,0.03000010770877527
39+
Mole fraction H2 shift out,0.6483754645123432,0.6483754645123432,0.6483754645123432,0.6483754645123432,0.6483754645123432,0.6483754645123432
40+
Oxygen-to-fuel ratio (O2/CH4),1.9999994133246006,1.9999994135508556,1.9999994137695771,1.9999994139757393,1.9999994200339997,1.9999994141804436
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Air-to-fuel ratio (Air/CH4),9.645047577602714,9.645047578693868,9.645047579748354,9.645047580742846,9.645047609958961,9.645047581730038
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Total water in (mol/h),146.36209532665805,149.28005531967617,152.02645266800153,154.65236985122507,157.1522324297366,157.16211093163864
43+
Total water out (mol/h),268.9443970917403,271.91034467359725,274.70190815427503,277.3710100917889,279.91198310887006,279.9220253154316
44+
Total water produced (mol/h),122.58230176508222,122.63028935392109,122.67545548627349,122.71864024056376,122.75975067913339,122.75991438379295
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Liquid water in (mol/h),140.39128078336782,143.30690336656448,146.05110073454463,148.6749144472311,151.17277457497022,151.18264512054503
46+
Liquid water out (mol/h),145.60475672786805,148.07739494775456,150.40465339623876,152.62981864729753,154.7481665051033,154.75653789929774
47+
Water vapour out (mol/h),123.33964036387225,123.8329497258427,124.29725475803626,124.74119144449132,125.16381660376668,125.16548741613387
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Fuel cell area (cm2),2292.0104472587664,2292.0104216688733,2292.0104355407784,2292.0104056381865,2292.0100760187197,2292.010390055469
49+
Catalyst mass reformer (g),0.6276416626518095,0.6388638373895605,0.649261597481892,0.6590571651379818,0.6682759030745784,0.6682886351692031
50+
Catalyst mass shift reactor (g),730.9812797254788,681.2389352379853,641.2794216239043,608.0013627455213,579.9733940789307,579.8686472714711

‎147OLD/260321_logit_sample_avg_prob_co2sav_rommel2017.csv‎

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# METADATA
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# Author: Marvin Meck
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# ORCiD: https://orcid.org/0000-0003-2930-8220
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# Date: 2026-03-21 07:16:01.449832
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# Description: Logit approximation for Rommel and Sagebiel (2017).
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# Probabilities computed over values of ICOST for different
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# values of CO2SAV (CSAV=20.0, ITYPE=0, DUR=5, FIT=8).
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# The results are based on an synthetic sample of decision makers
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# characterized by socio-demographic attributes and approximated
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# logit coefficients.
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#
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# The logit coeficients are given in
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#
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# "RESULTS_DIR/KQ5GQN/260321_logit_coefficients.json"
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#
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# The socio-demographic attributes are given in
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#
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# "RESULTS_DIR/gVHrJE/260321_socio_demographic_attributes_512.csv"
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#
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# References:
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# -----------
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# Rommel, K. and Sagebiel, J. (2017) 'Preferences for micro-cogeneration in
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# Germany: Policy implications for grid expansion from a discrete choice
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# experiment', Applied Energy, 206, pp. 612–622. Available at:
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# https://doi.org/10.1016/j.apenergy.2017.08.216.
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# END
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ICOST,CO2SAV=1.0,CO2SAV=2.0,CO2SAV=3.0
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10,0.5835882375120941,0.5921745928817317,0.6005926689724809
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11,0.570018356833877,0.5786999626644812,0.587230092790753
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12,0.5563784286505694,0.5651418661471604,0.5737713766287483
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13,0.5426950434227139,0.5515264077782368,0.5602420091631486
32+
14,0.5289949905396651,0.5378800087070117,0.5466679043172784
33+
15,0.515305012725934,0.5242291672955707,0.5330751704547448
34+
16,0.5016515623899738,0.5106002191068185,0.5194898767851772
35+
17,0.48806056476704857,0.49701910110701536,0.5059378215153103
36+
18,0.4745571924696737,0.48351112467314544,0.4924443062152008
37+
19,0.4611656556689579,0.470100761696122,0.4790339206604379
38+
20,0.44790901160761926,0.4568114476352449,0.46573034206713704
39+
21,0.43480899651628424,0.44366540482866806,0.45255615217359213
40+
22,0.4218858822990532,0.43068348872534934,0.43953267506261784
41+
23,0.4091583596046858,0.41788505900541095,0.42667983798752057
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24,0.3966434481390471,0.40528787682913625,0.41401605679018266
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25,0.38435643433387506,0.3929080287303882,0.4015581468105357

‎147OLD/260321_logit_sample_avg_prob_csav_rommel2017.csv‎

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# METADATA
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# Author: Marvin Meck
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# ORCiD: https://orcid.org/0000-0003-2930-8220
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# Date: 2026-03-21 07:16:01.451024
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# Description: Logit approximation for Rommel and Sagebiel (2017).
6+
# Probabilities computed over values of ICOST for different
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# values of CSAV (CO2SAV=2.0, ITYPE=0, DUR=5, FIT=8).
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# The results are based on an synthetic sample of decision makers
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# characterized by socio-demographic attributes and approximated
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# logit coefficients.
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#
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# The logit coeficients are given in
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#
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# "RESULTS_DIR/KQ5GQN/260321_logit_coefficients.json"
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#
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# The socio-demographic attributes are given in
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#
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# "RESULTS_DIR/gVHrJE/260321_socio_demographic_attributes_512.csv"
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#
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# References:
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# -----------
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# Rommel, K. and Sagebiel, J. (2017) 'Preferences for micro-cogeneration in
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# Germany: Policy implications for grid expansion from a discrete choice
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# experiment', Applied Energy, 206, pp. 612–622. Available at:
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# https://doi.org/10.1016/j.apenergy.2017.08.216.
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# END
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ICOST,CSAV=10.0,CSAV=20.0,CSAV=30.0
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10,0.5624810931138059,0.5921745928817317,0.6210849983110679
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11,0.5488025713090162,0.5786999626644812,0.6079082540973957
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12,0.5350892300749406,0.5651418661471604,0.594602533890709
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13,0.5213678707664204,0.5515264077782368,0.5811925067040346
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14,0.5076651869714306,0.5378800087070117,0.5677035498806605
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15,0.49400752308761176,0.5242291672955707,0.5541615261541509
34+
16,0.48042064073371055,0.5106002191068185,0.5405925523308568
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17,0.46692949749149726,0.49701910110701536,0.5270227640712388
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18,0.4535580420568447,0.48351112467314544,0.5134780813680736
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19,0.440329029336479,0.470100761696122,0.49998397928962707
38+
20,0.4272638583858159,0.4568114476352449,0.48656526837562186
39+
21,0.41438243537370123,0.44366540482866806,0.47324588875207513
40+
22,0.40170306301381475,0.43068348872534934,0.4600487215835606
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23,0.3892423571522676,0.41788505900541095,0.44699542092947797
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24,0.37701519047653764,0.40528787682913625,0.4341062684402418
43+
25,0.3650346626392238,0.3929080287303882,0.4214000526483602

‎147OLD/260321_mxl_sample_avg_prob_co2sav_rommel2017.csv‎

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# METADATA
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# Author: Marvin Meck
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# ORCiD: https://orcid.org/0000-0003-2930-8220
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# Date: 2026-03-21 07:16:01.449106
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# Description: Sample average choice probability according to
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# a mixed logit model by Rommel and Sagebiel (2017).
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# Probabilities computed over values of ICOST for different
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# values of CO2SAV (CSAV=20.0, ITYPE=0, DUR=5, FIT=8).
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# The results are based on an synthetic sample of decision makers
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# characterized by socio-demographic attributes and realizations of
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# the random coefficients to compute the mean probability for every
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# decision-maker.
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#
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# The random coeficients are given in
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#
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# "RESULTS_DIR/1d7OL3/260321_random_coefficients_512.csv"
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#
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# The socio-demographic attributes are given in
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#
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# "RESULTS_DIR/gVHrJE/260321_socio_demographic_attributes_512.csv"
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#
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# References:
23+
# -----------
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# Rommel, K. and Sagebiel, J. (2017) 'Preferences for micro-cogeneration in
25+
# Germany: Policy implications for grid expansion from a discrete choice
26+
# experiment', Applied Energy, 206, pp. 612–622. Available at:
27+
# https://doi.org/10.1016/j.apenergy.2017.08.216.
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# END
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ICOST,CO2SAV=1.0,CO2SAV=2.0,CO2SAV=3.0
30+
10,0.5927961546502032,0.6020482379409491,0.6106923013457889
31+
11,0.573749955812037,0.5830116448899788,0.5917162958384387
32+
12,0.5554740404860571,0.5647263035217692,0.5734604363233992
33+
13,0.5379687302791181,0.5472047230374446,0.5559549700290206
34+
14,0.5212514265730626,0.5304633785389314,0.5392198678193596
35+
15,0.5053450526378976,0.51451959144708,0.5232665918756579
36+
16,0.4902505661989825,0.4993751504128043,0.5080941107391028
37+
17,0.4759357007793747,0.48500515225531976,0.4936828652732233
38+
18,0.46234308575224015,0.47135951258413566,0.47999241378179724
39+
19,0.44940630395893016,0.45837415362456424,0.46696686701736984
40+
20,0.4370635326806008,0.4459839822372027,0.4545445068480371
41+
21,0.4252638512505661,0.4341322417383555,0.4426661544623695
42+
22,0.41396644410817734,0.4227744448368886,0.43128087826781836
43+
23,0.4031368769612988,0.41187684789590995,0.42034892086144193
44+
24,0.3927448946875174,0.4014118706440365,0.40984092550194146
45+
25,0.38276451944473483,0.39135450116042714,0.3997339191371435

‎147OLD/260321_mxl_sample_avg_prob_csav_rommel2017.csv‎

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# METADATA
2+
# Author: Marvin Meck
3+
# ORCiD: https://orcid.org/0000-0003-2930-8220
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# Date: 2026-03-21 07:16:01.450500
5+
# Description: Sample average choice probability according to
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# a mixed logit model by Rommel and Sagebiel (2017).
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# Probabilities computed over values of ICOST for different
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# values of CSAV (CO2SAV=2.0, ITYPE=0, DUR=5, FIT=8).
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# The results are based on an synthetic sample of decision makers
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# characterized by socio-demographic attributes and realizations of
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# the random coefficients to compute the mean probability for every
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# decision-maker.
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#
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# The random coeficients are given in
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#
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# "RESULTS_DIR/1d7OL3/260321_random_coefficients_512.csv"
17+
#
18+
# The socio-demographic attributes are given in
19+
#
20+
# "RESULTS_DIR/gVHrJE/260321_socio_demographic_attributes_512.csv"
21+
#
22+
# References:
23+
# -----------
24+
# Rommel, K. and Sagebiel, J. (2017) 'Preferences for micro-cogeneration in
25+
# Germany: Policy implications for grid expansion from a discrete choice
26+
# experiment', Applied Energy, 206, pp. 612–622. Available at:
27+
# https://doi.org/10.1016/j.apenergy.2017.08.216.
28+
# END
29+
ICOST,CSAV=10.0,CSAV=20.0,CSAV=30.0
30+
10,0.5683492163090377,0.6020482379409491,0.6328412421995526
31+
11,0.5493985873290503,0.5830116448899788,0.6140566517350801
32+
12,0.5313492562814057,0.5647263035217692,0.5958911154341976
33+
13,0.5141929965583347,0.5472047230374446,0.5783600867963921
34+
14,0.49791232173126587,0.5304633785389314,0.5614757884837513
35+
15,0.4824828153342555,0.51451959144708,0.5452694083256631
36+
16,0.46786779204282836,0.4993751504128043,0.529777662478089
37+
17,0.4540143016824888,0.48500515225531976,0.5150166788361947
38+
18,0.44085847023150687,0.47135951258413566,0.5009701968332301
39+
19,0.4283357621852303,0.45837415362456424,0.4875945574953315
40+
20,0.4163891072911405,0.4459839822372027,0.47483111743717316
41+
21,0.4049725462519864,0.4341322417383555,0.4626190521921807
42+
22,0.3940502319437497,0.4227744448368886,0.4509051738508517
43+
23,0.3835923289537763,0.41187684789590995,0.4396485175933292
44+
24,0.3735713380742239,0.4014118706440365,0.4288189486226392
45+
25,0.36396115995381734,0.39135450116042714,0.418392381354255

‎1d7OL3/260321_deterministic_coefficients.csv‎

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# METADATA
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# Author: Marvin Meck
3+
# ORCiD: https://orcid.org/0000-0003-2930-8220
4+
# Date: 2026-03-21 07:15:42.777566
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# Description: Deterministic coefficients used in the mixed logit model by
6+
# Rommel and Sagebiel (2017) based Rommel and Sagebiel (2017, table 4).
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#
8+
# References:
9+
# -----------
10+
# Rommel, K. and Sagebiel, J. (2017) 'Preferences for micro-cogeneration in
11+
# Germany: Policy implications for grid expansion from a discrete choice
12+
# experiment', Applied Energy, 206, pp. 612–622. Available at:
13+
# https://doi.org/10.1016/j.apenergy.2017.08.216.
14+
# END
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coefficient,deterministic coefficients
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ASC,-2.226
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ITYPE x AGE,-0.0174
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DUR x SEX,0.0777
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CO2SAV x AGE,-0.00624
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CSAV x AGE,-0.000459
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FIT x SEX,-0.0721
22+
FIT x FLATSIZE,0.00185
23+
ICOST x HEATSYS,0.106

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