model.energy hydrogen-based products

Introduction

This tool calculates the cost of producing and transporting green hydrogen and hydrogen-based products such as ammonia, methanol and methane. Green hydrogen is produced by water electrolysis where the electricity comes from wind and solar generators.

First choose the location where the hydrogen-based derivative is produced and the location to which it is delivered. This determines the weather data for the wind and solar generation where the derivative is produced, as well as the transport costs to the destination. Then choose your cost and technology assumptions to find the solution with least cost. Interesting cost levers are: capital costs of electrolysers and hydrogen storage, financing costs and minimum part-load rates of synthesis units.

This is a toy model with a strongly simplified setup. Please read the warnings below.

The tool is a simplified version of the trace model behind the research paper Import options for chemical energy carriers from renewable sources to Germany by Johannes Hampp, Michael Düren and Tom Brown, published in PLOS ONE in 2023.

All code and data behind this tool are openly available.

You may also be interested in our sister websites: model.energy for baseload electricity, an interface to run the detailed European model PyPSA-Eur and a future German renewable power system running on today's weather and demand.

Step 1: Select destination and source locations

Destination location (blue) Select from map above.

Source location (red) Select from map above.

Step 2: Choose product, technologies, costs and other assumptions

Product

Technology assumption scenario Individual cost assumptions can be changed below under "advanced assumption settings". 2030 is the default year for technology assumptions because this is a reasonable time by which green products can be scaled up to large volumes.

Choose which technologies should be included:

Onshore wind turbine

Utility-scale solar PV

Utility-scale battery storage

Hydrogen gas storage

Direct air capture for carbonaceous fuels If direct air capture is not selected, CO2 is costed according to assumptions specified below

Show advanced assumption settings

All costs are in 2020 euros. All energy units for fuels are given for the lower heating value (LHV). The exact configuration of technologies for each product can be found at the back of the paper Hampp et al, 2023.

Constant demand for product [MW] Demand should be large enough for utility-scale assets but small enough to neglect land availability constraints

Weather year Only the years 2011, 2012, 2013, 2014 are available (until we get a bigger hard-drive)

Sampling frequency for weather time-series n-hourly, n<3 is very slow, big n solves faster (n=25 or n=49 give fast and relatively accurate results)

Pipeline distance multiplicator factor As-the-crow-flies direct distance between source and destination is multiplied by this factor to create an estimate of the pipeline distance; represents rough average of distances considered in Hampp et al (2023)

Shipping distance multiplicator factor As-the-crow-flies direct distance between source and destination is multiplied by this factor to create an estimate of the shipping distance; represents rough average of distances considered in Hampp et al (2023)

Air separation unit for nitrogen capital cost (overnight) [EUR/t_N2/h] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

Air separation unit for nitrogen discount rate [percent]

Air separation unit output [tN2/MWhel] Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), pg.288 .

Air separation unit for nitrogen fixed operation and maintenance costs [percent of overnight cost per year] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

Air separation unit for nitrogen lifetime [years] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

ammonia shipping average speed [km/h] Assume same as for CH4 (l).

ammonia shipping energy capacity [MWh_LHV] Based on NH3 LHV of 5.1666 MWh/t_NH3 and 53000 t capacity.

ammonia shipping mass capacity [t_NH3] Cihlar et al 2020 based on IEA 2019, Table 3-B

ammonia shipping capital cost (overnight) [EUR] Cihlar et al 2020 based on IEA 2019, Table 3-B

ammonia shipping discount rate [percent]

ammonia shipping energy demand [MWh/km] Assume same as for CH4 (l).

ammonia shipping fixed operation and maintenance costs [%/year] Cihlar et al 2020 based on IEA 2019, Table 3-B

ammonia shipping lifetime [years] Guess estimated based on H2 (l) tanker, but more mature technology

ammonia shipping unloading/loading loss [%/transfer] Guesstimate, possibly negligible losses due to relatively close to ambient transport temperature.

ammonia shipping unloading/loading time [h] Assume same as for CH4 (l).

Liquid ammonia storage capital cost (overnight) [EUR/MWh_NH3] Calculated based on Morgan E. 2013: doi:10.7275/11KT-3F59 , Fig. 55, Fig 58.

Liquid ammonia storage discount rate [percent]

Liquid ammonia storage fixed operation and maintenance costs [percent of overnight cost per year] Guesstimate, based on H2 (l) storage tank.

Liquid ammonia storage lifetime [years] Morgan E. 2013: doi:10.7275/11KT-3F59 , pg. 290

Utility-scale battery energy capital cost (overnight) [EUR/kWh] Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx

Utility-scale battery energy discount rate [percent]

Utility-scale battery energy fixed operation and maintenance costs [percent of overnight cost per year] default

Utility-scale battery energy lifetime [years] Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx

Utility-scale battery converter power capital cost (overnight) [EUR/kW] Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx

Utility-scale battery converter power discount rate [percent]

Battery power charging efficiency [percent] Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx

Battery power discharging efficiency [percent] Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx

Utility-scale battery converter power fixed operation and maintenance costs [percent of overnight cost per year] Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx

Utility-scale battery converter power lifetime [years] Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx, Note K.

CO2 cost [EUR/tCO2]

CO2 storage tank capital cost (overnight) [EUR/t_CO2] Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, Table 3.

CO2 storage tank discount rate [percent]

CO2 storage tank fixed operation and maintenance costs [percent of overnight cost per year] Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .

CO2 storage tank lifetime [years] Lauri et al. 2014: doi: 10.1016/j.egypro.2014.11.297, pg. 2746 .

Direct air capture capital cost (overnight) [EUR/(tCO2/h)] Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx

Direct air capture discount rate [percent]

Direct air capture electricity consumption [MWh/tCO2] Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx

Direct air capture fixed operation and maintenance costs [percent of overnight cost per year] Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx

Direct air capture heat consumption [MWh/tCO2] Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx

Direct air capture lifetime [years] Danish Energy Agency, technology_data_for_carbon_capture_transport_storage.xlsx

Seawater desalination capital cost (overnight) [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.

Seawater desalination discount rate [percent]

Seawater desalination electricity input [MWh-el/m3-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 fixed operation and maintenance costs [percent of overnight cost per 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 lifetime [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.

Include dispatchable technology 1 Default assumption is based on open cycle natural gas turbine (OCGT)

Dispatchable technology 1 capital cost (overnight) [EUR/kW]

Dispatchable technology 1 discount rate [percent]

Dispatchable technology 1 emissions factor [gCO2eq/kWhel]

Dispatchable technology 1 fixed operation and maintenance costs [percent of overnight cost per year]

Dispatchable technology 1 lifetime [years]

Dispatchable technology 1 marginal cost (including fuel and VOM) [EUR/MWhel]

Include dispatchable technology 2 Default assumption is based on new nuclear EPR reactor in Europe

Dispatchable technology 2 capital cost (overnight) [EUR/kW]

Dispatchable technology 2 discount rate [percent]

Dispatchable technology 2 emissions factor [gCO2eq/kWhel]

Dispatchable technology 2 fixed operation and maintenance costs [percent of overnight cost per year]

Dispatchable technology 2 lifetime [years]

Dispatchable technology 2 marginal cost (including fuel and VOM) [EUR/MWhel]

Fischer-Tropsch synthesis carbon dioxide input [tCO2/MWh-FT-LHV] DEA technology data for Renewable fuels (2018): Hydrogen to Power.

Fischer-Tropsch synthesis capital cost (overnight) [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”.

Fischer-Tropsch synthesis discount rate [percent]

Fischer-Tropsch synthesis efficiency wrt hydrogen [MWh-FT-LHV/MWh-H2-LHV] DEA technology data for Renewable fuels (2018): Hydrogen to Power.

Fischer-Tropsch synthesis electricity input [MWhel/MWh-FT-LHV] DEA technology data for Renewable fuels (2018): Hydrogen to Power.

Fischer-Tropsch synthesis fixed operation and maintenance costs [percent of overnight cost per 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.

Fischer-Tropsch synthesis lifetime [years] Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.

Fischer-Tropsch synthesis minimum part load [percent] Private discussions.

ft shipping average speed [km/h] Assume same as for LOHC.

ft shipping energy capacity [MWh_LHV] Based on FT fuel LHV of 11.95 MWh/t_FTfuel and 75000t capacity.

ft shipping mass capacity [t_FTfuel] Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .

ft shipping capital cost (overnight) [EUR] Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .

ft shipping discount rate [percent]

ft shipping energy demand [MWh/km] Assume same as for LOHC.

ft shipping fixed operation and maintenance costs [%/year] Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .

ft shipping lifetime [years] Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .

ft shipping unloading/loading loss [%/transfer] Guesstimate, transfer of simple non-cryogenic fluid.

ft shipping unloading/loading time [h] Assume same as for LOHC.

Haber-Bosch ammonia synthesis capital cost (overnight) [EUR/kW_NH3] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

Haber-Bosch ammonia synthesis discount rate [percent]

Haber-Bosch ammonia synthesis hydrogen input [MWh-NH3-LHV/MWh-H2-LHV] DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry , pg. 57.

Haber-Bosch ammonia synthesis electricity input [MWhel/MWh-NH3-LHV] DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry , table 11.

Haber-Bosch ammonia synthesis fixed operation and maintenance costs [percent of overnight cost per year] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

Haber-Bosch ammonia synthesis lifetime [years] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

Haber-Bosch ammonia synthesis minimum part load [percent] Danish Energy Agency renewable fuels database

Haber-Bosch ammonia synthesis nitrogen input [tN2/MWh-NH3-LHV] DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry , pg. 57.

Industrial heat pump up to 125 C capital cost (overnight) [EUR/kW] Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx

Industrial heat pump up to 125 C discount rate [percent]

Industrial heat pump COP [percent] Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx

Industrial heat pump up to 125 C fixed operation and maintenance costs [percent of overnight cost per year] Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx

Industrial heat pump up to 125 C lifetime [years] Danish Energy Agency, technology_data_for_industrial_process_heat.xlsx

HVDC AC-DC inverter pair capital cost (overnight) [EUR/MW] Hagspiel et al. (2014): doi:10.1016/j.energy.2014.01.025 , table A.2 .

HVDC AC-DC inverter pair discount rate [percent]

HVDC AC-DC inverter pair fixed operation and maintenance costs [percent of overnight cost per year] Hagspiel et al. (2014): doi:10.1016/j.energy.2014.01.025 , table A.2 .

HVDC AC-DC inverter pair lifetime [years] Hagspiel et al. (2014): doi:10.1016/j.energy.2014.01.025 , table A.2 .

HVDC submarine cable capital cost (overnight) [EUR/MW/km] Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .

HVDC submarine cable discount rate [percent]

HVDC submarine cable fixed operation and maintenance costs [percent of overnight cost per year] Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .

HVDC submarine cable lifetime [years] Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .

HVDC submarine cable losses [percent/1000km] Purvins et al. (2018): https://doi.org/10.1016/j.jclepro.2018.03.095 .

Hydrogen storage compressor capital cost (overnight) [EUR/kWh_H2] Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.

Hydrogen storage compressor discount rate [percent]

Hydrogen storage compressor electricity input [MWhel/MWh-H2-LHV] DEA technology data for energy storage, pg. 82 .

Hydrogen storage compressor fixed operation and maintenance costs [percent of overnight cost per year] Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.

Hydrogen storage compressor lifetime [years] Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.4.

Hydrogen electrolyser capital cost (overnight) [EUR/kW_e] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

Hydrogen electrolyser discount rate [percent]

Hydrogen electrolyser efficiency ["percent, LHV"] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

Hydrogen electrolyser fixed operation and maintenance costs [percent of overnight cost per year] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

Hydrogen electrolyser lifetime [years] Danish Energy Agency, data_sheets_for_renewable_fuels.xlsx

Hydrogen electrolyser water input [m3-H2O/MWh-H2-LHV] -

Hydrogen overground tank storage capital cost (overnight) [EUR/kWh_H2] Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.

Hydrogen overground tank storage discount rate [percent]

Hydrogen overground tank storage fixed operation and maintenance costs [percent of overnight cost per year] Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.

Hydrogen overground tank storage lifetime [years] Based on Stöckl et al (2021): https://doi.org/10.48550/arXiv.2005.03464, table SI.9.

Hydrogen liquefaction capital cost (overnight) [EUR/kW_H2] IRENA (2022): Global Hydrogen Trade to Meet the 1.5° Climate Goal: Technology Review of Hydrogen Carriers, https://www.irena.org/publications/2022/Apr/Global-hydrogen-trade-Part-II , pg. 62f.

Hydrogen liquefaction discount rate [percent]

Hydrogen liquefaction efficiency [MWh-H2-liquid/MWh-H2-gas] Heuser et al. 2019: https://doi.org/10.1016/j.ijhydene.2018.12.156, table 1.

Hydrogen liquefaction electricity input [MWh-el/MWh-H2-LHV] Heuser et al. 2019: https://doi.org/10.1016/j.ijhydene.2018.12.156, table 1.

Hydrogen liquefaction fixed operation and maintenance costs [percent of overnight cost per year] DNV GL (2020): Study on the Import of Liquid Renewable Energy: Technology Cost Assessment, https://www.gie.eu/wp-content/uploads/filr/2598/DNV-GL_Study-GLE-Technologies-and-costs-analysis-on-imports-of-liquid-renewable-energy.pdf .

Hydrogen liquefaction lifetime [years] Reuß et al 2017, https://doi.org/10.1016/j.apenergy.2017.05.050 , Table 9.

Hydrogen submarine pipeline capital cost (overnight) [EUR/MW/km] Assume similar cost as for CH4 (g) submarine pipeline but with the same factor as between onland CH4 (g) pipeline and H2 (g) pipeline (2.86). This estimate is comparable to a 36in diameter pipeline calaculated based on d’Amore-Domenech et al (2021): 10.1016/j.apenergy.2021.116625 , supplementary material (=251 EUR/MW/km).

Hydrogen submarine pipeline discount rate [percent]

Hydrogen submarine pipeline fixed operation and maintenance costs [percent of overnight cost per year] Assume same as for CH4 (g) submarine pipeline.

Hydrogen submarine pipeline lifetime [years] Assume same as for CH4 (g) submarine pipeline.

Hydrogen submarine pipeline losses [percent/1000km] Danish Energy Agency, Technology Data for Energy Transport (2021), Excel datasheet: H2 140.

Hydrogen combined cycle turbine capital cost (overnight) [EUR/kW] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

Hydrogen combined cycle turbine discount rate [percent]

Hydrogen combined cycle turbine efficiency ["percent, LHV"] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

Hydrogen combined cycle turbine fixed operation and maintenance costs [percent of overnight cost per year] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

Hydrogen combined cycle turbine lifetime [years] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

lh2 shipping average speed [km/h]

lh2 shipping energy capacity [MWh_LHV] Corresponds to 11360 t H2 (l) with LHV of 33.3333 MWh/t_H2

lh2 shipping mass capacity [t_H2] Cihlar et al 2020: http://op.europa.eu/en/publication-detail/-/publication/7e4afa7d-d077-11ea-adf7-01aa75ed71a1/language-en , Table 3-B, based on IEA 2019.

lh2 shipping capital cost (overnight) [EUR] Cihlar et al 2020: http://op.europa.eu/en/publication-detail/-/publication/7e4afa7d-d077-11ea-adf7-01aa75ed71a1/language-en , Table 3-B, based on IEA 2019.

lh2 shipping discount rate [percent]

lh2 shipping energy demand [MWh/km]

lh2 shipping fixed operation and maintenance costs [%/year] Cihlar et al 2020: http://op.europa.eu/en/publication-detail/-/publication/7e4afa7d-d077-11ea-adf7-01aa75ed71a1/language-en , Table 3-B, based on IEA 2019.

lh2 shipping lifetime [years] Cihlar et al 2020: http://op.europa.eu/en/publication-detail/-/publication/7e4afa7d-d077-11ea-adf7-01aa75ed71a1/language-en , Table 3-B, based on IEA 2019.

lh2 shipping unloading/loading loss [%/transfer]

lh2 shipping unloading/loading time [h]

Liquid carbonaceous fuel storage tank capital cost (overnight) [EUR/m^3] Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 8F .

Liquid carbonaceous fuel storage tank discount rate [percent]

Liquid carbonaceous fuel storage tank fixed operation and maintenance costs [percent of overnight cost per year] Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , figure 7 and pg. 12 .

Liquid carbonaceous fuel storage tank lifetime [years] Stelter and Nishida 2013: https://webstore.iea.org/insights-series-2013-focus-on-energy-security , pg. 11.

Methanation carbon dioxide input [tCO2/MWh-CH4-LHV]

Methanation capital cost (overnight) [EUR/kW_CH4] 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 6: “Reference scenario”.

Methanation discount rate [percent]

Methanation efficiency wrt hydrogen [MWh-CH4-LHV/MWh-H2-LHV] Götz et al. (2016), fig. 11 .

Methanation fixed operation and maintenance costs [percent of overnight cost per 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 6.2.3.1

Methanation lifetime [years] Guesstimate.

Methanation minimum part load [percent] Danish Energy Agency renewable fuels database

Methane liquefaction capital cost (overnight) [EUR/kW_CH4] Calculated, based on Lochner and Bothe (2009): https://doi.org/10.1016/j.enpol.2008.12.012 and Fasihi et al 2017, table 1, https://www.mdpi.com/2071-1050/9/2/306

Methane liquefaction discount rate [percent]

Methane liquefaction electricity input [MWh-el/MWh-CH4-LHV] Pospíšil et al. 2019: https://doi.org/10.1016/j.rser.2018.09.027 , Table 2 and Table 3. Source 2: https://encyclopedia.airliquide.com/methane (2021-02-10).

Methane liquefaction fixed operation and maintenance costs [percent of overnight cost per year] Fasihi et al 2017, table 1, https://www.mdpi.com/2071-1050/9/2/306

Methane liquefaction lifetime [years] Fasihi et al 2017, table 1, https://www.mdpi.com/2071-1050/9/2/306

methane shipping average speed [km/h]

methane shipping energy capacity [MWh_LHV] Based on CH4 LHV of 13.8888 MWh/t_CH4 and 58300 t capacity.

methane shipping mass capacity [t_CH4] Calculated, based on Fasihi et al 2017, table 1, https://www.mdpi.com/2071-1050/9/2/306

methane shipping capital cost (overnight) [EUR] Fasihi et al 2017, table 1, https://www.mdpi.com/2071-1050/9/2/306

methane shipping discount rate [percent]

methane shipping energy demand [MWh/km] Taken from source (average fuel demand / average cruising speed) without 50% efficiency.

methane shipping fixed operation and maintenance costs [%/year] Fasihi et al 2017, table 1, https://www.mdpi.com/2071-1050/9/2/306

methane shipping lifetime [years] Fasihi et al 2017, table 1, https://www.mdpi.com/2071-1050/9/2/306

methane shipping unloading/loading loss [%/transfer]

methane shipping unloading/loading time [h]

Methane overground tank storage capital cost (overnight) [EUR/m^3] Storage costs per l: https://www.compositesworld.com/articles/pressure-vessels-for-alternative-fuels-2014-2023 (2021-02-10).

Methane overground tank storage discount rate [percent]

Methane overground tank storage fixed operation and maintenance costs [percent of overnight cost per year] Guesstimate, based on hydrogen storage tank by DEA.

Methane overground tank storage lifetime [years] Guesstimate, based on hydrogen storage tank by DEA.

methanol shipping average speed [km/h] Assume same as for LOHC.

methanol shipping energy capacity [MWh_LHV] Based on MeOH LHV of 5.53611 MWh/t_MeOH and 75000t capacity.

methanol shipping mass capacity [t_MeOH] Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .

methanol shipping capital cost (overnight) [EUR] Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .

methanol shipping discount rate [percent]

methanol shipping energy demand [MWh/km] Assume same as for LOHC.

methanol shipping fixed operation and maintenance costs [%/year] Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .

methanol shipping lifetime [years] Assume comparable tanker as for LOHC transport above, c.f. Runge et al 2020, Table 10, https://papers.ssrn.com/abstract=3623514 .

methanol shipping unloading/loading loss [%/transfer] Guesstimate, transfer of simple non-cryogenic fluid.

methanol shipping unloading/loading time [h] Assume same as for LOHC.

Methanol synthesis carbon dioxide input [tCO2/MWh-MeOH-LHV] DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry , pg. 66.

Methanol synthesis capital cost (overnight) [EUR/MW_MeOH] 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”.

Methanol synthesis discount rate [percent]

Methanol synthesis efficiency wrt hydrogen [MWh-MeOH-LHV/MWh-H2-LHV] DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry , pg. 64.

Methanol synthesis electricity input [MWhel/MWh-MeOH-LHV] DECHEMA 2017: DECHEMA: Low carbon energy and feedstock for the European chemical industry , pg. 65.

Methanol synthesis fixed operation and maintenance costs [percent of overnight cost per 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.

Methanol synthesis lifetime [years] Danish Energy Agency, Technology Data for Renewable Fuels (04/2022), Data sheet “Methanol to Power”.

Methanol synthesis minimum part load [percent] Private discussions.

Utility-scale solar PV capital cost (overnight) [EUR/kW_e] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

Utility-scale solar PV discount rate [percent]

Utility-scale solar PV fixed operation and maintenance costs [percent of overnight cost per year] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

Utility-scale solar PV lifetime [years] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

Solar maximum capacity allowed [MWp]

Solar minimum capacity allowed [MWp]

Onshore wind turbine capital cost (overnight) [EUR/kW] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

Onshore wind turbine discount rate [percent]

Onshore wind turbine fixed operation and maintenance costs [percent of overnight cost per year] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

Onshore wind turbine lifetime [years] Danish Energy Agency, technology_data_for_el_and_dh.xlsx

Wind maximum capacity allowed [MWp]

Wind minimum capacity allowed [MWp]

Step 3: Solve and wait for results

Ready in around 120 seconds, status: Waiting for job

Results

Costs are in 2020 euros. Energy units for fuels are given for the lower heating value (LHV). Total costs comprise production and transport of the products, but not taxes or other charges. Compare costs to historical market prices of fossil-based products in Europe.

electricity supply and demand time series (you can zoom and pan to see the details)

hydrogen supply and demand time series (you can zoom and pan to see the details)

Asset	Capacity	Cap Ftr used [%]	Cap Ftr avail [%]	Curtlmt [%]	LCOE [EUR/MWh]

Cap Ftr = Capacity Factor, used is after curtailment, avail is available before curtailment, Curtlmt = Curtailment, LCOE = Levelised Cost of Electricity

Background and warnings

This is a toy model with a strongly simplified setup. Please read the warnings before interpreting the results.

The tool is a simplified version of the model behind the research paper Import options for chemical energy carriers from renewable sources to Germany by Johannes Hampp, Michael Düren and Tom Brown, published in PLOS ONE in 2023. See the differences to the paper.

Warnings

Technologies such as direct air capture and shipping for liquid hydrogen are not yet widely commercially deployed, so results that rely on these technologies should be treated with caution.
Future cost projections are uncertain. If the technologies scale up faster than anticipated, costs will come down faster as manufacturers leverage experience and learning-by-doing.
The transport route is not calculated precisely. Instead, a factor is multiplied by the direct distance to account for indirect transport routes. For pipelines and cables the default factor is 1.2 and for shipping the default is 1.8 (since sea routes may have to go around continents). You can change this assumption.
At the moment all pipeline and cable costs use the submarine cost. In the future we will offer an overland cost where the user can change the fraction of the route that is over land or under sea. The submarine costs are higher than overland, so this is a conservative assumption.
Costs for shipping terminals at the exporting and importing locations are not included.
The shipping model is simplified. We assume that there are enough ships for a continuous delivery of products. The number of ships needed takes into account the time it takes the ships to do a return trip including loading and unloading the products. The continuous delivery means that the model neglects the need for buffer storage at the shipping terminals.
The default costs for hydrogen storage assumes that hydrogen is stored in overground steel tanks. If underground hydrogen storage is available, for example in salt caverns, change the overnight cost to 2 EUR/kWh and the lifetime to 50 years. Studies on salt deposit availability are available for most countries, see e.g. this map for Europe or Figure 2 of NREL 2009 for the United States.
The default setting restricts power generation to wind solar. Additional low-carbon generation technologies which may reduce costs include using existing hydroelectric generators, biomass from sustainable resources (such as waste and agricultural/forestry residues), offshore wind, concentrating solar thermal, geothermal, ocean energy, nuclear and fossil/biomass plants with CCS. Two additional dispatchable technologies are provided under "advanced assumption settings". Additional storage technologies include redox flow batteries, compressed air energy storage, etc.
No import or export capacities with other regions are assumed, so each region must supply demand as an island. Existing and planned transmission grid connections between regions can reduce electricity costs by up to 20% by smoothing wind over a continent-sized area (see e.g. this paper or this one).
The time series for wind and solar generation are based on weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis dataset. Reanalysis datasets are generated by fitting numerical weather simulations to real measured data. In regions where real measurements are sparse, reanalysis data may deviate from observed weather patterns. In addition, reanalysis datasets may not always capture clouds accurately, so for PV generation it is advisable to include satellite observations in calculations, such as the CMSAF SARAH II dataset (available only for Europe and Africa). Weather data is converted to power generation using the atlite library. No further corrections are applied, except a 7% linear scaling down of solar generation time series to fit measured European capacity factors for 2011-2013.
The wind profiles used here are converted from wind speed time series assuming an existing wind turbine model (Vestas V112 3MW with a hub height of 80m). Newer and future turbines will have higher capacity factors because e.g. they're taller, capturing wind energy where resources are better.
Solar profiles are calculated assuming that panels in the northern hemisphere face south, and that panels in the southern hemisphere face north, with a slope of 35 degrees against the horizontal in both cases.
Costs for ancillary services inside each production island are not included.
To avoid long job times, the default here is to run only for a single weather year sampled at most every 3 hours. You can download the code yourself and run hourly for multiple weather years.
The joint production of electricity and hydrogen can lead to lower costs for both, particularly for a low ratio of hydrogen to electricity production. Flexible electrolyser demand creates flexibility for integrating wind and solar for electricity production. However, operating the hydrogen production in island mode can save costs compared to joint production, since the alternating current doesn't have to match a perfect sine wave. This saves on power electronic costs.
We do not allow waste heat integration. For example, the waste heat from methanol or Fischer-Tropsch synthesis could be fed to supply the heat demand of direct air capture.
Lower reliability delivery of products than baseload may cost less, especially for electricity and hydrogen, which are hard to store.
The minimum must-run level also represents availability, taking into account maintenance sheduling for many aggregated units.

Differences to model in Hampp et al, 2023

This tool is a simplified version of the model behind the research paper Import options for chemical energy carriers from renewable sources to Germany by Johannes Hampp, Michael Düren and Tom Brown, published in PLOS ONE in 2023 (Hampp et al, 2023).

The following list highlights the main differences.

The default cost assumptions for hydrogen storage in steel tanks in the paper are higher than on this website. The paper assumes costs of steel tanks in 2030 of around 45 EUR/kWh, FOM 2% and lifetime of 30 years; use these values to reproduce the paper's results. The website uses for Type I steel tanks costs in 2030 of 12.23 EUR/kWh, FOM 2% and lifetime of 20 years.
The paper assumes that the best wind and solar resources in each country are used for domestic energy and excludes these areas from being used for hydrogen-based product exports. Since this website only allows point locations for wind and solar, we do not do this. This effect can lead to slightly lower costs on the website compared to the paper for densely-populated countries like Germany.
The model for the paper allows many wind and solar locations in each country to be combined. This leads to a smoothing effect across the country that reduces balancing costs compared to a plant at a single location. This effect makes the costs on the website higher than the paper in most countries with good wind and solar resources in sparsely-populated areas.
The paper assumes that product demand is fixed for the year, but is not strict about when the product is delivered during the year. The website requires baseload delivery of the product. This makes the website results more expensive.
The paper used GlobalEnergyGIS for processing the weather data, whereas the website uses the atlite library.
The paper uses a default minimum part load of 94.25% for methanol and Fischer-Tropsch synthesis, and 30% for NH3 and CH4. The website has a default of 50% for methanol synthesis, 90% for Fischer-Tropsch and 30% for NH3 and CH4.
The paper includes compression costs and energy demand for liquid CO2 tanks, whereas the website ignores these since the effect is tiny.
The paper displays results in 2015 euros, whereas the website uses 2020 euros.
The website has a simplified version of the shipping model in the paper (see warnings above).
The website includes the option for electricity delivered by HVDC cable, with hydrogen storage used as long-duration storage on the producer side. This option was not available in the paper, but the paper had instead electricity transmission followed by electrolysis on the consumer side.

Historical market prices of fossil-based products in Europe

The following graphic from Hampp et al, 2023 shows historical wholesale market prices in EUR-2015 of fossil-based products in Europe (black lines) versus the costs of hydrogen-based products the authors calculated for 2050 technology assumptions. Market prices for methanol are based on MMSA Europe Spot FOB, for ammonia on German export prices for ammonia, for natural gas on the Dutch TTF C1 future and for Fischer-Tropsch products on EU Diesel prices without taxes.

Only free software and open data

The graphical user interface, weather processing, optimisation model construction, solver and post-processing are all built with free software and open data only. The code for all parts except the solver can be downloaded from the GitHub repository efuels-server. It uses the Python for Power System Analysis (PyPSA) energy optimisation framework, open weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis dataset, the atlite library for converting weather data to generation profiles, D3.js for graphics, Mapbox, Leaflet and Natural Earth for maps, Clp for the solver, and free software for the server infrastructure (GNU/Linux, nginx, Flask, gunicorn, Redis).

The code is a simplified version of the trace model by Johannes Hampp used in Hampp et al, 2023.

New functionality coming soon

See the GitHub issue tracker.

Problems/comments/feedback/help out

If you encounter any problems please report them using the GitHub issue tracker. It would be helpful to note the jobid and any other error messages from your browser's JavaScript console (find the console via your browser's "Developer Tools").

If you want to help out and contribute improvements, please submit a pull request!

Any other feedback and comments can be sent to Tom Brown.

Contributors and thanks

Tom Brown conceived and developed the website.
Johannes Hampp developed the original trace model on which this website is based.
Bo Tranberg of Ento Labs implemented the user interface in bootstrap.
Jonas Hörsch developed the library atlite that processes the weather data and selects different geographical areas.

We thank also all the developers of the other free software on which model.energy depends.

Comparable websites

Here we list other websites that compute the costs of delivering hydrogen-based fuels. As far as we know, this website is the only one that does live optimisation with hourly time series.

Privacy statement

No personal information is stored on the server. There are no cookies.

Simulation assumptions and results for each job are stored on the server for statistical purposes.