model.energy

Introduction

This tool calculates the cost of meeting a constant electricity demand from a combination of wind power, solar power and storage for different regions of the world.

First choose your location to determine the weather data for the wind and solar generation. Then choose your cost and technology assumptions to find the solution with least cost. Storage options are batteries and hydrogen from electrolysis of water.

Fun things to try out:

remove technologies with the checkboxes, e.g. hydrogen gas storage or wind, and see system costs rise
set solar or battery costs very low, to simulate breakthroughs in manufacturing

See also this Twitter thread for an overview of the model's features and capabilities.

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

You may also be interested in our sister websites: model.energy for green hydrogen-derived products, 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 location and weather year

Select country

Select state or province (for Australia, China, Germany, India, Russia and United States)

Select point, rectangle or polygon, using the toolbox that appears at top-right

Location Select from map above.

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

Capacity factor exponent for spatial distribution of wind and solar If exponent is 0 generators are distributed evenly across the region, if it is 1 they are distributed proportional to capacity factor, if it is x they are distributed proportional to (capacity factor)^x

Step 2: Fetch available wind and solar output

Warning: It takes about 4 seconds to fetch the available wind and solar output (longer for custom selections).

Status: Waiting for job

Step 3: Choose technologies, costs and other assumptions

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

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

Show advanced assumption settings

All costs are in 2020 euros. All energy units for fuels are given for the lower heating value (LHV).

Constant hydrogen demand [MW-H2-LHV]

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)

Allam cycle CO2 capture efficiency [percent] [^mitchell2019]: Mitchell, C., Avagyan, V., Chalmers, H., & Lucquiaud, M. (2019). An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system. International Journal of Greenhouse Gas Control, 87, 1–18. https://doi.org/10.1016/j.ijggc.2019.04.020

Allam cycle overnight cost [EUR/kW] Assume 2x costs of CCGT based on [^mitchell2019], where CCGT is reported 556 gbp/kW, allam 1430 gbp with ASU and w/o ASU 1145.7 gbp, i.e. 2x as expensive as CCGT without the ASU costs; [^mitchell2019]: Mitchell, C., Avagyan, V., Chalmers, H., & Lucquiaud, M. (2019). An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system. International Journal of Greenhouse Gas Control, 87, 1–18. https://doi.org/10.1016/j.ijggc.2019.04.020

Allam Cycle turbine discount rate [percent]

Allam cycle efficiency [percent, LHV] [^mitchell2019]: Mitchell, C., Avagyan, V., Chalmers, H., & Lucquiaud, M. (2019). An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system. International Journal of Greenhouse Gas Control, 87, 1–18. https://doi.org/10.1016/j.ijggc.2019.04.020

Allam cycle FOM [percent of overnight cost per year] [^mitchell2019]: Mitchell, C., Avagyan, V., Chalmers, H., & Lucquiaud, M. (2019). An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system. International Journal of Greenhouse Gas Control, 87, 1–18. https://doi.org/10.1016/j.ijggc.2019.04.020

Allam cycle lifetime [years]

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.

Maximum average system emissions [gCO2eq/kWhel]

Limit on CO2 emissions

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) [USD/t_CO2/h] ICCT IRA e-fuels assumptions

Direct air capture discount rate [percent]

Direct air capture electricity consumption [MWh_el/t_CO2] ICCT IRA e-fuels assumptions

Direct air capture fixed operation and maintenance costs [percent of overnight cost per year] ICCT IRA e-fuels assumptions

Direct air capture heat consumption [MWh_th/t_CO2] ICCT IRA e-fuels assumptions

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]

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

Hydrogen storage compressor and above-ground facilities capital cost (overnight) [EUR/kW_H2] for surface processing unit of 126MGBP with 1 GW_H2 i.e. 126 MGBP/1 GW_H2*1.08/1.02^3*2 where 1 GBP:1.08 EUR exchange rate and inflation adjustment for 3 years with 2%/a from 2018 to 2015 is done. Cost are doubled because low-input pressure for alkaline electrolysis requires two-stage compression scheme with double the amount of facilities; H21 NoE (2018) report https://www.h21.green/app/uploads/2019/01/H21-NoE-PRINT-PDF-FINAL-1.pdf , table 3-30 and text

Hydrogen storage compressor discount rate [percent]

Hydrogen storage compressor and above-ground facilities electricity input [MWhel/MWh-H2-LHV] requiring with original report requiring 26.7 MW / 1000 MW_H2 of single stage compression; since alkaline electrolysis with low output pressure is assumed, assumed here twice the amount for two-stage compression; H21 NoE (2018) report https://www.h21.green/app/uploads/2019/01/H21-NoE-PRINT-PDF-FINAL-1.pdf , text below table 3-29

Hydrogen storage compressor and above-ground facilities fixed operation and maintenance costs [percent of overnight cost per year] H21 NoE (2018) report https://www.h21.green/app/uploads/2019/01/H21-NoE-PRINT-PDF-FINAL-1.pdf , text below table 3-30

Hydrogen storage compressor and above-ground facilities lifetime [years] Assume same value as for hydrogen_storage_tank_compressor

Hydrogen electrolyser capital cost (overnight) [EUR/kW_e] private communications; IEA https://iea.blob.core.windows.net/assets/9e0c82d4-06d2-496b-9542-f184ba803645/TheRoleofE-fuelsinDecarbonisingTransport.pdf

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 gas storage overnight cost [EUR/kWh] based on underground salt cavern storage CAPEX; based on cavern storage site with total 1 TWh storage volume considering all costs except for Surface Processing Unit costing 550 million EUR; INES (2023) https://energien-speichern.de/wp-content/uploads/2023/10/20231006_INES-Positionspapier_Vorschlaege-Marktrahmen_Entwicklung-H2-Speicher.pdf

Hydrogen energy discount rate [percent]

Hydrogen gas storage fixed operation and maintenance costs [percent of overnight cost per year] H21 NoE (2018) report https://www.h21.green/app/uploads/2019/01/H21-NoE-PRINT-PDF-FINAL-1.pdf , text below table 3-30

Hydrogen gas storage lifetime [years] Danish Energy Agency, technology_data_catalogue_for_energy_storage.xlsx

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

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.

Methanol storage (methanolisation for PtMeOH then Allam cycle for MeOHtP)

Methanol synthesis carbon dioxide input [tCO2/MWh-MeOH-LHV] based on 0.0702 kgCO2/MJ_(CH3OH,LHV); Concave & Aramco. (2022). E-Fuels: A techno-economic assessment of European domestic production and imports towards 2050 (Concawe Report 17/22). Retrieved 12 April 2023, from https://www.concawe.eu/publication/e-fuels-a-techno-economic-assessment-of-european-domestic-production-and-imports-towards-2050/ , table 83.

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 with respect to hydrogen [MWh-MeOH-LHV/MWh-H2-LHV] hydrogen input 1.161 MJ_H2/MJ_(CH3OH,LHV); Concave & Aramco. (2022). E-Fuels: A techno-economic assessment of European domestic production and imports towards 2050 (Concawe Report 17/22). Retrieved 12 April 2023, from https://www.concawe.eu/publication/e-fuels-a-techno-economic-assessment-of-european-domestic-production-and-imports-towards-2050/ , table 83.

Methanol synthesis electricity input [MWhel/MWh-MeOH-LHV] based on 0.0499 MJ/MJ_(CH3OH,LHV); Concave & Aramco. (2022). E-Fuels: A techno-economic assessment of European domestic production and imports towards 2050 (Concawe Report 17/22). Retrieved 12 April 2023, from https://www.concawe.eu/publication/e-fuels-a-techno-economic-assessment-of-european-domestic-production-and-imports-towards-2050/ , table 83.

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 4: Solve and wait for results

Ready in around 15 seconds, status: Waiting for job

Results

Average system cost [EUR/MWh]:

Cost is per unit of energy delivered in 2020 euros. For comparison, household electricity rates (including taxes and grid charges) averaged 211 EUR/MWh in the European Union in 2018 & 132 USD/MWh in the United States in 2019

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/S [EUR/MWh]	Rel Mkt Value [%]

Cap Ftr = Capacity Factor, used is after curtailment, avail is available before curtailment, Curtlmt = Curtailment, LCOE/S = Levelised Cost of Electricity/Storage per energy used, Rel Mkt Value = Relative Market Value (market revenue averaged over dispatch divided by average market price)

Background and warnings

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

Electricity systems with zero direct CO2 emissions can be built more cheaply by using additional technology options. The examples here are simply a toy model to put an upper bound on the costs for a very simple setup. Additional 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. Other options include allowing demand to adapt to renewable profiles (demand-side management).
No import or export capacities with other regions are assumed, so each region must meet the baseload profile by itself. Existing and planned transmission grid connections between regions can reduce costs by up to 20% by smoothing wind over a continent-sized area (see e.g. this paper or this one).
Including energy demand sectors other than electricity, like transport, heating and non-electric industrial demand can offer additional flexibility (e.g. load-shifting by battery electric vehicles and thermal storage for electrified heating), see e.g. this paper or this one.
Costs here are for completely decarbonised electricity systems. Reaching lower levels of decarbonisation is much cheaper and doesn't necessarily require any storage at all. A non-zero carbon dioxide emission target and options for fossil-fuelled generators can be set under "advanced assumption settings".
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.
Because the wind and solar profiles are computed using a fixed distribution of power plants, there is no possibility to optimise the distribution of power plants within each region to reduce the costs of providing a baseload profile.
The costs for hydrogen storage assume hydrogen gas is stored in salt caverns in suitable underground salt deposits. 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. Where suitable salt deposits are not available, hydrogen can be stored in aboveground steel tanks for 13 EUR/kWh, fixed operation and maintenance costs of 2% per year and a lifetime of 20 years. Alternatively, you can activate methanol storage in aboveground steel tanks.
Costs for the electricity grid inside each region and costs for ancillary services are not included.

To see results from a more sophisticated decarbonised European energy system, including more renewable energy technologies, sector coupling and cross-border transmission connections, go to the interface to run the detailed European model PyPSA-Eur.

Fuel cells could be used instead of combined or open cycle turbines for hydrogen to power. Fuel cells are less mature than turbines, but have better chances of cost reduction and efficiency improvement.

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 find solved versions run against every hour over 31 weather years here at the WHOBS repository.

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 whobs-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).

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.
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.

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.