This tool calculates the cost of meeting constant electricity demand from a combination of wind power, solar power and storage (using batteries and electrolysed hydrogen) 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.
Fun things to try out:
This is a toy model with a strongly simplified setup. Please read the warnings below.
Warning: It takes about 4 seconds to fetch the available wind and solar output.
Status: Waiting for job
Choose which technologies should be included:
Ready in 20 seconds, status: Waiting for job
Baseload demand: MW
|Asset||Capacity||Cap Ftr used [%]||Cap Ftr avail [%]||Curtlmt [%]||Rel Mkt Value [%]|
Average system cost [EUR/MWh]:
This is a toy model with a strongly simplified setup. Please read the warnings before interpreting the results. In particular:
To see an animation of a 95% decarbonised European electricity system, including more renewable energy technologies and cross-border transmission connections, see:
Here asset lifetimes are all assumed to be 25 years, with the exception of batteries (15 years) and hydrogen electrolysers (20 years). Battery charging and discharging efficiencies are both 90%. Fixed Operation and Maintenance (FOM) costs are set at 3% of the investment cost of the asset per year.
The sources for most of the assumptions can be found in this table.
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.
This tool is built only with free software and open data; the code for it can be downloaded from the GitHub repository whobs-server. In particular, 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, the Clp solver, D3.js for graphics, Mapbox, Leaflet and Natural Earth for maps, and free software for the server infrastructure (GNU/Linux, nginx, Flask, gunicorn, Redis).
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.
We thank also all the developers of the other free software on which model.energy depends.
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.