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:
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 and an interface to run the detailed European model PyPSA-Eur.
Warning: It takes about 4 seconds to fetch the available wind and solar output (longer for custom selections).
Status: Waiting for job
Choose which technologies should be included:
Show advanced assumption settings
All costs are in 2015 euros, following the Danish Energy Agency Technology Data Catalogue.
Ready in around 15 seconds, status: Waiting for job
Average system cost [EUR/MWh]:Cost is per unit of energy delivered in 2015 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
Dispatch over a year to meet the constant demand (electricity demand is the black line; you can zoom and pan to see the details; negative values correspond to storage consuming electricity):
|Asset||Capacity||Cap Ftr used [%]||Cap Ftr avail [%]||Curtlmt [%]||LCOE/S [EUR/MWh]||Rel Mkt Value [%]|
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 95%. Fixed Operation and Maintenance (FOM) costs are set at 3% of the investment cost of the asset per year.
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.
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).
See the GitHub issue tracker.
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.