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.
Step 1: Select location and weather year
Select from map above.
Only years from 2011 to 2013 inclusive are available (until we get a bigger hard-drive).
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
Demand should be large enough for utility-scale assets but small enough to neglect land availability constraints.
Individual cost assumptions can be changed below under "advanced assumption settings".
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):
Cap Ftr used [%]
Cap Ftr avail [%]
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
(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 (0.7 EUR/kWh) assume hydrogen gas is stored in salt caverns in suitable underground salt deposits based on assumptions from Welder et al 2018. These costs are higher than assumed in e.g. Table 3 of NREL 2009 (0.49 USD/kWh) using the most expensive option ("rock caverns created by excavating comparatively impervious rock formations"). 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 11 EUR/kWh.
Costs for the electricity grid inside each region and costs for ancillary services are not included.
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
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 and optimisation model construction and post-processing are all built with
free software and open data only. The commercial optimisation solver Gurobi is used for its
high performance on this website, but it can also be replaced with an open source solver such as CLP if
performance is not required. 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
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.