Each pathway leads to the number that drives your returns: the wholesale price your assets capture. The engine is identical in all three. Only the inputs change.
Upside and downside are framed from your point of view — higher wholesale prices help merchant returns, faster clean build-out squeezes them. Tilt the supply build and commodity prices within any scenario — down to individual VRE technologies — or commission a fully custom one.
The same engine runs every scenario, and every input it uses is visible on screen.
Hourly prices, capacity and demand from ENTSO-E, IRENA, Ember, Eurostat: 2024 hindcast to a mean error of 0.10 €/MWh (on the observed 2024 weather), in sample.
Fuel & CO₂, demand, nuclear, BESS and per-technology VRE — all visible and adjustable.
A merit-order LP (PyPSA-Eur formulation, HiGHS solver) clears every hour, per market, per year to 2050 — ~15s/year, cached in under 10.
Monte-Carlo weather re-draws turn a single curve into a P10–P90 band — every output ships with its interval.
Transparency includes the limitations. The model currently over-states how much of the day-ahead price a solar plant captures in France, Poland, the Netherlands and Belgium — by 3 to 17 percentage points against 2024 outturn — so a solar PPA in those markets looks better on our numbers than in the market. Quantified per zone in the methodology, stated up front, so sophisticated buyers can weight it themselves. Every assumption is yours to set, and every data source is publicly cited.
Run the three pathways yourself, tilt any assumption, or have us scope a bespoke scenario around your asset.