Offtakers buy firm energy, not capacity factors. Design for the hours.
The projects that win offtake now are hybrids shaped to a load: generation plus storage, sized so the delivery profile holds up hour by hour. Levelised lets you design that plant on a canvas and optimise it across a full year, so storage duration, curtailment and firming cost are outputs of a solve, not inputs to a pitch.
Questions this answers
- What storage capacity and duration lets this solar-wind hybrid meet a flat 50 MW offtake profile, and what does that firming add per MWh?
- Hour by hour, how much of this plant's output can we credibly commit to under a PPA before the shortfall risk lands on us?
- Does co-locating with the load beat a grid-delivered PPA on delivered cost once network charges and losses are in the model?
A capacity factor is an average, and offtakers no longer buy averages. Whether the counterparty wants a baseload strip, a matched hourly profile or a floor on delivered volume, the commercial question is the same: what can this plant deliver in its worst hours, and what does it cost to close the gap? Solving every hour of the year answers that directly.
Levelised optimises sizing and dispatch together. Add solar and wind resource profiles, let the solver choose capacities and storage, and read off the trade-off between over-building, storing and falling short. Test co-location against grid delivery, or a merchant tail against a contracted core, as variants of the same model.
Under the hood it is the open-source PyPSA framework with the HiGHS solver: transparent, published methods your offtaker's advisors can verify. Every model downloads as an open PyPSA file or results CSV, with a Python script export on the paid tier.
Run the numbers yourself
Build the hybrid, set the offtake profile, and solve for the least-cost design. Free, in the browser.
Or start from the Renewable Energy example : a complete, solvable model that opens straight in the app, no account needed.