Cheap electrons aren't the hard part. Keeping the plant fed through a calm week is.
Green hydrogen, ammonia and methanol projects live or die on how the whole chain handles intermittency: electrolysers that follow the wind, synthesis loops that can't, and storage buffering the two. Levelised lets you model that chain end to end (power, hydrogen, CO₂, product) and solve a full year hourly, so the cost per tonne reflects how the plant actually runs.
Questions this answers
- What does a tonne of green hydrogen or methanol really cost once electrolyser utilisation reflects a real wind–solar profile rather than a flat capacity factor?
- How much hydrogen or product storage keeps the synthesis loop running through a calm fortnight, and what does that buffer add per tonne?
- Is it cheaper to oversize the renewables, add a battery, or keep a grid connection to hold the electrolyser above its minimum load?
A renewable fuels plant is a chain of conversions with mismatched dynamics: variable renewables feeding an electrolyser that can ramp, feeding a synthesis unit that would rather not. The economics are set by the weakest link: every hour the loop runs below capacity is capital sitting idle, and every buffer that prevents it costs money too. Averages hide all of this; an hourly solve over a full year exposes it.
On the Levelised canvas each stage is a component: generation, electrolysis, storage at every intermediate, capture, synthesis, offtake. The optimiser sizes them together (renewables, electrolyser, tanks and loop) for least cost per unit of product, and shows where the constraint really sits. Test an islanded plant against a grid-supported one, or firm offtake against flexible, as variants of the same model.
The bundled Renewable Fuels example is an islanded green-methanol plant (electrolysis, direct air capture and methanolisation, with storage at every stage), solved and ready to pull apart. Under the hood it's the open-source PyPSA framework with the HiGHS solver: transparent, published methods, and every model exports as an open PyPSA file or results CSV.
Run the numbers yourself
Lay out the chain, set the product offtake, and solve for the least-cost design. Free, in the browser.
Or start from the Renewable Fuels example : a complete, solvable model that opens straight in the app, no account needed.