Module
Optimisation
Least-cost dispatch and capacity models. Open a model to see what it is made of — the fleet it can call on and the demand it has to serve — then run it and read the schedule it chooses.
Models, inputs and results
Every model the platform can solve or has already solved. Open one to see the fleet and demand it is asked to serve — and, where it has been run, what it chose.
Storage dispatch result
The battery optimiser's answer, plotted: state of charge, charge and discharge against the day-ahead price it arbitraged — from your stored runs, never solved on the fly.
Run one on your numbers
Battery dispatch, unit commitment, network dispatch and the rest — against real day-ahead prices or your own inputs, with the schedule plotted panel per unit.
Optimiser catalogue
The solvers of the problems themselves — LP, MILP and PyPSA formulations — with what each one assumes.
Solver backends
Which mathematical solvers are wired in and which are reachable from this deployment, stated rather than implied.