Renewables are being added faster than the network that has to carry them. These three demos run EnliteAI’s agents on real grid topologies — proposing, pricing and simulating the operational decisions that keep the system inside its limits.
288 decisions a day on transformer taps and injection setpoints, holding a real SimBench distribution grid inside its voltage and thermal limits, and curtailing only when nothing else will.
Take the operator’s seat for a simulated week: when a line approaches its thermal limit, the agent stops the clock, proposes switching plans and simulates each one before you decide.
Plan a full day on a 118-substation benchmark grid and turn the trade-off into a dial — from free topology switching to paid redispatch, with every setting planned out and priced.
All demos run on the same engine as the product — Maze, our open-source RL framework, with physics validation on every proposed action. What they show is what FLEX does; the difference in production is your grid, your data and your constraints.
The proof of concept runs the same engine on your own grid data — and ends in a quantified business case.
