Sebastien Behaghel

Renewables now provide nearly a third of Europe's electricity, and that share is climbing fast. The shift is good news for the climate, but it has created a new problem for the people who actually operate renewable assets and trade their output. Electricity has to be sold before it is produced. When actual production deviates from what was committed, operators pay heavy penalties to rebalance the grid. These penalties are growing fast: on Easter Monday 2026, Belgian imbalance prices hit €15,000/MWh, 250 times the normal level. A single five-hour window wiped out 50 days of revenue. The intuitive fix is to chase more accuracy. But accuracy isn't where the money is lost. Renewables are no longer a marginal contributor to the grid; they are a market mover. When one wind farm or solar park deviates from its committed volume, others usually deviate at the same time, the grid runs short or long, prices swing violently, and every operator is exposed at once. Asset risk and market risk are no longer two separate things, they happen together. Operators need tools that quantify that link and tell them what to do about it. WindLens is the AI risk-intelligence platform for renewable operators and traders. We combine portfolio production data, weather, grid conditions, and price signals to identify the moments when asset risk meets market risk, and turn those moments into clear bidding decisions that protect revenue. Operators receive this as a signal that plugs into their existing trading workflow, a dashboard they can audit, or through an AI trading assistant they can reason with. They stay in control. We make them sharper when it matters most. We are starting with wind, where the asset-market link is sharpest today and where European pilots are already running. We have demonstrated a $50K annual revenue uplift on a 40 MW park, and are working with design and dissemination partners to expand across their combined 15 GW of wind capacity. Solar is the natural next step. WindLens was founded by Matteo Peduto and Sebastien Behaghel. Matteo brings machine learning expertise from research at EPFL and MIT. Sebastien studied energy at Oxford and worked in European power markets before joining full-time. The company is incubated at EPFL and supported by the EPFL AI Center, the Swiss National Science Foundation, and Innosuisse.