LF Energy Summit Europe 2026

We bring LF Energy's PowSyBl into the control room as a decision support tool, combining grid analysis with Reinforcement Learning to solve post-contingency problems immediately. Increasing complexity amplifies overloads, demanding corrective actions beyond what manual operator heuristics and existing tools can deliver in time. Open-source RL agents backed by PowSyBl learn curative topology reconfigurations for the Simbench EHV network in Node-Breaker representation. Agents observe bus voltages, line loadings, injection profiles, and switching states. The action space controls breakers and disconnectors via valid action masks for node splitting, busbar re-assignment, and line (dis)connection. Remedial actions are injected as Operator Strategy actions inside PowSyBl's Security Analysis, faithfully reproducing N-1 assessment in training. We discuss reward design, bonuses, penalties, and action-masking. The talk ends with a live demo: a Leaflet map with colour-coded loadings, interactive single-line diagrams, forecast plots, and a control panel triggering Security Analysis and visualising corrective actions via PowSyBl's Operator Strategy in real time.