AI challenge for safe and low carbon power grid operation
Adrien Pavão, Antoine Marot, Jules Sintes, Viktor Eriksson Möllerstedt, Laure Crochepierre, Karim Chaouache, Benjamin Donnot, Van Tuan Dang, Isabelle Guyon
Abstract
Achieving carbon neutrality by 2050 will require power-grid operators to absorb unprecedented volumes of variable solar and wind generation while maintaining reliability. To tackle this systems-level bottleneck, Réseau de Transport d’Électricité (RTE) and the research community launched Learn To Run A Power Network (L2RPN), a crowd-sourced competition aiming to accelerate the integration of intermittent renewables into power-grid operations. L2RPN is based on 16 years of weekly scenarios (832 in total) on a 118-node grid under realistic constraints, and casts real-time grid operation as a Markov-Decision-Process. The six participating teams tackled the challenge by developing autonomous agents with various strategies blending heuristics, optimization, data scaling, supervised learning, and reinforcement learning. We provide a detailed overview of all six participants’ performance under the competition’s demanding design. In addition, we present an in-depth analysis of the winning solution - made publicly available - which achieves consistent decision making across scenarios, executes real-time multimodal actions in under five seconds, and performs efficient topology control via action-space reduction and a neural policy that predicts useful grid actions with over 80% accuracy. In parallel, we trained a neural alert module on 315,000 samples derived from top agents, achieving 93.9% recall in flagging dangerous states and allowing agents to predict future failure. Finally, this work not only demonstrates AI’s promise and current limits in real-time grid management but also lays a transparent foundation for more robust, trustworthy systems in the energy transition.