AI challenge for safe and low carbon power grid operation

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.