mlsanityData to 7 Oct 2026

Learning to run a Power Network Challenge: a Retrospective Analysis

arXiv 2103.03104 ↗First posted 2 Mar 2021cs.LG cs.SY eess.SYcited by 52

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Abstract (arXiv)

Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand and production profiles, with increasing renewable energy integration, as well as the high voltage network technology, constitute a real challenge for human operators when optimizing electricity transportation while avoiding blackouts. Motivated to investigate the potential of AI methods in enabling adaptability in power network operation, we have designed a L2RPN challenge to encourage the development of reinforcement learning solutions to key problems present in the next-generation power networks. The NeurIPS 2020 competition was well received by the international community attracting over 300 participants worldwide. The main contribution of this challenge is our proposed comprehensive 'Grid2Op' framework, and associated benchmark, which plays realistic sequential network operations scenarios. The Grid2Op framework, which is open-source and easily re-usable, allows users to define new environments with its companion GridAlive ecosystem. Grid2Op relies on existing non-linear physical power network simulators and let users create a series of perturbations and challenges that are representative of two important problems: a) the uncertainty resulting from the increased use of unpredictable renewable energy sources, and b) the robustness required with contingent line disconnections. In this paper, we give the competition highlights. We present the benchmark suite and analyse the winning solutions, including one super-human performance demonstration. We propose our organizational insights for a successful competition and conclude on open research avenues. Given the challenge success, we expect our work will foster research to create more sustainable solutions for power network operations.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

18 of 37 references.

References · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  3. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  4. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 300
  5. 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 79
  6. 2019The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithmscited by 63
  7. 2017Safe and Nested Subgame Solving for Imperfect-Information Gamescited by 39
  8. 2019Learning to run a power network challenge for training topology controllerscited by 33
  9. 2020Flatland-RL : Multi-Agent Reinforcement Learning on Trainscited by 32
  10. 2019Adaptive Power System Emergency Control using Deep Reinforcement Learningcited by 26
  11. 2020Reinforcement Learning for Electricity Network Operationcited by 20
  12. 2020Exploring grid topology reconfiguration using a simple deep reinforcement learning approachcited by 15
6 more
  1. 2019AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCscited by 14
  2. 2019Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansioncited by 13
  3. 2019Artificial Intelligence for Prosthetics - challenge solutionscited by 12
  4. 2020An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Searchcited by 12
  5. 2020pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Researchcited by 10
  6. 2020Adversarial Training for a Continuous Robustness Control Problem in Power Systemscited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 5; 2024: 15; 2025: 8; 2026: 12.

52 papers.

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  1. 2024Application-Driven Innovation in Machine Learningcited by 24
  2. 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
  3. 2021Learning to run a power network with trustcited by 17
  4. 2022PowRL: A Reinforcement Learning Framework for Robust Management of Power Networkscited by 17
  5. 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 15
  6. 2021A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Gridscited by 11
  7. 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
  8. 2021PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systemscited by 10
  9. 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approachcited by 10
  10. 2022Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Designcited by 8
  11. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operationscited by 8
  12. 2022Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AIcited by 6
38 more
  1. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  2. 2021A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systemscited by 5
  3. 2024Enhancing Safety in Reinforcement Learning with Human Feedback via Rectified Policy Optimizationcited by 5
  4. 2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Controlcited by 5
  5. 2025Reinforcement learning with combinatorial actions for coupled restless banditscited by 5
  6. 2024Blackout Mitigation via Physics-guided RLcited by 3
  7. 2024CommonPower: A Framework for Safe Data-Driven Smart Grid Controlcited by 3
  8. 2024Imitation Learning for Intra-Day Power Grid Operation through Topology Actionscited by 3
  9. 2024State and Action Factorization in Power Gridscited by 3
  10. 2024InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemmacited by 3
  11. 2021Soft Actor-Critic With Integer Actionscited by 2
  12. 2024Fault Detection for agents on power grid topology optimization: A Comprehensive analysiscited by 2
  13. 2024Reinforcement Learning for Sustainable Energy: A Surveycited by 2
  14. 2025Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representationscited by 2
  15. 2026Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challengecited by 2
  16. 2023Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Controlcited by 1
  17. 2024RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factorscited by 1
  18. 2025Multi-Objective Reinforcement Learning for Power Grid Topology Controlcited by 1
  19. 2025Machine Learning for Physical Simulation Challenge Results and Retrospective Analysis: Power Grid Use Casecited by 1
  20. 2026Hierarchical Reinforcement Learning with Runtime Safety Shielding for Power Grid Operationcited by 1
  21. 2026Audited Selective Verification for Risk-Controlled N-1 Thermal Contingency Screening under Deployment Shiftcited by 1
  22. 2026LLM Agents for Time-Series: A Surveycited by 1
  23. 2022Efficient entity-based reinforcement learningcited by 0
  24. 2022Climate Change Policy Exploration using Reinforcement Learningcited by 0
  25. 2022Reinforcement Learning for Resilient Power Gridscited by 0
  26. 2023Multi-Agent Reinforcement Learning for Power Grid Topology Optimizationcited by 0
  27. 2023Towards impactful challenges: post-challenge paper, benchmarks and other dissemination actionscited by 0
  28. 2024AI Competitions and Benchmarks, Practical issues: Proposals, grant money, sponsors, prizes, dissemination, publicitycited by 0
  29. 2024Challenge design roadmapcited by 0
  30. 2024Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agentscited by 0
  31. 2025On the Definition of Robustness and Resilience of AI Agents for Real-time Congestion Managementcited by 0
  32. 2025Power Grid Control with Graph-Based Distributed Reinforcement Learningcited by 0
  33. 2026MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Marketscited by 0
  34. 2026Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Gridscited by 0
  35. 2026Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programscited by 0
  36. 2026Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimizationcited by 0
  37. 2026Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildingscited by 0
  38. 2026Power Systems Agent Benchmark: Executable Evaluation of AI Agents in Electric Power Engineeringcited by 0

50 most cited of 52 All 52 (JSON)