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Reinforcement Learning for Electricity Network Operation

arXiv 2003.07339 ↗First posted 16 Mar 2020eess.SP cs.LG stat.MLcited by 20

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Abstract (arXiv)

This paper presents the background material required for the Learning to Run Power Networks Challenge. The challenge is focused on using Reinforcement Learning to train an agent to manage the real-time operations of a power grid, balancing power flows and making interventions to maintain stability. We present an introduction to power systems targeted at the machine learning community and an introduction to reinforcement learning targeted at the power systems community. This is to enable and encourage broader participation in the challenge and collaboration between these two communities.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 7; 2025: 4; 2026: 3.

20 papers.

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  1. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 52
  2. 2022Power Grid Congestion Management via Topology Optimization with AlphaZerocited by 20
  3. 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 15
  4. 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
  5. 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approachcited by 10
  6. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operationscited by 8
  7. 2021Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Trainingcited by 6
  8. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  9. 2024Blackout Mitigation via Physics-guided RLcited by 3
  10. 2024CommonPower: A Framework for Safe Data-Driven Smart Grid Controlcited by 3
  11. 2024State and Action Factorization in Power Gridscited by 3
  12. 2024Fault Detection for agents on power grid topology optimization: A Comprehensive analysiscited by 2
8 more
  1. 2025Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representationscited by 2
  2. 2022Reinforcement Learning and Tree Search Methods for the Unit Commitment Problemcited by 1
  3. 2024RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factorscited by 1
  4. 2025Multi-Objective Reinforcement Learning for Power Grid Topology Controlcited by 1
  5. 2025Power Grid Control with Graph-Based Distributed Reinforcement Learningcited by 0
  6. 2026Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Gridscited by 0
  7. 2026Interpretable Policy Distillation for Power Grid Topology Controlcited by 0
  8. 2026SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Marketscited by 0