mlsanityData to 7 Oct 2026

Power Grid Congestion Management via Topology Optimization with AlphaZero

arXiv 2211.05612 ↗First posted 10 Nov 2022cs.AI cs.LGcited by 20

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Abstract (arXiv)

The energy sector is facing rapid changes in the transition towards clean renewable sources. However, the growing share of volatile, fluctuating renewable generation such as wind or solar energy has already led to an increase in power grid congestion and network security concerns. Grid operators mitigate these by modifying either generation or demand (redispatching, curtailment, flexible loads). Unfortunately, redispatching of fossil generators leads to excessive grid operation costs and higher emissions, which is in direct opposition to the decarbonization of the energy sector. In this paper, we propose an AlphaZero-based grid topology optimization agent as a non-costly, carbon-free congestion management alternative. Our experimental evaluation confirms the potential of topology optimization for power grid operation, achieves a reduction of the average amount of required redispatching by 60%, and shows the interoperability with traditional congestion management methods. Our approach also ranked 1st in the WCCI 2022 Learning to Run a Power Network (L2RPN) competition. Based on our findings, we identify and discuss open research problems as well as technical challenges for a productive system on a real power grid.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 10 Nov 2022.

Builds on

11 of 30 references.

References · 11

  1. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  2. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 723
  3. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
  4. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 52
  5. 2019Learning to run a power network challenge for training topology controllerscited by 33
  6. 2020Reinforcement Learning for Electricity Network Operationcited by 20
  7. 2021Action Set Based Policy Optimization for Safe Power Grid Managementcited by 18
  8. 2021Learning to run a power network with trustcited by 17
  9. 2020Exploring grid topology reconfiguration using a simple deep reinforcement learning approachcited by 15
  10. 2020Towards an AI assistant for power grid operatorscited by 6
  11. 2020Substation-Level Grid Topology Optimization Using Bus Splittingcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 5; 2025: 7; 2026: 3.

20 papers.

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  1. 2023Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentscited by 15
  2. 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
  3. 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approachcited by 10
  4. 2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Controlcited by 5
  5. 2025Think Twice, Act Once: A Co-Evolution Framework of LLM and RL for Large-Scale Decision Makingcited by 5
  6. 2024Blackout Mitigation via Physics-guided RLcited by 3
  7. 2024State and Action Factorization in Power Gridscited by 3
  8. 2024Fault Detection for agents on power grid topology optimization: A Comprehensive analysiscited by 2
  9. 2025Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approachcited by 2
  10. 2025Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Controlcited by 2
  11. 2023Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Controlcited by 1
  12. 2023Active Third-Person Imitation Learningcited by 1
8 more
  1. 2025Multi-Objective Reinforcement Learning for Power Grid Topology Controlcited by 1
  2. 2026Learning to Run Power Networks: Effective AlphaZero-inspired Topological Controlcited by 1
  3. 2023Multi-Agent Reinforcement Learning for Power Grid Topology Optimizationcited by 0
  4. 2024Opportunities of Reinforcement Learning in South Africa's Just Transitioncited by 0
  5. 2025Power Grid Control with Graph-Based Distributed Reinforcement Learningcited by 0
  6. 2025Transferable Graph Learning for Transmission Congestion Management via Busbar Splittingcited by 0
  7. 2026Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Gridscited by 0
  8. 2026A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPNcited by 0