# Power Grid Congestion Management via Topology Optimization with AlphaZero

arXiv: https://arxiv.org/abs/2211.05612 · HTML: https://mlsanity.com/paper/2211.05612/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Learning to run a power network challenge for training topology controllers](https://mlsanity.com/paper/1912.04211/) (2019)
- [Action Set Based Policy Optimization for Safe Power Grid Management](https://mlsanity.com/paper/2106.15200/) (2021)
- [Reinforcement Learning for Electricity Network Operation](https://mlsanity.com/paper/2003.07339/) (2020)
- [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021)
- [Exploring grid topology reconfiguration using a simple deep reinforcement learning approach](https://mlsanity.com/paper/2011.13465/) (2020)
- [Towards an AI assistant for power grid operators](https://mlsanity.com/paper/2012.02026/) (2020)
- [Substation-Level Grid Topology Optimization Using Bus Splitting](https://mlsanity.com/paper/2009.14418/) (2020)

## Built on by (most cited first, 20 of 20)

- [Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents](https://mlsanity.com/paper/2304.00765/) (2023)
- [Hierarchical Reinforcement Learning for Power Network Topology Control](https://mlsanity.com/paper/2311.02129/) (2023)
- [HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach](https://mlsanity.com/paper/2405.00629/) (2024)
- [Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control](https://mlsanity.com/paper/2502.08681/) (2025)
- [Think Twice, Act Once: A Co-Evolution Framework of LLM and RL for Large-Scale Decision Making](https://mlsanity.com/paper/2506.02522/) (2025)
- [Blackout Mitigation via Physics-guided RL](https://mlsanity.com/paper/2401.09640/) (2024)
- [State and Action Factorization in Power Grids](https://mlsanity.com/paper/2409.04467/) (2024)
- [Fault Detection for agents on power grid topology optimization: A Comprehensive analysis](https://mlsanity.com/paper/2406.16426/) (2024)
- [Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach](https://mlsanity.com/paper/2503.15190/) (2025)
- [Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control](https://mlsanity.com/paper/2503.20688/) (2025)
- [Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control](https://mlsanity.com/paper/2302.07654/) (2023)
- [Active Third-Person Imitation Learning](https://mlsanity.com/paper/2312.16365/) (2023)
- [Multi-Objective Reinforcement Learning for Power Grid Topology Control](https://mlsanity.com/paper/2502.00040/) (2025)
- [Power Grid Control with Graph-Based Distributed Reinforcement Learning](https://mlsanity.com/paper/2509.02861/) (2025)
- [Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids](https://mlsanity.com/paper/2604.01830/) (2026)
- [Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control](https://mlsanity.com/paper/2608.14114/) (2026)
- [Multi-Agent Reinforcement Learning for Power Grid Topology Optimization](https://mlsanity.com/paper/2310.02605/) (2023)
- [Opportunities of Reinforcement Learning in South Africa's Just Transition](https://mlsanity.com/paper/2411.15145/) (2024)
- [Transferable Graph Learning for Transmission Congestion Management via Busbar Splitting](https://mlsanity.com/paper/2510.20591/) (2025)
- [A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN](https://mlsanity.com/paper/2609.02538/) (2026)

## 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.