# Learning to run a power network challenge for training topology controllers

arXiv: https://arxiv.org/abs/1912.04211 · HTML: https://mlsanity.com/paper/1912.04211/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 30 of 34)

- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Action Set Based Policy Optimization for Safe Power Grid Management](https://mlsanity.com/paper/2106.15200/) (2021)
- [Power Grid Congestion Management via Topology Optimization with AlphaZero](https://mlsanity.com/paper/2211.05612/) (2022)
- [PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks](https://mlsanity.com/paper/2212.02397/) (2022)
- [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021)
- [Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents](https://mlsanity.com/paper/2304.00765/) (2023)
- [PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.03970/) (2021)
- [A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids](https://mlsanity.com/paper/2110.06324/) (2021)
- [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)
- [Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems](https://mlsanity.com/paper/2203.07505/) (2022)
- [RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations](https://mlsanity.com/paper/2503.23101/) (2025)
- [Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading](https://mlsanity.com/paper/2112.09996/) (2021)
- [Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design](https://mlsanity.com/paper/2207.10330/) (2022)
- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [Blackout Mitigation via Physics-guided RL](https://mlsanity.com/paper/2401.09640/) (2024)
- [Imitation Learning for Intra-Day Power Grid Operation through Topology Actions](https://mlsanity.com/paper/2407.19865/) (2024)
- [Fault Detection for agents on power grid topology optimization: A Comprehensive analysis](https://mlsanity.com/paper/2406.16426/) (2024)
- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations](https://mlsanity.com/paper/2501.07186/) (2025)
- [Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach](https://mlsanity.com/paper/2503.15190/) (2025)
- [Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control](https://mlsanity.com/paper/2302.07654/) (2023)
- [Multi-Objective Reinforcement Learning for Power Grid Topology Control](https://mlsanity.com/paper/2502.00040/) (2025)
- [Machine Learning for Physical Simulation Challenge Results and Retrospective Analysis: Power Grid Use Case](https://mlsanity.com/paper/2505.01156/) (2025)
- [Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures](https://mlsanity.com/paper/2205.02108/) (2022)
- [Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review](https://mlsanity.com/paper/2205.08369/) (2022)
- [Multi-Agent Reinforcement Learning for Power Grid Topology Optimization](https://mlsanity.com/paper/2310.02605/) (2023)
- [Challenge design roadmap](https://mlsanity.com/paper/2401.13693/) (2024)
- [DIAL: Distribution-Informed Adaptive Learning of Multi-Task Constraints for Safety-Critical Systems](https://mlsanity.com/paper/2501.18086/) (2025)
- [Power Systems Agent Benchmark: Executable Evaluation of AI Agents in Electric Power Engineering](https://mlsanity.com/paper/2606.20950/) (2026)

## Abstract (arXiv)

For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities. However, a less costly and potentially more flexible option would be grid topology reconfiguration, as already partially exploited by Coreso (European RSC) and RTE (French TSO) operations. Beyond previous work on branch switching, bus reconfigurations are a broader class of action and could provide some substantial benefits to route electricity and optimize the grid capacity to keep it within safety margins. Because of its non-linear and combinatorial nature, no existing optimal power flow solver can yet tackle this problem. We here propose a new framework to learn topology controllers through imitation and reinforcement learning. We present the design and the results of the first "Learning to Run a Power Network" challenge released with this framework. We finally develop a method providing performance upper-bounds (oracle), which highlights remaining unsolved challenges and suggests future directions of improvement.