# Reinforcement Learning for Electricity Network Operation

arXiv: https://arxiv.org/abs/2003.07339 · HTML: https://mlsanity.com/paper/2003.07339/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

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

- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Power Grid Congestion Management via Topology Optimization with AlphaZero](https://mlsanity.com/paper/2211.05612/) (2022)
- [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)
- [RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations](https://mlsanity.com/paper/2503.23101/) (2025)
- [Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training](https://mlsanity.com/paper/2110.08956/) (2021)
- [Graph Reinforcement Learning for Power Grids: A Comprehensive Survey](https://mlsanity.com/paper/2407.04522/) (2024)
- [CommonPower: A Framework for Safe Data-Driven Smart Grid Control](https://mlsanity.com/paper/2406.03231/) (2024)
- [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)
- [Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations](https://mlsanity.com/paper/2501.07186/) (2025)
- [Reinforcement Learning and Tree Search Methods for the Unit Commitment Problem](https://mlsanity.com/paper/2212.06001/) (2022)
- [RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors](https://mlsanity.com/paper/2411.18050/) (2024)
- [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)
- [Interpretable Policy Distillation for Power Grid Topology Control](https://mlsanity.com/paper/2606.00561/) (2026)
- [SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets](https://mlsanity.com/paper/2607.08681/) (2026)

## 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.