# Blackout Mitigation via Physics-guided RL

arXiv: https://arxiv.org/abs/2401.09640 · HTML: https://mlsanity.com/paper/2401.09640/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 43 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Constrained Reinforcement Learning Has Zero Duality Gap](https://mlsanity.com/paper/1910.13393/) (2019)
- [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)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [Reinforcement Learning for Electricity Network Operation](https://mlsanity.com/paper/2003.07339/) (2020)
- [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)
- [AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs](https://mlsanity.com/paper/1911.04263/) (2019)
- [Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading](https://mlsanity.com/paper/2112.09996/) (2021)
- [GRNN-based Real-time Fault Chain Prediction](https://mlsanity.com/paper/2303.08864/) (2023)

## Built on by (most cited first, 3 of 3)

- [Transfer Learning for a Class of Cascade Dynamical Systems](https://mlsanity.com/paper/2410.06828/) (2024)
- [RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors](https://mlsanity.com/paper/2411.18050/) (2024)
- [Real-Time Risky Fault-Chain Search using Time-Varying Graph RNNs](https://mlsanity.com/paper/2503.09775/) (2025)

## Abstract (arXiv)

This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black box counterparts. One important observation is that strategically~removing transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts.