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Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation

Bo Meng, Chenghao Xu, Yongli Zhu

TL;DR

This paper addresses the challenge of mitigating multi-stage cascading failures in power grids by formulating MSCF as a reinforcement learning task with continuous actions. It develops a Python-MATPOWER based environment and uses the Deep Deterministic Policy Gradient (DDPG) algorithm within an actor–critic framework to learn mitigation policies that adjust generator outputs across stages. The key contributions include (1) a simulation environment for MSCF, (2) a DRL approach capable of continuous control, and (3) validation on IEEE 14-bus and 118-bus systems demonstrating superior performance over baselines in terms of win rate and reward stability. The work advances real-time, multi-stage resilience for complex grids and provides a foundation for integrating RL into grid operation and risk mitigation.

Abstract

Cascading failures in power grids can lead to grid collapse, causing severe disruptions to social operations and economic activities. In certain cases, multi-stage cascading failures can occur. However, existing cascading-failure-mitigation strategies are usually single-stage-based, overlooking the complexity of the multi-stage scenario. This paper treats the multi-stage cascading failure problem as a reinforcement learning task and develops a simulation environment. The reinforcement learning agent is then trained via the deterministic policy gradient algorithm to achieve continuous actions. Finally, the effectiveness of the proposed approach is validated on the IEEE 14-bus and IEEE 118-bus systems.

Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation

TL;DR

This paper addresses the challenge of mitigating multi-stage cascading failures in power grids by formulating MSCF as a reinforcement learning task with continuous actions. It develops a Python-MATPOWER based environment and uses the Deep Deterministic Policy Gradient (DDPG) algorithm within an actor–critic framework to learn mitigation policies that adjust generator outputs across stages. The key contributions include (1) a simulation environment for MSCF, (2) a DRL approach capable of continuous control, and (3) validation on IEEE 14-bus and 118-bus systems demonstrating superior performance over baselines in terms of win rate and reward stability. The work advances real-time, multi-stage resilience for complex grids and provides a foundation for integrating RL into grid operation and risk mitigation.

Abstract

Cascading failures in power grids can lead to grid collapse, causing severe disruptions to social operations and economic activities. In certain cases, multi-stage cascading failures can occur. However, existing cascading-failure-mitigation strategies are usually single-stage-based, overlooking the complexity of the multi-stage scenario. This paper treats the multi-stage cascading failure problem as a reinforcement learning task and develops a simulation environment. The reinforcement learning agent is then trained via the deterministic policy gradient algorithm to achieve continuous actions. Finally, the effectiveness of the proposed approach is validated on the IEEE 14-bus and IEEE 118-bus systems.
Paper Structure (17 sections, 1 equation, 7 figures, 3 tables, 1 algorithm)

This paper contains 17 sections, 1 equation, 7 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Methodology
  3. Multi-Stage Cascading Failure (MSCF) Problem
  4. Environment implementation
  5. Definitions of Step and Episode
  6. State design
  7. Action design
  8. Island detection and availability assessment
  9. Reward design
  10. Experiments and Results
  11. Conclusion
  12. Appendix
  13. The Union-Find Algorithm for Power Grid Island Detection
  14. The topology of the IEEE 118-bus system
  15. Reward comparison
  16. ...and 2 more sections

Figures (7)

  • Figure 1: An example of a multi-stage cascading failure.
  • Figure 2: Island Availability assessment.
  • Figure 3: (a) The overall workflow of grid simulation for MSCF study; (b) The IEEE 14-bus system.
  • Figure 4: The moving-average reward comparison.
  • Figure 5: The topology of the IEEE 118-bus system.
  • ...and 2 more figures