mlsanityData to 14 Oct 2026

Reinforcement Learning for Public Safety Power Shutoffs Under Decision-Dependent Uncertainty and Nonlinear Wildfire Ignition Models

arXiv 2604.26150 ↗First posted 28 Apr 2026math.OCcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Power grid infrastructure is an increasingly significant source of wildfire ignitions and poses severe risks to communities in fire-prone regions. Public Safety Power Shutoffs (PSPS) have emerged as a critical operational tool for utilities to mitigate this risk by proactively de-energizing portions of the grid under high-threat conditions. These shutoffs, however, impose costs on affected communities, and it is therefore essential that PSPS decisions be informed by realistic models of wildfire ignition risk. Current Mixed Integer Programming based methods require restrictive structural assumptions about the probability models for line failures caused by power line ignitions. While these simplifications yield tractable solutions, the resulting models may differ significantly from the true underlying dynamics. In this paper, we propose a reinforcement learning framework based on Proximal Policy Optimization that learns to adjust the topology of a distribution system by interacting directly with a simulator that accommodates any line failure probability model without imposing such restrictions. We test our methodology on 54-bus and 138-bus distribution systems and demonstrate its ability to lower operational costs compared to existing methods while allowing only marginally increased compute times as network size grows.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 25 references that are in the map. First posted on arXiv 28 Apr 2026.

Builds on

8 of 25 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2015Deep Reinforcement Learning in Large Discrete Action Spacescited by 161
  4. 2016Learning values across many orders of magnitudecited by 85
  5. 2020Balancing Wildfire Risk and Power Outages through Optimized Power Shut-Offscited by 9
  6. 2018Small vulnerable sets determine large network cascades in power gridscited by 8
  7. 2023Distribution System Operation Amidst Wildfire-Prone Climate Conditions Under Decision-Dependent Line Availability Uncertaintycited by 2
  8. 2024Decision-Dependent Uncertainty-Aware Distribution System Planning Under Wildfire Riskcited by 1

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  1. 2026Hierarchical Reinforcement Learning with Runtime Safety Shielding for Power Grid Operation
  2. 2024RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
  3. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systems
  4. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  5. 2026Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantification
  6. 2024Blackout Mitigation via Physics-guided RL
  7. 2024Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents
  8. 2024Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control