Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networks

2026-03-07

AI Summary

Extreme weather events and cyberattacks can severely disrupt distribution systems, necessitating efficient reconfiguration and load-shedding strategies to enhance resilience. This work proposes a graph-based reinforcement learning framework that integrates high-order topological features, uniquely embedding topological data analysis—specifically persistent homology—into graph neural networks and the reinforcement learning pipeline to improve the model’s understanding of network structure and self-healing capabilities. Experiments on the IEEE 123-node system demonstrate that the proposed method achieves a 9–18% higher cumulative reward, up to 6% more energy delivered, and a 6–8% reduction in voltage violations compared to baseline models, significantly optimizing power supply while maintaining operational stability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Extreme weather events and cyberattacks can cause component failures and disrupt the operation of power distribution networks (DNs), during which reconfiguration and load shedding are often adopted for resilience enhancement. This study introduces a topology-aware graph reinforcement learning (RL) framework for outage management that embeds higher-order topological features of the DN into a graph-based RL model, enabling reconfiguration and load shedding to maximize energy supply while maintaining operational stability. Results on the modified IEEE 123-bus feeder across 300 diverse outage scenarios demonstrate that incorporating the topological data analysis (TDA) tool, persistence homology (PH), yields 9-18% higher cumulative rewards, up to 6% increase in power delivery, and 6-8% fewer voltage violations compared to a baseline graph-RL model. These findings highlight the potential of integrating RL with TDA to enable self-healing in DNs, facilitating fast, adaptive, and automated restoration.

Problem

• power distribution networks
• resilience
• outage management
• reconfiguration
• load shedding

Innovation

• topology-aware reinforcement learning
• graph neural networks
• persistence homology
• power distribution networks
• topological data analysis