Graph Reinforcement Learning for Power Grids: A Comprehensive Survey

TL;DR

This survey reviews how Graph Reinforcement Learning combines Graph Neural Networks and Reinforcement Learning to enhance decision-making in power grids, highlighting current challenges and potential for future deployment.

Contribution

It provides a comprehensive analysis of Graph Reinforcement Learning applications in power grids, emphasizing architecture, data structure, and real-world challenges.

Findings

Demonstrates adaptability to unpredictable events and noisy data

Identifies current proof-of-concept stage and deployment challenges

Highlights open issues for real-world implementation

Abstract

The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this context, Graph Neural Networks are a promising solution due to their ability to learn from graph-structured data. Combined with Reinforcement Learning, they can be used as control approaches to determine remedial actions. This review analyses how Graph Reinforcement Learning can improve representation learning and decision-making in power grid applications, particularly transmission and distribution grids. We analyze the reviewed approaches in terms of the graph structure, the Graph Neural Network architecture, and the Reinforcement Learning approach. Although Graph Reinforcement Learning has demonstrated adaptability to unpredictable events and noisy data, its current stage is primarily proof-of-concept, and it is not yet deployable to real-world applications. We highlight the open challenges and limitations for real-world applications.