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.

翻译：可再生能源和分布式发电占比的不断提高，要求开发深度学习方法以解决传统电网方法固有的灵活性不足问题。在此背景下，图神经网络因其从图结构数据中学习的能力而成为一种有前景的解决方案。与强化学习相结合，它们可作为控制方法来确定补救措施。本文综述分析了图强化学习如何改进电网应用（特别是输电网和配电网）中的表征学习和决策制定。我们从图结构、图神经网络架构和强化学习方法三个方面对相关方法进行了分析。尽管图强化学习已展现出对不可预测事件和噪声数据的适应能力，但其当前阶段主要处于概念验证水平，尚未能部署到实际应用中。我们重点指出了实际应用中的开放挑战与局限性。