The rise of renewable energy and distributed generation requires new approaches to overcome the limitations of traditional methods. In this context, Graph Neural Networks are promising due to their ability to learn from graph-structured data. Combined with Reinforcement Learning, they can serve as control approaches to determine remedial network actions. This review analyses how Graph Reinforcement Learning (GRL) can improve representation learning and decision making in power grid use cases. Although GRL has demonstrated adaptability to unpredictable events and noisy data, it is primarily at a proof-of-concept stage. We highlight open challenges and limitations with respect to real-world applications.

翻译：可再生能源与分布式发电的兴起要求采用新方法以克服传统方法的局限性。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展现出广阔前景。与强化学习相结合后，它们可作为控制方法来确定电网的补救措施。本文综述分析了图强化学习（GRL）如何提升电力系统应用场景中的表征学习与决策能力。尽管GRL已展现出对不可预测事件和噪声数据的适应能力，但目前主要处于概念验证阶段。我们重点指出了其在现实世界应用中面临的开放性挑战与局限性。