The challenges posed by renewable energy and distributed electricity generation motivate the development of deep learning approaches to overcome the lack of flexibility of traditional methods in power grids use cases. The application of GNNs is particularly promising due to their ability to learn from graph-structured data present in power grids. Combined with RL, they can serve as control approaches to determine remedial grid actions. This review analyses the ability of GRL to capture the inherent graph structure of power grids to improve representation learning and decision making in different power grid use cases. It distinguishes between common problems in transmission and distribution grids and explores the synergy between RL and GNNs. In transmission grids, GRL typically addresses automated grid management and topology control, whereas on the distribution side, GRL concentrates more on voltage regulation. We analyzed the selected papers based on their graph structure and GNN model, the applied RL algorithm, and their overall contributions. Although GRL demonstrate adaptability in the face of unpredictable events and noisy or incomplete data, it primarily serves as a proof of concept at this stage. There are multiple open challenges and limitations that need to be addressed when considering the application of RL to real power grid operation.

利用深度学习方法以解决传统方法在电网应用中的灵活性不足所带来的可再生能源和分布式电力生成带来的挑战的文章中，重点分析了图神经网络在电力网中捕捉图结构以提高表示学习和决策制定能力的能力，同时探讨了强化学习与图神经网络之间的协同作用，尤其将GRL用于自动化电网管理、拓扑控制和电压调节等应用。然而，尽管GRL在面对不可预测事件、噪声或不完整数据时显示出适应性，但目前它主要是一个概念验证。在实际电力系统运行中，仍然需要解决许多开放性挑战和限制问题。

电网中的图强化学习：一项调查研究