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.

翻译：可再生能源和分布式发电带来的挑战促使深度学习方法的开发，以克服传统方法在电力系统应用场景中灵活性不足的问题。图神经网络（GNN）的应用尤其具有前景，因为它们能够从电力系统中存在的图结构数据中学习。与强化学习（RL）相结合，它们可以作为控制方法来确定电网的补救措施。本综述分析了图强化学习（GRL）捕捉电力系统固有图结构的能力，以改进不同电力系统应用场景中的表示学习和决策制定。它区分了输电网络和配电网络中的常见问题，并探讨了强化学习与图神经网络之间的协同作用。在输电网络中，图强化学习通常处理自动化电网管理和拓扑控制，而在配电侧，图强化学习更侧重于电压调节。我们根据所选论文的图结构和图神经网络模型、应用的强化学习算法及其整体贡献进行了分析。尽管图强化学习在面对不可预测事件以及噪声或不完整数据时表现出适应性，但目前它主要作为概念验证。在考虑将强化学习应用于实际电网运营时，仍存在许多需要解决的开放挑战和局限性。