  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.
