Towards Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks

To mitigate climate change, the share of renewable energies in power production needs to be increased. Renewables introduce new challenges to power grids regarding the dynamic stability due to decentralization, reduced inertia and volatility in production. Since dynamic stability simulations are intractable and exceedingly expensive for large grids, graph neural networks (GNNs) are a promising method to reduce the computational effort of analyzing dynamic stability of power grids. We provide new datasets of dynamic stability of synthetic power grids and find that GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only. Furthermore, we use GNNs to demonstrate the accurate identification of particularly vulnerable nodes in power grids, so-called troublemakers. Lastly, we find that GNNs trained on small grids generate accurate predictions on a large synthetic model of the Texan power grid, which illustrates the potential for real-world applications of the presented approach.