  To mitigate climate change, the share of renewable needs to be increased.
Renewable energies introduce new challenges to power grids due to
decentralization, reduced inertia and volatility in production. The operation
of sustainable power grids with a high penetration of renewable energies
requires new methods to analyze the dynamic stability. We provide new datasets
of dynamic stability of synthetic power grids and find that graph neural
networks (GNNs) are surprisingly effective at predicting the highly non-linear
target from topological information only. To illustrate the potential to scale
to real-sized power grids, we demonstrate the successful prediction on a Texan
power grid model.
