One of the key challenges for the success of the energy transition towards
renewable energies is the analysis of the dynamic stability of power grids.
However, dynamic solutions are intractable and exceedingly expensive for large
grids. Graph Neural Networks (GNNs) are a promising method to reduce the
computational effort of predicting dynamic stability of power grids, however
datasets of appropriate complexity and size do not yet exist. We introduce two
new datasets of synthetically generated power grids. For each grid, the dynamic
stability has been estimated using Monte-Carlo simulations. The datasets have
10 times more grids than previously published. To evaluate the potential for
real-world applications, we demonstrate the successful prediction on a Texan
power grid model. The performance can be improved to surprisingly high levels
by training more complex models on more data. Furthermore, the investigated
grids have different sizes, enabling the application of out-of-distribution
evaluation and transfer learning from a small to a large domain. We invite the
community to improve our benchmark models and thus aid the energy transition
with better tools.