  Power grids are critical infrastructures of paramount importance to modern
society and, therefore, engineered to operate under diverse conditions and
failures. The ongoing energy transition poses new challenges for the
decision-makers and system operators. Therefore, we must develop grid analysis
algorithms to ensure reliable operations. These key tools include power flow
analysis and system security analysis, both needed for effective operational
and strategic planning. The literature review shows a growing trend of machine
learning (ML) models that perform these analyses effectively. In particular,
Graph Neural Networks (GNNs) stand out in such applications because of the
graph-based structure of power grids. However, there is a lack of publicly
available graph datasets for training and benchmarking ML models in electrical
power grid applications. First, we present PowerGraph, which comprises
GNN-tailored datasets for i) power flows, ii) optimal power flows, and iii)
cascading failure analyses of power grids. Second, we provide ground-truth
explanations for the cascading failure analysis. Finally, we perform a complete
benchmarking of GNN methods for node-level and graph-level tasks and
explainability. Overall, PowerGraph is a multifaceted GNN dataset for diverse
tasks that includes power flow and fault scenarios with real-world
explanations, providing a valuable resource for developing improved GNN models
for node-level, graph-level tasks and explainability methods in power system
modeling. The dataset is available at
https://figshare.com/articles/dataset/PowerGraph/22820534 and the code at
https://github.com/PowerGraph-Datasets.
