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

TL;DR

This paper demonstrates that graph neural networks can effectively predict dynamic stability and identify vulnerabilities in power grid topologies, offering a computationally efficient alternative to traditional simulations.

Contribution

The study introduces large synthetic datasets for power grid stability, shows GNNs' high accuracy in predictions, and demonstrates transferability from small to large grid models.

Findings

GNNs achieve practical accuracy in stability prediction.

GNNs can identify vulnerable nodes in power grids.

Models trained on small grids generalize to large real-world-like grids.

Abstract

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 the dynamic stability of power grids. As a testbed for GNN models, we generate new, large datasets of dynamic stability of synthetic power grids, and provide them as an open-source resource to the research community. We find that GNNs are surprisingly effective at predicting the highly non-linear targets from topological information only. For the first time, performance that is suitable for practical use cases is achieved. Furthermore, we demonstrate the ability of these models to accurately identify particular vulnerable nodes in power grids, so-called troublemakers. Last, 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.