Towards dynamic stability analysis of sustainable power grids using graph neural networks

TL;DR

This paper introduces a novel approach using graph neural networks to analyze the dynamic stability of sustainable power grids with high renewable energy penetration, demonstrating effectiveness on synthetic and real-sized grid models.

Contribution

It provides new datasets and shows that GNNs can predict complex stability metrics from grid topology alone, enabling scalable stability analysis.

Findings

GNNs effectively predict dynamic stability from topology.

Successful application to a Texan power grid model.

New datasets for power grid stability analysis.

Abstract

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.