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Towards dynamical stability analysis of sustainable power grids using Graph Neural Networks
Christian Nauck · Michael Lindner · Ulrich Schürholt · Frank Hellmann
Event URL: https://www.climatechange.ai/papers/neurips2022/16 »

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 dynamical stability. We provide new datasets of dynamical 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.

Author Information

Christian Nauck (Potsdam Institute for Climate Impact Research)
Michael Lindner (PIK)
Ulrich Schürholt (University of St. Gallen)
Frank Hellmann (PIK)