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Toward dynamic stability assessment of power grid topologies using graph neural networks

Authors
/persons/resource/nauck

Nauck,  Christian
Potsdam Institute for Climate Impact Research;

/persons/resource/Michael.Lindner

Lindner,  Michael
Potsdam Institute for Climate Impact Research;

Schürholt,  Konstantin
External Organizations;

/persons/resource/frank.hellmann

Hellmann,  Frank
Potsdam Institute for Climate Impact Research;

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28774oa.pdf
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Citation

Nauck, C., Lindner, M., Schürholt, K., Hellmann, F. (2023): Toward dynamic stability assessment of power grid topologies using graph neural networks. - Chaos, 33, 10, 103103.
https://doi.org/10.1063/5.0160915


Cite as: https://publications.pik-potsdam.de/pubman/item/item_28774
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.