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

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

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 Creators:
Nauck, Christian1, Author              
Lindner, Michael1, Author              
Schürholt, Konstantin 2, Author
Hellmann, Frank1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2023-09-072023-10-022023-10-02
 Publication Status: Finally published
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0160915
MDB-ID: pending
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Dynamics, stability and resilience of complex hybrid infrastructure networks
Research topic keyword: Energy
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Degree: -

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Title: Chaos
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 33 (10) Sequence Number: 103103 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/180808
Publisher: American Institute of Physics (AIP)