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

• URL: http://arxiv.org/abs/2212.11130v1
• Date: Wed, 21 Dec 2022 15:57:12 GMT
• Title: Towards dynamic stability analysis of sustainable power grids using graph neural networks
• Authors: Christian Nauck, Michael Lindner, Konstantin Sch\"urholt, Frank Hellmann
• Abstract summary: Renewable energies introduce new challenges to power grids due to decentralization, reduced inertia and volatility in production. 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. To illustrate the potential to scale to real-sized power grids, we demonstrate the successful prediction on a Texan power grid model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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