Nauck Christian, Lindner Michael, Schürholt Konstantin, Hellmann Frank
Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam, Germany.
AIML Lab, University of St. Gallen, Rosenbergstrasse 30, CH-9000 St. Gallen, Switzerland.
Chaos. 2023 Oct 1;33(10). doi: 10.1063/5.0160915.
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.
为了缓解气候变化，需要提高可再生能源在电力生产中的占比。由于分散化、惯性降低和生产波动，可再生能源给电网的动态稳定性带来了新的挑战。由于动态稳定性模拟对于大型电网来说难以处理且成本极高，图神经网络（GNN）是一种很有前景的方法，可以减少分析电网动态稳定性的计算量。作为GNN模型的测试平台，我们生成了新的、大型的合成电网动态稳定性数据集，并将其作为开源资源提供给研究社区。我们发现，GNN仅从拓扑信息就能惊人地有效地预测高度非线性目标。首次实现了适用于实际用例的性能。此外，我们展示了这些模型准确识别电网中特定脆弱节点（即所谓的麻烦制造者）的能力。最后，我们发现，在小型电网上训练的GNN能对德克萨斯州电网的大型合成模型做出准确预测，这说明了其在实际应用中的潜力。
