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. However, 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 dynamic stability of power grids. We provide new datasets of dynamic stability of synthetic power grids and find that GNNs are surprisingly effective at predicting highly non-linear targets from topological information only. We show that large GNNs outperform GNNs from previous work as well as as handcrafted graph features and semi-analytic approximations. Further, we demonstrate GNNs can accurately identify trouble maker-nodes in the power grids. Lastly, we show that GNNs trained on small grids can perform accurately on a large synthetic Texan power grid model, which illustrates the potential of our approach.
翻译:为了减缓气候变化,需要增加可再生能源在发电中的份额。可再生能源由于权力下放、降低惰性和生产波动,对电网的动态稳定性提出了新的挑战。然而,动态稳定性模拟对大型电网来说是难以操作的,而且费用极高。图神经网络(GNN)是减少分析电网动态稳定性计算努力的有希望的方法。我们提供了合成电网动态稳定性的新数据集,发现GNN在预测仅来自地形信息的高度非线性目标方面是出乎意料的。我们显示,大型GNN在以往工作中超过了GNN,以及手工制作的图形特征和半分析近似。此外,我们展示GNNN能够准确地识别电网中的麻烦制造者。最后,我们显示,在小型电网上受过训练的GNN能够精确地使用大型合成德克萨斯电网模型来预测高度非线性目标,这说明了我们的方法的潜力。