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.

翻译：为了减缓气候变化,需要增加可再生能源的比例。可再生能源由于权力下放、降低惰性和生产波动而给电网带来新的挑战。可再生能源对电网提出了新的挑战。可再生能源高渗透可再生能源的可持续电网的运作需要新方法来分析动态稳定性。我们提供了合成电网动态稳定性的新数据集,发现图形神经网络(GNNs)在仅从地形信息预测高度非线性目标方面出人意料地有效。为了说明向实际规模的电网扩展的潜力,我们展示了对德克萨斯电网模型的成功预测。