Kruse Johannes, Schäfer Benjamin, Witthaut Dirk
Forschungszentrum Jülich, Institute of Energy and Climate Research - Systems Analysis and Technology Evaluation (IEK-STE), 52425 Jülich, Germany.
Institute for Theoretical Physics, University of Cologne, 50937 Köln, Germany.
Patterns (N Y). 2021 Oct 8;2(11):100365. doi: 10.1016/j.patter.2021.100365. eCollection 2021 Nov 12.
Stable operation of an electric power system requires strict operational limits for the grid frequency. Fluctuations and external impacts can cause large frequency deviations and increased control efforts. Although these complex interdependencies can be modeled using machine learning algorithms, the black box character of many models limits insights and applicability. In this article, we introduce an explainable machine learning model that accurately predicts frequency stability indicators for three European synchronous areas. Using Shapley additive explanations, we identify key features and risk factors for frequency stability. We show how load and generation ramps determine frequency gradients, and we identify three classes of generation technologies with converse impacts. Control efforts vary strongly depending on the grid and time of day and are driven by ramps as well as electricity prices. Notably, renewable power generation is central only in the British grid, while forecasting errors play a major role in the Nordic grid.
电力系统的稳定运行需要对电网频率设定严格的运行限制。波动和外部影响可能导致较大的频率偏差，并增加控制难度。尽管可以使用机器学习算法对这些复杂的相互依赖关系进行建模，但许多模型的黑箱特性限制了其洞察力和适用性。在本文中，我们介绍了一种可解释的机器学习模型，该模型能够准确预测三个欧洲同步区域的频率稳定性指标。通过夏普利值加法解释，我们确定了频率稳定性的关键特征和风险因素。我们展示了负荷和发电爬坡如何决定频率梯度，并识别出具有相反影响的三类发电技术。控制难度因电网和一天中的时间而异，并且受爬坡以及电价的驱动。值得注意的是，可再生能源发电仅在英国电网中处于核心地位，而预测误差在北欧电网中起着重要作用。
