The transition to a sustainable energy supply challenges the operation of electric power systems in manifold ways. Transmission grid loads increase as wind and solar power are often installed far away from the consumers. In extreme cases, system operators must intervene via countertrading or redispatch to ensure grid stability. In this article, we provide a data-driven analysis of congestion in the German transmission grid. We develop an explainable machine learning model to predict the volume of redispatch and countertrade on an hourly basis. The model reveals factors that drive or mitigate grid congestion and quantifies their impact. We show that, as expected, wind power generation is the main driver, but hydropower and cross-border electricity trading also play an essential role. Solar power, on the other hand, has no mitigating effect. Our results suggest that a change to the market design would alleviate congestion.

翻译：向可持续能源供应的转型以多种方式挑战着电力系统的运行。由于风能和太阳能发电厂通常远离用户，输电电网负荷随之增加。在极端情况下，系统运营商必须通过反向交易或重新调度进行干预，以确保电网稳定。本文对德国输电电网拥堵进行了数据驱动分析。我们开发了一种可解释的机器学习模型，用于逐小时预测重新调度和反向交易的规模。该模型揭示了驱动或缓解电网拥堵的因素，并量化了它们的影响。研究表明，与预期一致，风电是主要驱动因素，但水电和跨境电力交易也起着重要作用。另一方面，太阳能发电并未产生缓解效应。我们的结果表明，改变市场设计将有助于缓解电网拥堵。