Identifying drivers and mitigators for congestion and redispatch in the German electric power system with explainable AI
This work addresses grid stability challenges for energy operators during the renewable transition, though it is incremental as it applies existing explainable AI methods to a specific domain.
The study tackled predicting congestion and redispatch in Germany's electric power system using an explainable AI model, revealing that wind power is the main driver while hydropower and cross-border trading also significantly impact congestion, with solar power having no mitigating effect.
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.