2023/07/24 by Maurizio Titz, Sebastian Pütz, Titz, Maurizio +3 · 1 citation
Engineering · #Data Analysis #Electric Power System Optimization #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Integrated Energy Systems Optimization #Machine Learning (cs.LG) #Statistics and Probability (physics.data-an) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2307.12636
openalex publication_date 2023/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.