Identifying drivers and mitigators for congestion and redispatch in the German electric power system with explainable AI

TL;DR

This paper uses explainable AI to analyze and predict congestion in the German power grid, identifying key drivers like wind and hydropower, and suggesting market design changes to reduce congestion.

Contribution

It introduces an explainable machine learning model that predicts redispatch volumes and uncovers key factors influencing grid congestion in Germany.

Findings

Wind power is the main driver of congestion.

Hydropower and cross-border trading significantly impact congestion.

Solar power does not mitigate grid congestion.

Abstract

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.