  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.
