  The transition to a renewable energy system poses challenges for power grid
operation and stability. Secondary control is key in restoring the power system
to its reference following a disturbance. Underestimating the necessary control
capacity may require emergency measures, such as load shedding. Hence, a solid
understanding of the emerging risks and the driving factors of control is
needed. In this contribution, we establish an explainable machine learning
model for the activation of secondary control power in Germany. Training
gradient boosted trees, we obtain an accurate description of control
activation. Using SHapely Additive exPlanation (SHAP) values, we investigate
the dependency between control activation and external features such as the
generation mix, forecasting errors, and electricity market data. Thereby, our
analysis reveals drivers that lead to high reserve requirements in the German
power system. Our transparent approach, utilizing open data and making machine
learning models interpretable, opens new scientific discovery avenues.
