Exploring deterministic frequency deviations with explainable AI
This work addresses power grid stability issues for energy systems, particularly in the European grid, but is incremental as it applies existing explainable AI techniques to a specific domain problem.
The authors tackled the problem of understanding deterministic frequency deviations (DFDs) in power grids, which affect frequency quality and stability, by using explainable AI methods to analyze their relation to external features and identify solar ramps as critical for explaining patterns in the Rate of Change of Frequency.
Deterministic frequency deviations (DFDs) critically affect power grid frequency quality and power system stability. A better understanding of these events is urgently needed as frequency deviations have been growing in the European grid in recent years. DFDs are partially explained by the rapid adjustment of power generation following the intervals of electricity trading, but this intuitive picture fails especially before and around noonday. In this article, we provide a detailed analysis of DFDs and their relation to external features using methods from explainable Artificial Intelligence. We establish a machine learning model that well describes the daily cycle of DFDs and elucidate key interdependencies using SHapley Additive exPlanations (SHAP). Thereby, we identify solar ramps as critical to explain patterns in the Rate of Change of Frequency (RoCoF).