Exploring deterministic frequency deviations with explainable AI
Johannes Kruse, Benjamin Schäfer, Dirk Witthaut
Abstract
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 raise the need for substantial control actions and thereby increase cost of operation. 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. Thereby, we identify solar ramps as critical to explain patterns in the Rate of Change of Frequency (RoCoF).