Exploring deterministic frequency deviations with explainable AI

TL;DR

This paper uses explainable AI to analyze deterministic frequency deviations in power grids, revealing key external factors like solar ramps influencing these events and improving understanding of grid stability issues.

Contribution

It introduces a machine learning approach combined with explainability techniques to analyze DFDs and uncovers solar ramps as critical factors affecting frequency deviations.

Findings

Machine learning models effectively describe daily DFD cycles.

SHAP analysis identifies solar ramps as key to RoCoF patterns.

Enhanced understanding of external influences on power grid stability.

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 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).