Secondary control activation analysed and predicted with explainable AI

TL;DR

This paper develops an explainable AI model to analyze and predict secondary control activation in the German power grid, revealing key factors influencing reserve requirements and enhancing system stability understanding.

Contribution

It introduces an interpretable machine learning approach using gradient boosted trees and SHAP values to understand secondary control activation drivers in power systems.

Findings

Identifies generation mix and market data as key control activation drivers

Provides accurate predictions of secondary control needs

Enhances transparency in power system stability analysis

Abstract

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.