Secondary control activation analysed and predicted with explainable AI
This work addresses grid stability challenges for renewable energy systems, offering a transparent method to predict and analyze control needs, though it is incremental as it applies existing explainable AI techniques to a specific domain.
The paper tackled predicting secondary control activation in Germany's power grid using an explainable machine learning model, achieving an accurate description with gradient boosted trees and identifying key drivers like generation mix and forecasting errors through SHAP values.
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.