Regensburg 2022 – scientific programme
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 13: Energy Networks (joint session SOE/DY)
SOE 13.1: Talk
Wednesday, September 7, 2022, 12:45–13:00, H11
Revealing drivers and risks for power grid frequency stability with explainable AI — •Benjamin Schäfer1, Johannes Kruse2,3, and Dirk Witthaut2,3 — 1Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany — 2Forschungszentrum Jülich, Institute for Energy and Climate Research-Systems Analysis and Technology Evaluation (IEK-STE), Jülich, Germany — 3Institute for Theoretical Physics, University of Cologne, Köln, Germany
The transition to a sustainable energy system is challenging for the operation and stability of electric power systems as power generation becomes increasingly uncertain, grid loads increase, and their dynamical properties fundamentally change. At the same time, operational data are available at an unprecedented level of detail, enabling new methods of monitoring and control. To fully harness these data, advanced methods from machine learning must be used.
Here, we present explainable artificial intelligence (XAI) as a tool to quantify, predict, and explain essential aspects of power system operation and stability in three major European synchronous areas. We focus on the power grid frequency, which measures the balance of generation and load and thus provides the central observable for control and balancing. Combining XAI with domain knowledge, we identify the main drivers and stability risks, while our model and open dataset may enable further XAI research on power systems.