Regulatory Changes in German and Austrian Power Systems Explored with Explainable Artificial Intelligence

Abstract

A stable supply of electrical energy is essential for the functioning of our society. Therefore, energy and balancing markets of power grids are strictly regulated. With changes in technology, the economy and society, these regulations are also constantly adapted. However, whether these regulatory changes lead to the intended results is not easy to assess. Could eXplainable Artificial Intelligence (XAI) models distinguish regulatory settings and support the understanding of the effects of these changes? In this article, we explore two examples of regulatory changes: The splitting of the German-Austrian bidding zone and changes in the pricing schemes of the German balancing energy market. We find that boosted tree models and feedforward neural networks before and after a regulatory change differ in their respective parametrizations. Using Shapley additive explanations, we reveal model differences, e.g., in terms of feature importance, and identify key features of these distinct models. With this study, we demonstrate how XAI can be applied to investigate system changes in power systems.