Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market

Abhinav Das () and Stephan Schlüter
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Abhinav Das: Faculty of Mathematics and Economics, Ulm University, 89081 Ulm, Germany
Stephan Schlüter: Department of Mathematics, Natural and Economic Sciences, Ulm University of Applied Sciences, 89075 Ulm, Germany

Risks, 2025, vol. 13, issue 1, 1-18

Abstract: In this work, we introduce an innovative approach to managing electricity costs within Germany’s evolving energy market, where dynamic tariffs are becoming increasingly normal. In line with recent German governmental policies, particularly the Energiewende (Energy Transition) and European Union directives on clean energy, this work introduces a risk management strategy based on a combination of the well-known risk measures of the Value at Risk (VaR) and Conditional Value at Risk (CVaR). The goal is to optimize electricity procurement by forecasting hourly prices over a certain horizon and allocating a fixed budget using the aforementioned measures to minimize the financial risk. To generate price predictions, a Gaussian process regression model is used. The aim of this hybrid approach is to design a model that is easily understandable but allows for a comprehensive evaluation of potential financial exposure. It enables consumers to adjust their consumption patterns or market traders to invest and allows more cost-effective and risk-aware decision-making. The potential of our approach is shown in a case study based on the German market. Moreover, by discussing the political and economical implications, we show how the implementation of our method can contribute to the realization of a sustainable, flexible, and efficient energy market, as outlined in Germany’s Renewable Energy Act.

Keywords: electricity market; risk management; Gaussian process regression (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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