Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market
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 dire...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/13/1/13 |
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| author | Abhinav Das Stephan Schlüter |
| author_facet | Abhinav Das Stephan Schlüter |
| author_sort | Abhinav Das |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3f2c4796b0a5498795d8c05a654607c1 |
| institution | Kabale University |
| issn | 2227-9091 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj-art-3f2c4796b0a5498795d8c05a654607c12025-01-24T13:48:20ZengMDPI AGRisks2227-90912025-01-011311310.3390/risks13010013Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity MarketAbhinav Das0Stephan Schlüter1Faculty of Mathematics and Economics, Ulm University, 89081 Ulm, GermanyDepartment of Mathematics, Natural and Economic Sciences, Ulm University of Applied Sciences, 89075 Ulm, GermanyIn 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.https://www.mdpi.com/2227-9091/13/1/13electricity marketrisk managementGaussian process regression |
| spellingShingle | Abhinav Das Stephan Schlüter Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market Risks electricity market risk management Gaussian process regression |
| title | Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market |
| title_full | Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market |
| title_fullStr | Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market |
| title_full_unstemmed | Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market |
| title_short | Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market |
| title_sort | gaussian process regression with a hybrid risk measure for dynamic risk management in the electricity market |
| topic | electricity market risk management Gaussian process regression |
| url | https://www.mdpi.com/2227-9091/13/1/13 |
| work_keys_str_mv | AT abhinavdas gaussianprocessregressionwithahybridriskmeasurefordynamicriskmanagementintheelectricitymarket AT stephanschluter gaussianprocessregressionwithahybridriskmeasurefordynamicriskmanagementintheelectricitymarket |