Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach
Abstract Over time, the importance of virtual power plants (VPP) has markedly risen to seamlessly incorporate the sporadic nature of renewable energy sources into the existing smart grid framework. Simultaneously, there is a growing need for advanced forecasting methods to bolster the grid’s stabili...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87697-y |
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| author | Tirunagaru V. Sarathkumar Arup Kumar Goswami Baseem Khan Kamel A. Shoush Sherif S. M. Ghoneim Ramy N. R. Ghaly |
| author_facet | Tirunagaru V. Sarathkumar Arup Kumar Goswami Baseem Khan Kamel A. Shoush Sherif S. M. Ghoneim Ramy N. R. Ghaly |
| author_sort | Tirunagaru V. Sarathkumar |
| collection | DOAJ |
| description | Abstract Over time, the importance of virtual power plants (VPP) has markedly risen to seamlessly incorporate the sporadic nature of renewable energy sources into the existing smart grid framework. Simultaneously, there is a growing need for advanced forecasting methods to bolster the grid’s stability, flexibility, and dispatchability. This paper presents a dual-pronged, innovative approach to maximize income in the day-ahead power market through VPP. On one front, forecasting VPP generation units, including solar photovoltaic, wind power, and combined heat and power, employs a novel Adam Optimizer Long-Short-Term-Memory (AOLSTM) machine learning technique. Conversely, estimating the revenue’s superior frontier is accomplished by integrating energy storage and Monte-Carlo optimization. The proposed method effectively synergizes the concepts of VPP, energy storage, and AOLSTM to yield more substantial income in the day-ahead electricity market. Notably, the introduced AOLSTM approach demonstrates minimal error metrics compared to conventional methods such as persistence, Gradient Boost, and Random Forest. |
| format | Article |
| id | doaj-art-72eacdedc37d46218ac2789403f79c8a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-72eacdedc37d46218ac2789403f79c8a2025-02-02T12:22:07ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-87697-yForecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approachTirunagaru V. Sarathkumar0Arup Kumar Goswami1Baseem Khan2Kamel A. Shoush3Sherif S. M. Ghoneim4Ramy N. R. Ghaly5School of Engineering, Mallareddy UniversityDepartment of Electrical Engineering, National Institute of Technology SilcharDepartment of electrical and computer engineeringDepartment of Electrical Engineering, College of Engineering, Taif UniversityDepartment of Electrical Engineering, College of Engineering, Taif UniversityCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityAbstract Over time, the importance of virtual power plants (VPP) has markedly risen to seamlessly incorporate the sporadic nature of renewable energy sources into the existing smart grid framework. Simultaneously, there is a growing need for advanced forecasting methods to bolster the grid’s stability, flexibility, and dispatchability. This paper presents a dual-pronged, innovative approach to maximize income in the day-ahead power market through VPP. On one front, forecasting VPP generation units, including solar photovoltaic, wind power, and combined heat and power, employs a novel Adam Optimizer Long-Short-Term-Memory (AOLSTM) machine learning technique. Conversely, estimating the revenue’s superior frontier is accomplished by integrating energy storage and Monte-Carlo optimization. The proposed method effectively synergizes the concepts of VPP, energy storage, and AOLSTM to yield more substantial income in the day-ahead electricity market. Notably, the introduced AOLSTM approach demonstrates minimal error metrics compared to conventional methods such as persistence, Gradient Boost, and Random Forest.https://doi.org/10.1038/s41598-025-87697-yVirtual power plantRenewable energy sourcesPower forecastingMonte-Carlo optimizationEnergy storage |
| spellingShingle | Tirunagaru V. Sarathkumar Arup Kumar Goswami Baseem Khan Kamel A. Shoush Sherif S. M. Ghoneim Ramy N. R. Ghaly Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach Scientific Reports Virtual power plant Renewable energy sources Power forecasting Monte-Carlo optimization Energy storage |
| title | Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach |
| title_full | Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach |
| title_fullStr | Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach |
| title_full_unstemmed | Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach |
| title_short | Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach |
| title_sort | forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach |
| topic | Virtual power plant Renewable energy sources Power forecasting Monte-Carlo optimization Energy storage |
| url | https://doi.org/10.1038/s41598-025-87697-y |
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