An Integrated SVM-LSTM Method for VPP Resource Classification and Load Forecasting in Real-time Market Trading
The operation of virtual power plants in the electricity market requires handling complex resource scheduling and market trading decision-making problems. The research aims to enhance the participation efficiency and responsiveness of virtual power plants in the electricity market and solve practica...
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| Format: | Article |
| Language: | English |
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University of Zagreb Faculty of Electrical Engineering and Computing
2024-01-01
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| Series: | Journal of Computing and Information Technology |
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| Online Access: | https://hrcak.srce.hr/file/471978 |
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| _version_ | 1841551924687536128 |
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| author | He Zhao Yucheng Hou Zhifa Lin Xin Cao Xiao Yu |
| author_facet | He Zhao Yucheng Hou Zhifa Lin Xin Cao Xiao Yu |
| author_sort | He Zhao |
| collection | DOAJ |
| description | The operation of virtual power plants in the electricity market requires handling complex resource scheduling and market trading decision-making problems. The research aims to enhance the participation efficiency and responsiveness of virtual power plants in the electricity market and solve practical operational challenges by improving market trading strategies. Therefore, a resource grading model based on improved support vector machine was developed. The model is optimized using adaptive synthetic sampling, principal component analysis, and deep clustering algorithms. In addition, an improved long short-term memory network is utilized to achieve ultra short-term load forecasting. The results showed that the recall rate and F1 mean of the resource grading model based on the improved support vector machine algorithm were as high as 81.07% and 85.41%, respectively. The average prediction error of the improved long short-term memory neural network algorithm is 0.35%, and the maximum error is only 0.62%. In the basic scenario, the maximum deviation between the declared amount of backup auxiliary services based on load adjustable capacity prediction and the actual amount is only 88.62 kW. The method proposed by the research institute has significant advantages in improving the efficiency and responsiveness of virtual power plant market participation, which is conducive to promoting the overall economic benefits of virtual power plants in the electricity market. |
| format | Article |
| id | doaj-art-81fb1ff43dd54560bb5e84700b3aa855 |
| institution | Kabale University |
| issn | 1846-3908 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Zagreb Faculty of Electrical Engineering and Computing |
| record_format | Article |
| series | Journal of Computing and Information Technology |
| spelling | doaj-art-81fb1ff43dd54560bb5e84700b3aa8552025-01-09T14:17:30ZengUniversity of Zagreb Faculty of Electrical Engineering and ComputingJournal of Computing and Information Technology1846-39082024-01-0132421723310.20532/cit.2024.1005866An Integrated SVM-LSTM Method for VPP Resource Classification and Load Forecasting in Real-time Market TradingHe Zhao0Yucheng Hou1Zhifa Lin2Xin Cao3Xiao Yu4State Grid Beijing Electric Power Research Institute, Beijing, ChinaState Grid Beijing Electric Power Research Institute, Beijing, ChinaState Grid Beijing Electric Power Research Institute, Beijing, ChinaState Grid Beijing Electric Power Research Institute, Beijing, ChinaBeijing Tsintergy Technology Co., Ltd., Haidian District, Beijing, ChinaThe operation of virtual power plants in the electricity market requires handling complex resource scheduling and market trading decision-making problems. The research aims to enhance the participation efficiency and responsiveness of virtual power plants in the electricity market and solve practical operational challenges by improving market trading strategies. Therefore, a resource grading model based on improved support vector machine was developed. The model is optimized using adaptive synthetic sampling, principal component analysis, and deep clustering algorithms. In addition, an improved long short-term memory network is utilized to achieve ultra short-term load forecasting. The results showed that the recall rate and F1 mean of the resource grading model based on the improved support vector machine algorithm were as high as 81.07% and 85.41%, respectively. The average prediction error of the improved long short-term memory neural network algorithm is 0.35%, and the maximum error is only 0.62%. In the basic scenario, the maximum deviation between the declared amount of backup auxiliary services based on load adjustable capacity prediction and the actual amount is only 88.62 kW. The method proposed by the research institute has significant advantages in improving the efficiency and responsiveness of virtual power plant market participation, which is conducive to promoting the overall economic benefits of virtual power plants in the electricity market.https://hrcak.srce.hr/file/471978virtual power plantmarket tradingmachine learningSVMLSTM |
| spellingShingle | He Zhao Yucheng Hou Zhifa Lin Xin Cao Xiao Yu An Integrated SVM-LSTM Method for VPP Resource Classification and Load Forecasting in Real-time Market Trading Journal of Computing and Information Technology virtual power plant market trading machine learning SVM LSTM |
| title | An Integrated SVM-LSTM Method for VPP Resource Classification and Load Forecasting in Real-time Market Trading |
| title_full | An Integrated SVM-LSTM Method for VPP Resource Classification and Load Forecasting in Real-time Market Trading |
| title_fullStr | An Integrated SVM-LSTM Method for VPP Resource Classification and Load Forecasting in Real-time Market Trading |
| title_full_unstemmed | An Integrated SVM-LSTM Method for VPP Resource Classification and Load Forecasting in Real-time Market Trading |
| title_short | An Integrated SVM-LSTM Method for VPP Resource Classification and Load Forecasting in Real-time Market Trading |
| title_sort | integrated svm lstm method for vpp resource classification and load forecasting in real time market trading |
| topic | virtual power plant market trading machine learning SVM LSTM |
| url | https://hrcak.srce.hr/file/471978 |
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