Application of day-ahead optimal scheduling model based on multi-energy micro-grids with uncertainty in wind and solar energy and energy storage station
{"title":"Application of day-ahead optimal scheduling model based on multi-energy micro-grids with uncertainty in wind and solar energy and energy storage station","authors":"Hongxin Zhang","doi":"10.61435/ijred.2024.60218","DOIUrl":null,"url":null,"abstract":"Multi-energy micro-grid has received widespread attention in the wave of continuous promotion and development of renewable energy. However, in the face of wind and solar uncertainty, its scheduling model needs to be further optimized. Therefore, a multi-energy micro-grid day-ahead optimal scheduling model was proposed to construct wind and solar uncertainty scenarios, and the application of energy storage station was considered. Multiple algorithms were introduced to propose the multi-energy micro-grid day-ahead optimal scheduling model. Finally, the research content was validated. The results confirmed that the wind and solar power output probability model could describe the characteristics of wind and solar power output at different periods. The generated scenes had a large number of wind speeds in the range of 1.5 m/s to 5 m/s, and the light intensity reached its peak at 14:00, which was consistent with the historical data of the research object. In addition, the total pre-scheduling cost of this optimized scheduling model within a day was 45.16×105 yuan, while the actual scheduling cost within a day was only 21.46×105 yuan. It saved costs by 41.65% and 44.95%, respectively, compared to the comparison algorithms. The research has driven innovation and optimization of the multi-energy micro-grid scheduling model. This provides a useful theoretical and practical basis for addressing the uncertainty of wind and solar energy and improving the economic efficiency of energy systems, which is crucial for the sustainable development of new energy.","PeriodicalId":14200,"journal":{"name":"International Journal of Renewable Energy Development","volume":" 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Renewable Energy Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.61435/ijred.2024.60218","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Multi-energy micro-grid has received widespread attention in the wave of continuous promotion and development of renewable energy. However, in the face of wind and solar uncertainty, its scheduling model needs to be further optimized. Therefore, a multi-energy micro-grid day-ahead optimal scheduling model was proposed to construct wind and solar uncertainty scenarios, and the application of energy storage station was considered. Multiple algorithms were introduced to propose the multi-energy micro-grid day-ahead optimal scheduling model. Finally, the research content was validated. The results confirmed that the wind and solar power output probability model could describe the characteristics of wind and solar power output at different periods. The generated scenes had a large number of wind speeds in the range of 1.5 m/s to 5 m/s, and the light intensity reached its peak at 14:00, which was consistent with the historical data of the research object. In addition, the total pre-scheduling cost of this optimized scheduling model within a day was 45.16×105 yuan, while the actual scheduling cost within a day was only 21.46×105 yuan. It saved costs by 41.65% and 44.95%, respectively, compared to the comparison algorithms. The research has driven innovation and optimization of the multi-energy micro-grid scheduling model. This provides a useful theoretical and practical basis for addressing the uncertainty of wind and solar energy and improving the economic efficiency of energy systems, which is crucial for the sustainable development of new energy.