{"title":"基于加速粒子群算法的虚拟电厂可再生能源优化调度","authors":"D. Hropko, J. Ivanecký, J. Turček","doi":"10.1109/ELEKTRO.2012.6225637","DOIUrl":null,"url":null,"abstract":"Renewable energy sources (RES), such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of Virtual power plant (VPP) has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES cannot be perfectly forecasted, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an Accelerated particle swarm optimization is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.","PeriodicalId":343071,"journal":{"name":"2012 ELEKTRO","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"Optimal dispatch of renewable energy sources included in Virtual power plant using Accelerated particle swarm optimization\",\"authors\":\"D. Hropko, J. Ivanecký, J. Turček\",\"doi\":\"10.1109/ELEKTRO.2012.6225637\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Renewable energy sources (RES), such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of Virtual power plant (VPP) has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES cannot be perfectly forecasted, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an Accelerated particle swarm optimization is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.\",\"PeriodicalId\":343071,\"journal\":{\"name\":\"2012 ELEKTRO\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 ELEKTRO\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ELEKTRO.2012.6225637\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 ELEKTRO","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ELEKTRO.2012.6225637","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal dispatch of renewable energy sources included in Virtual power plant using Accelerated particle swarm optimization
Renewable energy sources (RES), such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of Virtual power plant (VPP) has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES cannot be perfectly forecasted, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an Accelerated particle swarm optimization is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.