M. Kamran, M. S. Naderi, Mehrdad Mallaki, G. Gharehpetian
{"title":"电动汽车负荷和充电方式对扩容规划的影响","authors":"M. Kamran, M. S. Naderi, Mehrdad Mallaki, G. Gharehpetian","doi":"10.1109/SGC.2015.7857386","DOIUrl":null,"url":null,"abstract":"The multi-objective generation expansion planning problem includes making the best decisions of selecting generation technologies to be added to the existing power system in order to minimize cost and other desired factors. In this paper, an optimal generation expansion planning scheme for power systems considering electric vehicle load effect is presented. The optimum value is achieved by solving multiobjective optimization problem. Furthermore, the impact of electric vehicle charging method (typical or optimum) on generation expansion planning are calculated and analysed. Mont Carlo simulation is used to represent uncertainty of system component and then problem is solved using a multi objective genetic algorithm method.","PeriodicalId":117785,"journal":{"name":"2015 Smart Grid Conference (SGC)","volume":"320 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Effect of electric vehicle load and charging pattern on generation expansion planning\",\"authors\":\"M. Kamran, M. S. Naderi, Mehrdad Mallaki, G. Gharehpetian\",\"doi\":\"10.1109/SGC.2015.7857386\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The multi-objective generation expansion planning problem includes making the best decisions of selecting generation technologies to be added to the existing power system in order to minimize cost and other desired factors. In this paper, an optimal generation expansion planning scheme for power systems considering electric vehicle load effect is presented. The optimum value is achieved by solving multiobjective optimization problem. Furthermore, the impact of electric vehicle charging method (typical or optimum) on generation expansion planning are calculated and analysed. Mont Carlo simulation is used to represent uncertainty of system component and then problem is solved using a multi objective genetic algorithm method.\",\"PeriodicalId\":117785,\"journal\":{\"name\":\"2015 Smart Grid Conference (SGC)\",\"volume\":\"320 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Smart Grid Conference (SGC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SGC.2015.7857386\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Smart Grid Conference (SGC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SGC.2015.7857386","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Effect of electric vehicle load and charging pattern on generation expansion planning
The multi-objective generation expansion planning problem includes making the best decisions of selecting generation technologies to be added to the existing power system in order to minimize cost and other desired factors. In this paper, an optimal generation expansion planning scheme for power systems considering electric vehicle load effect is presented. The optimum value is achieved by solving multiobjective optimization problem. Furthermore, the impact of electric vehicle charging method (typical or optimum) on generation expansion planning are calculated and analysed. Mont Carlo simulation is used to represent uncertainty of system component and then problem is solved using a multi objective genetic algorithm method.