Yifan Xu, Shufeng Dong, Liheng Lin, Shupeng Zhang, Hangyin Mao
{"title":"考虑电压稳定波动的分布式发电最优选址与规模","authors":"Yifan Xu, Shufeng Dong, Liheng Lin, Shupeng Zhang, Hangyin Mao","doi":"10.1109/CIEEC50170.2021.9510610","DOIUrl":null,"url":null,"abstract":"The fluctuation of voltage stability is considered in the distributed generation (DG) planning of this paper. Taking the mean value and standard deviation of the voltage stability index and the comprehensive investment cost of the distribution network as the goal, a model for optimal siting and sizing of DG is established. An improved multi-objective particle swarm algorithm is proposed to obtain optimal Parato solution set, in which adaptive inertia weight is used to improve convergence, cross mutation is used to avoid falling into local optima, and particle density is used to filter non-inferior solution sets. Taking the IEEE33 node system as an example, the location and capacity of wind turbines and photovoltaics are selected and the results prove the effectiveness of this method.","PeriodicalId":110429,"journal":{"name":"2021 IEEE 4th International Electrical and Energy Conference (CIEEC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal Siting and Sizing of Distributed Generation Considering Voltage Stability Fluctuation\",\"authors\":\"Yifan Xu, Shufeng Dong, Liheng Lin, Shupeng Zhang, Hangyin Mao\",\"doi\":\"10.1109/CIEEC50170.2021.9510610\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The fluctuation of voltage stability is considered in the distributed generation (DG) planning of this paper. Taking the mean value and standard deviation of the voltage stability index and the comprehensive investment cost of the distribution network as the goal, a model for optimal siting and sizing of DG is established. An improved multi-objective particle swarm algorithm is proposed to obtain optimal Parato solution set, in which adaptive inertia weight is used to improve convergence, cross mutation is used to avoid falling into local optima, and particle density is used to filter non-inferior solution sets. Taking the IEEE33 node system as an example, the location and capacity of wind turbines and photovoltaics are selected and the results prove the effectiveness of this method.\",\"PeriodicalId\":110429,\"journal\":{\"name\":\"2021 IEEE 4th International Electrical and Energy Conference (CIEEC)\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 4th International Electrical and Energy Conference (CIEEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIEEC50170.2021.9510610\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 4th International Electrical and Energy Conference (CIEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIEEC50170.2021.9510610","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal Siting and Sizing of Distributed Generation Considering Voltage Stability Fluctuation
The fluctuation of voltage stability is considered in the distributed generation (DG) planning of this paper. Taking the mean value and standard deviation of the voltage stability index and the comprehensive investment cost of the distribution network as the goal, a model for optimal siting and sizing of DG is established. An improved multi-objective particle swarm algorithm is proposed to obtain optimal Parato solution set, in which adaptive inertia weight is used to improve convergence, cross mutation is used to avoid falling into local optima, and particle density is used to filter non-inferior solution sets. Taking the IEEE33 node system as an example, the location and capacity of wind turbines and photovoltaics are selected and the results prove the effectiveness of this method.