{"title":"基于年风分布不确定集的风电场布局优化","authors":"Ying Wen, Mengxuan Song, Jun Wang","doi":"10.1109/CCTA41146.2020.9206258","DOIUrl":null,"url":null,"abstract":"This paper proposes a robust layout optimization method for maximizing the annual energy production of a wind farm under uncertain wind conditions. A novel method for describing the uncertainty set of wind distribution is proposed based on the variability of yearly wind distributions. Future wind conditions are assumed to vary within the uncertainty set. The lowest possible annual energy production in the future is set as the objective function. The optimization problem is solved by linear programming and genetic algorithms. The uncertainty and variability of energy production is reduced with the robust optimal layout and loss of energy production as the cost of robustness is compensated by the proper construction of uncertainty set. The proposed optimization method is compared with different optimization strategies. The simulation results demonstrate that the proposed optimization method can achieve a trade-off between limiting the influence of uncertain wind and maximizing overall energy production.","PeriodicalId":241335,"journal":{"name":"2020 IEEE Conference on Control Technology and Applications (CCTA)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Wind-Farm Layout Optimization based on Uncertain Set of Yearly Wind Distributions\",\"authors\":\"Ying Wen, Mengxuan Song, Jun Wang\",\"doi\":\"10.1109/CCTA41146.2020.9206258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a robust layout optimization method for maximizing the annual energy production of a wind farm under uncertain wind conditions. A novel method for describing the uncertainty set of wind distribution is proposed based on the variability of yearly wind distributions. Future wind conditions are assumed to vary within the uncertainty set. The lowest possible annual energy production in the future is set as the objective function. The optimization problem is solved by linear programming and genetic algorithms. The uncertainty and variability of energy production is reduced with the robust optimal layout and loss of energy production as the cost of robustness is compensated by the proper construction of uncertainty set. The proposed optimization method is compared with different optimization strategies. The simulation results demonstrate that the proposed optimization method can achieve a trade-off between limiting the influence of uncertain wind and maximizing overall energy production.\",\"PeriodicalId\":241335,\"journal\":{\"name\":\"2020 IEEE Conference on Control Technology and Applications (CCTA)\",\"volume\":\"162 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Conference on Control Technology and Applications (CCTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCTA41146.2020.9206258\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Conference on Control Technology and Applications (CCTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCTA41146.2020.9206258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wind-Farm Layout Optimization based on Uncertain Set of Yearly Wind Distributions
This paper proposes a robust layout optimization method for maximizing the annual energy production of a wind farm under uncertain wind conditions. A novel method for describing the uncertainty set of wind distribution is proposed based on the variability of yearly wind distributions. Future wind conditions are assumed to vary within the uncertainty set. The lowest possible annual energy production in the future is set as the objective function. The optimization problem is solved by linear programming and genetic algorithms. The uncertainty and variability of energy production is reduced with the robust optimal layout and loss of energy production as the cost of robustness is compensated by the proper construction of uncertainty set. The proposed optimization method is compared with different optimization strategies. The simulation results demonstrate that the proposed optimization method can achieve a trade-off between limiting the influence of uncertain wind and maximizing overall energy production.