{"title":"基于模拟退火的输电系统扩容规划","authors":"R. Romero, R. Gallego, A. Monticelli","doi":"10.1109/PICA.1995.515195","DOIUrl":null,"url":null,"abstract":"This paper presents a simulated annealing approach to the long term transmission expansion planning problem which is a hard, large scale combinatorial problem. The proposed approach has been compared with a more conventional optimization technique based on mathematical decomposition with a zero-one implicit enumeration procedure. Tests have been performed on three different systems. Two smaller systems for which optimal solutions are known have been used to tune the main parameters of the simulated annealing process. The simulated annealing method has then been applied to a larger example system for which no optimal solutions are known: as a result an entire family of interesting solutions have been obtained with costs about 7% less than the best solutions known for that particular example system.","PeriodicalId":294493,"journal":{"name":"Proceedings of Power Industry Computer Applications Conference","volume":"62 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"368","resultStr":"{\"title\":\"Transmission system expansion planning by simulated annealing\",\"authors\":\"R. Romero, R. Gallego, A. Monticelli\",\"doi\":\"10.1109/PICA.1995.515195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a simulated annealing approach to the long term transmission expansion planning problem which is a hard, large scale combinatorial problem. The proposed approach has been compared with a more conventional optimization technique based on mathematical decomposition with a zero-one implicit enumeration procedure. Tests have been performed on three different systems. Two smaller systems for which optimal solutions are known have been used to tune the main parameters of the simulated annealing process. The simulated annealing method has then been applied to a larger example system for which no optimal solutions are known: as a result an entire family of interesting solutions have been obtained with costs about 7% less than the best solutions known for that particular example system.\",\"PeriodicalId\":294493,\"journal\":{\"name\":\"Proceedings of Power Industry Computer Applications Conference\",\"volume\":\"62 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"368\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of Power Industry Computer Applications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PICA.1995.515195\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Power Industry Computer Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PICA.1995.515195","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Transmission system expansion planning by simulated annealing
This paper presents a simulated annealing approach to the long term transmission expansion planning problem which is a hard, large scale combinatorial problem. The proposed approach has been compared with a more conventional optimization technique based on mathematical decomposition with a zero-one implicit enumeration procedure. Tests have been performed on three different systems. Two smaller systems for which optimal solutions are known have been used to tune the main parameters of the simulated annealing process. The simulated annealing method has then been applied to a larger example system for which no optimal solutions are known: as a result an entire family of interesting solutions have been obtained with costs about 7% less than the best solutions known for that particular example system.