{"title":"机组承诺问题的遗传算法求解","authors":"Hatim S. Madraswala, A. Deshpande","doi":"10.1109/ICPEICES.2016.7853075","DOIUrl":null,"url":null,"abstract":"In this paper, Genetic Algorithm (GA) is used to solve the Unit Commitment (UC) Problem. Unit commitment problem was formulated with consideration of up & down time, startup cost (Hot & Cold start), and production cost. Unit commitment schedule as well as economic dispatch is obtained to obtain total cost of generation. Problem specific operators are used in the algorithm to improve the quality of the solution obtained and increase the convergence speed of problem. Performance of the GA is tested on 2 IEEE test systems, one of 5 units, 14 bus and another of 7 units, 56 bus respectively over the scheduling period of 24 hours. Results give an insight in the superiority of GA to other methods for solving UC problem.","PeriodicalId":305942,"journal":{"name":"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"280","resultStr":"{\"title\":\"Genetic algorithm solution to unit commitment problem\",\"authors\":\"Hatim S. Madraswala, A. Deshpande\",\"doi\":\"10.1109/ICPEICES.2016.7853075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, Genetic Algorithm (GA) is used to solve the Unit Commitment (UC) Problem. Unit commitment problem was formulated with consideration of up & down time, startup cost (Hot & Cold start), and production cost. Unit commitment schedule as well as economic dispatch is obtained to obtain total cost of generation. Problem specific operators are used in the algorithm to improve the quality of the solution obtained and increase the convergence speed of problem. Performance of the GA is tested on 2 IEEE test systems, one of 5 units, 14 bus and another of 7 units, 56 bus respectively over the scheduling period of 24 hours. Results give an insight in the superiority of GA to other methods for solving UC problem.\",\"PeriodicalId\":305942,\"journal\":{\"name\":\"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)\",\"volume\":\"120 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"280\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPEICES.2016.7853075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEICES.2016.7853075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic algorithm solution to unit commitment problem
In this paper, Genetic Algorithm (GA) is used to solve the Unit Commitment (UC) Problem. Unit commitment problem was formulated with consideration of up & down time, startup cost (Hot & Cold start), and production cost. Unit commitment schedule as well as economic dispatch is obtained to obtain total cost of generation. Problem specific operators are used in the algorithm to improve the quality of the solution obtained and increase the convergence speed of problem. Performance of the GA is tested on 2 IEEE test systems, one of 5 units, 14 bus and another of 7 units, 56 bus respectively over the scheduling period of 24 hours. Results give an insight in the superiority of GA to other methods for solving UC problem.