{"title":"Parallel genetic algorithm for generation expansion planning","authors":"Y. Fukuyama, Y. Nakanishi, H. Chiang","doi":"10.1109/59.496180","DOIUrl":null,"url":null,"abstract":"This paper presents an application of parallel genetic algorithm to optimal long-range generation expansion planning. The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for the string representation method are compared. The method is implemented on transputers, one of the practical multi-processors. The effectiveness of the proposed method is demonstrated on a typical generation expansion problem with four technologies, five intervals, and a various number of generation units. It is compared favorably with dynamic programming and conventional genetic algorithm. The results reveal the speed and effectiveness of the proposed method for solving this problem.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"4 1","pages":"49-56"},"PeriodicalIF":0.0000,"publicationDate":"1996-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/59.496180","citationCount":"106","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Intelligent Systems for Electrical Engineering and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/59.496180","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 106
Abstract
This paper presents an application of parallel genetic algorithm to optimal long-range generation expansion planning. The problem is formulated as a combinatorial optimization problem that determines the number of newly introduced generation units of each technology during different time intervals. A new string representation method for the problem is presented. Binary and decimal coding for the string representation method are compared. The method is implemented on transputers, one of the practical multi-processors. The effectiveness of the proposed method is demonstrated on a typical generation expansion problem with four technologies, five intervals, and a various number of generation units. It is compared favorably with dynamic programming and conventional genetic algorithm. The results reveal the speed and effectiveness of the proposed method for solving this problem.
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