{"title":"Biogeography Based Optimization technique applied to multi-constraints economic load dispatch problems","authors":"P. Roy, S. Ghoshal, S. S. Thakur","doi":"10.1109/TD-ASIA.2009.5356896","DOIUrl":null,"url":null,"abstract":"This paper presents Biogeography-Based Optimization (BBO) technique for solving constrained economic dispatch problems in power system. Many nonlinear characteristics of generators, like valve point loading, ramp rate limits, prohibited zone, and multiple fuels cost functions are considered. Two Economic Load Dispatch (ELD) problems with different characteristics are applied and compared its solution quality and computation efficiency to Genetic algorithm (GA), Particle swarm optimization (PSO), and other optimization techniques. The simulation results show that the proposed algorithm outperforms previous optimization methods.","PeriodicalId":131589,"journal":{"name":"2009 Transmission & Distribution Conference & Exposition: Asia and Pacific","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Transmission & Distribution Conference & Exposition: Asia and Pacific","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TD-ASIA.2009.5356896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
This paper presents Biogeography-Based Optimization (BBO) technique for solving constrained economic dispatch problems in power system. Many nonlinear characteristics of generators, like valve point loading, ramp rate limits, prohibited zone, and multiple fuels cost functions are considered. Two Economic Load Dispatch (ELD) problems with different characteristics are applied and compared its solution quality and computation efficiency to Genetic algorithm (GA), Particle swarm optimization (PSO), and other optimization techniques. The simulation results show that the proposed algorithm outperforms previous optimization methods.