{"title":"Uncertainty Modeling for the Management of Distributed Generation Units using PSO","authors":"V. S. Pappala, I. Erlich","doi":"10.1109/PCT.2007.4538367","DOIUrl":null,"url":null,"abstract":"This paper addresses a multistage stochastic model for the operation of distributed generation (DG) units under stochastic load demands. The stochastic load demands have a significant effect on the economic model of the DG units. The uncertainties are modeled as scenario trees. But as the number of decision making stages increase, the scenario tree becomes extremely large, which leads to complex computation. Therefore a novel approach to generate a scenario tree using classical particle swarm optimization (PSO) approach is presented. The resulting multistage nonlinear stochastic model is solved using adaptive PSO.","PeriodicalId":356805,"journal":{"name":"2007 IEEE Lausanne Power Tech","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Lausanne Power Tech","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PCT.2007.4538367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
This paper addresses a multistage stochastic model for the operation of distributed generation (DG) units under stochastic load demands. The stochastic load demands have a significant effect on the economic model of the DG units. The uncertainties are modeled as scenario trees. But as the number of decision making stages increase, the scenario tree becomes extremely large, which leads to complex computation. Therefore a novel approach to generate a scenario tree using classical particle swarm optimization (PSO) approach is presented. The resulting multistage nonlinear stochastic model is solved using adaptive PSO.