Transmission network expansion planning considering uncertainty in generation and demand

dc.contributorUniversidad Tecnólogica de Pereira
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:23:43Z
dc.date.accessioned2022-10-05T18:14:06Z
dc.date.available2014-05-27T11:23:43Z
dc.date.available2022-10-05T18:14:06Z
dc.date.created2014-05-27T11:23:43Z
dc.date.issued2008-12-01
dc.identifier2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America, T and D-LA.
dc.identifierhttp://hdl.handle.net/11449/70674
dc.identifier10.1109/TDC-LA.2008.4641803
dc.identifier2-s2.0-67650475765
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3919946
dc.description.abstractThis paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering uncertainty in demand and generation. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The model presented results in an optimization problem that is solved using a specialized genetic algorithm. The results obtained for known systems from the literature show that cheaper plans can be found satisfying the uncertainty in demand and generation. ©2008 IEEE.
dc.languageeng
dc.relation2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America, T and D-LA
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectDC model
dc.subjectExpansion planning
dc.subjectGenetic algorithm
dc.subjectLong-term
dc.subjectOptimization
dc.subjectTransmission network
dc.subjectUncertainty in demand
dc.subjectUncertainty in generation
dc.subjectElectric network topology
dc.subjectElectric power transmission networks
dc.subjectGenetic algorithms
dc.subjectMathematical models
dc.subjectProblem solving
dc.titleTransmission network expansion planning considering uncertainty in generation and demand
dc.typeTrabalho apresentado em evento


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