dc.creatorMontoya O.D.
dc.creatorGil-González W.
dc.creatorGrisales-Noreña L.F.
dc.date.accessioned2020-03-26T16:32:36Z
dc.date.accessioned2022-09-28T20:21:58Z
dc.date.available2020-03-26T16:32:36Z
dc.date.available2022-09-28T20:21:58Z
dc.date.created2020-03-26T16:32:36Z
dc.date.issued2018
dc.identifierWSEAS Transactions on Power Systems; Vol. 13, pp. 335-346
dc.identifier17905060
dc.identifierhttps://hdl.handle.net/20.500.12585/8915
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio UTB
dc.identifier56919564100
dc.identifier57191493648
dc.identifier55791991200
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3726540
dc.description.abstractThis paper addresses the optimal power flow (OPF) problem in direct current (DC) power grids via a hybrid Gauss-Seidel-Genetic-Algorithm methodology through a master-slave optimization strategy. In the master stage, a genetic algorithm is employed to select the power dispatch for any distributed generator while the slave stage, Gauss-Seidel method is used for solving the resulting power flow equations without recurring to matrix inversions. This approach is important since it can be easily implementable over any simple programming toolbox finding the optimal solution of the OPF problem. Genetic-Algorithm proposed in this paper corresponds to a continuous variant of the conventional binary approaches. Computational results show the efficiency and accuracy of the proposed optimization method when is compared to GAMS/CONOPT nonlinear solver. © 2018, World Scientific and Engineering Academy and Society. All rights reserved.
dc.languageeng
dc.publisherWorld Scientific and Engineering Academy and Society
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85062268252&partnerID=40&md5=46edbfab70f6fb6b8d2d51e4c46870ae
dc.titleOptimal power dispatch of DGS in DC power grids: A hybrid gauss-seidel-genetic-algorithm methodology for solving the OPF problem


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