Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/347156
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampLópez Amézquita, Juan Camilo-
dc.contributor.authorunicampRider Flores, Marcos Julio-
dc.typeArtigopt_BR
dc.titleOptimisation-based switch allocation to improve energy losses and service restoration in radial electrical distribution systemspt_BR
dc.contributor.authorLópez, Juan Camilo-
dc.contributor.authorFranco, John Fredy-
dc.contributor.authorRider, Marcos J.-
dc.subjectProgramação convexapt_BR
dc.subjectGeração de energia distribuídapt_BR
dc.subject.otherlanguageConvex programmingpt_BR
dc.subject.otherlanguageDistributed power generationpt_BR
dc.description.abstractThis study presents a new methodology for the optimal allocation of switching devices in radial electrical distribution systems (EDSs). A specialised greedy randomised adaptive search procedure (GRASP) algorithm defines the location of a number of switching devices in order to simultaneously improve the following optimisation subproblems related to the use of the allocated switches: (i) the optimal reconfiguration of EDS and (ii) the optimal service restoration of EDS. Eventually, the objective function of the proposed switch allocation algorithm minimises the cost of the total expected energy not supplied, computed after deploying the service restoration, plus the cost of the total annual energy loss computed for every load level in a year, plus the investment costs associated with the number of installed switches. Both optimisation subproblems, i.e. the reconfiguration and the restoration of EDS, are represented by mixed-integer non-linear programming (MINLP) models and transformed into mixed-integer linear programming (MILP) models, using linearisation strategies. MILP models guarantee convergence to optimality by using convex optimisation techniques. Finally, all tests were carried out using a real 136-node distribution system, considering dispatchable and non-dispatchable distributed generation resourcespt_BR
dc.relation.ispartofIET generation, transmission and distributionpt_BR
dc.relation.ispartofabbreviationIET gener., transm. distrib.pt_BR
dc.publisher.cityStevenagept_BR
dc.publisher.countryReino Unidopt_BR
dc.publisherInstitution of Engineering and Technologypt_BR
dc.date.issued2016-
dc.date.monthofcirculationAug.pt_BR
dc.language.isoengpt_BR
dc.description.volume10pt_BR
dc.description.issuenumber11pt_BR
dc.description.firstpage2792pt_BR
dc.description.lastpage2801pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1751-8687pt_BR
dc.identifier.eissn1751-8695pt_BR
dc.identifier.doi10.1049/iet-gtd.2015.1509pt_BR
dc.identifier.urlhttps://ieeexplore.ieee.org/document/7542774pt_BR
dc.date.available2020-08-12T12:51:03Z-
dc.date.accessioned2020-08-12T12:51:03Z-
dc.description.provenanceSubmitted by Mariana Aparecida Azevedo (mary1@unicamp.br) on 2020-08-12T12:51:03Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-01-08T19:02:56Z : No. of bitstreams: 1 000382791400026.pdf: 645480 bytes, checksum: d63b776bb57d7fdb24d4305487c6a472 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-08-12T12:51:03Z (GMT). No. of bitstreams: 0 Previous issue date: 2016en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/347156-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentDepartamento de Sistemas e Energiapt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.subject.keywordCost reductionpt_BR
dc.identifier.source000382791400026pt_BR
dc.creator.orcid0000-0001-5646-8612pt_BR
dc.creator.orcid0000-0001-5484-1161pt_BR
dc.type.formArtigo de pesquisapt_BR
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