Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand Scenarios
Main Author: | |
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Publication Date: | 2015 |
Other Authors: | , , , |
Format: | Conference object |
Language: | eng |
Source: | Repositório Institucional da UNESP |
Download full: | http://hdl.handle.net/11449/161771 |
Summary: | This paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers. |
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Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand ScenariosDistribution System ReconfigurationDemand scenariosSpecialized Genetic Algorithm of Chu-BeasleyMixed-Integer Nonlinear Programming ProblemThis paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers.UNESP Univ Estadual Paulista, Dept Elect Engn, Ilha Solteira, SP, BrazilUniv Porto, INESC TEC, Oporto, PortugalUniv Porto, Fac Econ, Oporto, PortugalUniv Porto, Fac Engn, Oporto, PortugalUNESP Univ Estadual Paulista, Dept Elect Engn, Ilha Solteira, SP, BrazilIeeeUniversidade Estadual Paulista (Unesp)Univ PortoSouza, Simone S. F. [UNESP]Romero, Ruben [UNESP]Pereira, JorgeSaraiva, J. T.IEEE2018-11-26T16:48:33Z2018-11-26T16:48:33Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject52015 Ieee Eindhoven Powertech. New York: Ieee, 5 p., 2015.http://hdl.handle.net/11449/161771WOS:000380546800065Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 Ieee Eindhoven Powertechinfo:eu-repo/semantics/openAccess2024-07-04T19:11:50Zoai:repositorio.unesp.br:11449/161771Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462024-07-04T19:11:50Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand Scenarios |
title |
Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand Scenarios |
spellingShingle |
Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand Scenarios Souza, Simone S. F. [UNESP] Distribution System Reconfiguration Demand scenarios Specialized Genetic Algorithm of Chu-Beasley Mixed-Integer Nonlinear Programming Problem |
title_short |
Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand Scenarios |
title_full |
Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand Scenarios |
title_fullStr |
Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand Scenarios |
title_full_unstemmed |
Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand Scenarios |
title_sort |
Specialized Genetic Algorithm of Chu-Beasley Applied to the Distribution System Reconfiguration Problem Considering Several Demand Scenarios |
author |
Souza, Simone S. F. [UNESP] |
author_facet |
Souza, Simone S. F. [UNESP] Romero, Ruben [UNESP] Pereira, Jorge Saraiva, J. T. IEEE |
author_role |
author |
author2 |
Romero, Ruben [UNESP] Pereira, Jorge Saraiva, J. T. IEEE |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Univ Porto |
dc.contributor.author.fl_str_mv |
Souza, Simone S. F. [UNESP] Romero, Ruben [UNESP] Pereira, Jorge Saraiva, J. T. IEEE |
dc.subject.por.fl_str_mv |
Distribution System Reconfiguration Demand scenarios Specialized Genetic Algorithm of Chu-Beasley Mixed-Integer Nonlinear Programming Problem |
topic |
Distribution System Reconfiguration Demand scenarios Specialized Genetic Algorithm of Chu-Beasley Mixed-Integer Nonlinear Programming Problem |
description |
This paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01 2018-11-26T16:48:33Z 2018-11-26T16:48:33Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2015 Ieee Eindhoven Powertech. New York: Ieee, 5 p., 2015. http://hdl.handle.net/11449/161771 WOS:000380546800065 |
identifier_str_mv |
2015 Ieee Eindhoven Powertech. New York: Ieee, 5 p., 2015. WOS:000380546800065 |
url |
http://hdl.handle.net/11449/161771 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2015 Ieee Eindhoven Powertech |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
5 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
repositoriounesp@unesp.br |
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1834484632221908992 |