Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration
Autor(a) principal: | |
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Data de Publicação: | 2025 |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TPEC63981.2025.10906970 https://hdl.handle.net/11449/305283 |
Resumo: | The distribution network is likely to experience higher active losses and lower voltage profiles due to its radial topology, the nature of loads and transformers, and recently with increased penetration of distributed generations. The power quality study in the distribution system aims to decrease power losses and mitigate voltage drops. This has been carried out using empirical techniques or exact optimization methods. However, distribution feeder reconfiguration (DFR) has become the best way to reduce losses and improve voltage profiles in the distribution system. In this study, the DFR was addressed simultaneously with the integration of distributed generation (DG) and capacitor bank (CB) into a large-scale distribution network. The stochastic fractal search algorithm (SFS) has been implemented to solve this problem. The loss-sensitive factor (LSF) approach was applied to reduce the search space of candidate buses for DG and CB placement when initializing the optimization variables and when correcting the boundary violation during the optimization process. Furthermore, we proposed two efficient mathematical strategies to control and correct the violation of tie-switches bounds at the diffusion and updating stages of the SFS, and to define CB sizes for better integration of them into the grid. The proposed algorithm was tested on the 136-bus distribution network. Different simulation cases have been carried out, and the results showed a significant impact on reducing the losses and enhancing the minimum voltage value of the feeder, especially when distributed generations and capacitor banks are simultaneously addressed in the DFR problem. |
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Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfigurationcapacitor bankcombinatorial optimizationdistributed generationDistribution feeder reconfigurationmathematical strategypower losses reductionstochastic fractal searchThe distribution network is likely to experience higher active losses and lower voltage profiles due to its radial topology, the nature of loads and transformers, and recently with increased penetration of distributed generations. The power quality study in the distribution system aims to decrease power losses and mitigate voltage drops. This has been carried out using empirical techniques or exact optimization methods. However, distribution feeder reconfiguration (DFR) has become the best way to reduce losses and improve voltage profiles in the distribution system. In this study, the DFR was addressed simultaneously with the integration of distributed generation (DG) and capacitor bank (CB) into a large-scale distribution network. The stochastic fractal search algorithm (SFS) has been implemented to solve this problem. The loss-sensitive factor (LSF) approach was applied to reduce the search space of candidate buses for DG and CB placement when initializing the optimization variables and when correcting the boundary violation during the optimization process. Furthermore, we proposed two efficient mathematical strategies to control and correct the violation of tie-switches bounds at the diffusion and updating stages of the SFS, and to define CB sizes for better integration of them into the grid. The proposed algorithm was tested on the 136-bus distribution network. Different simulation cases have been carried out, and the results showed a significant impact on reducing the losses and enhancing the minimum voltage value of the feeder, especially when distributed generations and capacitor banks are simultaneously addressed in the DFR problem.Sao Paulo State University (UNESP) School of Engineering Dept. of Electrical EngineeringSao Paulo State University (UNESP) School of Engineering Dept. of Electrical EngineeringUniversidade Estadual Paulista (UNESP)Ayikpa, Malinwo Estone [UNESP]2025-04-29T20:02:40Z2025-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/TPEC63981.2025.109069702025 IEEE Texas Power and Energy Conference, TPEC 2025.https://hdl.handle.net/11449/30528310.1109/TPEC63981.2025.109069702-s2.0-105001056703Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2025 IEEE Texas Power and Energy Conference, TPEC 2025info:eu-repo/semantics/openAccess2025-04-30T14:32:40Zoai:repositorio.unesp.br:11449/305283Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462025-04-30T14:32:40Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration |
title |
Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration |
spellingShingle |
Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration Ayikpa, Malinwo Estone [UNESP] capacitor bank combinatorial optimization distributed generation Distribution feeder reconfiguration mathematical strategy power losses reduction stochastic fractal search |
title_short |
Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration |
title_full |
Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration |
title_fullStr |
Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration |
title_full_unstemmed |
Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration |
title_sort |
Optimal Placement and Sizing of Distributed Generations and Capacitor Banks in Large-Scale Distribution Network Using Feeder Reconfiguration |
author |
Ayikpa, Malinwo Estone [UNESP] |
author_facet |
Ayikpa, Malinwo Estone [UNESP] |
author_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Ayikpa, Malinwo Estone [UNESP] |
dc.subject.por.fl_str_mv |
capacitor bank combinatorial optimization distributed generation Distribution feeder reconfiguration mathematical strategy power losses reduction stochastic fractal search |
topic |
capacitor bank combinatorial optimization distributed generation Distribution feeder reconfiguration mathematical strategy power losses reduction stochastic fractal search |
description |
The distribution network is likely to experience higher active losses and lower voltage profiles due to its radial topology, the nature of loads and transformers, and recently with increased penetration of distributed generations. The power quality study in the distribution system aims to decrease power losses and mitigate voltage drops. This has been carried out using empirical techniques or exact optimization methods. However, distribution feeder reconfiguration (DFR) has become the best way to reduce losses and improve voltage profiles in the distribution system. In this study, the DFR was addressed simultaneously with the integration of distributed generation (DG) and capacitor bank (CB) into a large-scale distribution network. The stochastic fractal search algorithm (SFS) has been implemented to solve this problem. The loss-sensitive factor (LSF) approach was applied to reduce the search space of candidate buses for DG and CB placement when initializing the optimization variables and when correcting the boundary violation during the optimization process. Furthermore, we proposed two efficient mathematical strategies to control and correct the violation of tie-switches bounds at the diffusion and updating stages of the SFS, and to define CB sizes for better integration of them into the grid. The proposed algorithm was tested on the 136-bus distribution network. Different simulation cases have been carried out, and the results showed a significant impact on reducing the losses and enhancing the minimum voltage value of the feeder, especially when distributed generations and capacitor banks are simultaneously addressed in the DFR problem. |
publishDate |
2025 |
dc.date.none.fl_str_mv |
2025-04-29T20:02:40Z 2025-01-01 |
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 |
http://dx.doi.org/10.1109/TPEC63981.2025.10906970 2025 IEEE Texas Power and Energy Conference, TPEC 2025. https://hdl.handle.net/11449/305283 10.1109/TPEC63981.2025.10906970 2-s2.0-105001056703 |
url |
http://dx.doi.org/10.1109/TPEC63981.2025.10906970 https://hdl.handle.net/11449/305283 |
identifier_str_mv |
2025 IEEE Texas Power and Energy Conference, TPEC 2025. 10.1109/TPEC63981.2025.10906970 2-s2.0-105001056703 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2025 IEEE Texas Power and Energy Conference, TPEC 2025 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus 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 |
_version_ |
1834482673425317888 |