Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.35833/MPCE.2018.000503 http://hdl.handle.net/11449/196830 |
Resumo: | The integration of network reconfiguration and distributed generation (DG) can enhance the performances of overall networks. Thus, proper sizing and siting of DG need to be determined, otherwise it will cause degradation in system performance. However, determining proper sizing and siting of DG together with network reconfiguration is a complex problem due to huge solution search space. This search space mostly contains non-radial network configurations. Eliminating these non-radial combinations during optimization process increases computational overhead and may end up at local optimal solution. To reduce the searching complexity, this paper considers the discretized network reconfiguration via dataset approach. Water cycle algorithm (WCA) is used to obtain the near optimal solution of network reconfiguration, and sizing and sitting of DG. In addition, the power factor of DG is also optimized to reduce the power loss. The proposed method is tested on an IEEE 33-bus network and an IEEE 69-bus network considering different scenarios to show the effectiveness of simultaneous approach considering variable power factor. The results show that the discretization of reconfiguration search space avoids that WCA to get trapped in local optima. The proposed method out-performs other technique such as harmony search algorithm (HSA), fireworks algorithm (FWA), Cuckoo search algorithm (CSA) and uniform voltage distribution based constructive algorithm (UVDA) and improves the solution quality of IEEE 33-bus network and 69-bus network by 29.20% and 27.88%, respectively. |
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Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle AlgorithmReactive power injectionsizing and siting of distributed generation (DG)dataset approachactive distribution networkpower systemThe integration of network reconfiguration and distributed generation (DG) can enhance the performances of overall networks. Thus, proper sizing and siting of DG need to be determined, otherwise it will cause degradation in system performance. However, determining proper sizing and siting of DG together with network reconfiguration is a complex problem due to huge solution search space. This search space mostly contains non-radial network configurations. Eliminating these non-radial combinations during optimization process increases computational overhead and may end up at local optimal solution. To reduce the searching complexity, this paper considers the discretized network reconfiguration via dataset approach. Water cycle algorithm (WCA) is used to obtain the near optimal solution of network reconfiguration, and sizing and sitting of DG. In addition, the power factor of DG is also optimized to reduce the power loss. The proposed method is tested on an IEEE 33-bus network and an IEEE 69-bus network considering different scenarios to show the effectiveness of simultaneous approach considering variable power factor. The results show that the discretization of reconfiguration search space avoids that WCA to get trapped in local optima. The proposed method out-performs other technique such as harmony search algorithm (HSA), fireworks algorithm (FWA), Cuckoo search algorithm (CSA) and uniform voltage distribution based constructive algorithm (UVDA) and improves the solution quality of IEEE 33-bus network and 69-bus network by 29.20% and 27.88%, respectively.University of MalayaUniv Malaya, Fac Engn, Dept Elect Engn, Kuala Lumpur 50603, MalaysiaUniv Kuala Lumpur, British Malaysian Inst, Elect Technol Sect, Bt 8,Jalan Sungai Pusu, Gombak 53100, Selangor Daryl, MalaysiaSao Paulo State Univ, Campus Rosana, BR-19274000 Rosana, BrazilSao Paulo State Univ, Campus Rosana, BR-19274000 Rosana, BrazilUniversity of Malaya: GPF016A-2019Ieee-inst Electrical Electronics Engineers IncUniv MalayaUniv Kuala LumpurUniversidade Estadual Paulista (Unesp)Muhammad, Munir AzamMokhlis, HazlieNaidu, KanendraAmin, AdilFranco, John F. [UNESP]Othman, Mohamadariff2020-12-10T19:57:32Z2020-12-10T19:57:32Z2020-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article86-93http://dx.doi.org/10.35833/MPCE.2018.000503Journal Of Modern Power Systems And Clean Energy. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 8, n. 1, p. 86-93, 2020.2196-5625http://hdl.handle.net/11449/19683010.35833/MPCE.2018.000503WOS:000528860300009Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal Of Modern Power Systems And Clean Energyinfo:eu-repo/semantics/openAccess2021-10-23T07:34:09Zoai:repositorio.unesp.br:11449/196830Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T07:34:09Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm |
title |
Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm |
spellingShingle |
Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm Muhammad, Munir Azam Reactive power injection sizing and siting of distributed generation (DG) dataset approach active distribution network power system |
title_short |
Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm |
title_full |
Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm |
title_fullStr |
Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm |
title_full_unstemmed |
Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm |
title_sort |
Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm |
author |
Muhammad, Munir Azam |
author_facet |
Muhammad, Munir Azam Mokhlis, Hazlie Naidu, Kanendra Amin, Adil Franco, John F. [UNESP] Othman, Mohamadariff |
author_role |
author |
author2 |
Mokhlis, Hazlie Naidu, Kanendra Amin, Adil Franco, John F. [UNESP] Othman, Mohamadariff |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Univ Malaya Univ Kuala Lumpur Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Muhammad, Munir Azam Mokhlis, Hazlie Naidu, Kanendra Amin, Adil Franco, John F. [UNESP] Othman, Mohamadariff |
dc.subject.por.fl_str_mv |
Reactive power injection sizing and siting of distributed generation (DG) dataset approach active distribution network power system |
topic |
Reactive power injection sizing and siting of distributed generation (DG) dataset approach active distribution network power system |
description |
The integration of network reconfiguration and distributed generation (DG) can enhance the performances of overall networks. Thus, proper sizing and siting of DG need to be determined, otherwise it will cause degradation in system performance. However, determining proper sizing and siting of DG together with network reconfiguration is a complex problem due to huge solution search space. This search space mostly contains non-radial network configurations. Eliminating these non-radial combinations during optimization process increases computational overhead and may end up at local optimal solution. To reduce the searching complexity, this paper considers the discretized network reconfiguration via dataset approach. Water cycle algorithm (WCA) is used to obtain the near optimal solution of network reconfiguration, and sizing and sitting of DG. In addition, the power factor of DG is also optimized to reduce the power loss. The proposed method is tested on an IEEE 33-bus network and an IEEE 69-bus network considering different scenarios to show the effectiveness of simultaneous approach considering variable power factor. The results show that the discretization of reconfiguration search space avoids that WCA to get trapped in local optima. The proposed method out-performs other technique such as harmony search algorithm (HSA), fireworks algorithm (FWA), Cuckoo search algorithm (CSA) and uniform voltage distribution based constructive algorithm (UVDA) and improves the solution quality of IEEE 33-bus network and 69-bus network by 29.20% and 27.88%, respectively. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-10T19:57:32Z 2020-12-10T19:57:32Z 2020-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.35833/MPCE.2018.000503 Journal Of Modern Power Systems And Clean Energy. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 8, n. 1, p. 86-93, 2020. 2196-5625 http://hdl.handle.net/11449/196830 10.35833/MPCE.2018.000503 WOS:000528860300009 |
url |
http://dx.doi.org/10.35833/MPCE.2018.000503 http://hdl.handle.net/11449/196830 |
identifier_str_mv |
Journal Of Modern Power Systems And Clean Energy. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 8, n. 1, p. 86-93, 2020. 2196-5625 10.35833/MPCE.2018.000503 WOS:000528860300009 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal Of Modern Power Systems And Clean Energy |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
86-93 |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
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 |
|
_version_ |
1803046821172346880 |