An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/ACCESS.2022.3144665 http://hdl.handle.net/11449/230263 |
Resumo: | Active power losses of distribution systems are higher than transmission ones, in which these losses affect the distribution operational costs directly. One of the efficient and effective methods for power losses reduction is distribution system reconfiguration (DSR). In this way, the network configuration is changed based on a specific power demand that has been already predicted by load forecasting techniques. The ohmic loss level in distribution system is affected by energy demand level, this is while an error in load forecasting can influence losses. Accordingly, including load uncertainty in DSR formulation is essential but this issue should not lead to change of the reconfiguration results significantly (i.e. the model should be robust). This paper presents a robust and efficient model for considering load uncertainty in network reconfiguration that is simple enough to implement in available commercial software packages and it is precise enough to find accurate solutions with low computational time. The analysis of results shows high efficiency and robustness of the proposed model for reconfiguration of distribution systems under demand uncertainty. |
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Repositório Institucional da UNESP |
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An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain LoadsComputational modelingDistribution networksLoad flow analysisLoad modelingMathematical modelsProbability density functionUncertaintyActive power losses of distribution systems are higher than transmission ones, in which these losses affect the distribution operational costs directly. One of the efficient and effective methods for power losses reduction is distribution system reconfiguration (DSR). In this way, the network configuration is changed based on a specific power demand that has been already predicted by load forecasting techniques. The ohmic loss level in distribution system is affected by energy demand level, this is while an error in load forecasting can influence losses. Accordingly, including load uncertainty in DSR formulation is essential but this issue should not lead to change of the reconfiguration results significantly (i.e. the model should be robust). This paper presents a robust and efficient model for considering load uncertainty in network reconfiguration that is simple enough to implement in available commercial software packages and it is precise enough to find accurate solutions with low computational time. The analysis of results shows high efficiency and robustness of the proposed model for reconfiguration of distribution systems under demand uncertainty.Bioenergy Research Institute (IPBEN) São Paulo State University Campus of Ilha Solteira Associated LaboratoryUniversity College Dublin School of Electrical and Electronic EngineeringKTH Royal Institute of Technology School of Electrical Engineering and Computer ScienceBioenergy Research Institute (IPBEN) São Paulo State University Campus of Ilha Solteira Associated LaboratoryUniversidade Estadual Paulista (UNESP)School of Electrical and Electronic EngineeringSchool of Electrical Engineering and Computer ScienceMahdavi, Meisam [UNESP]Alhelou, Hassan HaesHesamzadeh, Mohammad Reza2022-04-29T08:38:46Z2022-04-29T08:38:46Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10640-10652http://dx.doi.org/10.1109/ACCESS.2022.3144665IEEE Access, v. 10, p. 10640-10652.2169-3536http://hdl.handle.net/11449/23026310.1109/ACCESS.2022.31446652-s2.0-85123347443Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Accessinfo:eu-repo/semantics/openAccess2022-04-29T08:38:46Zoai:repositorio.unesp.br:11449/230263Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-29T08:38:46Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads |
title |
An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads |
spellingShingle |
An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads Mahdavi, Meisam [UNESP] Computational modeling Distribution networks Load flow analysis Load modeling Mathematical models Probability density function Uncertainty |
title_short |
An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads |
title_full |
An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads |
title_fullStr |
An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads |
title_full_unstemmed |
An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads |
title_sort |
An Efficient Stochastic Reconfiguration Model for Distribution Systems With Uncertain Loads |
author |
Mahdavi, Meisam [UNESP] |
author_facet |
Mahdavi, Meisam [UNESP] Alhelou, Hassan Haes Hesamzadeh, Mohammad Reza |
author_role |
author |
author2 |
Alhelou, Hassan Haes Hesamzadeh, Mohammad Reza |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) School of Electrical and Electronic Engineering School of Electrical Engineering and Computer Science |
dc.contributor.author.fl_str_mv |
Mahdavi, Meisam [UNESP] Alhelou, Hassan Haes Hesamzadeh, Mohammad Reza |
dc.subject.por.fl_str_mv |
Computational modeling Distribution networks Load flow analysis Load modeling Mathematical models Probability density function Uncertainty |
topic |
Computational modeling Distribution networks Load flow analysis Load modeling Mathematical models Probability density function Uncertainty |
description |
Active power losses of distribution systems are higher than transmission ones, in which these losses affect the distribution operational costs directly. One of the efficient and effective methods for power losses reduction is distribution system reconfiguration (DSR). In this way, the network configuration is changed based on a specific power demand that has been already predicted by load forecasting techniques. The ohmic loss level in distribution system is affected by energy demand level, this is while an error in load forecasting can influence losses. Accordingly, including load uncertainty in DSR formulation is essential but this issue should not lead to change of the reconfiguration results significantly (i.e. the model should be robust). This paper presents a robust and efficient model for considering load uncertainty in network reconfiguration that is simple enough to implement in available commercial software packages and it is precise enough to find accurate solutions with low computational time. The analysis of results shows high efficiency and robustness of the proposed model for reconfiguration of distribution systems under demand uncertainty. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-29T08:38:46Z 2022-04-29T08:38:46Z 2022-01-01 |
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/10.1109/ACCESS.2022.3144665 IEEE Access, v. 10, p. 10640-10652. 2169-3536 http://hdl.handle.net/11449/230263 10.1109/ACCESS.2022.3144665 2-s2.0-85123347443 |
url |
http://dx.doi.org/10.1109/ACCESS.2022.3144665 http://hdl.handle.net/11449/230263 |
identifier_str_mv |
IEEE Access, v. 10, p. 10640-10652. 2169-3536 10.1109/ACCESS.2022.3144665 2-s2.0-85123347443 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
IEEE Access |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
10640-10652 |
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 |
|
_version_ |
1803046088176828416 |