Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/ACCESS.2021.3116802 http://hdl.handle.net/11449/229695 |
Resumo: | Transmission expansion planning (TEP) is an important part of power system expansion planning. In TEP, optimal number of new transmission lines and their installation time and place are determined in an economic way. Uncertainties in load demand, place of power plants, and fuel price as well as voltage level of substations influence TEP solutions effectively. Therefore, in this paper, a scenario based-model is proposed for evaluating the fuel price impact on TEP considering the expansion of substations from the voltage level point of view. The fuel price is an important factor in power system expansion planning that includes severe uncertainties. This factor indirectly affects the lines loading and subsequent network configuration through the change of optimal generation of power plants. The efficiency of the proposed model is tested on the real transmission network of Azerbaijan regional electric company using a discrete artificial bee colony (DABC) and quadratic programming (QP) based method. Moreover, discrete particle swarm optimization (DPSO) and decimal codification genetic algorithm (DCGA) methods are used to verify the results of the DABC algorithm. The results evaluation reveals that considering uncertainty in fuel price for solving TEP problem affects the network configuration and the total expansion cost of the network. In this way, the total cost is optimized more and therefore the TEP problem is solved more precisely. Also, by comparing the convergence curve of the DABC with that of DPSO and DCGA algorithms, it can be seen that the efficiency of the DABC is more than DPSO and DCGA for solving the desired TEP problem. |
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Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony AlgorithmDABCexpansion of substationsnetwork lossesstatic TEP (STEP)uncertainty in fuel priceTransmission expansion planning (TEP) is an important part of power system expansion planning. In TEP, optimal number of new transmission lines and their installation time and place are determined in an economic way. Uncertainties in load demand, place of power plants, and fuel price as well as voltage level of substations influence TEP solutions effectively. Therefore, in this paper, a scenario based-model is proposed for evaluating the fuel price impact on TEP considering the expansion of substations from the voltage level point of view. The fuel price is an important factor in power system expansion planning that includes severe uncertainties. This factor indirectly affects the lines loading and subsequent network configuration through the change of optimal generation of power plants. The efficiency of the proposed model is tested on the real transmission network of Azerbaijan regional electric company using a discrete artificial bee colony (DABC) and quadratic programming (QP) based method. Moreover, discrete particle swarm optimization (DPSO) and decimal codification genetic algorithm (DCGA) methods are used to verify the results of the DABC algorithm. The results evaluation reveals that considering uncertainty in fuel price for solving TEP problem affects the network configuration and the total expansion cost of the network. In this way, the total cost is optimized more and therefore the TEP problem is solved more precisely. Also, by comparing the convergence curve of the DABC with that of DPSO and DCGA algorithms, it can be seen that the efficiency of the DABC is more than DPSO and DCGA for solving the desired TEP problem.Associated Laboratory Bioenergy Research Institute (IPBEN) São Paulo State University Campus of Ilha SolteiraDepartment of Electrical Engineering Faculty of Engineering University of ZanjanSchool of Electrical and Electronic Engineering University College DublinDepartment of Electrical Power Engineering Tishreen UniversityInstitute for Systems and Computer Engineering Technology and Science (INESC-TEC)Department of Electrical Engineering Khodabandeh Branch IAUFaculty of Engineering University of PortoAssociated Laboratory Bioenergy Research Institute (IPBEN) São Paulo State University Campus of Ilha SolteiraUniversidade Estadual Paulista (UNESP)University of ZanjanUniversity College DublinTishreen UniversityTechnology and Science (INESC-TEC)IAUUniversity of PortoMahdavi, Meisam [UNESP]Kimiyaghalam, AliAlhelou, Hassan HaesJavadi, Mohammad SadeghAshouri, AhmadCatalao, Joao P. S.2022-04-29T08:35:08Z2022-04-29T08:35:08Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article135983-135995http://dx.doi.org/10.1109/ACCESS.2021.3116802IEEE Access, v. 9, p. 135983-135995.2169-3536http://hdl.handle.net/11449/22969510.1109/ACCESS.2021.31168022-s2.0-85116970977Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIEEE Accessinfo:eu-repo/semantics/openAccess2022-04-29T08:35:08Zoai:repositorio.unesp.br:11449/229695Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-05-23T19:52:02.341116Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm |
title |
Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm |
spellingShingle |
Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm Mahdavi, Meisam [UNESP] DABC expansion of substations network losses static TEP (STEP) uncertainty in fuel price |
title_short |
Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm |
title_full |
Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm |
title_fullStr |
Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm |
title_full_unstemmed |
Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm |
title_sort |
Transmission Expansion Planning Considering Power Losses, Expansion of Substations and Uncertainty in Fuel Price Using Discrete Artificial Bee Colony Algorithm |
author |
Mahdavi, Meisam [UNESP] |
author_facet |
Mahdavi, Meisam [UNESP] Kimiyaghalam, Ali Alhelou, Hassan Haes Javadi, Mohammad Sadegh Ashouri, Ahmad Catalao, Joao P. S. |
author_role |
author |
author2 |
Kimiyaghalam, Ali Alhelou, Hassan Haes Javadi, Mohammad Sadegh Ashouri, Ahmad Catalao, Joao P. S. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) University of Zanjan University College Dublin Tishreen University Technology and Science (INESC-TEC) IAU University of Porto |
dc.contributor.author.fl_str_mv |
Mahdavi, Meisam [UNESP] Kimiyaghalam, Ali Alhelou, Hassan Haes Javadi, Mohammad Sadegh Ashouri, Ahmad Catalao, Joao P. S. |
dc.subject.por.fl_str_mv |
DABC expansion of substations network losses static TEP (STEP) uncertainty in fuel price |
topic |
DABC expansion of substations network losses static TEP (STEP) uncertainty in fuel price |
description |
Transmission expansion planning (TEP) is an important part of power system expansion planning. In TEP, optimal number of new transmission lines and their installation time and place are determined in an economic way. Uncertainties in load demand, place of power plants, and fuel price as well as voltage level of substations influence TEP solutions effectively. Therefore, in this paper, a scenario based-model is proposed for evaluating the fuel price impact on TEP considering the expansion of substations from the voltage level point of view. The fuel price is an important factor in power system expansion planning that includes severe uncertainties. This factor indirectly affects the lines loading and subsequent network configuration through the change of optimal generation of power plants. The efficiency of the proposed model is tested on the real transmission network of Azerbaijan regional electric company using a discrete artificial bee colony (DABC) and quadratic programming (QP) based method. Moreover, discrete particle swarm optimization (DPSO) and decimal codification genetic algorithm (DCGA) methods are used to verify the results of the DABC algorithm. The results evaluation reveals that considering uncertainty in fuel price for solving TEP problem affects the network configuration and the total expansion cost of the network. In this way, the total cost is optimized more and therefore the TEP problem is solved more precisely. Also, by comparing the convergence curve of the DABC with that of DPSO and DCGA algorithms, it can be seen that the efficiency of the DABC is more than DPSO and DCGA for solving the desired TEP problem. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-04-29T08:35:08Z 2022-04-29T08:35:08Z |
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.2021.3116802 IEEE Access, v. 9, p. 135983-135995. 2169-3536 http://hdl.handle.net/11449/229695 10.1109/ACCESS.2021.3116802 2-s2.0-85116970977 |
url |
http://dx.doi.org/10.1109/ACCESS.2021.3116802 http://hdl.handle.net/11449/229695 |
identifier_str_mv |
IEEE Access, v. 9, p. 135983-135995. 2169-3536 10.1109/ACCESS.2021.3116802 2-s2.0-85116970977 |
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 |
135983-135995 |
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 |
|
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1803045633090650112 |