Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions

Renewable distributed generators (RDGs) have been widely used in distribution networks for technological, economic, and environmental reasons. The main concern with renewable-based distributed generators, particularly photovoltaic and wind systems, is their intermittent nature, which causes output p...

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Autores: Ramadan, Ashraf, Ebeed, Mohamed, Kamel, Salah, Ahmed, Emad M., Tostado-Véliz, Marcos
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2023
País:España
Recursos:Universidad de Jaén
Repositorio:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/3591
Acesso em linha:https://www.sciencedirect.com/science/article/pii/S2090447922001836
https://hdl.handle.net/10953/3591
Access Level:acceso abierto
Palavra-chave:Uncertainties
Artificial hummingbird algorithm
Backward reduction algorithm
Monte-Carlo simulation
Renewable Energy
Wind
Solar
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spelling Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditionsRamadan, AshrafEbeed, MohamedKamel, SalahAhmed, Emad M.Tostado-Véliz, MarcosUncertaintiesArtificial hummingbird algorithmBackward reduction algorithmMonte-Carlo simulationRenewable EnergyWindSolarRenewable distributed generators (RDGs) have been widely used in distribution networks for technological, economic, and environmental reasons. The main concern with renewable-based distributed generators, particularly photovoltaic and wind systems, is their intermittent nature, which causes output power to fluctuate, increasing power system uncertainty. As a result, it's critical to think about the resource's uncertainty when deciding where it should go in the grid. The main innovation of this paper is proposing an efficient and the most recent technique for optimal sizing and placement of the RDGs in radial distribution systems considering the uncertainties of the loading and RDGs output powers. Monte-Carlo simulation approach and backward reduction algorithm are used to generate 12 scenarios to model the uncertainties of loading and RDG output power. The artificial hummingbird algorithm (AHA), which is considered the most recent and efficient technique, is used to determine the RDG ratings and placements for a multi-objective function that includes minimizing expected total cost, the expected total emissions, and the expected total voltage deviation, as well as improving expected total voltage stability with considering the uncertainties of loading and RDGs output powers. The proposed technique is tested using an IEEE 33-bus network and an actual distribution system in Portugal (94-bus network). Simulations show that the suggested method effectively solves the problem of optimal DG allocation. In addition of that the expected costs, the emissions, the voltage deviation, are reduced considerably and the voltage stability is also enhanced with inclusion of RDGs in the tested systems.Elsevier202420242023info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://www.sciencedirect.com/science/article/pii/S2090447922001836https://hdl.handle.net/10953/3591reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésAin Shams Engineering Journal [2023]; [14]: [101872]Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/35912026-06-24T12:41:07Z
dc.title.none.fl_str_mv Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
title Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
spellingShingle Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
Ramadan, Ashraf
Uncertainties
Artificial hummingbird algorithm
Backward reduction algorithm
Monte-Carlo simulation
Renewable Energy
Wind
Solar
title_short Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
title_full Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
title_fullStr Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
title_full_unstemmed Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
title_sort Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
dc.creator.none.fl_str_mv Ramadan, Ashraf
Ebeed, Mohamed
Kamel, Salah
Ahmed, Emad M.
Tostado-Véliz, Marcos
author Ramadan, Ashraf
author_facet Ramadan, Ashraf
Ebeed, Mohamed
Kamel, Salah
Ahmed, Emad M.
Tostado-Véliz, Marcos
author_role author
author2 Ebeed, Mohamed
Kamel, Salah
Ahmed, Emad M.
Tostado-Véliz, Marcos
author2_role author
author
author
author
dc.subject.none.fl_str_mv Uncertainties
Artificial hummingbird algorithm
Backward reduction algorithm
Monte-Carlo simulation
Renewable Energy
Wind
Solar
topic Uncertainties
Artificial hummingbird algorithm
Backward reduction algorithm
Monte-Carlo simulation
Renewable Energy
Wind
Solar
description Renewable distributed generators (RDGs) have been widely used in distribution networks for technological, economic, and environmental reasons. The main concern with renewable-based distributed generators, particularly photovoltaic and wind systems, is their intermittent nature, which causes output power to fluctuate, increasing power system uncertainty. As a result, it's critical to think about the resource's uncertainty when deciding where it should go in the grid. The main innovation of this paper is proposing an efficient and the most recent technique for optimal sizing and placement of the RDGs in radial distribution systems considering the uncertainties of the loading and RDGs output powers. Monte-Carlo simulation approach and backward reduction algorithm are used to generate 12 scenarios to model the uncertainties of loading and RDG output power. The artificial hummingbird algorithm (AHA), which is considered the most recent and efficient technique, is used to determine the RDG ratings and placements for a multi-objective function that includes minimizing expected total cost, the expected total emissions, and the expected total voltage deviation, as well as improving expected total voltage stability with considering the uncertainties of loading and RDGs output powers. The proposed technique is tested using an IEEE 33-bus network and an actual distribution system in Portugal (94-bus network). Simulations show that the suggested method effectively solves the problem of optimal DG allocation. In addition of that the expected costs, the emissions, the voltage deviation, are reduced considerably and the voltage stability is also enhanced with inclusion of RDGs in the tested systems.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S2090447922001836
https://hdl.handle.net/10953/3591
url https://www.sciencedirect.com/science/article/pii/S2090447922001836
https://hdl.handle.net/10953/3591
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Ain Shams Engineering Journal [2023]; [14]: [101872]
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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