A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/5252 |
Resumo: | The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach. |
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A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric VehiclesAnt colony optimizationEnergy resource managementElectric vehicleHybridizationSimulated annealingVirtual power playerThe massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.ElsevierRepositório Científico do Instituto Politécnico do PortoSousa, TiagoVale, ZitaCarvalho, JoãoPinto, TiagoMorais, Hugo2014-12-09T16:13:10Z20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/5252eng0360-544210.1016/j.energy.2014.02.025metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:45:16Zoai:recipp.ipp.pt:10400.22/5252Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:25:56.442085Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles |
title |
A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles |
spellingShingle |
A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles Sousa, Tiago Ant colony optimization Energy resource management Electric vehicle Hybridization Simulated annealing Virtual power player |
title_short |
A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles |
title_full |
A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles |
title_fullStr |
A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles |
title_full_unstemmed |
A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles |
title_sort |
A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles |
author |
Sousa, Tiago |
author_facet |
Sousa, Tiago Vale, Zita Carvalho, João Pinto, Tiago Morais, Hugo |
author_role |
author |
author2 |
Vale, Zita Carvalho, João Pinto, Tiago Morais, Hugo |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Sousa, Tiago Vale, Zita Carvalho, João Pinto, Tiago Morais, Hugo |
dc.subject.por.fl_str_mv |
Ant colony optimization Energy resource management Electric vehicle Hybridization Simulated annealing Virtual power player |
topic |
Ant colony optimization Energy resource management Electric vehicle Hybridization Simulated annealing Virtual power player |
description |
The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-12-09T16:13:10Z 2014 2014-01-01T00:00:00Z |
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://hdl.handle.net/10400.22/5252 |
url |
http://hdl.handle.net/10400.22/5252 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0360-5442 10.1016/j.energy.2014.02.025 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131353788186624 |