Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling
Autor(a) principal: | |
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Data de Publicação: | 2013 |
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/10400.22/5251 |
Resumo: | The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization. |
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Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources SchedulingEnergy resourcesGeneratorsLoad managementOptimizationParticle swarm optimizationPower generationReactive powerThe elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.IEEERepositório Científico do Instituto Politécnico do PortoFaria, PedroSoares, JoãoVale, ZitaMorais, HugoSousa, Tiago2014-12-09T16:06:49Z2013-032013-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/5251eng1949-305310.1109/TSG.2012.2235866metadata 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:RCAAP2023-03-13T12:45:15Zoai:recipp.ipp.pt:10400.22/5251Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:25:56.277641Repositório 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 |
Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling |
title |
Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling |
spellingShingle |
Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling Faria, Pedro Energy resources Generators Load management Optimization Particle swarm optimization Power generation Reactive power |
title_short |
Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling |
title_full |
Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling |
title_fullStr |
Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling |
title_full_unstemmed |
Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling |
title_sort |
Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling |
author |
Faria, Pedro |
author_facet |
Faria, Pedro Soares, João Vale, Zita Morais, Hugo Sousa, Tiago |
author_role |
author |
author2 |
Soares, João Vale, Zita Morais, Hugo Sousa, Tiago |
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 |
Faria, Pedro Soares, João Vale, Zita Morais, Hugo Sousa, Tiago |
dc.subject.por.fl_str_mv |
Energy resources Generators Load management Optimization Particle swarm optimization Power generation Reactive power |
topic |
Energy resources Generators Load management Optimization Particle swarm optimization Power generation Reactive power |
description |
The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-03 2013-03-01T00:00:00Z 2014-12-09T16:06:49Z |
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/10400.22/5251 |
url |
http://hdl.handle.net/10400.22/5251 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1949-3053 10.1109/TSG.2012.2235866 |
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 |
IEEE |
publisher.none.fl_str_mv |
IEEE |
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 |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131353783992320 |