Distributed energy resource short-term scheduling using signaled particle swarm optimization

Detalhes bibliográficos
Autor(a) principal: Soares, João
Data de Publicação: 2012
Outros Autores: Silva, Marco, Sousa, Tiago, Vale, Zita, Morais, H.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/1360
Resumo: Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.
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spelling Distributed energy resource short-term scheduling using signaled particle swarm optimizationDistributed energy resource schedulingMixed integer non-linear programmingParticle swarm optimizationShort-term schedulingDistributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.ElsevierRepositório Científico do Instituto Politécnico do PortoSoares, JoãoSilva, MarcoSousa, TiagoVale, ZitaMorais, H.2013-04-16T14:45:07Z20122013-04-12T10:52:34Z2012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/1360eng0360-544210.1016/j.energy.2012.03.022info: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:RCAAP2023-03-13T12:40:41Zoai:recipp.ipp.pt:10400.22/1360Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:22:31.650833Repositório 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 Distributed energy resource short-term scheduling using signaled particle swarm optimization
title Distributed energy resource short-term scheduling using signaled particle swarm optimization
spellingShingle Distributed energy resource short-term scheduling using signaled particle swarm optimization
Soares, João
Distributed energy resource scheduling
Mixed integer non-linear programming
Particle swarm optimization
Short-term scheduling
title_short Distributed energy resource short-term scheduling using signaled particle swarm optimization
title_full Distributed energy resource short-term scheduling using signaled particle swarm optimization
title_fullStr Distributed energy resource short-term scheduling using signaled particle swarm optimization
title_full_unstemmed Distributed energy resource short-term scheduling using signaled particle swarm optimization
title_sort Distributed energy resource short-term scheduling using signaled particle swarm optimization
author Soares, João
author_facet Soares, João
Silva, Marco
Sousa, Tiago
Vale, Zita
Morais, H.
author_role author
author2 Silva, Marco
Sousa, Tiago
Vale, Zita
Morais, H.
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 Soares, João
Silva, Marco
Sousa, Tiago
Vale, Zita
Morais, H.
dc.subject.por.fl_str_mv Distributed energy resource scheduling
Mixed integer non-linear programming
Particle swarm optimization
Short-term scheduling
topic Distributed energy resource scheduling
Mixed integer non-linear programming
Particle swarm optimization
Short-term scheduling
description Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-01-01T00:00:00Z
2013-04-16T14:45:07Z
2013-04-12T10:52:34Z
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url http://hdl.handle.net/10400.22/1360
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0360-5442
10.1016/j.energy.2012.03.022
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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