A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets
Autor(a) principal: | |
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Data de Publicação: | 2007 |
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/10400.22/1340 |
Resumo: | This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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A decision-support system based on particle swarm optimization for multiperiod hedging in electricity marketsContractsElectricity marketsGenetic algorithmsHedgingParticle swarm optimizationRisk managementThis paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.IEEERepositório Científico do Instituto Politécnico do PortoAzevedo, FilipeVale, ZitaOliveira, P. B. Moura2013-04-16T10:07:24Z20072013-04-12T10:24:14Z2007-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/1340eng0885-895010.1109/TPWRS.2007.901463info: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:RCAAP2023-03-13T12:40:41ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets |
title |
A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets |
spellingShingle |
A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets Azevedo, Filipe Contracts Electricity markets Genetic algorithms Hedging Particle swarm optimization Risk management |
title_short |
A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets |
title_full |
A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets |
title_fullStr |
A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets |
title_full_unstemmed |
A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets |
title_sort |
A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets |
author |
Azevedo, Filipe |
author_facet |
Azevedo, Filipe Vale, Zita Oliveira, P. B. Moura |
author_role |
author |
author2 |
Vale, Zita Oliveira, P. B. Moura |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Azevedo, Filipe Vale, Zita Oliveira, P. B. Moura |
dc.subject.por.fl_str_mv |
Contracts Electricity markets Genetic algorithms Hedging Particle swarm optimization Risk management |
topic |
Contracts Electricity markets Genetic algorithms Hedging Particle swarm optimization Risk management |
description |
This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007 2007-01-01T00:00:00Z 2013-04-16T10:07:24Z 2013-04-12T10:24:14Z |
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/10400.22/1340 |
url |
http://hdl.handle.net/10400.22/1340 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0885-8950 10.1109/TPWRS.2007.901463 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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 |
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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) |
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1777302284227575808 |