Adaptive Portfolio Optimization for Multiple Electricity Markets Participation
Autor(a) principal: | |
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Data de Publicação: | 2016 |
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/10400.22/9386 |
Resumo: | The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources’ particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players’ participation in electric power negotiations while improving energy efficiency. The opportunity for players’ participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator—MIBEL. |
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Adaptive Portfolio Optimization for Multiple Electricity Markets ParticipationAdaptive learningArtificial neural network (NN)Electricity marketsMultiagent simulationPortfolio optimizationSwarm intelligenceThe increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources’ particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players’ participation in electric power negotiations while improving energy efficiency. The opportunity for players’ participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator—MIBEL.Institute of Electrical and Electronics EngineersRepositório Científico do Instituto Politécnico do PortoPinto, TiagoMorais, HugoSousa, Tiago M.Sousa, TiagoVale, ZitaPraça, IsabelFaia, RicardoPires, Eduardo José Solteiro20162117-01-01T00:00:00Z2016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/9386eng10.1109/TNNLS.2015.2461491metadata 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:RCAAP2023-03-13T12:50:45Zoai:recipp.ipp.pt:10400.22/9386Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:29:59.574747Repositório 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 |
Adaptive Portfolio Optimization for Multiple Electricity Markets Participation |
title |
Adaptive Portfolio Optimization for Multiple Electricity Markets Participation |
spellingShingle |
Adaptive Portfolio Optimization for Multiple Electricity Markets Participation Pinto, Tiago Adaptive learning Artificial neural network (NN) Electricity markets Multiagent simulation Portfolio optimization Swarm intelligence |
title_short |
Adaptive Portfolio Optimization for Multiple Electricity Markets Participation |
title_full |
Adaptive Portfolio Optimization for Multiple Electricity Markets Participation |
title_fullStr |
Adaptive Portfolio Optimization for Multiple Electricity Markets Participation |
title_full_unstemmed |
Adaptive Portfolio Optimization for Multiple Electricity Markets Participation |
title_sort |
Adaptive Portfolio Optimization for Multiple Electricity Markets Participation |
author |
Pinto, Tiago |
author_facet |
Pinto, Tiago Morais, Hugo Sousa, Tiago M. Sousa, Tiago Vale, Zita Praça, Isabel Faia, Ricardo Pires, Eduardo José Solteiro |
author_role |
author |
author2 |
Morais, Hugo Sousa, Tiago M. Sousa, Tiago Vale, Zita Praça, Isabel Faia, Ricardo Pires, Eduardo José Solteiro |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Pinto, Tiago Morais, Hugo Sousa, Tiago M. Sousa, Tiago Vale, Zita Praça, Isabel Faia, Ricardo Pires, Eduardo José Solteiro |
dc.subject.por.fl_str_mv |
Adaptive learning Artificial neural network (NN) Electricity markets Multiagent simulation Portfolio optimization Swarm intelligence |
topic |
Adaptive learning Artificial neural network (NN) Electricity markets Multiagent simulation Portfolio optimization Swarm intelligence |
description |
The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources’ particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players’ participation in electric power negotiations while improving energy efficiency. The opportunity for players’ participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator—MIBEL. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 2016-01-01T00:00:00Z 2117-01-01T00:00:00Z |
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/10400.22/9386 |
url |
http://hdl.handle.net/10400.22/9386 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1109/TNNLS.2015.2461491 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
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metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers |
publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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