Adaptive Portfolio Optimization for Multiple Electricity Markets Participation

Detalhes bibliográficos
Autor(a) principal: Pinto, Tiago
Data de Publicação: 2016
Outros Autores: Morais, Hugo, Sousa, Tiago M., Sousa, Tiago, Vale, Zita, Praça, Isabel, Faia, Ricardo, Pires, Eduardo José Solteiro
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.
id RCAP_deeab565d8dc81020169255f3957bd4c
oai_identifier_str oai:recipp.ipp.pt:10400.22/9386
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling 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
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 Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
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
repository.mail.fl_str_mv
_version_ 1799131395837132800