Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization

Bibliographic Details
Main Author: Faia, Ricardo
Publication Date: 2018
Other Authors: Pinto, Tiago, Vale, Zita, Corchado, Juan Manuel
Format: Article
Language: eng
Source: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Download full: http://hdl.handle.net/10400.22/17117
Summary: The portfolio optimization is a well-known problem in the areas of economy and finance. This problem has also become increasingly important in electrical power systems, particularly in the area of electricity markets, mostly due to the growing number of alternative/complementary market types that are being introduced to deal with important issues, such as the massive integration of renewable energy sources in power systems. The optimization of electricity market players’ participation portfolio comprises significant time constraints, which cannot be satisfied by the use of deterministic techniques. For this reason, meta-heuristic solutions are used, such as particle swarm optimization. The inertia is one of the most important parameter in this method, and it is the main focus of this paper. This paper studies 18 popular inertia calculation strategies, by comparing their performance in the portfolio optimization problem. A strategic methodology for the automatic selection of the best inertia calculation method for the needs of each optimization is also proposed. Results show that the proposed approach is able to automatically adapt the inertia parameter according to the needs in each execution.
id RCAP_07635aa41dba37342f263985efad2273
oai_identifier_str oai:recipp.ipp.pt:10400.22/17117
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio OptimizationArtificial IntelligenceElectricity MarketInertia ParameterParticle Swarm OptimizationPortfolio OptimizationThe portfolio optimization is a well-known problem in the areas of economy and finance. This problem has also become increasingly important in electrical power systems, particularly in the area of electricity markets, mostly due to the growing number of alternative/complementary market types that are being introduced to deal with important issues, such as the massive integration of renewable energy sources in power systems. The optimization of electricity market players’ participation portfolio comprises significant time constraints, which cannot be satisfied by the use of deterministic techniques. For this reason, meta-heuristic solutions are used, such as particle swarm optimization. The inertia is one of the most important parameter in this method, and it is the main focus of this paper. This paper studies 18 popular inertia calculation strategies, by comparing their performance in the portfolio optimization problem. A strategic methodology for the automatic selection of the best inertia calculation method for the needs of each optimization is also proposed. Results show that the proposed approach is able to automatically adapt the inertia parameter according to the needs in each execution.This work has been developed in the scope of the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT) and grant agreement No 641794 (project DREAM-GO); and has also been supported by the CONTEST project – SAICT-POL/23575/2016. Ricardo Faia is supported by FCT Funds through SFRH/BD/133086/2017 (PhD scholarship).Taylor and FrancisRepositório Científico do Instituto Politécnico do PortoFaia, RicardoPinto, TiagoVale, ZitaCorchado, Juan Manuel2021-02-24T12:36:21Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/17117eng10.1080/08839514.2018.1506971info: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:RCAAP2023-03-13T13:06:29Zoai:recipp.ipp.pt:10400.22/17117Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:36:47.179210Repositório 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 Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization
title Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization
spellingShingle Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization
Faia, Ricardo
Artificial Intelligence
Electricity Market
Inertia Parameter
Particle Swarm Optimization
Portfolio Optimization
title_short Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization
title_full Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization
title_fullStr Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization
title_full_unstemmed Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization
title_sort Strategic Particle Swarm Inertia Selection for Electricity Markets Participation Portfolio Optimization
author Faia, Ricardo
author_facet Faia, Ricardo
Pinto, Tiago
Vale, Zita
Corchado, Juan Manuel
author_role author
author2 Pinto, Tiago
Vale, Zita
Corchado, Juan Manuel
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Faia, Ricardo
Pinto, Tiago
Vale, Zita
Corchado, Juan Manuel
dc.subject.por.fl_str_mv Artificial Intelligence
Electricity Market
Inertia Parameter
Particle Swarm Optimization
Portfolio Optimization
topic Artificial Intelligence
Electricity Market
Inertia Parameter
Particle Swarm Optimization
Portfolio Optimization
description The portfolio optimization is a well-known problem in the areas of economy and finance. This problem has also become increasingly important in electrical power systems, particularly in the area of electricity markets, mostly due to the growing number of alternative/complementary market types that are being introduced to deal with important issues, such as the massive integration of renewable energy sources in power systems. The optimization of electricity market players’ participation portfolio comprises significant time constraints, which cannot be satisfied by the use of deterministic techniques. For this reason, meta-heuristic solutions are used, such as particle swarm optimization. The inertia is one of the most important parameter in this method, and it is the main focus of this paper. This paper studies 18 popular inertia calculation strategies, by comparing their performance in the portfolio optimization problem. A strategic methodology for the automatic selection of the best inertia calculation method for the needs of each optimization is also proposed. Results show that the proposed approach is able to automatically adapt the inertia parameter according to the needs in each execution.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01T00:00:00Z
2021-02-24T12:36:21Z
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/10400.22/17117
url http://hdl.handle.net/10400.22/17117
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1080/08839514.2018.1506971
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 Taylor and Francis
publisher.none.fl_str_mv Taylor and Francis
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_ 1799131458703458304