Decision support for energy contracts negotiation with game theory and adaptive learning

Detalhes bibliográficos
Autor(a) principal: Pinto, Tiago
Data de Publicação: 2015
Outros Autores: Vale, Zita, Praça, Isabel, Pires, E. J. Solteiro, Lopes, Fernando
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.9/2959
Resumo: This paper presents a decision support methodology for electricity market players’bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.
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spelling Decision support for energy contracts negotiation with game theory and adaptive learningElectricity marketsBilateral contractingMulti-agent systemsNegotiation strategiesThis paper presents a decision support methodology for electricity market players’bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.MDPIRepositório do LNEGPinto, TiagoVale, ZitaPraça, IsabelPires, E. J. SolteiroLopes, Fernando2016-05-04T11:19:30Z20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.9/2959engPinto, T.; Vale, Z.; Praça, I.; Pires, E.J. Solteiro; Lopes, F. - Decision support for energy contracts negotiation with game theory and adaptive learning. In: Energies, 2015, Vol. 8, p. 9817-98421996-107310.3390/en8099817info: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:RCAAP2023-08-13T06:26:49Zoai:repositorio.lneg.pt:10400.9/2959Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T15:36:07.445848Repositório 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 Decision support for energy contracts negotiation with game theory and adaptive learning
title Decision support for energy contracts negotiation with game theory and adaptive learning
spellingShingle Decision support for energy contracts negotiation with game theory and adaptive learning
Pinto, Tiago
Electricity markets
Bilateral contracting
Multi-agent systems
Negotiation strategies
title_short Decision support for energy contracts negotiation with game theory and adaptive learning
title_full Decision support for energy contracts negotiation with game theory and adaptive learning
title_fullStr Decision support for energy contracts negotiation with game theory and adaptive learning
title_full_unstemmed Decision support for energy contracts negotiation with game theory and adaptive learning
title_sort Decision support for energy contracts negotiation with game theory and adaptive learning
author Pinto, Tiago
author_facet Pinto, Tiago
Vale, Zita
Praça, Isabel
Pires, E. J. Solteiro
Lopes, Fernando
author_role author
author2 Vale, Zita
Praça, Isabel
Pires, E. J. Solteiro
Lopes, Fernando
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Repositório do LNEG
dc.contributor.author.fl_str_mv Pinto, Tiago
Vale, Zita
Praça, Isabel
Pires, E. J. Solteiro
Lopes, Fernando
dc.subject.por.fl_str_mv Electricity markets
Bilateral contracting
Multi-agent systems
Negotiation strategies
topic Electricity markets
Bilateral contracting
Multi-agent systems
Negotiation strategies
description This paper presents a decision support methodology for electricity market players’bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-01-01T00:00:00Z
2016-05-04T11:19:30Z
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/10400.9/2959
url http://hdl.handle.net/10400.9/2959
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Pinto, T.; Vale, Z.; Praça, I.; Pires, E.J. Solteiro; Lopes, F. - Decision support for energy contracts negotiation with game theory and adaptive learning. In: Energies, 2015, Vol. 8, p. 9817-9842
1996-1073
10.3390/en8099817
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 MDPI
publisher.none.fl_str_mv MDPI
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
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