TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading

Detalhes bibliográficos
Autor(a) principal: Thiago R. P. M. Rúbio
Data de Publicação: 2015
Outros Autores: Jonas Queiroz, Henrique Lopes Cardoso, Ana Paula Rocha, Eugénio Oliveira
Tipo de documento: Livro
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/83662
Resumo: Smart Grid technologies are changing the way energy is generated, distributed and consumed. With the increasing spread of renewable power sources, new market strategies are needed to guarantee a more sustainable participation and less dependency of bulk generation. In PowerTAC (Power Trading Agent Competition), different software agents compete in a simulated energy market, impersonating broker companies to create and manage attractive tariffs for customers while aiming to profit. In this paper, we present TugaTAC Broker, a PowerTAC agent that uses a fuzzy logic mechanism to compose tariffs based on its customers portfolio. Fuzzy sets allow adaptive configurations for brokers in different scenarios. To validate and compare the performance of TugaTAC, we have run a local version of the PowerTAC competition. The experiments comprise TugaTAC competing against other simple agents and a more realistic configuration, with instances of the winners of previous editions of the competition. Preliminary results show a promising dynamic: our approach was able to manage imbalances and win the competition in the simple case, but need refinements to compete with more sophisticated market. (c) Springer International Publishing Switzerland 2016.
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spelling TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy TradingSmart Grid technologies are changing the way energy is generated, distributed and consumed. With the increasing spread of renewable power sources, new market strategies are needed to guarantee a more sustainable participation and less dependency of bulk generation. In PowerTAC (Power Trading Agent Competition), different software agents compete in a simulated energy market, impersonating broker companies to create and manage attractive tariffs for customers while aiming to profit. In this paper, we present TugaTAC Broker, a PowerTAC agent that uses a fuzzy logic mechanism to compose tariffs based on its customers portfolio. Fuzzy sets allow adaptive configurations for brokers in different scenarios. To validate and compare the performance of TugaTAC, we have run a local version of the PowerTAC competition. The experiments comprise TugaTAC competing against other simple agents and a more realistic configuration, with instances of the winners of previous editions of the competition. Preliminary results show a promising dynamic: our approach was able to manage imbalances and win the competition in the simple case, but need refinements to compete with more sophisticated market. (c) Springer International Publishing Switzerland 2016.20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/83662eng10.1007/978-3-319-33509-4_16Thiago R. P. M. RúbioJonas QueirozHenrique Lopes CardosoAna Paula RochaEugénio Oliveirainfo: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:RCAAP2023-11-29T15:08:23Zoai:repositorio-aberto.up.pt:10216/83662Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:16:33.312850Repositório 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 TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading
title TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading
spellingShingle TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading
Thiago R. P. M. Rúbio
title_short TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading
title_full TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading
title_fullStr TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading
title_full_unstemmed TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading
title_sort TugaTAC Broker: A Fuzzy Logic Adaptive Reasoning Agent for Energy Trading
author Thiago R. P. M. Rúbio
author_facet Thiago R. P. M. Rúbio
Jonas Queiroz
Henrique Lopes Cardoso
Ana Paula Rocha
Eugénio Oliveira
author_role author
author2 Jonas Queiroz
Henrique Lopes Cardoso
Ana Paula Rocha
Eugénio Oliveira
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Thiago R. P. M. Rúbio
Jonas Queiroz
Henrique Lopes Cardoso
Ana Paula Rocha
Eugénio Oliveira
description Smart Grid technologies are changing the way energy is generated, distributed and consumed. With the increasing spread of renewable power sources, new market strategies are needed to guarantee a more sustainable participation and less dependency of bulk generation. In PowerTAC (Power Trading Agent Competition), different software agents compete in a simulated energy market, impersonating broker companies to create and manage attractive tariffs for customers while aiming to profit. In this paper, we present TugaTAC Broker, a PowerTAC agent that uses a fuzzy logic mechanism to compose tariffs based on its customers portfolio. Fuzzy sets allow adaptive configurations for brokers in different scenarios. To validate and compare the performance of TugaTAC, we have run a local version of the PowerTAC competition. The experiments comprise TugaTAC competing against other simple agents and a more realistic configuration, with instances of the winners of previous editions of the competition. Preliminary results show a promising dynamic: our approach was able to manage imbalances and win the competition in the simple case, but need refinements to compete with more sophisticated market. (c) Springer International Publishing Switzerland 2016.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-01-01T00:00:00Z
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dc.language.iso.fl_str_mv eng
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dc.relation.none.fl_str_mv 10.1007/978-3-319-33509-4_16
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