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Learning Bidding Strategies in Local Electricity Markets using Ant Colony optimization

2020Conference object Open access
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dc.contributor.authorLezama, Fernando
dc.contributor.authorFaia, R.
dc.contributor.authorSoares, João
dc.contributor.authorFaria, Pedro
dc.contributor.authorVale, Zita
dc.date.accessioned2021-06-17T10:20:00Z
dc.date.available2021-06-17T10:20:00Z
dc.date.issued2020
dc.description.abstractLocal energy markets (LM) are attracting significant interest due to their potential of balancing generation and consumption and supporting the adoption of distributed renewable sources at the distribution level. Besides, LMs aim at increasing the participation of small end-users in energy transactions, setting the stage for transactive energy systems. In this work, we explore the use of ant colony optimization (ACO) for learning bidding strategies under a bi-level optimization framework that arises when trading energy in an LM. We performed an empirical analysis of the impact of ACO parameters have in the learning process and the obtained profits of agents. After that, we analyze and compare ACO performance against an evolutionary algorithm under a realistic case study with nine agents trading energy in the day-ahead LM. Results suggest that ACO can be efficient for strategic learning of agents, providing solutions in which all agents can improve their profits. Overall, it is shown the advantages that an LM can bring to market participants, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition.pt_PT
dc.description.sponsorshipThis work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and CENERGETIC (POCI-01-0145- FEDER-028983 and PTDC/EEI-EEE/28983/2017), from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and grants CEECIND/02814/2017, CEECIND/02887/2017, SFRH/BD/133086/2017.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/CEC48606.2020.9185520pt_PT
dc.identifier.isbn978-1-7281-6929-3
dc.identifier.urihttp://hdl.handle.net/10400.22/18057
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationPOCI-01-0145- FEDER-028983pt_PT
dc.relationSmart Distribution Grid: a Market Driven Approach for the Next Generation of Advanced Operation Models and Services
dc.relationCEECIND/02814/2017pt_PT
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relationApoio à decisão para participação em mercados de energia elétrica
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9185520pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectAnt Colony optimizationpt_PT
dc.subjectEvolutionary computationpt_PT
dc.subjectLearning Strategypt_PT
dc.subjectLocal energy marketpt_PT
dc.subjectRenewable energypt_PT
dc.titleLearning Bidding Strategies in Local Electricity Markets using Ant Colony optimizationpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleSmart Distribution Grid: a Market Driven Approach for the Next Generation of Advanced Operation Models and Services
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardTitleApoio à decisão para participação em mercados de energia elétrica
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/771066/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEEI-EEE%2F28983%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00760%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F133086%2F2017/PT
oaire.citation.conferencePlaceGlasgow, UKpt_PT
oaire.citation.endPage8pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title2020 IEEE Congress on Evolutionary Computation (CEC)pt_PT
oaire.fundingStreamH2020
oaire.fundingStream9471 - RIDTI
oaire.fundingStream6817 - DCRRNI ID
person.familyNameLezama
person.familyNameFaia
person.familyNameSoares
person.familyNameFaria
person.familyNameVale
person.givenNameFernando
person.givenNameRicardo Francisco Marcos
person.givenNameJoão
person.givenNamePedro
person.givenNameZita
person.identifier78FtZwIAAAAJ
person.identifier1043580
person.identifier632184
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person.identifier.ciencia-idB212-2309-F9C3
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8638-8373
person.identifier.orcid0000-0002-1053-7720
person.identifier.orcid0000-0002-4172-4502
person.identifier.orcid0000-0002-5982-8342
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridA-6945-2017
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id36810077500
person.identifier.scopus-author-id35436109600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
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