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Metalearning to support competitive electricity market players’strategic bidding

2016Journal article Open access
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dc.contributor.authorPinto, Tiago
dc.contributor.authorSousa, Tiago M.
dc.contributor.authorMorais, Hugo
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.date.accessioned2017-01-25T12:08:39Z
dc.date.embargo2117
dc.date.issued2016
dc.description.abstractElectricity markets are becoming more competitive, to some extent due to the increasing number ofplayers that have moved from other sectors to the power industry. This is essentially resulting fromincentives provided to distributed generation. Relevant changes in this domain are still occurring, such asthe extension of national and regional markets to continental scales. Decision support tools have therebybecome essential to help electricity market players in their negotiation process. This paper presentsa metalearner to support electricity market players in bidding definition. The proposed metalearneruses a dynamic artificial neural network to create its own output, taking advantage on several learningalgorithms already implemented in ALBidS (Adaptive Learning strategic Bidding System). The proposedmetalearner considers different weights for each strategy, based on their individual performance. Themetalearner’s performance is analysed in scenarios based on real electricity markets data using MASCEM(Multi-Agent Simulator for Competitive Electricity Markets). Results show that the proposed metalearneris able to provide higher profits to market players when compared to other current methodologies andthat results improve over time, as consequence of its learning process.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.epsr.2016.03.012pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/9397
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.relation.ispartofseriesElectric Power Systems Research;Vol. 135
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0378779616300591pt_PT
dc.subjectAdaptive learningpt_PT
dc.subjectArtificial neural networkpt_PT
dc.subjectElectricity marketspt_PT
dc.subjectMetalearningpt_PT
dc.subjectMulti-agent simulationpt_PT
dc.titleMetalearning to support competitive electricity market players’strategic biddingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage34pt_PT
oaire.citation.startPage27pt_PT
oaire.citation.titleElectric Power Systems Researchpt_PT
oaire.citation.volume135pt_PT
person.familyNamePinto
person.familyNameMorais
person.familyNamePraça
person.familyNameVale
person.givenNameTiago
person.givenNameHugo
person.givenNameIsabel
person.givenNameZita
person.identifierR-000-T7J
person.identifier80878
person.identifier299522
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id2010-D878-271B
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0001-5906-4744
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridK-8430-2014
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id21834170800
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationb159f8c9-5ee1-444e-b890-81242ee0738e
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscoveryee4ecacd-c6c6-41e8-bca1-21a60ff05f50

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