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Strategic bidding methodology for electricity markets using adaptive learning

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dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorRodrigues, Fátima
dc.contributor.authorMorais, H.
dc.contributor.authorPraça, Isabel
dc.date.accessioned2013-05-09T15:26:58Z
dc.date.available2013-05-09T15:26:58Z
dc.date.issued2011
dc.date.updated2013-04-12T17:05:33Z
dc.description.abstractThe very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.por
dc.identifier.doi10.1007/978-3-642-21827-9_50pt_PT
dc.identifier.isbn978-3-642-21826-2
dc.identifier.isbn978-3-642-21827-9
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10400.22/1530
dc.language.isoengpor
dc.publisherSpringer Berlin Heidelbergpor
dc.relation.ispartofseriesLecture Notes in Computer Science; Vol. 6704
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007%2F978-3-642-21827-9_50
dc.subjectAdaptive learningpor
dc.subjectElectricity marketspor
dc.subjectForecasting methodspor
dc.subjectIntelligent agentspor
dc.subjectMultiagent systemspor
dc.titleStrategic bidding methodology for electricity markets using adaptive learningpor
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage500por
oaire.citation.startPage490por
oaire.citation.titleModern Approaches in Applied Intelligence - 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, Syracuse, NY, USA, June 28 – July 1, 2011, Proceedings, Part IIpor
oaire.citation.volumeVol. 6704
person.familyNamePinto
person.familyNameVale
person.familyNamePraça
person.givenNameTiago
person.givenNameZita
person.givenNameIsabel
person.identifierR-000-T7J
person.identifier632184
person.identifier299522
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0002-2519-9859
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.ridK-8430-2014
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
person.identifier.scopus-author-id22734900800
rcaap.rightsclosedAccesspor
rcaap.typebookPartpor
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublication.latestForDiscoveryff1df02d-0c0f-4db1-bf7d-78863a99420b

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