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Intelligent decision making in electricity markets: simulated annealing Q-Learning

2012Conference object Restricted access
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dc.contributor.authorPinto, Tiago
dc.contributor.authorSousa, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorMorais, H.
dc.contributor.authorPraça, Isabel
dc.date.accessioned2013-04-15T14:20:10Z
dc.date.available2013-04-15T14:20:10Z
dc.date.issued2012
dc.date.updated2013-04-11T14:33:10Z
dc.description.abstractElectricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.por
dc.identifier.doi10.1109/PESGM.2012.6345606pt_PT
dc.identifier.isbn978-1-4673-2728-2
dc.identifier.isbn978-1-4673-2727-5
dc.identifier.issn1944-9925
dc.identifier.urihttp://hdl.handle.net/10400.22/1308
dc.language.isoengpor
dc.publisherIEEEpor
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6345606por
dc.subjectAdaptive learningpor
dc.subjectElectricity marketspor
dc.subjectQ-Learningpor
dc.subjectMultiagent simulationpor
dc.subjectReinforcement learningpor
dc.subjectSimulated annealingpor
dc.titleIntelligent decision making in electricity markets: simulated annealing Q-Learningpor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceSan Diego CA, USApor
oaire.citation.endPage8por
oaire.citation.startPage1por
oaire.citation.titleIEEE Power and Energy Society General Meeting 2012por
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.typeconferenceObjectpor
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relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublication.latestForDiscoveryee4ecacd-c6c6-41e8-bca1-21a60ff05f50

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