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Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

2014Book part Restricted access
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dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorSousa, Tiago
dc.contributor.authorSousa, Tiago
dc.contributor.authorMorais, Hugo
dc.contributor.authorPraça, Isabel
dc.date.accessioned2015-05-05T10:16:17Z
dc.date.available2015-05-05T10:16:17Z
dc.date.issued2014
dc.description.abstractEnergy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.por
dc.identifier.doi10.1007/978-3-319-07551-8_41
dc.identifier.urihttp://hdl.handle.net/10400.22/5909
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSpringerpor
dc.relation.ispartofseriesCommunications in Computer and Information Science;Vol. 430
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007/978-3-319-07767-3_25por
dc.subjectMulti-agent based simulationpor
dc.subjectMASCEMpor
dc.subjectALBidSpor
dc.titleParticle Swarm Optimization of Electricity Market Negotiating Players Portfoliopor
dc.typebook part
dspace.entity.typePublication
oaire.citation.conferencePlaceSalamanca, Spainpor
oaire.citation.endPage284por
oaire.citation.startPage273por
oaire.citation.titleHighlights of Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collectionpor
oaire.citation.volume430por
person.familyNamePinto
person.familyNameVale
person.familyNameMorais
person.familyNamePraça
person.givenNameTiago
person.givenNameZita
person.givenNameHugo
person.givenNameIsabel
person.identifierR-000-T7J
person.identifier632184
person.identifier80878
person.identifier299522
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.ciencia-id2010-D878-271B
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0001-5906-4744
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-id21834170800
person.identifier.scopus-author-id22734900800
rcaap.rightsclosedAccesspor
rcaap.typebookPartpor
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relation.isAuthorOfPublication.latestForDiscoveryb159f8c9-5ee1-444e-b890-81242ee0738e

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