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Portfolio Optimization for Electricity Market Participation with NPSO-LRS

dc.contributor.authorFaia, R.
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.date.accessioned2021-03-09T16:25:49Z
dc.date.available2021-03-09T16:25:49Z
dc.date.issued2016-09
dc.description.abstractMassive changes in electricity markets have occurred during the last years, as a consequence of the massive introduction of renewable energies. These changes have led to a restructuring process that had an impact throughout the electrical industry. The case of the electricity markets is a relevant example, where new forms of trading emerged and new market entities were created. With these changes, the complexity of electricity markets increased as well, which brought out the need from the involved players for adequate support to their decision making process. Artificial intelligence plays an important role in the development of these tools. Multi-agent systems, in particular, have been largely explored by stakeholders in the sector. Artificial intelligence also provides intelligent solutions for optimization, which enable troubleshooting in a short time and with very similar results to those achieved by deterministic techniques, which usually result from very high execution times. The work presented in this paper aims at solving a portfolio optimization problem for electricity markets participation, using an approach based on NPSO-LRS (New Particle Swarm Optimization with Local Random Search). The proposed method is used to assist decisions of electricity market players.pt_PT
dc.description.sponsorshipThis work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/DEXA.2016.042pt_PT
dc.identifier.isbn978-1-5090-3635-6
dc.identifier.issn2378-3915
dc.identifier.urihttp://hdl.handle.net/10400.22/17357
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationEnabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/7816640pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectDecision supportpt_PT
dc.subjectElectricity marketspt_PT
dc.subjectPortfolio optimizationpt_PT
dc.subjectSwarm Optimizationpt_PT
dc.titlePortfolio Optimization for Electricity Market Participation with NPSO-LRSpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleEnabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/641794/EU
oaire.citation.conferencePlacePorto, Portugalpt_PT
oaire.citation.endPage152pt_PT
oaire.citation.startPage148pt_PT
oaire.citation.title27th International Workshop on Database and Expert Systems Applications (DEXA)pt_PT
oaire.fundingStream5876
oaire.fundingStreamH2020
person.familyNameFaia
person.familyNamePinto
person.familyNameVale
person.givenNameRicardo Francisco Marcos
person.givenNameTiago
person.givenNameZita
person.identifier78FtZwIAAAAJ
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-id9B12-19F6-D6C7
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-1053-7720
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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