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Adaptive Portfolio Optimization for Multiple Electricity Markets Participation

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dc.contributor.authorPinto, Tiago
dc.contributor.authorMorais, Hugo
dc.contributor.authorSousa, Tiago M.
dc.contributor.authorSousa, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorPraça, Isabel
dc.contributor.authorFaia, Ricardo
dc.contributor.authorPires, Eduardo José Solteiro
dc.date.accessioned2017-01-25T10:35:24Z
dc.date.embargo2117
dc.date.issued2016
dc.description.abstractThe increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources’ particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players’ participation in electric power negotiations while improving energy efficiency. The opportunity for players’ participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator—MIBEL.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/TNNLS.2015.2461491pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/9386
dc.language.isoengpt_PT
dc.publisherInstitute of Electrical and Electronics Engineerspt_PT
dc.relation.ispartofseriesIEEE Transactions on Neural Networks and Learning Systems;Vol. 27, Issue. 8,
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7239617/pt_PT
dc.subjectAdaptive learningpt_PT
dc.subjectArtificial neural network (NN)pt_PT
dc.subjectElectricity marketspt_PT
dc.subjectMultiagent simulationpt_PT
dc.subjectPortfolio optimizationpt_PT
dc.subjectSwarm intelligencept_PT
dc.titleAdaptive Portfolio Optimization for Multiple Electricity Markets Participationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1733pt_PT
oaire.citation.issue8pt_PT
oaire.citation.startPage1720pt_PT
oaire.citation.titleIEEE Transactions on Neural Networks and Learning Systemspt_PT
oaire.citation.volume27pt_PT
person.familyNamePinto
person.familyNameMorais
person.familyNameVale
person.familyNamePraça
person.givenNameTiago
person.givenNameHugo
person.givenNameZita
person.givenNameIsabel
person.identifierR-000-T7J
person.identifier80878
person.identifier632184
person.identifier299522
person.identifier.ciencia-id2414-9B03-C4BB
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person.identifier.ciencia-id721B-B0EB-7141
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0001-5906-4744
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-id21834170800
person.identifier.scopus-author-id7004115775
person.identifier.scopus-author-id22734900800
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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