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Forecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industry

2015Doctoral thesis Open access
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datacite.subject.fosEngenharia e Tecnologia::Gestão Industrialpt_PT
dc.contributor.advisorCatalão, João Paulo da Silva
dc.contributor.advisorMatias, João Carlos de Oliveira
dc.contributor.authorSilva, Gerardo José Osório da
dc.date.accessioned2016-01-27T11:04:52Z
dc.date.available2016-01-27T11:04:52Z
dc.date.issued2015
dc.description.abstractNowadays, the paradigm shift in the electricity sector and the advent of the smart grid, along with the growing impositions of a gradual reduction of greenhouse gas emissions, pose numerous challenges related with the sustainable management of power systems. The insular power systems industry is heavily dependent on imported energy, namely fossil fuels, and also on seasonal tourism behavior, which strongly influences the local economy. In comparison with the mainland power system, the behavior of insular power systems is highly influenced by the stochastic nature of the renewable energy sources available. The insular electricity grid is particularly sensitive to power quality parameters, mainly to frequency and voltage deviations, and a greater integration of endogenous renewables potential in the power system may affect the overall reliability and security of energy supply, so singular care should be placed in all forecasting and system operation procedures. The goals of this thesis are focused on the development of new decision support tools, for the reliable forecasting of market prices and wind power, for the optimal economic dispatch and unit commitment considering renewable generation, and for the smart control of energy storage systems. The new methodologies developed are tested in real case studies, demonstrating their computational proficiency comparatively to the current state-of-the-art.pt_PT
dc.identifier.tid101482558
dc.identifier.urihttp://hdl.handle.net/10400.6/3968
dc.language.isoengpt_PT
dc.subjectIndústria de energia eléctrica - Gestão sustentávelpt_PT
dc.subjectIndústria de energia eléctrica - Despacho económicopt_PT
dc.subjectIndústria de energia eléctrica - Energias renováveispt_PT
dc.subjectIndústria de energia eléctrica - Armazenamento de energiapt_PT
dc.titleForecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industrypt_PT
dc.typedoctoral thesis
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876-PPCDTI/PTDC%2FEEA-EEL%2F110102%2F2009/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PEst-OE%2FEEI%2FLA0021%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876-PPCDTI/PTDC%2FEEA-EEL%2F118519%2F2010/PT
oaire.fundingStream5876-PPCDTI
oaire.fundingStream3599-PPCDT
oaire.fundingStream5876-PPCDTI
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typedoctoralThesispt_PT
relation.isProjectOfPublication53612759-ddd6-4f19-8094-c39eb582db0f
relation.isProjectOfPublicationdf97e844-9ef2-4fcd-ab25-82df5d1a7090
relation.isProjectOfPublicationb812ab6f-2380-422c-9a34-214cf697e8f8
relation.isProjectOfPublication.latestForDiscoverydf97e844-9ef2-4fcd-ab25-82df5d1a7090
thesis.degree.nameDoutoramento em Engenharia e Gestão industrialpt_PT

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