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A novel microgrid support management system based on stochastic mixed-integer linear programming

2021-05-15Journal article Restricted access
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dc.contributor.authorGomes, Isaías
dc.contributor.authorMelício, R.
dc.contributor.authorMendes, Victor
dc.date.accessioned2021-06-04T08:55:16Z
dc.date.available2021-06-04T08:55:16Z
dc.date.issued2021-05-15
dc.description.abstractThis paper focuses on a support management system for the management and operation planning of a microgrid by the new electricity market agent, the microgrid aggregator. The aggregator performs the management of microturbines, wind and photovoltaic systems, energy storage, electric vehicles, and usage of energy aiming at having the best participation in the market. Nowadays, the electricity market participation entails making decisions aided by a support and information system, which is an important part of a microgrid support management system. The microgrid support management system developed in this paper has a formulation based on a stochastic mixed-integer linear programming problem that depends on knowledge of the stochastic processes that describe the uncertain parameters. A set of plausible scenarios computed by Kernel Density Estimation sets the characterization of the random variables. But as commonly happen, a scenario reduction is necessary to avoid the need to have significant computational requirements due to the high degree of uncertainty. The scenario reduction carried out is a two-tier procedure, following a K-means clustering technique and a fast backward scenario reduction method. The case studies reveal the performance of the microgrid and validate the methodology basis conceived for the microgrid support management system.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGOMES, Isaias L. R.; MELÍCIO, R.; MENDES, Victor M. F. – A novel microgrid support management system based on stochastic mixed-integer linear programming. Energy. ISSN 0360-5442. Vol. 223 (2021), pp. 1-13pt_PT
dc.identifier.doi10.1016/j.energy.2021.120030pt_PT
dc.identifier.eissn1873-6785
dc.identifier.issn0360-5442
dc.identifier.urihttp://hdl.handle.net/10400.21/13413
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationUIDB/04683/2020 - FCT under the ICT (Institute of Earth Sciences)pt_PT
dc.relationUIDB/50022/2020 - FCTpt_PT
dc.relationUIDB/04131/2020 - FCTpt_PT
dc.relationUIDP/04131/2020 - FCTpt_PT
dc.relation.publisherversionhttps://reader.elsevier.com/reader/sd/pii/S0360544221002796?token=21AC406D7D80E942DE1971183C6755B62E5CF1FE51761F7DFFD23F3A538DF2ACC17153BDFA9FFC93524710340B1A0BA2&originRegion=eu-west-1&originCreation=20210604084619pt_PT
dc.subjectMicrogridpt_PT
dc.subjectMicrogrid aggregatorpt_PT
dc.subjectRisk managementpt_PT
dc.subjectRenewable energypt_PT
dc.subjectEnergy storagept_PT
dc.subjectElectric vehiclespt_PT
dc.subjectDemand responsept_PT
dc.titleA novel microgrid support management system based on stochastic mixed-integer linear programmingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage13pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleEnergypt_PT
oaire.citation.volume223pt_PT
person.familyNameGomes
person.familyNameMendes
person.givenNameIsaías
person.givenNameVictor
person.identifier.ciencia-id9A16-51D0-5AF9
person.identifier.orcid0000-0003-3110-6644
person.identifier.orcid0000-0002-4599-477X
person.identifier.ridD-2332-2012
person.identifier.scopus-author-id57188648074
person.identifier.scopus-author-id55138675600
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication104a0c6b-46e6-423f-8fe8-3edc1a2406dc
relation.isAuthorOfPublicationa86b9291-f23c-4f09-83d7-9ee691696705
relation.isAuthorOfPublication.latestForDiscovery104a0c6b-46e6-423f-8fe8-3edc1a2406dc

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