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dc.contributor.authorGruosso, Giambattista
dc.contributor.authorDaniel, Luca
dc.contributor.authorMaffezzoni, Paolo
dc.date.accessioned2022-07-11T14:39:30Z
dc.date.available2022-07-11T14:39:30Z
dc.date.issued2022-06-28
dc.identifier.urihttps://hdl.handle.net/1721.1/143638
dc.description.abstractThis paper aims at presenting a novel effective approach to probabilistic analysis of distribution power grid with high penetration of PV sources. The novel method adopts a Gaussian Mixture Model for reproducing the uncertainty of correlated PV sources along with a piece-wise-linear approximation of the voltage-power relationship established by load flow problem. The method allows the handling of scenarios with a large number of uncertain PV sources in an efficient yet accurate way. A distinctive feature of the proposed probabilistic analysis is that of directly providing, in closed-form, the joint probability distribution of the set of observable variables of interest. From such a comprehensive statistical representation, remarkable information about grid uncertainty can be deduced. This includes the probability of violating the safe operation conditions as a function of PV penetration.en_US
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.relation.isversionofhttp://dx.doi.org/10.3390/en15134752en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceMultidisciplinary Digital Publishing Instituteen_US
dc.titlePiece-Wise Linear (PWL) Probabilistic Analysis of Power Grid with High Penetration PV Integrationen_US
dc.typeArticleen_US
dc.identifier.citationEnergies 15 (13): 4752 (2022)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-07-08T11:55:00Z
dspace.date.submission2022-07-08T11:55:00Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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