dc.contributor.author | Gruosso, Giambattista | |
dc.contributor.author | Daniel, Luca | |
dc.contributor.author | Maffezzoni, Paolo | |
dc.date.accessioned | 2022-07-11T14:39:30Z | |
dc.date.available | 2022-07-11T14:39:30Z | |
dc.date.issued | 2022-06-28 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/143638 | |
dc.description.abstract | This 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.publisher | Multidisciplinary Digital Publishing Institute | en_US |
dc.relation.isversionof | http://dx.doi.org/10.3390/en15134752 | en_US |
dc.rights | Creative Commons Attribution | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Multidisciplinary Digital Publishing Institute | en_US |
dc.title | Piece-Wise Linear (PWL) Probabilistic Analysis of Power Grid with High Penetration PV Integration | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Energies 15 (13): 4752 (2022) | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.identifier.mitlicense | PUBLISHER_CC | |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2022-07-08T11:55:00Z | |
dspace.date.submission | 2022-07-08T11:55:00Z | |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | en_US |