A Surrogate-Enhanced Scheme in Decision Making under Uncertainty in Power Systems

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dc.contributor.authorXu, Yijunen
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorKorkali, Merten
dc.contributor.authorChen, Xiaoen
dc.contributor.authorValinejad, Jaberen
dc.contributor.authorPeng, Longen
dc.date.accessioned2024-01-23T16:06:42Zen
dc.date.available2024-01-23T16:06:42Zen
dc.date.issued2021en
dc.description.abstractFacing stochastic variations of the loads due to an increasing penetration of renewable energy generation, online decision making under uncertainty in modern power systems is capturing power researchers' attention in recent years. To address this issue while achieving a good balance between system security and economic objectives, we propose a surrogate-enhanced scheme under a joint chance-constrained (JCC) optimal power-flow (OPF) framework. Starting from a stochastic-sampling procedure, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo (MC) method, we propose to use a polynomial-chaos-based surrogate that allows us to efficiently evaluate the power-system model at non-Gaussian distributed sampled values with a negligible computing cost. Learning from the MC simulated samples, we further proposed a hybrid adaptive approach to overcome the conservativeness of the JCC-OPF by utilizing correlation of the system states, which is ignored in the traditional Boole's inequality. The simulations conducted on the modified Illinois test system demonstrate the excellent performance of the proposed method.en
dc.description.versionPublished versionen
dc.format.extent5 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/PESGM46819.2021.9637922en
dc.identifier.eissn1944-9933en
dc.identifier.isbn9781665405072en
dc.identifier.issn1944-9925en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117608en
dc.identifier.volume2021-Julyen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.subjectDecision making under uncertaintyen
dc.subjectsurrogate modelen
dc.subjectuncertainty quantificationen
dc.subjectFLOWen
dc.titleA Surrogate-Enhanced Scheme in Decision Making under Uncertainty in Power Systemsen
dc.title.serial2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)en
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherProceedings Paperen
dc.type.otherBook in seriesen
pubs.finish-date2021-07-29en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.start-date2021-07-26en

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