An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty

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dc.contributor.authorXu, Yijunen
dc.contributor.authorKorkali, Merten
dc.contributor.authorMili, Lamine M.en
dc.contributor.authorValinejad, Jaberen
dc.contributor.authorChen, Taoen
dc.contributor.authorChen, Xiaoen
dc.date.accessioned2024-01-23T18:16:27Zen
dc.date.available2024-01-23T18:16:27Zen
dc.date.issued2021-01-12en
dc.description.abstractA modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, 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 method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.en
dc.description.versionPublished versionen
dc.format.extentPages 2696-2707en
dc.format.extent12 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier3 (Article number)en
dc.identifier.doihttps://doi.org/10.1109/TSG.2021.3051088en
dc.identifier.eissn1949-3061en
dc.identifier.issn1949-3053en
dc.identifier.issue3en
dc.identifier.orcidMili, Lamine [0000-0001-6134-3945]en
dc.identifier.urihttps://hdl.handle.net/10919/117617en
dc.identifier.volume12en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsPublic Domain (U.S.)en
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.subjectUncertaintyen
dc.subjectComputational modelingen
dc.subjectStochastic processesen
dc.subjectResponse surface methodologyen
dc.subjectRenewable energy sourcesen
dc.subjectReactive poweren
dc.subjectIterative methodsen
dc.subjectAC optimal power flowen
dc.subjectresponse surfaceen
dc.subjectchance constraintsen
dc.subjectdependenceen
dc.titleAn Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertaintyen
dc.title.serialIEEE Transactions on Smart Griden
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
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

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