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dc.contributor.authorLima, Ricardo
dc.contributor.authorConejo, Antonio J.
dc.contributor.authorLangodan, Sabique
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorKnio, Omar
dc.date.accessioned2017-12-21T13:57:05Z
dc.date.available2017-12-21T13:57:05Z
dc.date.issued2017-12-15
dc.identifier.citationLima RM, Conejo AJ, Langodan S, Hoteit I, Knio OM (2017) Risk-averse formulations and methods for a virtual power plant. Computers & Operations Research. Available: http://dx.doi.org/10.1016/j.cor.2017.12.007.
dc.identifier.issn0305-0548
dc.identifier.doi10.1016/j.cor.2017.12.007
dc.identifier.urihttp://hdl.handle.net/10754/626422
dc.description.abstractIn this paper we address the optimal operation of a virtual power plant using stochastic programming. We consider one risk-neutral and two risk-averse formulations that rely on the conditional value at risk. To handle large-scale problems, we implement two decomposition methods with variants using single- and multiple-cuts. We propose the utilization of wind ensembles obtained from the European Centre for Medium Range Weather Forecasts (ECMWF) to quantify the uncertainty of the wind forecast. We present detailed results relative to the computational performance of the risk-averse formulations, the decomposition methods, and risk management and sensitivities analysis as a function of the number of scenarios and risk parameters. The implementation of the two decomposition methods relies on the parallel solution of subproblems, which turns out to be paramount for computational efficiency. The results show that one of the two decomposition methods is the most efficient.
dc.description.sponsorshipThe authors acknowledge the support of the Center for Uncertainty Quantification in Computational Science & Engineering at KAUST.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0305054817303076
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Computers & Operations Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Operations Research, [, , (2017-12-15)] DOI: 10.1016/j.cor.2017.12.007 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectOptimization under uncertainty
dc.subjectstochastic programming
dc.subjectconditional value at risk
dc.subjectenergy
dc.subjectvirtual power plant
dc.titleRisk-averse formulations and methods for a virtual power plant
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.identifier.journalComputers & Operations Research
dc.eprint.versionPost-print
dc.contributor.institutionIntegrated Systems Engineering-Electrical and Computer Engineering, The Ohio State University, OH, USA
kaust.personLangodan, Sabique
kaust.personHoteit, Ibrahim
kaust.personLima, Ricardo
kaust.personKnio, Omar
dc.date.published-online2017-12-15
dc.date.published-print2018-08


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