Risk-averse formulations and methods for a virtual power plant

Handle URI:
http://hdl.handle.net/10754/626422
Title:
Risk-averse formulations and methods for a virtual power plant
Authors:
Lima, Ricardo M.; Conejo, Antonio J.; Langodan, Sabique ( 0000-0003-0513-1790 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Knio, Omar M.
Abstract:
In 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.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
Lima 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.
Publisher:
Elsevier BV
Journal:
Computers & Operations Research
Issue Date:
15-Dec-2017
DOI:
10.1016/j.cor.2017.12.007
Type:
Article
ISSN:
0305-0548
Sponsors:
The authors acknowledge the support of the Center for Uncertainty Quantification in Computational Science & Engineering at KAUST.
Additional Links:
http://www.sciencedirect.com/science/article/pii/S0305054817303076
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLima, Ricardo M.en
dc.contributor.authorConejo, Antonio J.en
dc.contributor.authorLangodan, Sabiqueen
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorKnio, Omar M.en
dc.date.accessioned2017-12-21T13:57:05Z-
dc.date.available2017-12-21T13:57:05Z-
dc.date.issued2017-12-15en
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.en
dc.identifier.issn0305-0548en
dc.identifier.doi10.1016/j.cor.2017.12.007en
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.en
dc.description.sponsorshipThe authors acknowledge the support of the Center for Uncertainty Quantification in Computational Science & Engineering at KAUST.en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0305054817303076en
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/en
dc.subjectOptimization under uncertaintyen
dc.subjectstochastic programmingen
dc.subjectconditional value at risken
dc.subjectenergyen
dc.subjectvirtual power planten
dc.titleRisk-averse formulations and methods for a virtual power planten
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalComputers & Operations Researchen
dc.eprint.versionPost-printen
dc.contributor.institutionComputer, Electrical and Mathematical Sciences & Engineering Division, Saudi Arabiaen
dc.contributor.institutionIntegrated Systems Engineering-Electrical and Computer Engineering, The Ohio State University, OH, USAen
kaust.authorLangodan, Sabiqueen
kaust.authorHoteit, Ibrahimen
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