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dc.contributor.authorCheng, Jianqiang
dc.contributor.authorChen, Richard Li-Yang
dc.contributor.authorNajm, Habib N.
dc.contributor.authorPinar, Ali
dc.contributor.authorSafta, Cosmin
dc.contributor.authorWatson, Jean-Paul
dc.date.accessioned2018-05-29T16:24:53Z
dc.date.available2018-05-29T16:24:53Z
dc.date.issued2018-06
dc.identifier.citationCheng, J., Chen, R.LY., Najm, H.N. et al. Comput Optim Appl (2018) 70: 479. https://doi.org/10.1007/s10589-018-0006-2en_US
dc.identifier.issn0926-6003
dc.identifier.issn1573-2894
dc.identifier.doi10.1007/s10589-018-0006-2
dc.identifier.urihttp://hdl.handle.net/10150/627809
dc.description.abstractIncreasing penetration levels of renewables have transformed how power systems are operated. High levels of uncertainty in production make it increasingly difficulty to guarantee operational feasibility; instead, constraints may only be satisfied with high probability. We present a chance-constrained economic dispatch model that efficiently integrates energy storage and high renewable penetration to satisfy renewable portfolio requirements. Specifically, we require that wind energy contribute at least a prespecified proportion of the total demand and that the scheduled wind energy is deliverable with high probability. We develop an approximate partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chance-constrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed satisfaction tolerance, and approximately 100 times faster than standard sample average approximation. Finally, the improved efficiency of our PSAA approach enables solution of a larger WECC-240 test system in minutes.en_US
dc.description.sponsorshipLaboratory Directed Research and Development (LDRD) program of the Sandia National Laboratories; U.S. Department of Energys National Nuclear Security Administration [DE-NA0003525]; Bisgrove Scholars program (Science Foundation Arizona)en_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.urlhttp://link.springer.com/10.1007/s10589-018-0006-2en_US
dc.rights© Springer Science+Business Media, LLC, part of Springer Nature 2018en_US
dc.subjectChance constraintsen_US
dc.subjectSample average approximationen_US
dc.subjectPartial sample average approximationen_US
dc.subjectEconomic dispatchen_US
dc.subjectRenewable energy integrationen_US
dc.subjectEnergy storageen_US
dc.titleChance-constrained economic dispatch with renewable energy and storageen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Syst & Ind Engnen_US
dc.identifier.journalCOMPUTATIONAL OPTIMIZATION AND APPLICATIONSen_US
dc.description.note12 month embargo; published online: 19 April 2018en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleComputational Optimization and Applications
dc.source.volume70
dc.source.issue2
dc.source.beginpage479
dc.source.endpage502


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