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dc.contributor.advisorPaternotte, Hande Yaman
dc.contributor.authorŞahin, Munise Kübra
dc.date.accessioned2018-08-08T13:30:15Z
dc.date.available2018-08-08T13:30:15Z
dc.date.copyright2018-07
dc.date.issued2018-08
dc.date.submitted2018-08-06
dc.identifier.urihttp://hdl.handle.net/11693/47729
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 44-47).en_US
dc.description.abstractThe increase in the energy consumption puts pressure on natural resources and environment and results in a rise in the price of energy. This motivates residents to schedule their energy consumption through demand response mechanism. We propose a multi-stage stochastic programming model to schedule di erent kinds of electrical appliances under uncertain weather conditions and availability of renewable energy. We incorporate appliances with internal batteries to better utilize the renewable energy sources. Our aim is to minimize the electricity cost and the residents' dissatisfaction. We use a scenario groupwise decomposition approach to compute lower and upper bounds for instances with a large number of scenarios. The results of our computational experiments show that the approach is very e ective in nding high quality solutions in small computation times. We provide insights about how optimization and renewable energy combined with batteries for storage result in peak demand reduction, savings in electricity cost and more pleasant schedules for residents with di erent levels of price sensitivity.en_US
dc.description.statementofresponsibilityby Munise Kübra Şahin.en_US
dc.format.extentxii, 51 leaves : illustrations ; 30 cmen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSmart Griden_US
dc.subjectDemand Responseen_US
dc.subjectMulti-Stage Stochastic Programmingen_US
dc.subjectScenario Groupwise Decompositionen_US
dc.titleMulti-stage stochastic programming for demand response optimizationen_US
dc.title.alternativeTalep tepkisi optimizasyonu için çok aşamalı rassal programlamaen_US
dc.typeThesisen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreexii, 51 leaves : illustrations ; 30 cm.en_US
dc.identifier.itemidB158761
dc.embargo.release2019-08-06


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