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dc.contributor.advisorJewell, Ward T.
dc.contributor.authorBasnet, Saurav Man Singh
dc.descriptionThesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
dc.description.abstractDemand response programs are becoming an integral part of the power system, helping create a closer alignment between the electrical service providers and customers. The research described in this dissertation uses the residential demand response (DR) program during a peak demand event to determine the demand reduction capacity as a virtual storage (VS). The amount of demand that is reduced due to the demand response program is analogous to the amount of energy discharged by storage to reduce the demand. Since there is no hard storage involved, demand reduction is taken as VS. The aggregator is a third party who communicates between the client (electrical service provider) and customers to utilize the virtual storage capacity. The aggregator provides incentive to customers to take control over their thermostat and receive a reward from the client for load reduction. A mathematical model was developed based on reward and incentive to maximize the aggregator profit. Incentives must benefit both clients and customers in order for programs to succeed. This dissertation is based on two concrete areas: predictive analytics to estimate the level of residential participation, and mathematical modeling of the load reduction capacity of a demand response program as a virtual storage system and its optimization.
dc.format.extentix, 94 pages
dc.publisherWichita State University
dc.rightsCopyright 2017 by Saurav Man Singh Basnet All Rights Reserved
dc.subject.lcshElectronic dissertations
dc.titleResidential demand response program: predictive analytics and virtual storage model

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  • CE Theses and Dissertations
    Doctoral and Master's theses authored by the College of Engineering graduate students
  • Dissertations
    This collection includes Ph.D. dissertations completed at the Wichita State University Graduate School (Fall 2005 --)
  • EECS Theses and Dissertations
    Collection of Master's theses and Ph.D. dissertations completed at the Dept. of Electrical Engineering and Computer Science

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