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Residential demand response program: predictive analytics and virtual storage model
Basnet, Saurav Man Singh
Basnet, Saurav Man Singh
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Dissertation
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2017-05
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Electronic dissertations
Electronic dissertations
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Abstract
Demand 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.
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Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
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Wichita State University
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Copyright 2017 by Saurav Man Singh Basnet
All Rights Reserved