Residential demand response program: virtual storage model and its optimization

No Thumbnail Available
Authors
Basnet, Saurav Man Singh
Jewell, Ward T.
Advisors
Issue Date
2019-03-21
Type
Conference paper
Keywords
Clustering , Demand response , Direct load control , Genetic algorithm , Heating ventilation and air conditioning , Incentives , Thermal integrity
Research Projects
Organizational Units
Journal Issue
Citation
S. M. Basnet and W. Jewell, "Residential Demand Response Program: Virtual Storage Model and Its Optimization," 2018 IEEE Conference on Technologies for Sustainability (SusTech), Long Beach, CA, USA, 2018, pp. 35-42
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 paper 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. Incentives must benefit both clients and customers in order for programs to succeed. A mathematical modeling of the load reduction capacity of a demand response program as a virtual storage system and its optimization is presented in this paper.

Table of Contents
Description
Click on the DOI link to access the article (may not be free).
Publisher
IEEE
Journal
Book Title
Series
2018 IEEE Conference on Technologies for Sustainability (SusTech);
PubMed ID
DOI
ISSN
EISSN