DocumentCode :
3666136
Title :
An approach to assess the responsive residential demand to financial incentives
Author :
Qinran Hu; Xin Fang; Fangxing Li; Xiaojing Xu;Chien-fei Chen; Haolu Hu
Author_Institution :
Dept. of EECS, The University of Tennessee, Knoxville, USA
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
1
Lastpage :
5
Abstract :
Due to the development of intelligent energy management system with automatic control, the large population of residential appliances have the opportunity to be effectively utilized by load serving entities (LSEs) to reduce their operating costs and increase total profit. In practice, LSEs are promoting various demand response (DR) programs to stimulate the flexibility of industrial and commercial demand. However, in the residential sector, due to customers´ versatile electricity consumption patterns, fully utilizing the responsive residential demand through DR programs such as incentive based demand response (I-DR) is difficult. Specifically, in I-DR, the most crucial issue for LSEs is how to estimate the residents´ potential responses to certain financial incentives. Therefore, this paper presents an approach which integrates three data sets (1. the residential energy consumption survey by the U.S. energy information administration; 2. the American time use survey by the U.S. Department of Labor; and 3. the survey of customers´ reactions to financial incentives in DR program by the center for ultra-wide-area resilient electric energy transmission networks) to assess responsive residential demand in a stochastic model. This proposed approach can be easily customized for any given times, locations, financial incentives, and residents´ portfolios. Also, it will help LSEs get the valuable insights on regulating the residential demand by adjusting the financial incentives to customers and improving the mechanism existing demand response programs.
Keywords :
"Load management","Probability distribution","Home appliances","Stochastic processes","Electricity supply industry","Mathematical model","Portfolios"
Publisher :
ieee
Conference_Titel :
Power & Energy Society General Meeting, 2015 IEEE
ISSN :
1932-5517
Type :
conf
DOI :
10.1109/PESGM.2015.7286623
Filename :
7286623
Link To Document :
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