DocumentCode
116235
Title
Distributional analysis for model predictive deferrable load control
Author
Niangjun Chen ; Lingwen Gan ; Low, Steven H. ; Wierman, Adam
Author_Institution
California Inst. of Tech., Pasadena, CA, USA
fYear
2014
fDate
15-17 Dec. 2014
Firstpage
6433
Lastpage
6438
Abstract
Deferrable load control is essential for handling the uncertainties associated with the increasing penetration of renewable generation. Model predictive control has emerged as an effective approach for deferrable load control, and has received considerable attention. Though the average-case performance of model predictive deferrable load control has been analyzed in prior works, the distribution of the performance has been elusive. In this paper, we prove strong concentration results on the load variation obtained by model predictive deferrable load control. These results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance.
Keywords
load regulation; optimal control; predictive control; uncertain systems; average-case performance; distributional analysis; load variation; model predictive deferrable load control; performance distribution; renewable generation penetration; uncertainty handling; Algorithm design and analysis; Analytical models; Load flow control; Load management; Load modeling; Prediction algorithms; Predictive models;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
Conference_Location
Los Angeles, CA
Print_ISBN
978-1-4799-7746-8
Type
conf
DOI
10.1109/CDC.2014.7040398
Filename
7040398
Link To Document