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
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;
Conference_Titel :
Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-1-4799-7746-8
DOI :
10.1109/CDC.2014.7040398