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Model predictive control for deferrable loads scheduling

Citation

Chen, Niangjun (2014) Model predictive control for deferrable loads scheduling. Master's thesis, California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:06022014-205438709

Abstract

Real-time demand response is essential for handling the uncertainties of renewable generation. Traditionally, demand response has been focused on large industrial and commercial loads, however it is expected that a large number of small residential loads such as air conditioners, dish washers, and electric vehicles will also participate in the coming years. The electricity consumption of these smaller loads, which we call deferrable loads, can be shifted over time, and thus be used (in aggregate) to compensate for the random fluctuations in renewable generation.

In this thesis, we propose a real-time distributed deferrable load control algorithm to reduce the variance of aggregate load (load minus renewable generation) by shifting the power consumption of deferrable loads to periods with high renewable generation. The algorithm is model predictive in nature, i.e., at every time step, the algorithm minimizes the expected variance to go with updated predictions. We prove that suboptimality of this model predictive algorithm vanishes as time horizon expands in the average case analysis. Further, we prove strong concentration results on the distribution of the load variance obtained by model predictive deferrable load control. These concentration results highlight that the typical performance of model predictive deferrable load control is tightly concentrated around the average-case performance. Finally, we evaluate the algorithm via trace-based simulations.

Item Type:Thesis (Master's thesis)
Subject Keywords:smart grid; model predictive control; deferrable load scheduling; stochastic control; distributional analysis; EV charging
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computer Science
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Wierman, Adam C. (co-advisor)
  • Low, Steven H. (co-advisor)
Thesis Committee:
  • None, None
Defense Date:2 June 2014
Non-Caltech Author Email:ncchen (AT) caltech.edu
Record Number:CaltechTHESIS:06022014-205438709
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:06022014-205438709
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8468
Collection:CaltechTHESIS
Deposited By: Niangjun Chen
Deposited On:03 Jun 2014 19:07
Last Modified:03 Jun 2014 19:07

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