DocumentCode :
2917957
Title :
Decentralized control of aggregated loads for demand response
Author :
Di Guo ; Wei Zhang ; Gangfeng Yan ; Zhiyun Lin ; Minyue Fu
Author_Institution :
Dept. of Syst. Sci. & Eng., Zhejiang Univ., Hangzhou, China
fYear :
2013
fDate :
17-19 June 2013
Firstpage :
6601
Lastpage :
6606
Abstract :
This paper focuses on the aggregated control of a large number of residential responsive loads for various demand response applications. We propose a general hybrid system model which can capture the dynamics of both Thermostatically Controlled Loads (TCLs) such as air conditioners and water heaters, as well as deferrable loads such as washers, dryers, and Plug-in Hybrid Electric Vehicles (PHEVs). Based on the hybrid system model, the aggregated control problem is formulated as a large scale optimal control problem that determines the energy use plans for a heterogeneous population of hybrid systems. A decentralized cooperative control algorithm is proposed to solve the aggregated control problem. Convergence of the proposed algorithm is proved using potential game theory. The simulation results indicate that the aggregated power response can accurately track a reference trajectory and effectively reduce the peak power consumption.
Keywords :
convergence; cooperative systems; decentralised control; demand side management; game theory; load regulation; optimal control; Aggregated Loads; TCL dynamics; aggregated control problem; aggregated power response; convergence; decentralized cooperative control algorithm; demand response applications; energy use plans; heterogeneous population; hybrid system model; large scale optimal control problem; peak power consumption; potential game theory; reference trajectory tracking; residential responsive loads; thermostatically controlled load dynamics; Atmospheric modeling; Decentralized control; Games; Heuristic algorithms; Load management; Load modeling; Optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2013
Conference_Location :
Washington, DC
ISSN :
0743-1619
Print_ISBN :
978-1-4799-0177-7
Type :
conf
DOI :
10.1109/ACC.2013.6580875
Filename :
6580875
Link To Document :
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