DocumentCode :
42338
Title :
Scalable and Robust Demand Response With Mixed-Integer Constraints
Author :
Seung-Jun Kim ; Giannakis, Georgios
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA
Volume :
4
Issue :
4
fYear :
2013
fDate :
Dec. 2013
Firstpage :
2089
Lastpage :
2099
Abstract :
A demand response (DR) problem is considered entailing a set of devices/subscribers, whose operating conditions are modeled using mixed-integer constraints. Device operational periods and power consumption levels are optimized in response to dynamic pricing information to balance user satisfaction and energy cost. Renewable energy resources and energy storage systems are also incorporated. Since DR becomes more effective as the number of participants grows, scalability is ensured through a parallel distributed algorithm, in which a DR coordinator and DR subscribers solve individual subproblems, guided by certain coordination signals. As the problem scales, the recovered solution becomes near-optimal. Robustness to random variations in electricity price and renewable generation is effected through robust optimization techniques. Real-time extension is also discussed. Numerical tests validate the proposed approach.
Keywords :
demand side management; distributed algorithms; energy storage; integer programming; parallel algorithms; power engineering computing; power markets; pricing; renewable energy sources; DR coordinator; DR problem; DR subscribers; coordination signals; device operational periods; dynamic pricing information; electricity price; energy cost; energy storage systems; mixed-integer constraints; numerical tests; parallel distributed algorithm; power consumption levels; random variations; real-time extension; renewable energy resources; renewable generation; robust demand response; robust optimization techniques; user satisfaction; Electricity; Energy storage; Optimization; Power demand; Real-time systems; Robustness; Uncertainty; Lagrange relaxation; mixed-integer programs; parallel and distributed algorithms; real-time demand response; robust optimization;
fLanguage :
English
Journal_Title :
Smart Grid, IEEE Transactions on
Publisher :
ieee
ISSN :
1949-3053
Type :
jour
DOI :
10.1109/TSG.2013.2257893
Filename :
6510544
Link To Document :
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