Scalable and Robust Demand Response With Mixed-Integer Constraints

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 a...

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Veröffentlicht in:IEEE transactions on smart grid Jg. 4; H. 4; S. 2089 - 2099
Hauptverfasser: Seung-Jun Kim, Giannakis, Georgios B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.12.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Electricity
Energy storage
Lagrange relaxation
mixed-integer programs
Optimization
parallel and distributed algorithms
Power demand
real-time demand response
Real-time systems
robust optimization
Robustness
Uncertainty
ISSN:1949-3053, 1949-3061
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2013.2257893