Efficient and scalable demand response for the smart power grid

Seung Jun Kim, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

A demand response setup is considered entailing a set of appliances with deferrable and non-interruptible tasks. A mixed-integer linear programming model for scheduling the operational periods and power levels of the appliances is formulated in response to known dynamic pricing information with the objective of minimizing the total electricity cost and consumer dissatisfaction. A scalable algorithm yielding a near-optimal solution is developed by enforcing a separable structure, and using Lagrangian relaxation. Thus, the original problem is decomposed to per-appliance subproblems, which can be solved exactly based on dynamic programming. The proximal bundle method is employed to obtain a solution to the convexified version, which helps recovery of a primal feasible solution. Numerical tests validate the proposed approach.

Original languageEnglish (US)
Title of host publication2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
Pages109-112
Number of pages4
DOIs
StatePublished - Dec 1 2011
Event2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011 - San Juan, Puerto Rico
Duration: Dec 13 2011Dec 16 2011

Publication series

Name2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011

Other

Other2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2011
CountryPuerto Rico
CitySan Juan
Period12/13/1112/16/11

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