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Efficient computation for sparse load shifting in demand side management Li, C, Yu, X, Yu, W, Chen, G and Wang, J 2016, 'Efficient computation for sparse load shifting in demand side management', IEEE Transactions on Smart Grid, vol. 8, no. 1, pp. 250-261. Document type: Journal Article Collection: Journal Articles Title Efficient computation for sparse load shifting in demand side management Author(s) Li, C Yu, X Yu, W Chen, G Wang, J Year 2016 Journal name IEEE Transactions on Smart Grid Volume number 8 Issue number 1 Start page 250 End page 261 Total pages 11 Publisher IEEE Abstract This paper introduces a distributed algorithm for sparse load shifting in demand-side management with a focus on the scheduling problem of residential smart appliances. By the sparse load shifting strategy, customers' discomfort is reduced. Although there are many game theoretic models for the demand-side management problem, the computational efficiency of finding Nash equilibrium that globally minimizes the total energy consumption cost and the peak-to-average ratio is still an outstanding issue. We develop a bidirectional framework for solving the demand-side management problem in a distributed way to substantially improve the search efficiency. A Newton method is employed to accelerate the centralized coordination of demand side management strategies that superlinearly converge to a better Nash equilibrium minimizing the peak-to-average ratio. Furthermore, dual fast gradient and convex relaxation are applied to tackle the sub-problem for customers' best response, which is able to relieve customers' discomfort from load shifting or interrupting. Detailed results from illustrative case studies are presented and discussed, which shows the costs of energy consumption and daily peak demand by our algorithm are reduced. Finally, some conclusions are drawn. Subject Power and Energy Systems Engineering (excl. Renewable Power) DOI - identifier 10.1109/TSG.2016.2521377 Copyright notice © 2016 IEEE ISSN 1949-3053 Related Links Link Description https://dx.doi.org/10.1109/TSG.2016.2521377 Link to Published Version   Versions Version Filter Type Thu, 30 Jun 2016, 11:08:00 EST Wed, 23 Jan 2019, 10:36:12 EST Filtered Full Citation counts:   Cited 71 times in Thomson Reuters Web of Science Article | Citations Cited 21 times in Scopus Article | Citations Altmetric details: Access Statistics: 183 Abstract Views  -  Detailed Statistics Created: Thu, 30 Jun 2016, 10:04:00 EST by Catalyst Administrator