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Particle swarm optimization for demand side management in smart grid
Authors: Dipti Srinivasan; Thillainathan Logenthiran; Ei Phyu;
Abstract
Demand side management is a useful and necessary tool in smart grid energy management system to reduce total power demand during peak demand periods and hence, enhancing grid sustainability and reducing overall cost. This paper discusses a new load shifting approach for demand side management in smart grid energy management. This approach optimizes the consumption curves of household, commercial and industrial consumers. The proposed algorithm in this approach minimizes the cost incurred by users while taking into account users' individual preferences for the loads by setting priorities and preferred time intervals for load scheduling.
Related Organizations
- Newcastle University United Kingdom
- National University of Singapore Singapore
- Newcastle University Singapore Singapore
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).33 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%

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citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
popularity
Popularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
33
Top 10%
Top 10%
Top 10%