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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2022 - 11
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Clustering-based Energy-aware Scheduling of Smart Residential Area

MUTHUSELVI, G. See more information about MUTHUSELVI, G. on SCOPUS See more information about MUTHUSELVI, G. on IEEExplore See more information about MUTHUSELVI, G. on Web of Science, SARAVANAN, B. See more information about SARAVANAN, B. on SCOPUS See more information about SARAVANAN, B. on SCOPUS See more information about SARAVANAN, B. on Web of Science
 
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Download PDF pdficon (3,629 KB) | Citation | Downloads: 661 | Views: 599

Author keywords
clustering algorithms, energy management, load management, meter reading, smart grids

References keywords
energy(18), smart(15), residential(15), demand(13), response(10), load(10), grid(10), clustering(7), data(6), analysis(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-02-28
Volume 22, Issue 1, Year 2022, On page(s): 95 - 102
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.01011
Web of Science Accession Number: 000762769600010
SCOPUS ID: 85126765661

Abstract
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Updating power system networks without changing the existing network facilities is done by modifying the consumer's energy demand curve using the Demand Response (DR) program. The increase in energy consumption, its environmental impact and limits in generation illustrates the importance of energy savings and alternate usage as Demand side management (DSM). Clustering methods provide proper planning and management of loads during the DR program. DR congestion of residential electrical loads scheduling is effectively managed by clustering of all the load curves in the smart residential area. The purpose of clustering the consumers is to understand the different energy behaviour better and identify the typical seasonal consumption patterns for the residential consumers, thereby creating a smart control strategy for the DR program. This work mainly focuses on applying load clustering method to reshape the load curve in the residential area during summer. The optimal scheduling of loads using this proposed method provide peak load management, Peak to Average Ratio (PAR) reduction, and the minimization of electricity cost of the consumer. The proposed seasonal clustering-based scheduling framework is solved using CPLEX solver.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 96]


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References Weight

Web of Science® Citations for all references: 1,535 TCR
SCOPUS® Citations for all references: 1,977 TCR

Web of Science® Average Citations per reference: 55 ACR
SCOPUS® Average Citations per reference: 71 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2023-11-28 17:35 in 156 seconds.




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