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


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  1/2024 - 5

An Improved Sine Cosine Algorithm for the Day-ahead Microgrid Management in the Presence of Electric Vehicles

QIU, C. See more information about QIU, C. on SCOPUS See more information about QIU, C. on IEEExplore See more information about QIU, C. on Web of Science
 
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Download PDF pdficon (1,401 KB) | Citation | Downloads: 382 | Views: 279

Author keywords
energy management, microgrids, optimization methods, renewable energy sources, scheduling

References keywords
energy(31), electric(17), algorithm(15), microgrid(13), optimization(12), vehicles(11), renewable(11), management(11), systems(10), scheduling(10)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2024-02-29
Volume 24, Issue 1, Year 2024, On page(s): 41 - 50
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.01005
SCOPUS ID: 85189452589

Abstract
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Microgrid (MG) is capable of accommodating renewable energy sources (RESs) with high flexibility. With the rapid development of MGs, plug-in hybrid electric vehicles (PHEVs) are gaining increasing attention since they can alleviate pollution and reduce energy consumption. The appearance of PHEVs would exacerbate the power supply shortages and bring new challenges to the power system. This paper develops an effective day-ahead optimal scheduling of a MG, taking into account RESs, storage devices and PHEVs. The Monte Carlo simulation is utilized to model the uncertainties of PHEVs. A smart charging/discharging strategy incorporating the V2G technique is proposed to smooth the demand curve and reduce the operational costs. To handle the MG scheduling problem in the presence of PHEVs, an improved sine cosine algorithm with simulated annealing based local search operator and chaotic opposition learning strategy (CSCASA) is proposed to minimize the total costs. The proposed algorithm can keep a better balance between global and local search abilities. CSCASA is first validated on some benchmark problems. Then, CSCASA is employed to generate optimal schedule of a grid-connected MG with PHEVs. The experimental results demonstrate the superior performance of CSCASA in the optimal MG scheduling problem with and without PHEVs.


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References Weight

Web of Science® Citations for all references: 33,479 TCR
SCOPUS® Citations for all references: 40,685 TCR

Web of Science® Average Citations per reference: 728 ACR
SCOPUS® Average Citations per reference: 884 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 2024-05-22 07:26 in 303 seconds.




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