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JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
Avg review time: 57 days
Avg accept to publ: 60 days
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  3/2017 - 6

A New V2G Control Strategy for Load Factor Improvement Using Smoothing Technique

CHANHOM, P. See more information about CHANHOM, P. on SCOPUS See more information about CHANHOM, P. on IEEExplore See more information about CHANHOM, P. on Web of Science, NUILERS, S. See more information about  NUILERS, S. on SCOPUS See more information about  NUILERS, S. on SCOPUS See more information about NUILERS, S. on Web of Science, HATTI, N. See more information about HATTI, N. on SCOPUS See more information about HATTI, N. on SCOPUS See more information about HATTI, N. on Web of Science
 
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Download PDF pdficon (2,168 KB) | Citation | Downloads: 1,056 | Views: 3,216

Author keywords
electric vehicles, energy storage, finite impulse response filters, power smoothing, smart grids

References keywords
grid(20), power(19), vehicle(13), energy(12), smart(10), electric(10), vehicles(9), systems(7), system(5), capacity(5)
No common words between the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 43 - 50
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03006
Web of Science Accession Number: 000410369500006
SCOPUS ID: 85028564493

Abstract
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This paper proposes a new vehicle-to-grid (V2G) control strategy for improving the load factor in the power network. To operate the proposed strategy, the available storage capacity of the PEVs batteries is considered as a battery energy storage system (BESS) for charging and discharging an amount of power corresponding to the V2G power command. Due to the remarkable advantages of the technique so-called simple moving average, it is selected for applying in the proposed V2G control strategy. In this research, for investigating the load factor improvement, the essential data including the daily-load profiles with 7-day and 14-day periods are used for the 3 studied cases. These 3 studied cases present the power network with variation of the PEVs locations for describing the PEVs usage and charging or discharging behavior. The performance of the proposed strategy is simulated and verified by the MATPOWER software. The simulation results show that the load factors of the 3 studied cases are improved. Moreover, the encouragement of energy arbitrage for the PEVs owners is also discussed in this paper.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 3
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Cited-By CrossRef

[1] An optimization scheduling method of electric vehicle virtual energy storage to track planned output based on multiobjective optimization, Han, Xiaojuan, Liang, Dengxiang, Wang, Hui, International Journal of Energy Research, ISSN 0363-907X, Issue 11, Volume 44, 2020.
Digital Object Identifier: 10.1002/er.5534
[CrossRef]

[2] Real Measure of a Transmission Line Data with Load Fore-cast Model for The Future, YILMAZ, Musa, Balkan Journal of Electrical and Computer Engineering, ISSN 2147-284X, Issue 2, Volume 6, 2018.
Digital Object Identifier: 10.17694/bajece.419646
[CrossRef]

[3] Intelligent Charging Control of Power Aggregator for Electric Vehicles Using Optimal Control, ALKAWAZ, A. N., KANESAN, J., MOHD KHAIRUDDIN, A. S., CHOW, C. O., SINGH, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 21, 2021.
Digital Object Identifier: 10.4316/AECE.2021.04003
[CrossRef] [Full text]

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