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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Aug 2018
Next issue: Nov 2018
Avg review time: 82 days


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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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  1/2018 - 10

Improving Voltage Profile and Optimal Scheduling of Vehicle to Grid Energy based on a New Method

NAZARLOO, A. See more information about NAZARLOO, A. on SCOPUS See more information about NAZARLOO, A. on IEEExplore See more information about NAZARLOO, A. on Web of Science, FEYZI, M. R. See more information about  FEYZI, M. R. on SCOPUS See more information about  FEYZI, M. R. on SCOPUS See more information about FEYZI, M. R. on Web of Science, SABAHI, M. See more information about  SABAHI, M. on SCOPUS See more information about  SABAHI, M. on SCOPUS See more information about SABAHI, M. on Web of Science, BANNAE SHARIFIAN, M. B. See more information about BANNAE SHARIFIAN, M. B. on SCOPUS See more information about BANNAE SHARIFIAN, M. B. on SCOPUS See more information about BANNAE SHARIFIAN, M. B. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,926 KB) | Citation | Downloads: 135 | Views: 304

Author keywords
discharges (electric), electric vehicles, energy management, optimal scheduling, power grids

References keywords
grid(23), power(14), vehicle(13), electric(10), vehicles(8), energy(7), plug(6), smart(5), hybrid(5), distribution(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 81 - 88
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01010
Web of Science Accession Number: 000426449500010
SCOPUS ID: 85043286801

Abstract
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The electric vehicles (EVs), depending on the charging or discharging modes, can act as flexible loads or as flexible energy sources. Therefore, this paper proposes a method for achieving the following objectives: improvement the voltage profile of the point of common coupling (PCC), control the charging and discharging of EVs in an appropriate scheduling so that at the end of the charging and discharging process all EVs are fully charged, improvement the profiles of active and reactive loads based on the peak shaving and the valley filling, charging rate control and energy management for the economic justification of vehicle to grid (V2G) technology based on the proposed method. Considering that the penetration of EVs and state of charge (SOC) of battery at any time is random, this paper extracts and analyzes the data that is available through national household travel surveys (NHTS). In order to determine the desired parameters, two stochastic algorithms are integrated with Monte Carlo simulations. To prove the performance superiority of the proposed method over conventional methods under high EVs-penetration, an IEEE 14-bus system is used for real-time simulation.


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

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[2] Y. Fan, W. Zhu, Z. Xue, L. Zhang, and Z. Zou, "A multi-function conversion technique for vehicle-to-grid applications," Energies, vol. 8, no. 8, pp. 7638-7653, 2015.
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[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 53]


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[CrossRef] [Web of Science Times Cited 278] [SCOPUS Times Cited 340]


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[CrossRef] [Web of Science Times Cited 986] [SCOPUS Times Cited 1425]


[14] Y. Ota, H. Taniguchi, T. Nakajima, K. Liyanage, A. Yokoyama, and J. Baba, "Autonomous distributed V2G (Vehicle to Grid) satisfying scheduled charging," IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 559–564, Mar. 2012.
[CrossRef] [Web of Science Times Cited 153] [SCOPUS Times Cited 203]


[15] Y. Ma, T. Houghton, A. Cruden, and D. Infield, "Modeling the benefits of vehicle to grid technology to a power system," IEEE Trans. Power Syst., vol. 27, no. 2, pp. 1012–1020, May 2012.
[CrossRef] [Web of Science Times Cited 90] [SCOPUS Times Cited 135]


[16] M. Singh, P. Kumar, and I. Kar, "Implementation of vehicle to grid infrastructure using fuzzy logic controller," IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 565–577, Mar. 2012.
[CrossRef] [Web of Science Times Cited 82] [SCOPUS Times Cited 102]


[17] M. Singh, P. Kumar, and I. Kar, "Designing a multi charging station for electric vehicles and its utilization for the grid support," IEEE PES General Meeting, pp. 1-8, San Diego, CA, Jul. 2012.
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[18] E. Pashajavid, M. Aliakbar Golkar, "Charging of plug-in electric vehicles: Stochastic modelling of load demand within domestic grids", 20th Iranian Conf. Electrical Engineering (ICEE), Tehran, 2012, pp. 535 - 539.
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References Weight

Web of Science® Citations for all references: 6,322 TCR
SCOPUS® Citations for all references: 7,742 TCR

Web of Science® Average Citations per reference: 253 ACR
SCOPUS® Average Citations per reference: 310 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 2018-09-19 21:07 in 173 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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