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FACTS & FIGURES

JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Nov 2018
Next issue: Feb 2019
Avg review time: 81 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: 171 | Views: 437

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.


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SCOPUS® Times Cited: 2
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Cited-By CrossRef

[1] Research on the effect of large-scale electric vehicle based on smart wearable equipment access to grid, Cai, Li, Hu, Pengqiu, Tan, Zefu, Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, 2018.
Digital Object Identifier: 10.1007/s12652-018-1041-7
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Copyright ©2001-2018
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

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