Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.650
JCR 5-Year IF: 0.639
Issues per year: 4
Current issue: Aug 2019
Next issue: Nov 2019
Avg review time: 72 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


TRAFFIC STATS

2,357,770 unique visits
611,213 downloads
Since November 1, 2009



Robots online now
SemrushBot
SemanticScholar


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 19 (2019)
 
     »   Issue 3 / 2019
 
     »   Issue 2 / 2019
 
     »   Issue 1 / 2019
 
 
 Volume 18 (2018)
 
     »   Issue 4 / 2018
 
     »   Issue 3 / 2018
 
     »   Issue 2 / 2018
 
     »   Issue 1 / 2018
 
 
 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
 Volume 16 (2016)
 
     »   Issue 4 / 2016
 
     »   Issue 3 / 2016
 
     »   Issue 2 / 2016
 
     »   Issue 1 / 2016
 
 
  View all issues  








LATEST NEWS

2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

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.

Read More »


    
 

  1/2018 - 15

Optimization of Charge/Discharge Coordination to Satisfy Network Requirements Using Heuristic Algorithms in Vehicle-to-Grid Concept

DOGAN, A. See more information about DOGAN, A. on SCOPUS See more information about DOGAN, A. on IEEExplore See more information about DOGAN, A. on Web of Science, BAHCECI, S. See more information about  BAHCECI, S. on SCOPUS See more information about  BAHCECI, S. on SCOPUS See more information about BAHCECI, S. on Web of Science, DALDABAN, F. See more information about  DALDABAN, F. on SCOPUS See more information about  DALDABAN, F. on SCOPUS See more information about DALDABAN, F. on Web of Science, ALCI, M. See more information about ALCI, M. on SCOPUS See more information about ALCI, M. on SCOPUS See more information about ALCI, M. 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 (1,247 KB) | Citation | Downloads: 283 | Views: 1,915

Author keywords
electric vehicles, genetic algorithms, heuristic algorithms, smart grids, optimization

References keywords
grid(33), power(31), electric(28), vehicle(23), vehicles(21), energy(21), charging(18), plug(16), systems(14), smart(14)
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): 121 - 130
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01015
Web of Science Accession Number: 000426449500015
SCOPUS ID: 85043247244

Abstract
Quick view
Full text preview
Image thresholding is the most crucial step in microscopic image analysis to distinguish bacilli objects causing of tuberculosis disease. Therefore, several bi-level thresholding algorithms are widely used to increase the bacilli segmentation accuracy. However, bi-level microscopic image thresholding problem has not been solved using optimization algorithms. This paper introduces a novel approach for the segmentation problem using heuristic algorithms and presents visual and quantitative comparisons of heuristic and state-of-art thresholding algorithms. In this study, well-known heuristic algorithms such as Firefly Algorithm, Particle Swarm Optimization, Cuckoo Search, Flower Pollination are used to solve bi-level microscopic image thresholding problem, and the results are compared with the state-of-art thresholding algorithms such as K-Means, Fuzzy C-Means, Fast Marching. Kapur's entropy is chosen as the entropy measure to be maximized. Experiments are performed to make comparisons in terms of evaluation metrics and execution time. The quantitative results are calculated based on ground truth segmentation. According to the visual results, heuristic algorithms have better performance and the quantitative results are in accord with the visual results. Furthermore, experimental time comparisons show the superiority and effectiveness of the heuristic algorithms over traditional thresholding algorithms.


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

[1] U.S. Energy Administration Office, International Energy Outlook, Washington, DC, USA, DOE/EIA-0484(2016), May 2016. [Online] Available: Temporary on-line reference link removed - see the PDF document

[2] Electric Power Research Institute, Environmental assessment of plug-in hybrid electric vehicles. Volume 1: Nationwide Greenhouse Gas Emissions, CA, USA, 1015325, July 2007. [Online] Available: Temporary on-line reference link removed - see the PDF document

[3] International Energy Agency. Global EV Outlook 2017 Two million and counting. Paris, FR, OECD/IEA 2017, July 2017. [Online] Available: Temporary on-line reference link removed - see the PDF document

[4] X. Fang, S. Misra, G. Xue, and D. Yang, "Smart Grid – The New and Improved Power Grid: A Survey ," IEEE Commun. Surveys Tuts., vol. 14, no. 4, pp. 944–980, 2012.
[CrossRef] [Web of Science Times Cited 1039] [SCOPUS Times Cited 1296]


[5] S. Xie, W. Zhong, K. Xie, R. Yu, and Y. Zhang, "Fair Energy Scheduling for Vehicle-to-Grid Networks Using Adaptive Dynamic Programming," IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 8, pp. 1697–1707, 2016.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 47]


[6] E. De Caluwé, Grid-supportive charging infrastructure for plug-in electric vehicles, PhD thesis, K.U.Leuven, – Faculty of Engineering Science, 2015. [Online] Available: Temporary on-line reference link removed - see the PDF document

[7] J. Taylor, A. Maitra, M. Alexander, D. Brooks, and M. Duvall, "Evaluation of the impact of plug-in electric vehicle loading on distribution system operations," in Proc. IEEE Power Energy Soc. Gen. Meet., Calgary, Canada, 26-30 July 2009 pp. 1–6.
[CrossRef] [SCOPUS Times Cited 305]


[8] M. J. Scott, M. K. Meyers, D. B. Elliott, W. M. Warwick, "Impacts Assessment of Plug-in Hybrid Vehicles on Electric Utilities and Regional US Power Grids Part 2: Economic Assessment," Pacific Northwest Nat. Lab., Richland, WA., DE-AC05-76RL01830, Nov. 2017 [Online] Available: Temporary on-line reference link removed - see the PDF document

[9] A. Dogan, M. Kuzlu, M. Pipattanasomporn, S. Rahman, and T. Yalcinoz, "Impact of EV charging strategies on peak demand reduction and load factor improvement," in Proc. Inter. Conf. on Elect.l and Electronics Eng., Bursa, Turkey, 26-28 Nov. 2015, pp. 374-378.
[CrossRef] [SCOPUS Times Cited 6]


[10] C. Guille and G. Gross, "A conceptual framework for the vehicle-to-grid ( V2G ) implementation," Energy Policy, vol. 37, no. 11, pp. 4379–4390, 2009.
[CrossRef] [Web of Science Times Cited 415] [SCOPUS Times Cited 565]


[11] M. Yilmaz and P. T. Krein, "Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces," IEEE Trans. Power Electron., vol. 28, no. 12, pp. 5673–5689, Dec. 2013.
[CrossRef] [Web of Science Times Cited 331] [SCOPUS Times Cited 388]


[12] C. S. Antunez, J. F. Franco, M. J. Rider, R. Romero, "A New Methodology for the Optimal Charging Coordination of Electric Vehicles Considering Vehicle-to-Grid Technology," IEEE Trans. Sustain. Energy. vol. 7, no. 2, pp. 596–607, 2016.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 15]


[13] K. Clement-nyns, E. Haesen, and J. Driesen, "The impact of vehicle-to-grid on the distribution grid," Electr. Power Syst. Res., vol. 81, no. 1, pp. 185–192, 2011.
[CrossRef] [Web of Science Times Cited 180] [SCOPUS Times Cited 228]


[14] H. Liu, Z. Hu, Y. Song, and J. Lin, "Decentralized vehicle-to-grid control for primary frequency regulation considering charging demands," IEEE Trans. Power Syst., vol. 28, no. 3, pp. 3480–3489, Aug. 2013.
[CrossRef] [Web of Science Times Cited 173] [SCOPUS Times Cited 205]


[15] C. D. White and K. M. Zhang, "Using vehicle-to-grid technology for frequency regulation and peak-load reduction," J. Power Sources, vol. 196, no. 8, pp. 3972–3980, 2011.
[CrossRef] [Web of Science Times Cited 130] [SCOPUS Times Cited 167]


[16] Z. Wang and S. Wang, "Grid Power Peak Shaving and Valley Filling Using Vehicle-to-Grid Systems," IEEE Trans. Power Del., vol. 28, no. 3, pp. 1822–1829, 2013.
[CrossRef] [Web of Science Times Cited 120] [SCOPUS Times Cited 141]


[17] M. Brenna, F. Foiadelli, and M. Longo, "The Exploitation of Vehicle-to-Grid Function for Power Quality Improvement in a Smart Grid," IEEE Intell. Transp. Syst. vol. 15, no. 5, pp. 2169–2177, 2014.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 26]


[18] H. Liu, Z. Hu, Y. Song, J. Wang, and X. Xie, "Vehicle-to-Grid Control for Supplementary Frequency Regulation Considering Charging Demands," IEEE Trans. Power Syst., vol. 30, no. 6, pp. 3110–3119, 2015.
[CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 87]


[19] M. Kesler, M. C. Kisacikoglu, and L. M. Tolbert, "Vehicle-to-Grid Reactive Power Operation Using Plug-In Electric Vehicle Bidirectional Offboard Charger," IEEE Ind. Electron., vol. 61, no. 12, pp. 6778–6784, 2014.
[CrossRef] [Web of Science Times Cited 87] [SCOPUS Times Cited 111]


[20] J. Lin, S. Member, K. Leung, V. O. K. Li, and A. In, "Optimal Scheduling With Vehicle-to-Grid Regulation Service," IEEE Internet Things J., vol. 1, no. 6, pp. 556–569, 2014.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 49]


[21] X. Wu, L. Li, J. Zou, and G. Zhang, "EV-Based Voltage Regulation in Line Distribution Grid." IEEE Instr. and Meas. Tech. Conf. Taipei 2016.
[CrossRef] [SCOPUS Times Cited 4]


[22] A. Andreotti, G. Carpinelli, F. Mottola, and D. Proto, "A review of single-objective optimization models for plug-in vehicles operation in smart grid- Part I: Theoretical aspects," in Proc. Power and Energy Society General Meeting, San Diego, USA, 22-26 July 2012, pp. 1-8
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 23]


[23] A. Andreotti, G. Carpinelli, F. Mottola, and D. Proto "A review of single-objective optimization models for plug-in vehicles operation in smart grids part ii: Numerical applications to vehicles fleets," in Proc. Power and Energy Society General Meeting, San Diego, USA, 22-26 July 2012, pp. 1-8.
[CrossRef] [SCOPUS Times Cited 21]


[24] X. Bai, W. Qiao, "Robust optimization for bidirectional dispatch coordination of large-scale V2G," IEEE Trans Smart Grid, vol. 6, no. 4, pp. 1944–1954, 2015.
[CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 43]


[25] W. Qi, Z. Xu , Z-J. Shen, Hu Z, Song Y. "Hierarchical coordinated control of plug- in electric vehicles charging in multifamily dwellings,"IEEE Trans Smart Grid, vol. 5, no. 3, pp. 1465–1474, 2014.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 59]


[26] C. Jin, , J. Tang, and P. Ghosh, "Optimizing Electric Vehicle Charging: A Customer’s Perspective," IEEE Trans. Veh. Technol., vol. 62, no. 7, pp. 2919–2927, 2013.
[CrossRef] [Web of Science Times Cited 100] [SCOPUS Times Cited 115]


[27] A. H. Hajimiragha, C. A. Canizares, M. W. Fowler, S. Moazeni, and A. Elkamel, "A robust optimization approach for planning the transition to plug-in hybrid electric vehicles," IEEE Trans. Power Syst, vol. 26, no. 4, pp. 2264–2274, 2011.
[CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 104]


[28] K. Zhang, L. Xu, M. Ouyang, H. Wang, L. Lu, J. Li, "Optimal decentralized valley-filling charging strategy for electric vehicles,"Energy Convers Manag., vol. 78, no. 57, pp. 537–550, 2009.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 56]


[29] X. Wang, Q. Liang, "Energy management strategy for plug-in hybrid electric vehicles via bidirectional vehicle-to-grid," IEEE Syst J, vol. 37, no. 3, pp. 1789 - 1798, 2017.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 23]


[30] M. Shafie-khah, M. P. Moghaddam, M. K. Sheikh-El-Eslami, M. Rahmani- Andebili, "Modeling of interactions between market regulations and behavior of plug-in electric vehicle aggregators in a virtual power market environment," Energy, vol. 40, no. 1, pp. 139-150, 2012.
[CrossRef] [Web of Science Times Cited 47] [SCOPUS Times Cited 50]


[31] Z. Yang, K. Li , A. Foley, C. Zhang, "Optimal Scheduling Methods to Integrate Plug-in Electric Vehicles with the Power System: A Review," in Proc. 19th IFAC World Congress, Cape Town, South Africa, 24-29 August 2014.
[CrossRef]


[32] Z. Yang, K. Li , A. Foley, "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, vol. 51, no. 28, pp. 396-416, 2015.
[CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 70]


[33] Y. Sugii, K. Tsujino, T. Nagano, "A Genetic-Algorithm based scheduling method of charging of electric vehicles," in Proc. IEEE Systems, Man, and Cybernetics, Conference Proceedings, Tokyo, Japan, 12-15 Oct. 1999, pp. 1-8.
[CrossRef]


[34] G. Celli, E. Ghiani, F. Pilo, G. Pisano, G. G. Soma, "Particle Swarm Optimization for Minimizing the Burden of Electric Vehicles in Active Distribution Networks," in Proc. Power and Energy Society General Meeting, in Proc. Power and Energy Society General Meeting, San Diego, USA, 22-26 July 2012, pp. 1-7.
[CrossRef] [SCOPUS Times Cited 17]


[35] S. Xu, D. Feng, Z. Yan, L. Zhang, N. Li, L. Jing, J. Wang, "Ant-Based Swarm Algorithm for Charging Coordination of Electric Vehicles," Int. J. Dist. Sensor Network, vol. 9, no. 5, pp. 1–13, 2013.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 27]


[36] I. Rahman, P. Vasant, B. S. M. Singh, M. Abdullah-Al-WadudHybrid, "Swarm Intelligence-Based Optimization for Charging Plug-in Hybrid Electric Vehicle," In: Nguyen N., Trawinski B., Kosala R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science, vol 9012. Springer, Cham
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[37] M. Alonso, H. Amaris, J. G. Germain, J. M. Galan, Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms," Energies, vol. 7, no. 4, pp. 2449-2475, 2014.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 69]


[38] C. Jin, J. Tang, P. Ghosh, "Optimizing electric vehicle charging with energy storage in the electricity market," IEEE Trans Smart Grid, vol. 4, no. 1, pp. 311-320, 2013.
[CrossRef] [Web of Science Times Cited 111] [SCOPUS Times Cited 116]


[39] S. Shao, M. Pipattanasomporn, and S. Rahman, "Challenges of PHEV penetration to the residential distribution network," in Proc. IEEE Power Energy Soc. Gen. Meeting, 2009, Calgary, Canada, 26-30 July 2009, pp. 1–8.
[CrossRef] [SCOPUS Times Cited 258]


[40] C. D. White and K. M. Zhang, "Using vehicle-to-grid technology for frequency regulation and peak-load reduction," J. Power Sources, vol. 196, no. 8, pp. 3972-3980, 2011.
[CrossRef] [Web of Science Times Cited 130] [SCOPUS Times Cited 167]


[41] P. Richardson, D. Flynn, A. Keane, "Optimal Charging of Electric Vehicles in Low-Voltage Distribution Systems," IEEE Trans. Power Syst., vol. 27, no. 1, pp. 268 - 279, 2012.
[CrossRef] [Web of Science Times Cited 264] [SCOPUS Times Cited 328]


[42] S. Deilami, A. S. Masoum, P. S. Moses, M. A. S. Masoum, "Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile," IEEE Trans. Smart Grid, vol. 2, no. 3, pp. 456 - 467, 2011.
[CrossRef] [Web of Science Times Cited 501] [SCOPUS Times Cited 626]


[43] N. Banol A., J. F. Franco, M. Lavorato, M. J. Rider, R. Romero, "Plug-In Electric Vehicle Charging Coordination in Electrical Distribution Systems Using a Tabu Search Algorithm," IEEE 15th Int. Conf. Environment and Electrical Engineering (EEEIC), Rome, Italy, 10-13 June 2015, pp. 1-6.
[CrossRef] [SCOPUS Times Cited 2]


[44] O. Sundstrom, C. Binding, "Flexible Charging Optimization for Electric Vehicles Considering Distribution Grid Constraints," IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 26 - 37, 2011.
[CrossRef] [Web of Science Times Cited 259] [SCOPUS Times Cited 316]


[45] A. Dogan, T. Yalcinoz, M. Alci, "A Comparison of Heuristic Methods for Optimum Power Flow Considering Valve Point Effect", Elektronika Ir Elektrotechnika, vol. 22, no.5, pp.32-37, 2016.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]


[46] P. Richardson, D. Flynn, and A. Keane, "Optimal charging of electric vehicles in low-voltage distribution systems," IEEE Trans. Power Syst., vol. 27, no. 1, pp. 268–279, 2012.
[CrossRef] [Web of Science Times Cited 264] [SCOPUS Times Cited 328]


[47] C. Wu and H. Mohsenian-rad, "Vehicle-to-Aggregator Interaction Game," IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 434–442, 2012.
[CrossRef] [Web of Science Times Cited 216] [SCOPUS Times Cited 234]


[48] H. Liang, B. J. Choi, and W. Zhuang, "Optimizing the Energy Delivery via V2G Systems Based on Stochastic Inventory Theory," IEEE Trans. Smart Grid., vol. 4, no. 4, pp. 2230–2243, 2013.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 40]


[49] R.-E. Precup, S. Preitl, "Optimisation criteria in development of fuzzy controllers with dynamics," Engineering Applications of Artificial Intelligence, vol. 17, no. 6, pp. 661-674, 2004.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 55]


[50] T. S. Li, C. T. Su, T. L.Chiang, "Applying robust multi-response quality engineering for parameter selection using a novel neural–genetic algorithm," Computers in Industry, vol. 50, no. 1, pp. 113-122, 2003.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 39]


[51] S. Vrkalovic, T.-A. Teban, I.-D. Borlea, "Stable Takagi-Sugeno fuzzy control designed by optimization," International Journal of Artificial Intelligence, vol. 15, no. 2, pp. 17-29, 2017.

[52] R. D. Baruah, P. Angelov, "DEC: Dynamically Evolving Clustering and its application to structure identification of evolving fuzzy models," IEEE Trans. Cybern., vol. 44, no. 9, pp. 1619-1631, 2014.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 35]


[53] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading: Addison-Wesley Publishing Company, p.62, 1989

[54] K. V. Price, "Differential evolution: a fast and simple numerical optimizer," in Proc. Fuzzy Inf. Process. Soc. Conf. North Am., Berkeley, CA, USA, 19-22 June 1996 pp. 524–527.
[CrossRef]


[55] J. Kennedy, R. Eberhart, "Particle swarm optimization", in Proc. IEEE Int. Conf Neural Networks, Perth, Austuralia, 27 Nov.-1 Dec. 1995, pp. 1942–1948.
[CrossRef] [Web of Science Times Cited 24264]


[56] D. Karaboga, "An idea based on honey bee swarm for numerical optimization". Technical Report TR06, Erciyes University, Eng. Faculty, Computer Engineering Department, Oct. 2005. [Online] Available: Temporary on-line reference link removed - see the PDF document

[57] R. Ranjan, D Das, "Simple and efficient computer algorithm to solve radial distribution networks," Electr Power Compon Syst., vol. 31 pp.95–107, 2003
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 57]


[58] GridLAB-D, [Online]. Available: http://www.gridlabd.org/

[59] D. P. Chassin, K. Schneider, C. Gerkensmeyer, "GridLABD: An open-source power systems modeling and simulation environment," in Proc. Transmission and Distribution Conference and Exposition, Chicago, USA, 21-24 April 2008, pp.1-5.
[CrossRef] [SCOPUS Times Cited 181]


[60] MATLAB, [Online]. Available: https://www.mathworks.com/products/matlab.html

[61] J. C. Fuller, B. Vyakaranam, N. Prakash Kumar, S. M. Leistritz, G. B. Parker, "Modeling of GE Appliances in GridLAB-D: Peak Demand Reduction," Technical Report-PNNL-21358 [Online] Available: Temporary on-line reference link removed - see the PDF document

[62] Z. T. Taylor, K . Gowri, S. Katipamula, "GridLAB-D Technical Support Document: Residential End-Use Module Version 1.0," Technical Report-PNNL- 17694 [Online] Available: Temporary on-line reference link removed - see the PDF document

[63] R. G. Pratt, C. C. Conner, E. E. Richman, K. G. Ritland, W. F. Sandusky, and M. E. Taylor, "Description of Electric Energy Use in Single Family Residences in the Pacific Northwest," DOE/BP 13795 21, Bonneville Power Administration, Portland, OR, 1989. [Online] Available: Temporary on-line reference link removed - see the PDF document

[64] EN 50160, voltage characteristics of electricity supplied by public distribution systems, 1999.

[65] S. Shao, M. Pipattanasomporn, and S. Rahman, "Grid Integration of Electric Vehicles and Demand Response With Customer Choice," IEEE Trans. Smart Grid., vol. 3, no. 1, pp. 543–550, 2012.
[CrossRef] [Web of Science Times Cited 174] [SCOPUS Times Cited 207]


[66] FHA, "Summary of Travel Trends: 2009 National Household Travel Survey," p. 82, 2011.

[67] J. D. Dogger, B. Roossien, and F. D. J. Nieuwenhout, "Characterization of Li-ion batteries for intelligent management of distributed grid connected storage," IEEE Trans. Energy Convers., vol. 26, no. 1, pp. 256– 263, 2011.
[CrossRef] [Web of Science Times Cited 83] [SCOPUS Times Cited 102]


[68] E. Bompard, E. Carpaneto, G. Chicco, and R. Napoli, "Convergence of the backward / forward sweep method for the load-flow analysis of radial distribution systems," Int. J. of Elect. Power & Energy Syst., vol. 22, pp. 521–530, 2000.
[CrossRef] [Web of Science Times Cited 61]


[69] R.-E. Precup, R.-C. David, E. M. Petriu, M.-B. Radac, S. Preitl, J. Fodor, "Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems," Knowledge-Based Systems, vol. 38, no. 9, pp. 74-84, 2013.
[CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 74]


[70] D. Zaharie, "Influence of crossover on the behavior of Differential Evolution Algorithms," Applied Soft Computing, vol. 9, no. 3, pp. 1126-1138, 2009.
[CrossRef] [Web of Science Times Cited 165] [SCOPUS Times Cited 201]


[71] A. W.Mohamed, H. Z. Sabry, M. Khorshid, "An alternative differential evolution algorithm for global optimization," Journal of Advanced Research, vol. 3, no. 2, pp. 149-165, 2012.
[CrossRef] [SCOPUS Times Cited 47]


[72] D. Karaboga B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied Soft Computing, vol. 8, no. 1, pp. 687–697, 2008.
[CrossRef] [Web of Science Times Cited 1682] [SCOPUS Times Cited 2253]




References Weight

Web of Science® Citations for all references: 31,642 TCR
SCOPUS® Citations for all references: 9,996 TCR

Web of Science® Average Citations per reference: 433 ACR
SCOPUS® Average Citations per reference: 137 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 2019-10-12 21:25 in 390 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2019
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.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: