Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 1.221
JCR 5-Year IF: 0.961
SCOPUS CiteScore: 2.5
Issues per year: 4
Current issue: Aug 2021
Next issue: Nov 2021
Avg review time: 88 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

1,776,471 unique visits
596,896 downloads
Since November 1, 2009



Robots online now
bingbot


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 21 (2021)
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
 Volume 20 (2020)
 
     »   Issue 4 / 2020
 
     »   Issue 3 / 2020
 
     »   Issue 2 / 2020
 
     »   Issue 1 / 2020
 
 
 Volume 19 (2019)
 
     »   Issue 4 / 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
 
 
  View all issues  








LATEST NEWS

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

2021-Apr-15
Release of the v3 version of AECE Journal website. We moved to a new server and implemented the latest cryptographic protocols to assure better compatibility with the most recent browsers. Our website accepts now only TLS 1.2 and TLS 1.3 secure connections.

2020-Jun-29
Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

2020-Jun-11
Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

Read More »


    
 

  4/2020 - 1
View TOC | « Previous Article | Next Article »

Multi-objective Environmental-economic Load Dispatch Considering Generator Constraints and Wind Power Using Improved Multi-objective Particle Swarm Optimization

YALCINOZ, T. See more information about YALCINOZ, T. on SCOPUS See more information about YALCINOZ, T. on IEEExplore See more information about YALCINOZ, T. on Web of Science, RUDION, K. See more information about RUDION, K. on SCOPUS See more information about RUDION, K. on SCOPUS See more information about RUDION, K. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
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 (1,254 KB) | Citation | Downloads: 849 | Views: 895

Author keywords
optimization, particle swarm optimization, power generation dispatch, power system economics, wind energy

References keywords
power(29), dispatch(29), economic(26), optimization(21), swarm(20), algorithm(13), energy(11), objective(9), multiobjective(8), multi(8)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-11-30
Volume 20, Issue 4, Year 2020, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.04001
Web of Science Accession Number: 000594393400001
SCOPUS ID: 85098149157

Abstract
Quick view
Full text preview
One of the vital optimization issues in energy systems is the problem of economic load dispatch (ED). On the other hand, solar, wind, and other renewable energies are important energy sources for reducing hazardous emissions. This paper suggests an improved multi-objective particle swarm optimization algorithm (IMOPSO) that uses a functional inertial weight and a functional constriction factor to solve the multi-objective environmental-economic load dispatch (MEED) problem. A mutation strategy is used in IMOPSO, and a mutation operator, which is implemented for each particle in the swarm, is used to find optimum Pareto fronts. In this paper, the proposed IMOPSO is applied to the MEED problem under consideration of emission pollution, wind energy, prohibited operating zone, ramp limits, valve point effects, and transmission losses. The proposed technique is tested on the IEEE 30-bus, the IEEE 118-bus test system, and the modified IEEE 118-bus test system with emission coefficients, ramp rate limits, wind power, and prohibited operating zone. The IMOPSOs are compared with the results of various multi-objective algorithms to solve the MEED problem. The simulation results indicate that the IMOPSO produces better results than the compared multi-objective optimization algorithms for various test systems.


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

[1] A. Farag, S. Al-Baiyat, T.C. Cheng, "Economic load dispatch multiobjective optimization procedures using linear programming techniques," IEEE Trans. Power Syst., vol.10, pp. 731-738, 1995.
[CrossRef] [Web of Science Times Cited 274] [SCOPUS Times Cited 382]


[2] A. J. Wood, B. F. Wollenberg, G. B. Sheble, Power Generation, Operation and Control, New Jersey, Wiley-IEEE, 3rd ed., 2013

[3] W. M. Lin, S. J. Chen, "Bid-based dynamic economic dispatch with an efficient interior point algorithm," Int J of Electrical Power & Energy Syst., vol. 24, pp. 51-57, 2002.
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 60]


[4] L. G. Papageorgiou, E. S. Fraga, "A mixed integer quadratic programming formulation for the economic dispatch of generators with prohibited operating zones," Electr. Power Syst. Res., vol. 77, pp. 1292-1296, 2007.
[CrossRef] [Web of Science Times Cited 107] [SCOPUS Times Cited 131]


[5] B. Mohammadi-Ivatloo, A. Rabiee and A. Soroudi, "Nonconvex dynamic economic power dispatch problems solution using hybrid immune-genetic algorithm," IEEE Systems Journal, vol. 7, no. 4, pp. 777-785, Dec. 2013,
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 63]


[6] Y. Qu, Y. S. Zhu, Y. C. Jiao, et al, "A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems," Swarm and Evolutionary Comp., vol. 38, pp. 1-11, 2018.
[CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 113]


[7] N. Noman, H. Iba, "Differential evolution for economic load dispatch problems," Elect. Power Syst. Res., vol. 78, pp. 1322-1331, 2008.
[CrossRef] [Web of Science Times Cited 300] [SCOPUS Times Cited 379]


[8] C. Chelladurai, A. A. Victoire, "Crisscross optimization with comprehensive vertical crossover to solve combined economic emission dispatch," Advances in Electrical and Computer Engineering, vol.18, no.3, pp.131-140, 2018,
[CrossRef] [Full Text]


[9] A. Bhattacharya, P. K. Chattopadhyay, "Biogeography-based optimization for different economic load dispatch problems," IEEE Trans. Power Syst., vol. 25, pp. 1064-1077, 2010.
[CrossRef] [Web of Science Times Cited 285] [SCOPUS Times Cited 382]


[10] T. Yalcinoz, K. Rudion, "Economic load dispatch using an improved particle swarm optimization based on functional constriction factor and functional inertia weight," The 19th IEEE Int. Conf. on Environment and Electrical Engineering, Genoa, Italy, 11-14 June 2019.
[CrossRef] [SCOPUS Times Cited 4]


[11] G. Abbas, J. Gu, U. Farooq, et al, "Solution of an economic dispatch problem through particle swarm optimization: A detailed survey - Part I," IEEE Access, vol. 5, pp. 15105-15141, 2017.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 58]


[12] V. K. Jadoun, N. Gupta, K. R. Niazi, A. Swarnkar, "Modulated particle swarm optimization for economic emission dispatch," Int J Electr Power Energy Syst., vol. 73, pp. 80-88, 2015.
[CrossRef] [Web of Science Times Cited 59] [SCOPUS Times Cited 70]


[13] K. K. Mandal, S. Mandal, B. Bhattacharya, N. Chakraborty, "Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique," Appl. Soft Comput., vol. 28, pp. 188-195, 2015.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 49]


[14] A. Y. Abdelaziz, E. S. Ali, S. M. Abd Elazim, "Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems," Energy, vol. 101, 506-518, 2016.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 110]


[15] D. C. Secui, "A method based on the ant colony optimization algorithm for dynamic economic dispatch with valve-point effects," Int Trans Electr Energ Syst., vol. 25, pp. 262-287, 2015.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 15]


[16] S. Hemamalini, S. P. Simon, "Dynamic economic dispatch using artificial immune system for units with valve-point effect," Int J Electr Power Energy Syst., vol. 33, pp. 868-874, 2011.
[CrossRef] [Web of Science Times Cited 108] [SCOPUS Times Cited 129]


[17] I. A. Farhat, M. E. El-Hawary, "Dynamic adaptive bacterial foraging algorithm for optimum economic dispatch with valve-point effects and wind power," IET Gener Trans Distrib., vol. 4, pp. 989-999, 2010.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 77]


[18] S. Ma, Y. Wang, Y. Lv, "Multiobjective environment/ economic power dispatch using evolutionary multiobjective optimization," IEEE Access, vol. 6, pp. 13066-13074, 2018.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 12]


[19] D. W. Gong, Y. Zhang, C. L. Qi, "Environmental/economic power dispatch using a hybrid multi-objective optimization algorithm," Int J. Electr Power Energy Syst., vol. 32, pp. 607-614, 2010.
[CrossRef] [Web of Science Times Cited 112] [SCOPUS Times Cited 148]


[20] D. B. Das, C. Patvardhan, "New multi-objective stochastic search technique for economic load dispatch," IEE Proc Gener Trans Distrib., vol. 145, pp. 747-752, 1998.
[CrossRef] [Web of Science Times Cited 131] [SCOPUS Times Cited 172]


[21] M. A. Abido, "A niched Pareto genetic algorithm for multiobjective environmental/ economic dispatch," Int J Electr Power Energy Syst., vol. 25, pp. 97-105, 2003.
[CrossRef] [Web of Science Times Cited 227] [SCOPUS Times Cited 279]


[22] M. A. Abido, "Multiobjective evolutionary algorithms for electric power dispatch problem," IEEE Trans Evol Comput., vol. 10, pp. 315-329, 2006.
[CrossRef] [Web of Science Times Cited 392] [SCOPUS Times Cited 572]


[23] S. Agrawal, B. K. Panigrahi, M. K. Tiwari, "Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch," IEEE Trans Evol Comput., vol. 12, pp. 529-541, 2008.
[CrossRef] [Web of Science Times Cited 215] [SCOPUS Times Cited 280]


[24] M. A. Abido, "A novel multiobjective evolutionary algorithm for environmental/economic power dispatch," Electr Power Syst Res., vol. 65, pp. 71-81, 2003.
[CrossRef] [Web of Science Times Cited 210] [SCOPUS Times Cited 291]


[25] R. H. Bhesdadiya, I. N. Trivedi, P. Jangir, et al, "An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem," Cogent Eng., vol. 3, pp. 1269383, 2016.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 26]


[26] S. Sivasubramani, K. S. Swarup, "Environmental/economic dispatch using multi-objective harmony search algorithm," Electr Power Syst Res., vol. 81, pp. 1778-1785, 2011.
[CrossRef] [Web of Science Times Cited 104] [SCOPUS Times Cited 122]


[27] J. Kennedy, R.C. Eberhart, "Particle swarm optimization," Proceedings of ICNN'95 - International Conference on Neural Networks Perth, WA, Australia, pp. 1942-1948, 1995.
[CrossRef] [Web of Science Times Cited 27165]


[28] M. R. Bonyadi, Z. Michalewicz, "Particle swarm optimization for single objective continuous space problems: a review," Evolutionary Computation, vol. 25, pp. 1-54, 2017.
[CrossRef] [Web of Science Times Cited 226] [SCOPUS Times Cited 265]


[29] M. Clerc, J. Kennedy, "The particle swarm explosion, stability, and convergence in a multidimensional complex space," IEEE Trans Evolutionary Computation," vol. 6, pp.58-73, 2002.
[CrossRef] [Web of Science Times Cited 5418] [SCOPUS Times Cited 6897]


[30] A. Ratnaweera, S. K. Halgamuge, H. C. Watson, "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," IEEE Trans Evolutionary Computation, vol. 8, pp. 240-255, 2004.
[CrossRef] [Web of Science Times Cited 1883] [SCOPUS Times Cited 2441]


[31] C. Sun, J. Zeng, J. Pan, S. Xue, Y. Jin, "A new fitness estimation strategy for particle swarm optimization," Information Sciences," vol. 221, pp. 355-370, 2013.
[CrossRef] [Web of Science Times Cited 74] [SCOPUS Times Cited 83]


[32] A. Nickabadi, M. M. Ebadzadeh, R. Safabakhsh, "A novel particle swarm optimization algorithm with adaptive inertia weight," Applied Soft Comp., vol. 11, pp. 3658-3670, 2011.
[CrossRef] [Web of Science Times Cited 452] [SCOPUS Times Cited 550]


[33] Y. Lu, M. Liang, Z. Ye, L. Cao, "Improved particle swarm optimization algorithm and its application in text feature selection," Applied Soft Comp., vol. 35, pp. 629-636, 2015.
[CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 83]


[34] M. Abido, "Optimal power flow using particle swarm optimization," Int J Electr Power Energy Syst., vol. 24, pp. 563-571, 2002.
[CrossRef] [Web of Science Times Cited 690] [SCOPUS Times Cited 873]


[35] J. G. Vlachogiannis, K. Y. Lee, "A comparative study on particle swarm optimization for optimal steady-state performance of power systems," IEEE Trans. Power Syst., vol. 21, pp. 1718-1727, 2006.
[CrossRef] [Web of Science Times Cited 165] [SCOPUS Times Cited 228]


[36] G. Pranava, P.V. Prasad, "Constriction coefficient particle swarm optimization for economic load dispatch with valve point loading effects," Int Conf Power Energy Control, Sri Rangalatchum Dindigul, India, pp. 350-354, 2013.
[CrossRef] [SCOPUS Times Cited 12]


[37] C. C. A. Coello, G. T. Pulido, M. S. Lechuga, "Handling multiple objectives with particle swarm optimization," IEEE Trans Evol Comput., vol. 8, pp. 256-279, 2004.
[CrossRef] [Web of Science Times Cited 2288] [SCOPUS Times Cited 3008]


[38] C. C. A. Coello, "An introduction to multi-objective particle swarm optimizers," Gaspar-Cunha A, Takahashi R, Schaefer G, Costa L, (eds) Soft Computing in Industrial Applications, Advances in Intelligent and Soft Computing, Springer, Berlin, Heidelberg, pp. 96, 2011.
[CrossRef] [SCOPUS Times Cited 20]


[39] P. K. Tripathi, S. Bandyopadhyay, S. K. Pal, "Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients," Information Sciences, vol. 177, pp. 5033-5049, 2007.
[CrossRef] [Web of Science Times Cited 362] [SCOPUS Times Cited 446]


[40] O. R. Castro, G. M. Fritsche, A. Pozo, "Evaluating selection methods on hyper-heuristic multi-objective particle swarm optimization," J Heuristics, vol. 24, pp. 581-616, 2018.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 4]


[41] C. L. Chen, T. Y. Lee, R. M. Jan, "Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity," Energy Conversion and Management, vol. 47, pp. 3456-3472, 2006.
[CrossRef] [Web of Science Times Cited 83] [SCOPUS Times Cited 112]


[42] S. S. Reddy, "Optimal scheduling of thermal-wind-solar power system with storage," Renewable Energy, vol. 101, pp. 1357-1368, 2017.
[CrossRef] [Web of Science Times Cited 119] [SCOPUS Times Cited 139]


[43] P. Partha, P. N. Biswas, B. Y. Suganthan, et al, "Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power," Energy, vol. 150, pp. 1039-1057, 2018.
[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 97]




References Weight

Web of Science® Citations for all references: 42,062 TCR
SCOPUS® Citations for all references: 19,182 TCR

Web of Science® Average Citations per reference: 956 ACR
SCOPUS® Average Citations per reference: 436 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 2021-11-22 10:32 in 317 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 ©2001-2021
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: