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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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  4/2020 - 1
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 HIGHLY CITED PAPER 

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
 
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Download PDF pdficon (1,254 KB) | Citation | Downloads: 1,656 | Views: 2,112

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
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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

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Cited-By SCOPUS

SCOPUS® Times Cited: 6
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Cited-By CrossRef

[1] Optimal power flow solution using opposition based modified Rao’s algorithm, Shaik, Mabhu Jani, Kottala, Padma, Rama Sudha, K, Engineering Research Express, ISSN 2631-8695, Issue 4, Volume 5, 2023.
Digital Object Identifier: 10.1088/2631-8695/acfc15
[CrossRef]

[2] Generalized Model of Economic Dispatch Optimization as an Educational Tool for Management of Energy Systems, PAPAZIS, S. A., BAKOS, G. C., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 21, 2021.
Digital Object Identifier: 10.4316/AECE.2021.02009
[CrossRef] [Full text]

[3] Achieving Sustainability and Cost-Effectiveness in Power Generation: Multi-Objective Dispatch of Solar, Wind, and Hydro Units, Lotfi Akbarabadi, Mohammad, Sirjani, Reza, Sustainability, ISSN 2071-1050, Issue 3, Volume 15, 2023.
Digital Object Identifier: 10.3390/su15032407
[CrossRef]

[4] The Multi-Objective Optimal Scheduling of the Water–Wind–Light Complementary System Based on an Improved Pigeon Flock Algorithm, Wang, Kangping, Ge, Pengjiang, Duan, Naixin, Wang, Jili, Lv, Jinli, Liu, Meng, Wang, Bin, Energies, ISSN 1996-1073, Issue 19, Volume 16, 2023.
Digital Object Identifier: 10.3390/en16196787
[CrossRef]

[5] A hybrid differential evolution for multi-objective optimisation problems, Song, Erping, Li, Hecheng, Connection Science, ISSN 0954-0091, Issue 1, Volume 34, 2022.
Digital Object Identifier: 10.1080/09540091.2021.1984396
[CrossRef]

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