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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  2/2022 - 10
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Impact of Photovoltaic Systems Allocation on Congestion in Distribution Network: Iraq Case Study

BADR, H. M. See more information about BADR, H. M. on SCOPUS See more information about BADR, H. M. on IEEExplore See more information about BADR, H. M. on Web of Science, ALI, R. S. See more information about  ALI, R. S. on SCOPUS See more information about  ALI, R. S. on SCOPUS See more information about ALI, R. S. on Web of Science, MAHMOOD, J. R. See more information about MAHMOOD, J. R. on SCOPUS See more information about MAHMOOD, J. R. on SCOPUS See more information about MAHMOOD, J. R. on Web of Science
 
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Download PDF pdficon (1,568 KB) | Citation | Downloads: 633 | Views: 1,218

Author keywords
distributed power generation, optimization methods, photovoltaic systems, power demand, voltage control

References keywords
distribution(11), optimal(10), generation(10), power(9), photovoltaic(9), algorithm(9), solar(7), location(7), distributed(7), systems(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-05-31
Volume 22, Issue 2, Year 2022, On page(s): 79 - 86
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.02010
Web of Science Accession Number: 000810486800010
SCOPUS ID: 85131723748

Abstract
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As Photovoltaic Distributed Generation (PVDG) becomes increasingly popular in modern power systems, it has raised concerns for system operators, despite its remarkable and valuable opportunities, such as reduction in voltage deviation and active power loss. On another side, random distribution of PVDGs in the distribution network can lead to system security violations and congestion. Optimal allocation of PVDGs is one of the efficient methods to enhance the power systems' efficiency. This paper proposes a new version of the Modified Camel Algorithm (NMCA) based on the L technique to optimize PVDGs. The proposed technique can retain a good solution group for each generation due to the expansion in the search space. In order to verify the validity of the NMCA, it has been tested with IEEE 69- bus network and the Baghdad distribution network built a simulation model for the Baghdad distribution network. The simulation model has been created depending on its obtained load profiles, feeders, voltage, and current settings in addition to available PVDG stations in this grid to determine optimum allocation PVDGs in the network.


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

[1] Z. A. Kamaruzzaman, A. Mohamed, and H. Shareef, "Effect of grid-connected photovoltaic systems on static and dynamic voltage stability with analysis techniques - A review," Przeglad Elektrotechniczny, pp. 134-138, 2015.
[CrossRef] [SCOPUS Times Cited 19]


[2] M. Q. Duong, T. D. Pham, T. T. Nguyen, A. T. Doan, and H. V. Tran, "Determination of optimal location and sizing of solar photovoltaic distribution generation units in radial distribution systems," Energies, vol. 12, no. 1, 2019.
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 121]


[3] T. Wang, Y. Xiang, C. Li, D. Mi, Z. Wang, "An improved analytical methodology for joint distribution in probabilistic load flow," Advances in Electrical and Computer Engineering, vol.20, no.1, pp.49-56, 2020.
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[CrossRef] [Full Text] [SCOPUS Times Cited 7]


[5] S. Visalakshi and S. Baskar, "Covariance matrix adapted evolution strategy-based decentralised congestion management for multilateral transactions," IET Generation, Transmission & Distribution, vol. 4, no. 3, pp. 400-417

[6] W. Phuangpornpitak and K. Bhumkittipich, "Principle optimal placement and sizing of single distributed generation for power loss reduction using particle swarm optimization," Research Journal of Applied Sciences, Engineering and Technology, vol. 7, no. 6, pp. 1211-1216, 2014

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[CrossRef] [SCOPUS Times Cited 23]


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[CrossRef] [SCOPUS Times Cited 17]


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[14] I. Gasparovic and M. Gasparovic, "Determining optimal solar power plant locations based on remote sensing and GIS methods: A case study from Croatia," Remote Sensing, vol. 11, no. 12, 2019.
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[15] M. Mokarram, M. J. Mokarram, M. R. Khosravi, A. Saber, and A. Rahideh, "Determination of the optimal location for constructing solar photovoltaic farms based on multi-criteria decision system and Dempster-Shafer theory," Scientific Reports, vol. 10, no. 1, p. 8200, 2020.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 50]


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[22] A. Wazir and N. Arbab, "Analysis and optimisation of IEEE 33 Bus radial distributed system using optimisation algorithm," 2016

[23] T. D. Pham, T. T. Nguyen, and B. H. Dinh, "Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimisation algorithm," Neural Computing and Applications, vol. 33, no. 9, pp. 4343-4371, 2021.
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[CrossRef]




References Weight

Web of Science® Citations for all references: 1,012 TCR
SCOPUS® Citations for all references: 1,350 TCR

Web of Science® Average Citations per reference: 35 ACR
SCOPUS® Average Citations per reference: 47 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 2024-10-06 13:04 in 109 seconds.




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