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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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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
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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: 531 | Views: 866

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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Full text preview
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]


[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 87]


[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.
[CrossRef] [Full Text] [Web of Science Times Cited 1]


[4] X. Wu, X. Shen, J. Zhang, Y. Zhang, "A wind energy prediction scheme combining cauchy variation and reverse learning strategy," Advances in Electrical and Computer Engineering, vol. 21, no. 4, pp. 3-10, 2021.
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[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

[7] T. J. Sahib, M. R. Ab Ghani, Z. Jano, and I. H. Mohamed, "Optimum allocation of distributed generation using PSO: IEEE test case studies evaluation," International Journal of Applied Engineering Research, vol. 12, pp. 2900-2906, 2017

[8] H. Sadeghian, M. H. Athari, and Z. Wang, "Optimized solar photovoltaic generation in a real local distribution network,". 2017,
[CrossRef]


[9] N. M. Saad et al., "Impacts of photovoltaic distributed generation location and size on distribution power system network," Int. J. Power Electron. Drive Syst. International Journal of Power Electronics and Drive Systems, vol. 9, no. 2, pp. 905-913, 2018.
[CrossRef]


[10] U. Raut and S. Mishra, "Enhanced Sine-Cosine algorithm for optimal planning of distribution network by incorporating network reconfiguration and distributed generation," Arabian Journal for Science and Engineering, vol. 46, 08/12, 2020

[11] A. Ymeri and S. Mujovic, "Optimal location and sizing of photovoltaic systems in order to reduce power losses and voltage drops in the distribution grid," International Review of Electrical Engineering (IREE), vol. 12, p. 498, 12/31, 2017.
[CrossRef]


[12] S. O. Fadlallah and D. E. Benhadji Serradj, "Determination of the optimal solar photovoltaic system for Sudan," Solar Energy, vol. 208, pp. 800-813, 2020

[13] B. H. Dinh, T. T. Nguyen, T. T. Nguyen, and T. D. Pham, "Optimal location and size of photovoltaic systems in high voltage transmission power networks," Ain Shams Engineering Journal, vol. 12, no. 3, pp. 2839-2858

[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.
[CrossRef] [Web of Science Times Cited 34]


[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 33]


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[17] R. S. Ali, F. M. Alnahwi, and A. S. Abdullah, "A modified camel travelling behaviour algorithm for engineering applications," Australian Journal of Electrical and Electronics Engineering, vol. 16, no. 3, pp. 176-186, 2019.
[CrossRef]


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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: 945 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 33 ACR
SCOPUS® Average Citations per reference: 0

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 2024-03-24 14:01 in 92 seconds.




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