• Various optimization algorithm...

Treffer: Various optimization algorithms for efficient placement and sizing of photovoltaic distributed generations in different networks.

Title:
Various optimization algorithms for efficient placement and sizing of photovoltaic distributed generations in different networks.
Authors:
Diab AAZ; Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, Egypt.; Minia National University, Minia, Egypt., Mahmoud FS; Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, Egypt., Sultan HM; Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, Egypt., El-Sayed AM; Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, Egypt.; Minia National University, Minia, Egypt., Ismeil MA; Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha, Saudi Arabia., Kamel OM; Electrical and Computer Engineering Department, Minia Higher Institute of Engineering and Technology, New El-Minya, Minia, Egypt.
Source:
PloS one [PLoS One] 2025 Apr 02; Vol. 20 (4), pp. e0319422. Date of Electronic Publication: 2025 Apr 02 (Print Publication: 2025).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science
MeSH Terms:
Algorithms* , Electric Power Supplies* , Renewable Energy* , Solar Energy*, Models, Theoretical
References:
IEEE Trans Neural Netw Learn Syst. 2018 Mar;29(3):681-694. (PMID: 28092578)
Sci Rep. 2019 May 9;9(1):7181. (PMID: 31073211)
Environ Sci Pollut Res Int. 2023 Feb;30(9):23714-23735. (PMID: 36327068)
Entry Date(s):
Date Created: 20250402 Date Completed: 20250515 Latest Revision: 20250515
Update Code:
20250519
PubMed Central ID:
PMC11964283
DOI:
10.1371/journal.pone.0319422
PMID:
40173203
Database:
MEDLINE

Weitere Informationen

Recent research has concentrated on emphasizing the significance of incorporating renewable distributed generations (RDGs), like photovoltaic (PV) and wind turbines (WTs), into the distribution system to address issues related to distributed generation (DG) allocation. The key implications of integrating RDGs include the improvement of voltage profiles and the minimization of power losses. Various optimization techniques, namely Salp Swarm Algorithm (SSA), Marine Predictor Algorithm (MPA), Grey Wolf Optimizer (GWO), Improved Grey Wolf Optimizer (IGWO), and Seagull Optimization Algorithm (SOA), have been applied to achieve optimal allocation and sizing of RDGs in radial distributed systems (RDS). The present paper is structured in two phases. In the initial phase, the Loss Sensitivity Factor (LSF) is employed to identify the most suitable nodes for integrating RDGs. In the second phase, within the selected candidate nodes from the first phase, the optimal location and capacity of RDGs are determined. Additionally, a comprehensive comparison of the proposed optimization methods is conducted to select the most effective solutions for the allocation of units of RDGs. The efficacy of the utilized techniques is validated through testing on two distinct networks, namely the IEEE 33 and 69 buses RDS in MATLAB, with attainments compared against other techniques. Moreover, a larger RDS system of 118- bus IEEE system has been considered in order to enhance its power quality indices. Moreover, a real case of study from Egypt of 15 bus has been considered and evaluated with considering the applied techniques. The results show the enhancement of the voltage profile and decreasing the power losses of the tested system with the DG systems with the superiority of the MPA and SSA algorithms.
(Copyright: © 2025 Diab et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

No authors have competing interests.