Open Access

REVIEW

A Comprehensive Review of Sizing and Allocation of Distributed Power Generation: Optimization Techniques, Global Insights, and Smart Grid Implications

Abdullrahman A. Al-Shamma’a¹, Hassan M. Hussein Farh^1,*, Ridwan Taiwo², Al-Wesabi Ibrahim³, Abdulrhman Alshaabani¹, Saad Mekhilef ⁴, Mohamed A. Mohamed^5,6,*

1 Electrical Engineering Department, Imam Mohammad Ibn Saud Islamic University (IMISU), Riyadh, 11564, Saudi Arabia
2 Faculty of Construction and Environment, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, 310028, China
3 College of Electrical and Information Engineering, Hunan University, Changsha, 410083, China
4 School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
5 Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61519, Egypt
6 College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China

* Corresponding Authors: Hassan M. Hussein Farh. Email: ; Mohamed A. Mohamed. Email:

Computer Modeling in Engineering & Sciences 2025, 145(2), 1303-1347. https://doi.org/10.32604/cmes.2025.071302

Received 04 August 2025; Accepted 14 October 2025; Issue published 26 November 2025

Abstract

Optimal sizing and allocation of distributed generators (DGs) have become essential computational challenges in improving the performance, efficiency, and reliability of electrical distribution networks. Despite extensive research, existing approaches often face algorithmic limitations such as slow convergence, premature stagnation in local minima, or suboptimal accuracy in determining optimal DG placement and capacity. This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement. It integrates both quantitative and qualitative analyses of the scholarly landscape, mapping influential research domains, co-authorship structures, the articles’ citation networks, keyword clusters, and international collaboration patterns. Moreover, the study classifies and evaluates the most prominent objective functions, key computational models and optimization algorithms, DG technologies, and strategic approaches employed in the field. The findings reveal that advanced algorithmic frameworks substantially enhance network stability, minimize real power losses, and improve voltage profiles under various operational constraints. This review serves as a foundational resource for researchers and practitioners, highlighting emerging algorithmic trends, modelling innovations, and data-driven methodologies that can guide future development of intelligent, optimization-based DG integration strategies in smart distribution systems.

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Keywords

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Supplementary Material

Supplementary Material File

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