@Article{cmes.2025.071302,
AUTHOR = {Abdullrahman A. Al-Shamma’a, Hassan M. Hussein Farh, Ridwan Taiwo, Al-Wesabi Ibrahim, Abdulrhman Alshaabani, Saad Mekhilef , Mohamed A. Mohamed},
TITLE = {A Comprehensive Review of Sizing and Allocation of Distributed Power Generation: Optimization Techniques, Global Insights, and Smart Grid Implications},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {2},
PAGES = {1303--1347},
URL = {http://www.techscience.com/CMES/v145n2/64567},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2025.071302}
}