A Comparative Analysis of Optimization Techniques for Distributed Generation (DG) in a 33 kV Radial Distribution System

Abstract

The incorporation of renewable distributed generation (DG) stands as a pivotal component in implementing strategies directed toward mitigating power losses within electrical networks. With the continual rise in electricity demand and the accompanying maintenance expenditures, the importance of DG has become increasingly apparent. The effectiveness of reducing power losses, improving voltage profiles, and lowering system expenses was evaluated in this study by comparing the techniques of Artificial Bee Colony (ABC), differential evolution (DE), and particle swarm optimization (PSO). Among these methods, ABC emerged as the most effective, showing substantial potential in enhancing DG performance within a 33 kV distribution system, resulting in a 98.97% reduction in system costs. The findings of this analysis provide appreciated insights for guiding future research efforts in optimizing electrical systems.