Comprehensive Framework for Optimal Distributed Generation Allocation in Unbalanced Distribution Networks using Genetic-Algorithm

The increasing integration of renewable energy sources and the inherent uncertainties in load demand have intensified unbalances in modern distribution systems. These unbalances lead to increased power losses, voltage deviations, and reduced system reliability. This paper presents a comprehensive framework for the optimal allocation of distributed generators (DGs) in unbalanced distribution networks using Genetic Algorithms (GAs). The framework based on a multi-objective function aims to minimize power losses, enhance voltage profiles, reduce DG penetration levels, and minimize the number of DG units installed. The Newton-Raphson method, adapted for unbalanced systems, is employed for load flow analysis, ensuring accurate modeling of unbalanced conditions. Realistic load profiles are generated using the standardlastprofile package in R, capturing the variability and uncertainty of real-world demand. The proposed methodology is validated on the IEEE 37-bus test system. The results demonstrate a significant reduction in active power losses by approximately 71%, improved voltage profiles across all phases, and enhanced overall system performance. The 24-hour operational analysis confirms the effectiveness of the DG placement under dynamic load conditions. The findings highlight the potential of GAs in addressing the challenges of unbalanced distribution systems and contribute to the efficient integration of renewable energy sources.