Multi-Objective Optimal Placement of Distributed Generation in IEEE 33-Bus System Using Grey Wolf Optimization.

This study presents a Grey Wolf Optimization (GWO)-based approach for the optimal placement and sizing of distributed generation (DG) in radial distribution systems to improve power system performance. The increasing penetration of renewable energy sources in modern power networks has created challenges associated with voltage instability, high power losses, and reduced reliability, thereby necessitating efficient optimization techniques for DG planning. The IEEE 33-bus radial distribution system was adopted as the benchmark network, while MATLAB R2023a with the MATPOWER toolbox was used for modelling and simulation. Load-flow analysis was performed using the Newton–Raphson and Backward/Forward Sweep methods to evaluate system performance before and after DG integration. A photovoltaic (PV)-based DG model with controllable active power injection was incorporated into the network. The optimization problem was formulated as a constrained multi-objective function aimed at minimizing active power losses and voltage deviation while maximizing voltage stability. Operational constraints, including bus voltage limits, DG penetration limits, branch thermal capacities, and power balance equations, were incorporated into the model. GWO was employed to determine the optimal DG locations and capacities, while Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) were implemented for comparative analysis using identical parameters of 30 search agents and 50 iterations. Simulation results showed that GWO outperformed PSO and ACO in all evaluated indices. The minimum bus voltage improved from 0.9131 p.u. to 0.9784 p.u., while total active power loss decreased from 202.67 kW to 92.35 kW, representing a 54.43% reduction. Furthermore, GWO converged faster within 18 iterations compared to 29 and 42 iterations for PSO and ACO, respectively. Reliability indices SAIFI and SAIDI also improved significantly after DG integration, confirming the effectiveness of GWO for optimal DG planning in radial distribution networks.