A Multi-Objective Puma Optimization Approach for Distributed Generation Planning in Distribution Networks with Mixed Load Models and Plug-in Electric Vehicle Loads

The rapid proliferation of plug-in electric vehicles (PEVs) significantly affects distribution networks (DNs) due to increased charging demand and uncertainty in charging behavior. Stochastic PEV charging can result in excessive power losses, voltage deviations, and reduced voltage stability margins. To mitigate these impacts, this study investigates the optimal integration of renewable distributed generators (DGs) in DNs while explicitly considering the stochastic characteristics of renewable generation and PEV demand. Wind turbine and solar photovoltaic-based DG units are strategically integrated using a realistic 24-hour time-varying mixed load model comprising residential, commercial, and industrial loads. Four PEV charging scenarios are examined: Electric Power Research Institute (EPRI), off-peak, peak, and stochastic charging profiles. A weighted multi-objective function is formulated to simultaneously minimize daily active power loss and voltage deviation while enhancing the voltage stability index. The proposed methodology is validated on IEEE 33-bus and IEEE 69-bus test systems. The Puma Optimizer (PO) algorithm is employed to determine the optimal placement and sizing of renewable DGs. Its performance is compared with Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Simulation results demonstrate that the PO-based approach achieves superior reduction in power losses and improved voltage stability under varying PEV charging conditions.