Optimal DG Capacity for IEEE 33 and 66 Bus RDS

Optimal Distributed Generation (DG) Capacity in IEEE 33 and 69 Bus Radial Distribution System (RDS) Using Modern Optimization Methods

The recently developed optimization algorithms have shown strong potential for solving complex and nonlinear optimization problems. In this study, best-performing methods among the Sine-Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Dragonfly Algorithm (DA), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), and Rao Algorithm (RAO) will be implemented and compared. They search the solution space to minimize objectives such as real power losses and voltage deviations while respecting constraints like bus voltages, line limits, and DG capacity. Modern algorithms; both metaheuristic and non-metaheuristic (e.g., WOA, GWO, PSO, ACO, DA, ALO, SSA, EOO, Starfish, Rao, JAYA, TLBO) help handle the nonlinearity and multiple local minima typical of DG planning problems. By exploring many candidate solutions efficiently, these methods can balance trade-offs between loss reduction, voltage improvement, cost, and reliability. They also allow easy inclusion of practical factors such as load variation, DG intermittency, and power factor settings, making the optimization results more realistic and useful for planning. However, the use of these algorithms in electrical distribution systems is still limited, and detailed comparative studies are lacking. Therefore, this research aims to apply advanced optimization techniques, verify the results through load flow analysis, and test practical constraints using standard IEEE 33-bus and 69-bus Radial Distribution System test cases with Type I and Type III DGs.