Abstract
turbines, into power networks significantly increases the complexity of the optimal power flow (OPF) problem due to the intermittent and uncertain nature of these resources. Traditional optimization methods often face challenges such as convergence to local optima and slow computational speed under these conditions. In this paper, the Particle Swarm Optimization (PSO) algorithm is employed to solve the multi-objective OPF problem in a hybrid energy system consisting of thermal generators, solar PV panels, and wind turbines. The optimization objectives include: minimizing the fuel cost of thermal generators, reducing active power losses in transmission lines, and improving the voltage profile by minimizing voltage deviations from the nominal value (1 p.u.). The proposed model is implemented on a modified IEEE 30-bus test system, where the generators at buses 5 and 11 are replaced with solar PV panels, and the generators at buses 8 and 13 are replaced with wind turbines. Simulation results demonstrate that the proposed PSO-based approach can effectively provide a balanced solution, reducing the fuel cost to $635.2, active power losses to 2.1 MW, and voltage deviation to 0.075 p.u. Furthermore, a comparative analysis with other metaheuristic algorithms, such as Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO), confirms the superiority of PSO in terms of convergence speed and quality of the final solution.