Optimal Reactive Power Flow in RES Based System Using Hybrid Grey Wolf with PSO

The increasing penetration of renewable energy sources (RES) such as solar and wind introduces significant uncertainty and nonlinearity in power systems, making optimal reactive power flow (ORPF) a challenging optimization problem. ORPF aims to minimize active power losses while maintaining voltage stability and satisfying system constraints. Conventional optimization and standalone metaheuristic techniques often suffer from premature convergence and reduced performance under high renewable penetration levels. To overcome these limitations, this paper proposes a hybrid Grey Wolf Optimizer–Particle Swarm Optimization (GWO–PSO) algorithm for efficient ORPF in RES-based power systems.The proposed hybrid approach combines the strong global exploration capability of the Grey Wolf Optimizer with the fast local exploitation characteristics of Particle Swarm Optimization through a sequential embedded strategy. PSO is adaptively activated in the later stages of the optimization process to refine solutions obtained by GWO, ensuring improved convergence and solution quality. The effectiveness of the proposed method is validated on IEEE 33-bus and 69-bus distribution systems with varying renewable penetration levels and loading conditions. Simulation results demonstrate significant reductions in active power losses and notable improvements in voltage profiles compared to PSO, Genetic Algorithm, Simulated Annealing, and standalone GWO methods. The hybrid GWO–PSO also exhibits faster convergence and robust performance under high renewable penetration, making it suitable for practical implementation in energy management systems for modern RES-integrated power networks.