Enhancing Optimal Power Flow Using Grey Wolf Intelligence-Integrated Particle Swarm Optimization

The accurate and efficient resolution of the power flow problem is crucial for planning, operating, and controlling power systems. While conventional iterative methods are commonly used, they often experience incomplete or slow convergence. This has prompted an increasing interest in meta-heuristic optimization techniques. This study presents a novel Hybrid Particle Swarm Optimization (HPSO) algorithm that combines the exploration-exploitation balance of Particle Swarm Optimization (PSO) with the adaptive hunting strategy of the Grey Wolf Optimization (GWO) algorithm. By incorporating the sensitive parameter control and convergence reliability of GWO into PSO, the proposed HPSO enhances both search diversity and solution accuracy. The algorithm is validated using the IEEE 30-bus test system (IEEE 30-BTS) in MATLAB R2020b. Its performance is evaluated based on power losses, voltage deviations (VD), and computation time. Furthermore, a multi-objective Pareto-optimal analysis shows that HPSO consistently achieves better trade-offs between minimizing power losses and maintaining voltage stability compared to traditional PSO and GWO methods. The simulation results illustrate the robustness and efficiency of the proposed HPSO, highlighting its potential as a valuable tool for optimal power flow (OPF) applications in modern power systems.