Enhancing Power Flow Control: A Comparative Study of Genetic Algorithm and Particle Swarm Optimization

Abstract

This research investigates the enhancement of power quality in power systems through the application of Soft Computing Techniques, with a particular focus on addressing the Optimal Power Flow (OPF) problem, known for its nonlinear optimization complexity within power systems. A novel hybrid optimization approach, combining the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) technique, is introduced to tackle this challenge. This hybridization is designed to capitalize on the strengths of both methods, with the overarching goal of achieving environmental, technical, and economic benefits. The study explores both single and multi-objective optimization scenarios, encompassing diverse objectives such as minimizing generation costs, reducing emissions, minimizing transmission power losses, and maximizing voltage stability and profiles. Experimental validation of the developed PSO-GA hybrid algorithm is conducted on three standard bus systems, demonstrating significant enhancements in the efficiency and reliability of OPF outcomes. Notably, the hybrid technique showcases superior levels of techno-economic-environmental advantages compared to conventional methods. Additionally, sensitivity analysis confirms the robustness of the proposed algorithm against parameter variations. In conclusion, this research underscores the potential of integrating Soft Computing Techniques to effectively optimize power systems and elevate power quality.