Hybrid Genetic Algorithm and Particle Swarm Optimization Algorithm for Optimal Power Flow in Power System

Abstract

In power systems, the Optimal Power Flow (OPF) problem is considered as the majority extensively nonlinear optimization problems. This work presents a new hybrid optimization approach that integrates the advantages of the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) approach to solve the OPF problem. The developed hybrid algorithm is considered to achieve environmental, technical, and economic, advantages. The proposed technique is used to single and multi-objective optimization problems using diverse objective models like minimization of generation cost, reduction of emission, minimization of transmission power loss, maximization of voltage profile, and maximization of voltage stability. To show the ability of developed hybrid optimization method, single-objective cases are used and verified on three standard bus systems. The developed PSO-GA technique attains considerably the efficiency, and the robustness of OPF outcomes for the cases, are represented. The experimentation outcomes show that the developed technique tends to better levels of techno-economic-environmental advantages evaluated. Additionally, the sensitivity analysis study verifies that the proposed hybrid algorithm constructs robust outcomes over parameter variations.