Improved PSO Method for Combined Economic Dispatch Using Hybrid Differential Evolution

Abstract

A new method for solving the total economical dispatch (ED) issue in power system optimisation is presented in this abstract. It is called differential evolution based Better Particle Swarm Optimisation (DEBIPSO). In order to satisfy demand at the lowest possible cost, the ED problem entails scheduling the power generation of several units in an electrical grid. The suggested hybrid method combines Particle Swarm Optimization's (PSO) global search capability with Differential Evolution's (DE) exploration capabilities in a synergistic manner. The hybrid algorithm uses PSO's particle update mechanism along with DE's crossover and mutation operators to combine the best features of both approaches while minimising their drawbacks. As a result, speed of convergence and solution quality are enhanced. The efficiency of the DEBIPSO method in providing close to perfect solutions to the mixed ED issue is assessed using standard test systems. The suggested approach's superiority is further confirmed by comparison with independent the DE and algorithms for PSO as well as traditional approaches. All things considered, this hybridization approach offers a viable way to tackle difficult optimisation problems in the power system management, resulting in increased production efficiency and economy.