Efficient Energy Load Distribution Model using Modified Particle Swarm Optimization Algorithm

Abstract

Reduction of emission and energy conservation plays a major role in the current power system for realizing sustainable socio-economic development. The application prospects and practical significance of economic load dispatch issue in the electric power market is remarkable. The various generating sets must be assigned with load capacity in a reasonable manner for reducing the cost of electric power generation. This problem may be overcome by the proposed modified particle swarm optimization (PSO) algorithm. The practical issue is converted and modelled into its corresponding mathematical counterpart by establishing certain constraints. Further, a novel interdependence strategy along with a modified PSO algorithm is implemented for balancing the local search capability and global optimization. Multiple swarms are introduced in the modified PSO algorithm. Certain standard test functions are executed for specific analysis. Finally, the proposed modified PSO algorithm can optimize the economic load dispatch problem while saving the energy resources to a larger extent. The algorithm evaluation can be performed using real-time examples for verifying the efficiency. When compared to existing schemes like artificial bee colony (ABC), genetic algorithms (GAs), and conventional PSO algorithms, the proposed scheme offers lowest electric power generation cost and overcomes the load dispatch issue according to the simulation results.