Economic Load Dispatch Optimization for Thermal Power Plant Using Particle Swarm Optimization and Lambda Iteration

Abstract

Nigeria’s socioeconomic growth cannot improve effectively if the rate of electricity generation and dispatch continues at its current status which is due to several challenges facing the country’s power industry. This study investigates the optimization of Economic Load Dispatch for six thermal power generators using Particle Swarm Optimization (PSO) and Lambda Iteration methods. The primary objective is to minimize fuel costs while meeting a total power demand of 600MW. The study employs quadratic cost functions obtained from historical datas derived from input-output characteristics of the generators and simulates both methods using MATLAB. Results indicate that PSO achieves superior cost efficiency with a total generation cost of N507,103,545 compared to N507,232,335 using Lambda Iteration. PSO with a 0.0254% improvement in cost over the Lambda iteration method, and saves about N128,790.0, which is minor but could be significant for large-scale dispatches Furthermore, PSO demonstrates faster convergence and greater flexibility, making it a more effective tool for modern power systems. The findings underscore the potential of advanced optimization techniques like PSO in enhancing operational efficiency and cost-effectiveness in power generation.