Combined Economic And Emission Dispatch Using Random Drift Particle Swarm Optimization

The efficient and optimum economic operations of electric power generation systems have always occupied an important position in the electric power industry. In recent years, this problem area has taken a new direction as the public has become increasingly concerned with environmental matters. The harmful ecological effects caused by the emission of these gaseous pollutants can be reduced by optimal distribution of load between the plants of a power system. However, this leads to a noticeable increase in system operating cost. Therefore both need to be balanced. In this work, three techniques, i.e., one Conventional Technique- Lambda Iteration Technique and two AI Techniques- Particle Swarm Optimization (PSO) and Random Drift Particle Swarm Optimization (RDPSO) Techniques, are investigated to solve Combined Economic and Emission Dispatch (CEED) problem. This multi-objective CEED problem is converted into a single optimization problem using a Price Penalty factor approach. The investigation is carried out on 15- Generating Unit Test Systems with respect to Total Operating Cost, Total Emission, System Losses. Results show that in solving Combined Economic and Emission Dispatch (CEED) problem, Random Drift Particle Swarm Optimization (RDPSO) Technique is superior to other two techniques in terms of reduced operating cost. Therefore, Random Drift Particle Swarm Optimization (RDPSO) Technique is very much suitable and recommended for On-Line application for solving Combined Economic and Emission Dispatch problem of power system operation