Combined Economic and Emission Dispatch Using Evolutionary Algorithms-a Case Study

An efficient and optimum economic operation of electric power generation systems has always occupied an important position in the electric power industry. This involves allocation of the total load between the available generating units in such a way that the total cost of operation is kept at a minimum. In recent years this problem has taken a suitable twist as the public has become increasingly concerned with environmental matters, so that economic dispatch now includes the dispatch of systems to minimize pollutants, as well as to achieve minimum cost. This paper proposes a lambda based approach for solving the Combined Economic and Emission Dispatch (CEED) problem using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methodologies considering the power limits of the generator. The purpose of Combined Economic and Emission Dispatch (CEED) is to minimize both the operating fuel cost and emission level simultaneously while satisfying load demand and operational constraints. This multi-objective CEED problem is converted into a single objective function using a modified price penalty factor approach.