Solution of the combined environmental economic dispatch problem using multi-objective cat swarm optimization

Abstract

This paper deals with the realistic combined environmental economic dispatch (CEED) problem, considering the running fuel cost along with emission as objectives taking power balance and operating limits of the generators as constraints. A new dynamic algorithm for multi-objective optimization, namely the modified multi-objective cat swarm optimization (MOCSO) has been formulated and successfully implemented to address the nonlinear multi-modal CEED problem. A new constraint handling technique is incorporated in the algorithm to satisfy the nonlinear constraints. Further, to select a single solution from the set of Pareto solutions, best tradeoff solution is obtained through fuzzy inference. A thorough investigation has been carried out on two standard test cases namely, the IEEE 14 bus and IEEE 30 bus test systems. The effectiveness of the algorithm is compared with other competitive evolutionary algorithms, such as the non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm2 (SPEA2), multi-objective particle swarm optimization (MOPSO) and multi-objective differential evolution (MODE). Performance estimation is done on the basis of computational time, Pareto fronts and non-parametric performance measures. The statistical analysis is also performed, to show the superiority of the proposed modified MOCSO algorithm. Examination of the performance measures shows that the modified MOCSO provides good Pareto solutions while maintaining diversity. It provides a wide scope to trade a balance between running cost and environmental emission of thermal power plants under different types of challenging constraints.