Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system

Abstract

This article proposes an efficient meta-heuristic approach, namely, oppositional grey wolf optimization (OGWO) algorithm for resolving the optimal operating strategy of economic load dispatch (ELD) problem. The proposed algorithm combines two basic concepts. Firstly, the hunting behavior and social hierarchy of grey wolves are used to search optimal solutions and secondly, oppositional concept is integrated with the grey wolf optimization (GWO) algorithm to accelerate the convergence rate of the conventional GWO algorithm. To show the performance of the proposed algorithm, it is applied on small, medium and large scale test systems for solving ELD problems of 13-unit, 40-unit and 160-unit systems. Comparative studies are carried out to scrutinize the efficiency of the proposed OGWO approach over the conventional GWO and other approaches available in the literature. The simulation results clearly suggest that the proposed OGWO approach is capable of finding better solutions in terms of computational time and fuel cost than the other techniques. Keywords: Economic load dispatch, Evolutionary algorithm, Grey wolf optimization, Oppositional based learning, Power system