Indian Journal of Science and Technology

Abstract

Objectives: To minimize the fuel price of generator while satisfying different constraints. Valve point effects and multiple fuel option are also considered in some cases. Methods: Quasi-Oppositional Grey Wolf Optimizer algorithm is applied here for solving different economic dispatch problems. Grey Wolf Optimizer is a meta-heuristic method, motivated by social behaviour of grey wolves. Quasi-Oppositional learning is implemented in the present work within Grey Wolf Optimizer for improving the quality of solution in minimum time. Quasi-opposite numbers are used within the algorithm in place of normal random numbers for improving the convergence speed. Findings: The proposed technique is applied to six different systems to test the efficiency of the algorithm. Simulation results obtained by this method are compared with those obtained by some well-known optimization methods to show the robustness and superiority of this technique. Simulation results also show that the computational efficiency of Quasi-Oppositional Grey Wolf optimizer is better as compared to several previously developed optimization methods. Improvement: It is found that the convergence speed, success rate, efficiency and solution quality of the proposed algorithm is improved. 

Keywords: Economic Load Dispatch, Oppositional Based Learning, Quasi-oppositional Grey Wolf Optimizer, Valve Point Effects