Constrained multi-objective population extremal optimization based economic-emission dispatch incorporating renewable energy resources

Economic emission dispatch (EED) problem of an electrical power system can be considered as one of the most popular constrained multi-objective optimization problems to minimize the cost and emission simultaneously subjecting to various constraints. Although many approaches have been presented to deal with this problem, it is still a challenge issue especially when more and more renewable energy sources such as wind power and solar power are incorporated into the system due to their intermittence and uncertainty. To improve the EED performance with those renewable power generations, a constrained multi-objective population extremal optimization algorithm called CMOPEO-EED is proposed by utilizing an advanced constraint handling technique, i.e., the superiority of feasible solution approach. To demonstrate the effectiveness of the proposed method, three versions of a modified IEEE 30-bus and 6-generator system with renewable power generations are considered as the test systems. The comprehensive experimental results and analyses fully validate that the proposed CMOPEO-EED method in this paper outperforms these recently reported single-objective success history based adaptive differential evolutionary algorithm (SHADE)-based EED method and constrained non-dominated sorting genetic algorithm-based EED (CNSGAII-EED) method in terms of cost and emission indices.