A new hybrid method for multi-objective economic power/emission dispatch in wind energy based power system

The meta-heuristic methods are very efficient for the search of global solution for complex problems better than deterministic methods. However meta-heuristic methods, have the disadvantage of convergence time, which is due to the high number of agents and iterations. To solve this problem we have combined two meta-heuristic methods, the GA and the PSO with a classical method SQP. The proposed hybrid method performs the first step with a global search using the method of PSO on the best 10 % among the members of the population of the genetic algorithm. On the other hand, other operators of GA are applied to the remaining 90 % of the population. In the second step, a conventional method (SQP) is applied, to give a final value with high precision and accuracy. Since the work is done step by step around the approximate solution, the proposed approach is applied for solving a multi-objective problem in order to achieve the set of optimal solutions and best compromising solution between the cost function and emission function. In this work, this technique is used on a power network, in which wind energy was injected. To validate the robustness of the proposed approach, the proposed algorithm is tested on the norme IEEE 30-bus and on the Algerian 59-bus electrical network. Comparison of the results with recent global optimization methods show the superiority of the proposed approach and confirm its potential for solving practical optimal power flow in terms of solution quality and convergence characteristics. © 2013, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.