Multiobjective evolutionary algorithms for electric power dispatch problem

The potential of Multiobjective Evolutionary Algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem is comprehensively presented and discussed. In this work, the Non-dominated Sorting Genetic Algorithm (NSGA), Niched Pareto Genetic Algorithm (NPGA), and Strength Pareto Evolutionary Algorithm (SPEA) have been developed and successfully applied to the Environmental/Economic electric power Dispatch (EED) problem. These multiobjective evolutionary algorithms have been individually examined and applied to a standard test system. A hierarchical clustering algorithm is imposed to provide the power system operator with a representative and manageable Pareto set. Moreover, a fuzzy set theory based approach is developed to extract one of the Pareto-optimal solutions as the best compromise solution. Several optimization runs have been carried out on different cases of problem complexity. The results of the MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the performance of MOEA have been assessed and evaluated using different measures of diversity, distribution, and quality of the obtained non-dominated solutions.