Optimization of economic load dispatch for a microgrid using evolutionary computation

Abstract

Economic load dispatch of a microgrid system is a highly nonlinear and multi-objective problem. The two objectives are minimizing the emission of the thermal generators and minimizing the total operating cost. This microgrid system consists of thermal generators, wind turbines and polymer electrolyte membrane (PEM) fuel cells. Two state-of-the-art multi-objective methods, strength pareto evolutionary algorithm 2 (SPEA2) and non-dominated sorting genetic algorithm (NSGA-II), are adopted to perform the optimization. The results show that SPEA2 has a faster convergence speed and NSGA-II has a better convergence eventually for large number of generations. It is suggested that SPEA2 is recommended if time is the most important concern. However, if the accuracy of the results is top priority, NSGA-II is preferred.