A hybrid crossover multi-agent multi-objective evolutionary algorithm and its application in microgrid operation optimization

Abstract

The power generation industry needs to adopt renewable energy so as to reduce the utilization of fossil energy and pollution emission. In renewable energy power generation, microgrid operation optimization needs to consider multiple objectives such as economy and environmental protection, which is a multi-objective optimization problem. Aiming at the multi-objective optimization problem, based on the Pareto optimal concept, a hybrid crossover multi-agent multi-objective evolutionary algorithm is proposed and applied to the multi-objective optimization problem of microgrid systems, in which the economical cost and environmental protection are considered. The simulation results under three operating conditions show that compared with the classical NSGA-Ⅱ algorithm, the proposed algorithm can obtain higher quality Pareto optimal solution in a shorter time. The efficiency of the proposed algorithm in this problem is higher than that of the classical NSGA-Ⅱ algorithm. It can provide a higher quality solution for the optimal operation of a microgrid.