A Knowledge based Multi-objective Optimization Strategy for Microgrid Environmental/Economic Scheduling problems

The environmental/economic scheduling for Microgrid (MGEES) is a complex multi-objective optimization problem, which usually is always difficult for the intelligent algorithms to obtain reliable optimization results. In this paper, a typical MGEES mathematical model is established. Then a knowledge-based strategy for multi-objective evolutionary algorithm (MOEA) is proposed. The knowledge is obtained by simplifying the models and a second-mutation approach is introduced to apply the knowledge during optimization process. Finally, the strategy is applied to three MOEAs in three MGEES scenarios. The simulation results show that by introducing the proposed strategy, the efficiency of the algorithm is obviously improved and the specific requirement of the algorithm performance is reduced. (C) 2019 The Authors. Published by Elsevier