Microgrid system contains a variety of distributed generations. In order to reduce the power generation cost of the microgrid,it is necessary to apply an optimization algorithm to dispatch the microgrid. It is prone to fall into local optimum by traditional optimization algorithms when solving microgrid scheduling,resulting in a decrease in convergence speed. Therefore,based on the sparrow search algorithm (SSA),a reverse mutation sparrow search algorithm (RMSSA) is proposed. Firstly,the reverse learning strategy and adaptive t-distribution variation are used to expand the optimization range of SSA,so as to improve the diversity of the population and the search ability of SSA. Then,a microgrid optimization scheduling model aiming at the lowest comprehensive operating cost is established. Constraints such as constant power balance,charge and discharge rate,ramp rate,are used to solve the optimal scheduling model of the microgrid by using RMSSA. The comparative simulation results show that the proposed algorithm has good global search ability. The algorithm is superior to the original sparrow search algorithm,gray wolf algorithm and bat algorithm in terms of convergence speed,optimization accuracy and stability,and it brings comprehesive benefits to the microgrid.
