A Novel Sparrow Search Algorithm for Microgrid Optimal Scheduling with Electric Vehicles

In this paper, a new improved sparrow search algorithm (ISSA) based on the swarm-intelligence technique method is proposed for the optimal scheduling problem of the microgrid (MG) model with electric vehicles (EVs). In order to make a better reasonable scheduling plan for the power generation plan of each power source on the MG, the ISSA is used to optimize the power distribution. Two strategies are used to improve the basic SSA. One is replace the producer update formula with the fractional particle swarm position update formula, and it can enhance the searching ability of the ISSA. The other one is multi-producer following strategy, which can better balance the global exploration and local exploitation of the ISSA. The effectiveness of the ISSA is verified via some well-known benchmark functions. The experimental results demonstrate that the ISSA performs better compared to other algorithms. Finally, the ISSA is successfully applied to a MG model based on two EV charge-discharge control strategies.