A Real-Time Schedule Optimization of Massive Electric Vehicles and Energy Storage System Based on Grey Wolf Optimizer

摘要

A real-time coordinated schedule model for massive electric vehicles (EVs) and energy storage system (ESS) is proposed to reduce real-time scheduling difficulties considering the accurate constrains of each EV and the safe operation of the distribution network. Newly connected EVs are clustered into different clusters by the charging completion time which is set by their owners. The optimization process includes two steps. In the first step, the Grey Wolf Optimizer (GWO) is adapted to calculate the charging/discharging strategy of EV clusters and ESS, in the second step, allocation algorithm based on energy buffer factor consensus is proposed to make detail strategy for each EV in the cluster considering accurate constrains. The simulation results show that, the model proposed has a great performance on massive EVs and ESS real time scheduling optimization and the validity and practicability of GWO and the allocation algorithm is verified when compared to other algorithms.