A Three-Stage Multitimescale Framework for Online Dispatch in a Microgrid with EVs and Renewable Energy

To accomplish more practical charging dispatch for electric vehicles (EVs) and effectively consider the multiuncertainties of renewable energy and load demand, this article presents a three-stage multitimescale framework for online dispatch of a grid-connected microgrid. In the first stage, a reference scheduling model is proposed to obtain the referral plans with a sampling period of 30 min. Considering these uncertainties, the second stage proposes a real-time operation method based on stochastic model predictive control, with a sampling period of 10 min, to update the referral plans by minimizing the deviation of the real results from the referral results. To sufficiently capture the operations of EV charging, a rule-based energy management model for the EV charging station is designed in the third stage. The controller of the third stage runs every minute and allocates the charging station power to the available EVs while meeting the charging demand and technical constraints. Numerical simulations are used to investigate the effectiveness and superiority of the proposed dispatch framework. The simulation results show that the proposed framework reaches 98% of ideal performance achieved by off-line optimization.