A multi-time scale coordinated control and scheduling strategy of EVs considering guidance impacts in multi-areas with uncertain RESs

With the increased electrification of transportation sector, the electric vehicles (EVs) are deemed to be key players in energy scheduling act to realize more economical operation of distribution networks. EVs have the function of energy space-time transfer, and energy-space coupling effects need to be considered in scheduling. In this paper, EVs' spatial characteristics from multi-areas and characteristics of transferable charging power are both taken into account, then a multi-time scale coordinated control and scheduling strategy is proposed to achieve optimal schedules in both day-ahead (DA) and real-time (RT) periods. First, in DA periods, EVs are modeled as shiftable and location-flexible loads to participate in multi-areas' scheduling task managed by a unified distribution system operator (DSO). To attract more spatially distributed EVs to different charging stations and avoid charging congestion, a price-based transfer model (PBTM) is established to realize EVs charging guidance in different areas while integrated into the DA stochastic scheduling. Next, in RT periods, EVs are modeled as controllable loads to compensate RT power errors caused by uncertain renewable energy sources (RESs) and inaccuracies associated with DA prediction. Both DA and RT scheduling are coordinated with a RT control strategy for EVs, in which a state space model (SSM) is constructed to calculate charging power and then form 1-min control signals to realize the tracking of multi-time scale schedule. Simulation results demonstrate that the proposed coordinated control and scheduling strategy can guide more EVs to be grid-connected, promote multi-time scale economic scheduling, and benefit the EV users meanwhile.