Electric vehicles (EVs), as carriers of moveable loads, can not only store energy during off-peak electricity consumption but also feed energy back to the grid during peak electricity consumption. However, along with the scale surge of the EV industry, massive EV charging–discharging behaviors present randomness and disorder characteristics, affecting the stable operation as well as the balance of supply and demand for the grid. Thus, this paper proposes a spatiotemporal bilevel charging–discharging scheduling strategy for urban EVs based on functional zoning. First, the EV charging demand, geographic characteristics, and node coupling degree within each functional region are analyzed. A functional zoning method based on reactive voltage sensitivity and modularity coefficients is proposed. Second, a spatiotemporal bilevel charging–discharging optimization scheduling model is established. The upper-level performs rolling prediction of EV charging–discharging time window length, whereas the lower-level provides spatial guidance of EV charging–discharging locations. Specifically, the upper-level utilizes long short term memory to predict EV charging–discharging time, minimizing the voltage deviation. The obtained temporal features are imported into the rolling optimization model, adjusting the EV charging–discharging time window length. Then, the lower-level inputs the optimal results of the upper-level. Additionally, differentiated EV user behavior guidance strategies are established based on the regret theory and the dynamic charging–discharging cost model, achieving spatial optimal scheduling. Finally, a modified binary gravity search algorithm is developed, integrating the binary coding mechanism, adaptive gravitational constant, and dynamic particle update scheme. Case studies are conducted within an IEEE-123 bus system. Numerous experimental results show that the proposed methodology optimizes the spatiotemporal distribution of urban EV charging loads and improves the operation efficiency of the grid. It provides a novel idea for the friendly interaction of vehicle-to-grid as well.
Gao et al. (Thu,) studied this question.