## 1 Introduction
In terms of PV and storage sources, the investment and initial installation costs are high and the means to reduce these costs are to extend the service life and improve the energy conversion rate. However, in operation, the energy costs for recharging EVs are minimized by the use of renewable PV energy. Regarding the public grid, taking into account a power limit reduces the negative impact on the stability of the system. EV charging stations empowered by PV-based microgrid have been supported by government departments and achieved social acceptance [21]. Considering the EV charging station modelling proposed in [PERSON], [PERSON] et al. [22; 23], this paper presents an EV charging station model empowered by a direct current (DC) microgrid. The DC microgrid aggregates EVs, a storage system, a public grid connection, and a PV source, aiming at power balancing of the system [24], and bringing various interests [PERSON], [PERSON] et al. [25], such as the peak power of the public grid is lowered, the profits of the charging station are increased, and the EV drivers' charging costs are reduced. Due to the intermittency of the PV power, the capacity limitation of the storage, and the power limitation of the public grid, the power of an EV charging station can be reduced. In addition, this power limitation of an EV charging station is variable in different situations, according to the weather and the charging time slots. Drivers without private chargers must go to public charging stations. EV charging stations mainly includes three types of charging systems: single cable with single charger, multiple cables integrated in single charger, multiple cables with multiple chargers. The second and the third are considered suitable for installation in residential areas or near workplaces taking investment costs into consideration [26]. Intelligently controlled charging can reduce manual maintenance, and more importantly, can meet the drivers' needs and overcome the intermittency of the PV power. The EV charging station requires precise management taking drivers' needs and power limitations into consideration. It should be noted that in real life, charging modes chosen by drivers are impossible to predict. The choices of the same drivers are not necessarily the same at different times and in different conditions. Meanwhile, the arrival time and its initial state of charge (\(SOC_{EV}\)) are random. In the EV charging process, the driver has the possibility to leave at any time. When the power of the station is insufficient, the shedding operation should be considered. The restoration operation should be considered if there are EVs on waiting because of EV shedding operation. A spatial and temporal model of EV charging demand for a rapid charging station is developed in [27], and the charging demand is forecasted by the queueing theory with the arrival rate of discharged vehicles. The paper [28] proposes a mathematical model for the spatial-temporal charging demand for EVs, which helps to obtain the charging spatial-temporal parameters: charging mode, charging location and charging time based on the input parameters. EV parameters include the number of EVs, EV charging power, and battery capacity. However, load shedding and load restoration are not considered in this model. The authors of [29; 30] present a load shedding/restoration real-time optimization for DC microgrid building-integrated, which solves the load shedding and load restoration when the available microgrid power is less than the load demanded power and provides a reference for the shedding operation and restoration operation of the EV charging station. However, designing a complete power management strategy for an EV charging station remains an open issue. The motivation for the proposed power management strategy comes from the fact that charging station stability and drivers' satisfaction are of great value for the future development of EVs, which requires managing an EV charging station in a reliable and intelligent way.