## 1 Introduction
In this paper, the real-time management strategy of an EV charging station is investigated under power limitation and considering most of the drivers' need from the perspective of combining theory with practice, in which drivers' choices, the disconnection operation, the standby mode, the shedding operation, and the restoration operation are included. Meanwhile, the arrival time of EVs, the initial \(SOC_{EV}\), and drivers' choices are random, which emulates the uncertainty of the EV charging behavior, and provides drivers' interface with the largest right to choose the charging modes. The intelligent shedding operation and the restoration operation based on the same theory as computer stack are proposed. The main contributions of this work are:  
(1) This paper proposes a microgrid-based EV charging station topology. The microgrid is composed of PV sources, an electrochemical storage system, a public grid connection, five chargers associated with their parking spots, and a DC common bus. The PV system can operate in maximum power point tracking algorithm (MPPT) mode and power-constrained mode. The public grid can operate under its power constraints obtained from the public grid operator in real-time. The storage system operates under its power and capacity constraints to protect the over-charging and, over-discharging for long-life support. The five chargers in five parking spots are designed to respect the real small charging station. (2) This paper proposes a real-time power management strategy for an EV charging station and a real-time interface. The management strategy fully considers the randomness and unpredictability of drivers' choices based on power availability. Meanwhile, the real-time power management strategy considers the constraints of the physical components of the microgrid. The real-time interface can deal with microgrid's sources and interact with drivers. When an EV arrives, the driver can set his/her choice through the interface according to the microgrid's current condition. (3) This paper deals with the uncertainties of EV drivers' behaviors. The arrival time of EVs and the initial \(SOC_{EV}\) are randomly and non-schedulable to emulate the real condition of EV arrival. An EV charging algorithm is presented to deal with the uncertainties of EV driver behaviors. Drivers may choose three charging modes: fast mode, average mode, and slow mode. In addition, it is also considered that the driver can disconnect the EV from the charger at any time. (4) A simple EV shedding and restoration method is applied according to the available power of the proposed microgrid and the EV power demand. The EV shedding and restoration method is based on the number of parking spots. The earliest arrival EV has the highest parking spot number. The EV with a higher parking spot number has a higher priority to be shed and a less priority to be restored. Following the presented considerations, the topology and modelling of the studied system are described in Section 2. The EV charging station management strategy is introduced in Section 3. The simulation results and analyses are presented in Section 4. Conclusions and further works are given in Section 5.