## 1 Introduction
After LSTM, the GRU network is also proposed with another RNN gate structure, which has only two gates used to control the influence of datasets in previous time, to solve problems of RNN such as vanishing or exploding gradient. GRU has high forecasting accuracy and alleviates the overfitting problem of LSTM [47]. In [48], LSTM based framework is applied to forecast a residential load by capturing temporal power consumption pattern in single-meter load profile. The LSTM-RNN was presented to establish an accurate PV power forecasting model reflecting the temporal changes in PV power production [49]. The GRU neural network is used to establish the model to forecast the electricity load with stacked auto-encoding compressing the historical dataset [50]. The hybrid DL approach was developed based on the sequence-to-sequence autoencoder and GRU for short-term PV power prediction, which has higher performance than other DL models [51]. In this paper, multi-EV charging/discharging scheduling strategy is proposed for the power management of interdependent MG and EV fleets located in a charging station. The proposed charging/discharging scheduling algorithm is presented to solve the problem of the grid system instability that may occur when the charging demand of EVs is added to the local load demand, and to minimize the operating cost of the PV-based charging station while satisfying the charging requirements of parked EVs. A portion of produced PV power and discharging power of EVs can be supplied to the local load demand and sold to the electric company at the sum of SMP and weighted REC, and the rest of these power can be directly supplied to the charging demand of EVs. If the PV power and discharging power of EVs are insufficient for the load demand, the power is supplied from the utility grid. It is assumed that the charging station is located in workplace, and EVs can be charged or discharged during the parking period that is determined by the distribution of arrival time at home and workplace. The proposed power management scheme is mainly composed of two stages: prediction and scheduling. Using a time series model trained by the GRU network, the local load demand and PV power production are forecasted in the prediction stage. In the scheduling stage, parked EVs are assigned to the charging/discharging operations depending on the EV charging/discharging scheduling algorithm considering the future trend of the local load demand and PV power output. For the coordinated power distribution of power sources and EV charging/discharging scheduling, multi-objective optimization is applied to the MG and EV fleets to minimize the operating cost of the PV-based charging station and grid dependency of the MG and maximize the utilization of EVs as ESSs while considering each EV's charging requirements. The contributions of this paper are listed as follows. 1. A novel power management scheme for independent MG and EV fleets in the PV-based charging station is presented. The PV power produced from the PV-based charging station is used as a secondary source to satisfy the local load demand and charging demand of EVs, and it is consumed prior to grid power. With the proposed EV charging/discharging scheduling algorithm, discharging power from parked EVs is also be used to meet the local load demand and charging demand of EVs. In addition, the system operator of the PV-based charging station can decrease the operating cost by selling the produced PV power and discharging power of EVs to electric companies which are supplemented to the local load demand. For economical and energy-efficient power management of the PV-based charging station, a demand response (DR) program, SMP, and REC are considered. 2. The multi-EV charging/discharging scheduling algorithm with GRU network is proposed for economical and energy-efficient power management of parked EVs in the PV-based charging station.