## 1 Introduction
When traded with the electric company, the commercial price of electricity produced from the RESs is generally determined based on the system marginal price (SMP) and the renewable energy certificates (RECs) issued by authorities such as the Korea Power Exchange (KPX) [36]. In study [37], a real-time model performing V2G power transactions is applied for EV fleets. The electrical energy can be sold/purchased to/from the utility grid through bi-directional charging in V2G networks [38; 39]. In the charging station, a communication system is significant to extract each EV's operation, such as charging and discharging, and its charging rate [40]. Smooth and reliable communication is an essential component to operate charging stations with sharing networks and effectively scheduling EVs' operations for the customers [41]. To prevent compatibility problems between charging stations, a diversity of internet-based communication methods have been presented and communication standards have been established [42; 43]. Data and energy flow of charging stations are bidirectionally transmitted among EVs, charging stations, and the utility grid. The ISO/IEC 15118 standard is defined as a complementary international standard supporting internet protocol-based bidirectional communication system [44]. It is mainly used to develop improved and autonomous charging/discharging control mechanism between EVs and charging station. The IEC 61850 is used to standardize EV charging systems consisting of AC or DC and define requirements of general EV, electric vehicle supply equipment (EVSE), and different charging modes. Additionally, its communication standard defines information models to be applied to the information exchange between electrical devices in the charging station [40]. Electric power industry has significant impact on our daily life, and stable and efficient electricity supply can maintain the balanced operation of the power system [45]. Therefore, accurately forecasting the change of load demand is essential to ensure the stable power system and reliable electricity market. In addition, as RESs are integrated with MGs, they are utilized to supply electricity to households, buildings, factories, and EV charging stations. Among various types of RESs, many studies are currently implemented for PV output forecasting due to its characteristic of intermittent power generation. PV power forecasting is helpful for planning and scheduling of power distribution, energy management,increasing the operational efficiency of the power system, and minimizing the electricity cost. Typically, short-term forecasting of electricity load demand and RESs' output can be used for power system control or energy flow scheduling of electrical components in the MG. Recently, deep learning (DL) is applied to improve the accuracy of data forecasting since a large amount of data can be collected from several sensing devices. Different DL algorithms, such as convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and other hybrid deep neural network architectures, are generally used as beneficial tools in time-series data forecasting [46]. The function of DL based neural network is to learn complex mappings from the patterns of input and output datasets by supporting numerous inputs and outputs. The advantage of artificial neural network (ANN) includes that the solution is very fast and simple compared with the physics-based models. In addition, it does not require knowledge about parameters on internal system, and it can solve the complex mathematical problems with high accuracy. RNN is generally used to deal with time-series data, however, it is easy to have long-term dependence. To prevent long-term dependence on RNN, the gate structure of LSTM performs the function of selective memory of past information.