## 1 Introduction
In [18], a two-stage demand response model is described to coordinate the peak load incurred by charging EVs. To reduce the stress on the grid, renewable energy systems (RESs) can be installed in the charging station infrastructure and utilized with smart charging strategies to charge EVs. Additionally, the photovoltaic (PV)-based EV charging station can participate in the energy markets or EV arbitrage markets as the grid-connected PV systems are widely deployed and EV markets rapidly grow. To maximize the revenues of the PV-based EV charging station and minimize the battery energy storage system (BESS) capacity fading, a multi-objective optimization is applied [19]. A dynamic searching peak and valley algorithm is applied to PV-based EV charging stations to mitigate the effect on the public grid and reduce the electricity cost of the public grid. In [20], the impact of a PV-based charging station on the economics and emissions from the power grid was analyzed. A study in [21] described smart control strategies to integrate EVs and PV systems with the future electricity systems. A PV generation can be used to supplement increased peak electricity demands due to the mid-day charging of PHEVs [22]. In [23], the effect of fast EV chargers on a retail building's load demand was analyzed, and 38% of the PHEV charging load was supplied with demand management and produced PV power. V2G technologies can be applied to power distribution systems of larger capacity, facilitate a smart grid with power management, and provide ancillary services to the users [24]. Through interactions between smart grid and EV aggregator in the V2G applications,charging-discharging coordination can be achieved with supply-demand equilibrium as V2G operates faster than traditional power plants, eliminating the problems such as stress on the power grid, power balance, urgent charging demand, and energy deviation [25; 26]. Moreover, V2G provides backup capabilities for renewable energy resources, such as wind and solar power generations, efficiently supplementing intermittent power production of RESs [27; 28]. According to the total number of EVs in a local network, distributed power capacity provided by V2G can have a profound effect on the power system [29]. In [30], the particle swarm optimization (PSO)-based approach was presented to improve balances between operating cost and emission in the V2G framework. V2G has the effect of reducing the overall cost of services and the electricity bills of customers and improving load factors by selling energy to the utility grid. With the utilization of V2G, intelligent charging-discharging methods are carried out in real-time when the grid system requires an economical and energy-efficient solution and ancillary services. Intelligent charging-discharging methods can be applied to the grid system where EVs and charging station are interconnected to a data communication network, and the charging station is linked to a transmission-distribution system operator. It can also track, control, and constrain the use of electric devices in a charging station to optimize the electricity demand. Optimization techniques are usually used for energy management of the grid system, and the optimized operation of the power system is determined based on optimization variables, such as charging power, charging-discharging status, price prediction, and power balance [31; 32; 33]. With the cooperative charging-discharging operations of EVs, the vehicle-to-vehicle (V2V) charging mechanism can be utilized to stabilize the electricity network, which is beneficial to both EVs having the role of energy consumers and energy providers [34]. A semi-distributed online V2V charging strategy based on electricity price control was presented to obtain high revenue for discharging and low cost for charging EVs [35].