# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
Therefore, vehicle-to-grid (V2G) services have emerged as a promising technology in the field of smart grids [10], where they can improve frequency [11] and voltage regulations while providing benefits to the EV users [12], and this depends on the number of available EVs [13; 14]. Additionally, such services can enhance power quality and promote the integration of renewable energy with developed smart control algorithms [15; 16; 17]. In [18], the benefits are highlighted for V2G service participants, as these services can decrease the total ownership cost of EVs. For the grid operator, V2G is seen as a power source that is able to mitigate fluctuations caused by renewable energy sources and provide ancillary services. As for EV owners, participating in V2G services should have financial incentives without limiting their travel needs. Moreover, V2G optimization plays a crucial role in maximizing the benefits. In [19], the authors found that total cost of EV ownership could be reduced by implementing V2G service in the Flanders region of Belgium. This service helped to smooth out electricity demand by filling in valleys and reducing power peaks, ultimately leading to improved grid stability. A charging and discharging strategy for EVs proving the effectiveness of their V2G operation in different cities in China with different trip patterns was developed in [20]. Their aim was to minimize the cost of operation of the distribution grid considering grid congestion and voltage constraints. These factors were particularly important, given the variation in the distribution of EVs across areas of operations. The potential locations for EV charging and their participation in V2G service have been predicted in [21] using automated machine learning, based on historical data collected over 42 weeks. The authors of [22] have demonstrated that the participation of EVs in V2G services, when they are idle at charging stations, can reduce the demand for charging EVs. In [23], an improved harmony particle swarm optimization problem was investigated in a bi-level model (low level: EV cluster scheduling, upper level: planning) to determine the optimal allocation of distributed generation and charging stations within a V2G service. The results indicated that the optimized model could satisfy the charging demand of EV users, improve the voltage quality, mitigate load fluctuations, encourage the use of renewable energy, and improve the global performance of the planning scheme. An optimization framework hasbeen developed in [24] to reduce greenhouse gases and intensive electricity imports in the Switzerland power system with controlled charging/discharging of EVs. To jointly install EV charging stations and distributed energy resources in a distribution system in China, an optimization model has been presented in [25], where V2G service is considered with minimized annual costs (considering also social aspects). An optimization problem has been modeled in [26] as a nonlinear stochastic programming problem with uncertainty of PV energy. The EVs can operate in V2G mode, where this allows EVs to charge during off-peak hours and discharge during peak hours to reduce energy costs. The proposed problem can optimize the operation of EVs and minimize the cycles of their batteries to reduce battery degradation speed. A novel control system has been presented in [27] to underpin V2G service by deploying a fleet of EVs, which allows a V2G aggregator to provide voltage and frequency services while reducing the charging cost with the minimization of battery-level degradation. A case study of an EV charging station based on a university campus in Jordan has been presented in [28], which investigates the feasibility of a V2G service to minimize the global consumption of energy drawn from the public grid. A computational model of an EV with battery degradation has been studied in [29] to supply power to the grid while gaining profit for the EV owner by alleviating the load on the main grid.