AI-Empowered Game Theoretic-Enabled Dynamic Electric Vehicles Charging Price Scheme in Smart City

The huge number of electric vehicles (EVs) penetration resulted in the rapid deployment of charging stations as well as an increase in electricity demands. Due to this, there is a growing interest in the load balance in the power grid. In the power grid, whenever there is a decrease in the overall usable voltage and reactive power, harmonics will be injected, which affects the overall power quality of the grid. In addition to the abovementioned issues, most of the existing works face issues, such as an imbalance of load at the grid, inefficient utilization of resources, and nonoptimal power transactions cost. To reduce and avoid the abovementioned issues, we propose a real-time intelligent method-based energy utilization, conservation, and optimal price at the grid and charging stations using artificial intelligence (AI). We develop the IEEE 69 bus test system and analyze the grid and charging stations. Using the analyzed factors of the grid and charging station, designed an edge-computing enabled optimal charging price scheme using the game theoretic approach. The designed system provides efficient utilization, energy conservation, and adaptive optimal charging price for EVs at charging stations. Discrete-time event simulator is developed to test the proposed scheme with parameters, such as arrival rates, queue length, and reactive power. The proposed scheme's results (analytical, simulation, and comparison) show adaptability and realism.