Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System

As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles.