Distributed Charging Scheduling and Pricing Strategy for Plug-in Electric Vehicles Based on Stackelberg-Nash and Multi-Cluster Aggregative Games

In this paper, we propose a distributed and interactive Plug-in Electric Vehicle (PEV) charging scheduling approach, which is also combined with an optimal pricing strategy. This method tackles challenges such as fluctuations in charging currents, potential supply congestion, and uneven demand distribution that arise as PEV penetration increases. The objective is to improve the robust stability of the charging system while also reducing the costs for PEV users. This study designs a multi-cluster aggregative game mechanism to handle the competitive dynamics among operational clusters and the collective behavior of individual PEVs. Additionally, a strategic pricing method, based on Stackelberg game theory, is designed to refine the determination of basic electricity prices. We further introduce a distributed update method that efficiently seeks the Nash Equilibrium (NE) of the hierarchical game described. The effectiveness of the proposed architecture and solution methodology is validated through experimental studies.