随着电动汽车保有量与普及率的逐年提升，农村地区现有充电设施布局不均衡、数量不充足，已成为制约农村地区新能源汽车发展的主要原因。基于主从博弈定价策略，建立了面向高比例光伏渗透农村电网的充储电站优化配置模型。首先，基于主从博弈理论建立面向高比例光伏渗透农村电网的充储电站优化配置模型：上层领导者为充储电站投资方，综合考虑农村配电网与充储电站建设运行条件，优化决策充储电站建设位置、配套储能设备容量及电价策略；下层跟随者为电动汽车车主，根据上层领导者模型制定的电价策略，优化决策电动汽车充电策略。其次，利用Karush Kuhn Tucker (KKT)条件与对偶原理实现模型转换，获得易于求解的混合整数线性规划模型。最后，利用包含2个充电区域的IEEE 33节点典型算例进行算法验证。算例分析表明所提模型能够协调充储电站投资方与电动汽车车主的利益，获得经济效益好、光伏消纳能力强的充储电站配置运行策略。

With the increase in the number and penetration rate of electric vehicles year by year, the uneven layout and insufficient number of existing charging facilities in rural areas have become the main reason restricting the development of new energy vehicles in rural areas. Based on the master-slave game pricing strategy, this paper establishes a charging and storage plant optimisation model for rural power grids with a high percentage of photovoltaic penetration. First of all, based on the master-slave game theory to establish the charging and storage power station optimization model for the high proportion of PV penetration of rural power grid: the upper leader is the charging and storage power station investor, taking into account the construction and operation conditions of the rural distribution network and charging and storage power station, to optimize the decision-making charging and storage power station construction location, the supporting capacity of the storage equipment and the tariff strategy; the lower follower is the electric vehicle owner, according to the tariff strategy formulated in the upper leader model, to optimize the decision-making charging strategy for the electric vehicle. The lower level followers are EV owners, who make optimal decisions on EV charging strategies based on the tariff strategies developed by the upper level leader model. Secondly, the Karush Kuhn Tucker (KKT) condition and the dyadic principle are used to transform the model to obtain an easy-to-solve mixed-integer linear programming model. Finally, the algorithm is validated using a typical IEEE 33-node arithmetic system containing two charging regions. The example analysis shows that the proposed model can coordinate the interests of charging and storage plant investors and EV owners, and obtain a charging and storage plant configuration and operation policy with good economic benefits and strong PV consumption capacity.