面向配电网环境下智能台区内部灵活性可调资源，提出了一种基于场景生成方法的配电网运营商主从博弈定价与电动汽车（electric vehicle，EV）充电管理策略，以引导用户侧灵活性资源通过市场化手段实现与配电网的高效互动。首先针对EV负荷，基于条件密度网络（conditional density networks，CDNs），采用Gibbs抽样方法得到计及EV用户多类型充电特征联合概率分布的负荷预测结果，同时构建EV聚合模型以降低模型维度；在配电网环境下得到基于二阶锥最优潮流模型的配网节点电价（distribution locational marginal pricing，DLMP），在此基础上构建配电网运营商主从博弈定价策略与用户侧激励机制；利用Karush-Kuhn-Tucker （KKT）条件和最优对偶理论将主从博弈双层模型转化为混合整数线性规划问题进行求解，最终得到全局最优定价策略，并采用功率分配模块实现EV用户充电功率的合理分配。基于改进IEEE 33节点配电系统的算例仿真结果验证了所提策略的有效性。

Facing the flexible and adjustable resources inside the intelligent distribution area of distribution network, a Stackelberg game pricing strategy and electric vehicle ( EV) charging management strategy for distribution network operator based on scenario generation method is proposed to guide the user-side flexible resources to realize efficient interaction with the distribution network through market-oriented means. Firstly, based on conditional density networks (CDNs), Gibbs sampling method is used to predict EV load, which takes into account the joint probability distribution of EV users′ charging characteristics, and EV aggregation model is constructed to reduce the model dimension. The distribution locational marginal pricing (DLMP) based on the second-order conical optimal power flow model of distribution network is obtained, and on this basis, the Stackelberg game pricing strategy and user-side incentive mechanism of distribution network operator are constructed. The bi-level Stackelberg game model is transformed into a mixed-integer linear programming problem using the Karush-Kuhn-Tucker (KKT) conditions and optimal duality theory for solution, and finally the global optimal pricing strategy is obtained, and the power allocation module is used to realize the reasonable distribution of charging power for EV users. Simulation results based on improved IEEE 33-node power distribution system verify the effectiveness of the proposed strategy.