针对大规模纯电重卡（electric truck, ET）无序充电抬高电网负荷峰值影响电能质量的问题，提出适用于调峰场景的集群划分及双层优化调度策略。首先，考虑实时道路流量及多能耗因素建立ET参与电网调峰的需求响应模型，以物流因素作为特征量，结合改进的模糊聚类算法对ET进行日前集群划分；其次，基于分群结果，结合电网调度与企业用户两方主体的不同利益诉求，在主从博弈框架下考虑柔性时间窗建立双层规划调度模型，实时求解集群内纯电重卡充放电功率；最后，采用克里金模型优化的粒子群算法加快模型求解的速度。通过某物流区实际ET数据进行仿真验证，结果表明，采取的集群划分及考虑柔性时间窗的双层调度策略能更好地平滑负荷曲线，降低集群的调度偏差。同时，克里金优化算法在求解双层优化模型上，更具有快速性。

Aiming at the problem that the unordered charging of large-scale electric truck (ET) increases the peak load of the grid and affects the power quality, a cluster division and two-tier optimal scheduling strategy for peak load balancing scenarios were proposed. Firstly, the demand response model of ET participating in power grid peak regulation was established considering real-time road flow and multi-energy consumption factors. With logistics factors as characteristic quantities, ET was divided into day-ahead clusters by an improved fuzzy clustering algorithm. Secondly, based on the clustering results, combined with the different interests of power grid dispatching and enterprise users, a two-tier scheduling model was established under the framework of master-slave game considering the flexible time window to solve the charging and discharging power of pure electric heavy duty card in the cluster in real time. Finally, particle swarm optimization based on Kriging model was used to speed up the solving of the model. The simulation results of ET data in a logistics area show that the two-tier scheduling strategy based on cluster division and flexible time window can better smooth the load curve and reduce the scheduling deviation of clusters. At the same time, Kriging optimization algorithm is more fast in solving the two-tier optimization model.