针对风光荷时变的配电网动态重构问题，提出了一种Kmeans ++融合PAM（Kmeans ++ -partitioning around medoid）聚类方法，按时间顺序划分日等值负荷曲线。以综合成本、电压偏移和负荷均衡度为优化目标构建配电网多目标动态重构模型。提出了一种改进星鸦优化算法（improved Nutcracker optimization algorithm, INOA）提升模型求解效率，利用Tent映射+准反射学习提供优质初始种群，引入动态适应度-距离平衡选择方法和切线飞行策略提高全局搜索能力，加入柯西-高斯变异扰动克服局部最优。基于IEEE33节点系统进行对比分析，结果表明所提方法能够实现负荷最优划分并对重构模型进行高效求解。

A novel clustering approach combining Kmeans + + and PAM was introduced to segment the daily load curve chronologically for the dynamic reconfiguration of distribution networks incorporating time-varying wind solar power and loads. Multi-objective dynamic reconfiguration model of distribution network based on the optimal objectives of comprehensive cost, voltage offset and load balance. To enhance the computational efficiency of the model, an INOA(improved Nutcracker optimization algorithm) was proposed, which used Tent mapping + quasi-reflection learning to provide high-quality initial population. Dynamic fitness-distance balance selection method and tangential flight strategy were introduced to enhance the global search capability. The Cauchy-Gaussian variation perturbation was incorporated to augment the algorithm's capability to escape from local optima. Using the IEEE 33-node system as a basis, the outcomes indicate that the suggested approach effectively achieves optimal load distribution and efficiently addresses the restructured model.