针对高渗透率分布式光伏逐一控制复杂且随机变量之间的相关性影响分布式光伏规划的问题，提出了一种考虑运行分区和相关性的高渗透率分布式光伏双层规划方法。首先，依据模块度指标和有功功率平衡度指标对配电网的负荷节点进行集群划分。其次，为分析光伏与负荷之间相关性，建立基于Nataf逆变换的三点估计法（three-point estimate method，3PEM）随机潮流模型。然后建立高渗透率分布式光伏双层规划方法，外层以年综合费用最小为目标，优化各集群分布式光伏与储能的配置容量，采用改进的遗传算法进行求解；内层基于机会约束，以综合节点电压期望值偏差率最小、运行风险值最小为多目标函数，优化各节点分布式光伏的出力与储能位置，采用快速非支配排序遗传算法（non-dominated sorting genetic algorithm 2，NSGA2）和基于熵的逼近理想解排序法（technique for order preference by similarity to ideal solution，TOPSIS）求解最优配置方案。最后，以IEEE 33节点系统为算例，结果表明了所提模型的可行性。

To address the complexity of individually controlling high-penetration distributed photovoltaics (PV) and the impact of correlation between random variables on distributed PV planning, a dual-layer planning method for high-penetration distributed PV considering operational partitioning and correlation is proposed. Firstly, load nodes in the distribution network are clustered based on modularity and active power balance indicators. Secondly, to analyze the correlation between PV and loads, a 3PEM stochastic power flow model based on the Nataf inverse transform is established. Then, a dual-layer planning method for high-penetration distributed PV is developed. The outer layer minimizes the annual comprehensive cost by optimizing the allocation capacities of distributed PV and energy storage for each cluster, utilizing an improved genetic algorithm. The inner layer, based on chance constraints, minimizes the deviation rate of comprehensive node voltage expectation and the operational risk value as multi-objective functions, optimizing the output of distributed PV and the placement of energy storage at each node, employing non-dominated sorting genetic algorithm 2(NSGA2) and entropy-based technique for order preference by similarity to ideal solution(TOPSIS) to find the optimal configuration. Finally, the feasibility of the proposed model is demonstrated through a case study of IEEE 33-node system.