配电网中灵活性资源与分布式光伏的协同运行调度对提升分布式可再生能源消纳水平和促进国家“双碳”目标的实现具有重要意义，然而，传统调度方法往往要求配电网的详细拓扑及线路阻抗信息，而这些模型信息在实际低压配电网中经常未知。为了克服这一挑战，提出了一种基于决策树的配电网非参运行优化方法。首先，通过无监督聚类算法从历史数据中提取拓扑状态信息，并生成“伪标签”；然后，结合运行数据训练决策树，学习运行策略与潮流约束可行性的关系，并将决策树等效转化为易于求解的混合整数线性形式，实现潮流约束的无模型非参重构；最后，算例仿真验证了该方法在网络拓扑变化、线路阻抗信息等未知条件下仍可精准地刻画潮流约束的可行域，且计算效率远高于基于模型的方法。

In the distribution network, the coordinated scheduling of flexible resources and distributed photovoltaics(PV) plays a crucial role in enhancing the consumption level of distributed renewable energy and promoting the realization of national "dual carbon" goals. However, traditional scheduling methods often require detailed topology and line impedance information of the distribution network, which is frequently unknown in actual distribution networks. To overcome this challenge, a non-parametric operation optimization method for distribution networks based on decision tree is proposed. This method first leverages unsupervised clustering to extract the topology status implied in the historical data to generate“pseudo labels”, and then train a decision tree to learn the relationship between operation strategies and the feasibility of flow constraints from historical operation data of the distribution network by training decision trees, and equivalently transforms the decision trees into mixed-integer linear forms that are easy to solve, thereby achieving a non-parametric reconstruction of flow constraints. Case studies show that this method can accurately depict the feasible domain of flow constraints under conditions where network topology and line impedance information are unknown, and its computational efficiency is significantly higher than that of traditional model-based methods.