[Objective] The high-penetration integration of distributed renewable energy sources poses dual uncertainties to distribution networks， including spatiotemporal mismatches between generation and load and deviations in scenario probabilities， necessitating planning methods that jointly balance structural autonomy and uncertainty adaptivity. This paper proposes an operation-feedback-driven closed-loop framework integrating dynamic cluster partitioning， planning configuration， and dispatch operation. A collaborative configuration model is developed for distributed generation （DG） （including wind turbines and photovoltaics）， soft open points （SOP）， and distributed energy storage systems （DESS）. [Methods] First， a weighted and normalized source-grid-load-storage matching index based on critical periods is constructed and combined with the modularity index to form a comprehensive performance metric for cluster partitioning. Second， to address renewable output uncertainty under biased scenario probabilities， a two-stage distributionally robust coordinated planning model is established. Furthermore， marginal operational information， such as cross-zone mutual support and voltage-constraint stress， is extracted from dispatch results to adaptively adjust partition boundaries， ensuring the partitioning outcomes are better aligned with planning and operational needs. The resulting model is solved using a dual-loop algorithm that couples outer partition iterations with an inner column-and-constraint generation （C&CG） procedure. [Results] Case studies on the IEEE 69-node system demonstrate that the proposed method achieves synergistic improvements in annual comprehensive cost， renewable energy accommodation， and weakly-coupled autonomous operation. [Conclusions] This research provides theoretical support for the collaborative configuration of DGs and flexible resources， offering significant implications for the planning and operation of modern distribution networks.