To address the coordination and autonomy challenges posed by large-scale distributed PV integration and the limitations of existing clustering methods regarding energy storage supply-demand coordination and source-load uncertainty

this paper proposes a clustering method that considers the coordination of source-load-storage power. By integrating an improved grey wolf algorithm with source-load uncertainty analysis

the proposed method enhances the applicability and stability of clustering under extreme scenarios. First

a comprehensive performance index system combines electrical distance-based modularity

cluster active and reactive power balance

and energy storage supply-demand coordination. Next

the grey wolf algorithm is enhanced through Tent chaotic mapping and nonlinear control parameter strategies to increase population diversity and global search capability

thereby avoiding local optima. Finally

scenario analysis is used to convert the source-load uncertainty model into specific scenarios for optimizing the distribution network clustering model. The improved IEEE 33-bus and 10kV actual distribution network clustering case studies demonstrate that the proposed clustering method exhibits significant adaptability and effectiveness in distribution networks requiring autonomy and coordination capabilities.