Abstract：Under the carbon peaking and carbon neutrality goals，the high integration of new energy sources

like photovoltaics（PV）into the power system makes it difficult for traditional dispatching schemes to effectively

handle their randomness and volatility in output. PV curtailment occurs frequently and local counter-peak

regulation further exacerbates the peak-valley load difference. To address these issues，focused on a microgrid with

large-scale PV integration，and a coordinated optimization strategy for wind，PV，and storage operations，

considering dynamic clustering and partitioning of PV clusters was proposed. First，with the goal of improving the

smoothness of PV output，a dynamic clustering division based on trend inconsistency distance was performed using

the K-means algorithm. The optimal clustering scheme and dynamic adjustment strategy were determined using

sum of squared error（SSE）indicator. Next，based on the clustering results，a coordinated optimization strategy for wind，PV，and storage in the microgrid was proposed，integrating the operational costs of generation，load，and storage. Finally，case study analysis demonstrated that the proposed method effectively utilized the smoothing

effect of PV clusters，enhanced the integration level of new energy，and achieved multiple operational objectives

such as improving system economic efficiency and peak shaving and valley filling.

      Key words：dynamic cluster partitioning；renewable energy consumption；microgrid optimization dispatch；

multi-timescale