To enhance the accuracy and stability of dynamic modeling for smart station areas under the interactive influence of renewable energy uncertainty and power electronic control，this paper proposes an adaptive dimension-increasing dynamic modeling method based on Koopman theory. Firstly，comprehensively considering multi-energy coordination and load fluctuations，a mathematical model of the smart station area is constructed by integrating energy balance，power flow coupling，and power electronic control characteristics，achieving real-time feedback and collaborative optimization of the operational state. Secondly，based on online collected high-dimensional data，an adaptive dimension-increasing dynamic model is built. This model utilizes nonlinear mapping to achieve global linearization of the system dynamics，enabling real-time extraction and robust reconstruction of the dominant modes. Simulation results show that compared to the dynamic mode decomposition（DMD） method，the proposed Koopman modeling approach reduces the minimum relative error by 3.1%，more accurately captures the dynamic evolution of the system across multiple time scales，and effectively enhances the modeling capability and stability analysis level of smart station areas in high-penetration renewable energy scenarios.