Aiming at the accommodation challenges of high-proportion new energy integration into power systems, an intelligent regulation method for new energy accommodation resources under uncertainty is proposed. A joint probability model of wind and photovoltaic output is constructed based on Monte Carlo simulation and Copula theory to quantify new energy uncertainty. A refined model of multiple adjustable resources such as energy storage and electric vehicle clusters is established, and their operational characteristics and dispatchable potential are analyzed. A three-layer optimization architecture is designed:in the day-ahead layer, two-stage stochastic programming is used to formulate the dispatch plan;in the intra-day layer, rolling correction is conducted based on model predictive control;and in the real-time layer, power balance is achieved through distributed feedback. Simulation results show that the proposed strategy significantly improves new energy accommodation, with wind power accommodation reaching 96.8% and photovoltaic accommodation reaching 94.2%, increased by 14.5 and 17.7 percentage points respectively;meanwhile, total operation costs are reduced by 8.5% compared to traditional strategies. The effectiveness of the proposed method in enhancing accommodation rates, improving operational economy, and increasing dispatch flexibility is verified, providing theoretical basis and technical support for optimal operation of high-proportion new energy power systems.