| This paper addresses the accommodation challenges brought by the high proportion of new energy integration into power systems and proposes an intelligent regulation method for new energy accommodation resources under uncertainty. Firstly, a joint probability distribution model of wind and photovoltaic power output is constructed based on Monte Carlo simulation and Copula theory, achieving precise quantification of new energy uncertainty. Secondly, a refined mathematical model of multiple adjustable resources such as energy storage systems and electric vehicle clusters is established, analyzing their operational characteristics and dispatchable potential. On this basis, a three-layer optimization regulation architecture is designed, including day-ahead stochastic programming, intraday rolling optimization, and real-time feedback correction: the upper layer uses two-stage stochastic programming to formulate the day-ahead dispatch plan, the middle layer conducts intraday rolling correction based on model predictive control, and the lower layer achieves real-time power balance through distributed feed-back. Simulation results show that the proposed strategy significantly improves the system""s new energy accommodation capacity, with wind power accommodation reaching 96.8% and photovoltaic power accommodation reaching 94.2%, increasing by 14.5 and 17.7 percentage points respectively compared to traditional dispatch strategies; at the same time, it effectively reduces system operation costs, with total operation costs reduced by 8.5% compared to traditional strategies. The research verifies the effectiveness of the pro-posed method in enhancing new energy accommodation rates, improving system operational economy, and increasing dis-patch flexibility, providing theoretical basis and technical support for the optimal operation of high-proportion new energy power systems. |