Under the background of“dual carbon”and new power systems, the installed capacity of new energy has increased year by year,new loads such as data centers and 5G base stations have continued to grow, and the user-side load patterns and load characteristics have undergone major changes, while superposing the influence of external environment and other factors, and the power supply and demand situation is grim. In order to ensure the safe and stable operation of power grid and tap the user’s adjustable potential, a dynamic evaluation method of multi-element customer load adjustable potential based on BP neural network based on particle swarm optimization is proposed.The influence mechanism of the adjustable potential of multi-customer load is analyzed, the feature label system is constructed, the relevant features of the adjustable potential are selected by the grey relational degree analysis method, and the adjustable potential assessment model is built to realize the assessment of the load control ability of multi-customer load. The accuracy of the assessment model is verified according to the actual response results, and the user load and demand response data are regularly updated. Dynamic evaluation of userside adjustability potential enables the model to adapt to changing user behavior.