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.