The new power system consists of four major elements such as source

network

load and storage. The distributed“energy stor?age”characteristics of electric vehicles can complement new energy generation

alleviate the impact of intermittent and fluctuation of newenergy output on the power grid

and effectively reduce the capacity of storage allocation in the power grid. However

the disorderly charg?ing behavior of EVs can aggravate the difference between new energy output and load

so it is necessary to control the charging of EVs inan aggregated manner. Firstly

the disordered charging behavior of different types of electric vehicles is simulated using the Monte Carlomethod

and the impact of disordered charging on the balance between source and load is analyzed. Secondly

the improved k-means++clustering algorithm is used to divide the result of disorderly charging and the difference of new energy into four clustering periods

name?ly

“sharp

peak

flat and valley”. Finally

an optimization strategy for charging aggregation control during overlapping clustering periods isproposed

which is verified in combination with the“Lingfeng Zero-Carbon Evolution Park”project in Ningbo

Zhejiang Province

and theresults show that

under the premise of impairing the charging satisfaction of EV owners as less as possible

this strategy can effectively re?duce the imbalance between new energy output and load

and combined with fixed-energy storage configurations

it can achieve a new typeof electric power and load imbalance. Combined with the fixed energy storage configuration

it enables source-load balancing in new powersystems.