Battery energy storage station（BESS） in a commercial park can reduce the peak load of electricity consumption and improve the economic efficiency of the park. However

the existing control strategy is relatively simple

which is difficult to adapt to the large-scale development and application in the future. Consequently

taking a BESS in a commercial park as the main research object

we proposed a multi-objective operation control strategy for BESS in the commercial park after taking battery life into consideration. Firstly

we developed a statistical method of battery charging and discharging cycle based on the rain flow count method. Then

using 5-order function fitting circle life and depth of discharge

we calculated the relationship between attenuation to estimate the battery life of control as a child one of the objective function so as to achieve the goal of delay battery life decay. According to the control strategy of BESS proposed in this paper

based on the distributed photovoltaic and load prediction curves in the park

genetic algorithm was adopted to optimize the charging and discharging power of the BESS every 1 min with an optimization interval of 10 min. The min-level control of BESS can be realized. Finally

a typical summer day in a Shanghai commercial park is taken as an example to verify the effectiveness and superiority of the control strategy.