As the penetration rate of distributed energy increases gradually and the load fluctuation on the user side becomes larger

it is difficult for the power grid to maintain its original better operation mode all the time. With the maturity of battery energy storage technology

the application is extensive

and the benefits of peak cutting and valley filling are also recognized. The application of battery energy storage in distribution network is considered

and the future distribution network is dynamically reconstructed. Taking the minimum network loss as the objective function and considering the network topology constraint

node voltage constraint

branch current constraint and power flow balance constraint

an upper optimization model is established for network reconfiguration. Taking the maximum profit as the objective function and considering the energy storage charge and discharge power constraints

the lower optimization model is established to formulate the charge and discharge strategy. The two-layer optimization model is solved by the genetic algorithm embedded with fmincon function. Then a mathematical model is established. Genetic algorithm is solved by embedding graphminspantree function. Finally

a modified IEEE-33 node system is used as an example to verify the effectiveness of the proposed method.