Abstract: The integration of distributed photovoltaic (PV) systems on a large scale brings challenges to the safe and stable operation of distribution net-work, with voltage stability issue being one of them. The location and capacity determination of battery energy storage systems (BESS) is of great importance to voltage stability of photovoltaic (PV) distribution networks. However, unreasonable location and capacity determination strategy will lead to unnecessary cost and undermine the economics of distribution network operation. Battery storage capacity-location configuration method for photovoltaic distribution networks is proposed based on the center of mass theory. Firstly, the concepts of BESS center of mass and voltage interval center of mass in PV distribution network are put forward. The BESS center of mass is utilized to establish the evaluation index of BESS concentration degree, while the voltage interval center of mass is employed to construct the evaluation index of static voltage stability. The voltage stability objective function is established by classifying whether the voltage is in the normal operation interval based on the voltage interval center of mass. Subsequently, the impact of the BESS concentration degree on the deployment cost is investigated with the help of the BESS center of mass. Considering the voltage deviation constraint, tidal current constraint and network balance, a model for the BESS location and capacity determination in the PV distribution network is developed with the objective of minimizing the static voltage stability, operating network loss and investment cost of BESS in the distribution network. Finally, the solution to the location and capacity determination of BESS in the PV distribution network is obtained based on the multi-objective particle swarm optimization (MOPSO) algorithm. The IEEE-33 node distribution network connected by PV systems is utilized as the case study system to verify the proposed method and the analysis of concentration degree of BESS’s impact on the cost. The simulation results demonstrate that the proposed algorithm is capable of reducing the cost of energy storage configuration and improving the static voltage stability of the distribution network as well.