Abstract: At present, the economic and reliability issues of power distribution networks urgently need to be solved due to the high proportion of renewable energy sources connected to them. To address this, this paper proposes a coordinated optimization model for power distribution networks that takes into account the uncertainty of hydro-photovoltaic-load. First, the Monte Carlo sampling method is used to generate scenarios for photovoltaic, hydro, and load, and then a backward scenario reduction method is used to obtain typical output scenarios. Second, the mixed integer nonlinear nonconvex model is transformed into a mixed integer second order cone programming problem using the second-order cone relaxation and big M method. Finally, the Gurobi solver is used to solve the problem, and the case study uses power distribution network data from a region in Sichuan Province containing hydro and photovoltaic power, which is verified on the IEEE33 node system. The results show that the proposed model effectively reduces economic costs and improves the supply reliability of the power distribution network.