Abstract: Aiming at the system robustness and economic coordination caused by large-scale renewable energy access to the grid, this paper proposed a distributionally robust dynamic optimal power flow model considering wind-solar-hydrothermal energy sources. The uncertainty of wind and solar power was described by distributionally robust optimization method through the ambiguity set that containing all possible probability distributions. The ambiguity set was constructed as a Wasserstein ball centered at the empirical distribution of the wind-solar forecasting error with the radius of Wasserstein metric. The optimal solution was obtained by minimizing the operational cost while satisfying the worst-case probability distribution of the wind-solar forecasting error. Because the cascade hydropower model is a mixed integer model, the AC power flow was approximated as a decoupled linear power flow to improve computational efficiency. The simulation results of a 703-node system show that the proposed method can effectively balance the robustness and economy of the system by controlling sample size and Wasserstein radius confidence.