With the increasing prevalence of intermittent power generation, the volatility, intermittency, and randomness of renewable energy pose significant challenges to the planning and operation of distribution networks. In this study, a data-driven distributionally robust optimization model is introduced. This model takes into account the forecasting errors of wind power generation, as well as the operational constraints and coordinated control of energy storage, demand-side loads, and conventional generating units. The model can obtain the scheduling scheme with the lowest cost in scenarios with uncertain wind power. Unlike traditional stochastic methods, this model uses the Wasserstein metric to construct the uncertainty set from wind power big data without the need to pre-determine the probability distribution or distribution interval of errors. This is achieved through a Wasserstein ball centered on empirical distribution. As the amount of historical data grows, the model adjusts the radius of the Wasserstein ball, thus reducing the conservatism of the results. Compared with traditional robust optimization methods, this system can achieve lower operating costs. Compared with traditional stochastic programming methods, this system has higher reliability. Finally, the superiority of the proposed model over traditional models is verified through simulation analysis.