To mitigate new energy fluctuations, the joint optimization and scheduling of different units as a unified generating system is generally applied. In this study, to address the impact of uncertainties on scheduling plans, research on optimizing scheduling of new energy under uncertain conditions is necessary. In this point of view, this study proposes a distributionally robust optimization method based on Wasserstein distance to tackle the uncertainty in photovoltaic outputs. The proposed methodology first constructs an uncertainty set based on the historical output data of photovoltaic power plants, and converts the original model into a mixed-integer linear model easy to solve by using the duality theory and Karush-Kuhn-Tucher (KKT) conditions. The converted model is then solved by using column-and-constraint generation (CCG) algorithm. Finally, numerical experiments on a comprehensive system comprising multiple units to compare the optimization results of the deterministic model, the robust optimization model and the distributionally robust optimization model are given, demonstrating the effectiveness and the superiority of the proposed distributionally robust optimization model.