With a large number of renewable energy sources such as wind power connected to the grid, how to maximize the use of renewable energy and reduce the cost of traditional thermal power units have become urgent problems. In particular the co-optimization of energy and reserve dispatch has become a hot research topic. Considering the uncertainty of wind power output, a two-stage economic dispatch model is established to minimize the cost of the objective value. In the first stage, day ahead dispatching is determined according to the predicted wind power output. In the second stage, i.e., the real-time dispatching stage, feedback regulation is used for the first stage scheduling considering the real-time output of wind power. Given the uncertainty of wind power output, Kullback-Leibler divergence is used to model the uncertain variables. Combined with the distributed robust method, a two-stage distributionally robust energy and reserve co-optimization model with an extreme probability distribution is established, and it is transformed into a solvable mixed integer nonlinear programming problem. Based on the generalized Benders decomposition method, a decomposition coordination algorithm is proposed to solve the optimization model. Finally, IEEE 6-bus system and IEEE 118-bus system are taken as tested simulation systems, and the feasibility and superiority of the proposed method are verified by comparing the traditional robust and stochastic programming methods. This work is supported by National Natural Science Foundation of China (No. 51777126).