With the increased penetration of renewable energy sources (RESs) and plug-and-play loads, Microgrids (MGs) bring direct challenges in energy management due to the uncertainties in both supply and demand sides. In this paper, we present a coordinated energy dispatch based on Distributed Model Predictive Control (DMPC), where the upper level provides an optimal scheduling for energy exchange between Distribution Network Operator (DNO) and MGs, whereas the lower level guarantees a satisfactory tracking between supply and demand. With the proposed scheme, not only we maintain a supply-demand balance in an economic way, but also improve the renewable energy utilization of distributed MG systems. To describe the dynamic process of energy trading, a novel conditional probability distribution model is introduced, which can characterize randomness of charging/discharging and uncertainties of energy dispatch. Moreover, we formulate a two-layer optimization problem and the corresponding algorithm is given. Finally, simulation results show the effectiveness of the proposed method.