Abstract: In the context of a high proportion of renewable energy access to electric power system, a multi-energy power scheduling model was established, after considering the uncertain output of wind power and photovoltaic power generation. In order to solve the large-scale scheduling model using commercial solver, the problem of dealing with the multi-objective and chance-constraint was studied. In the process of multi-objective optimization problem, the existing ε-constrained method was improved from the viewpoint of improving the uniformity of Pareto frontier set. The multi-objective optimization problem was transformed into a series of single-objective optimization problem with parameters, and the Pareto frontier set with multi-objective optimization was obtained by changing the value of the parameter. With regard to the chance-constrained constraints, a deterministic transformation method based on sampling was proposed, which transforms the opportunity constraint into multiple mixed integer constraints. According to the characteristics of the model which the random variables can be separated from the optimize variables, the transformation model was further simplified, which greatly reduces the calculation time of the chance-constrained programming and achieves the goal of solving the model within the required time. Finally, an example was given to demonstrate the effectiveness of the proposed method.