Abstract: The uncertainty of wind power output presents certain risks to the stable operation of integrated energy systems. A multiobjective distributionally robust optimization approach that incorporates chance constraint is introduced. Initially,to balance low-carbon economy and robustness during system operation,to minimize both the comprehensive operational costs and carbon emissions,a multiobjective distributionally robust chance-constrained optimization model is constructed. Subsequently,a distributionally robust bound is determined to address the uncertainty of wind power,transforming the multi-objective distributionally robust chance-constrained model into a multi-objective deterministic optimization model. To achieve a well-distributed Pareto frontier,the model is solved by using the Normalized Normal Constraint（NNC）method. Finally,through comparative case study analysis,it is shown that the proposed multiobjective optimization model can effectively balance low-carbon economic considerations and robustness in system decision-making,offer a new approach to solve the wind power uncertainty in integrated energy systems.