Multiple uncertain factors such as renewable energy output, purchase price and integrated demand response pose serious challenges to the safe and economic operation of integrated energy systems. Precision modeling of uncertain factors can effectively improve the effectiveness and superiority of the integrated energy system scheduling strategy. Based on the analysis of the characteristics of multiple uncertainties, this paper first uses a random scenario and robust optimization methods to model multiple uncertainties such as energy purchase price, wind and solar output, and integrated demand response. In addition, because Advanced Adiabatic Compressed Air Energy Storage (AA-CAES) has the advantages of cold-heat-electricity combined storage, it can enhance the flexibility of system operation. Therefore, this paper chooses AA-CAES as the IES Energy storage system. Then we construct an IES robust-stochastic optimization model with AA-CAES, and use the bat algorithm to analyse the model. Finally, a simulation analysis is carried out based on the data of a certain area. The results show that the proposed method can reduce the operating cost of the system and realize the coordination and optimization of economy and robustness. This work is supported by the National Natural Science Foundation of China (No. 51777088) and the Science and Technology Project of State Grid Hebei Electric Power Co., Ltd. (No. SGHEJY00NYJS2000061).