Abstract: The advancement of clean heating projects has led to a growing share of thermal storage electric heating in winter loads, making it an important demand-side resource for virtual power plant (VPP) optimal scheduling. Aiming at the problem that the traditional fully rational VPP scheduling model is difficult to accurately describe the actual decision-making behavior of electric heating users, a two-layer Stackelberg game optimization scheduling model of virtual power plant considering users' bounded rationality is proposed. Firstly, based on the prospect theory (PT), the decision-making value model of electric heating users is constructed to describe the subjective perception of electricity cost and heating comfort, and the Logit model is introduced to describe the user response probability. Secondly, the Stackelberg game game model is established. The upper layer formulates the time-of-use electricity price strategy with the goal of maximizing the operating income of VPP, and the lower layer users make the demand response decision with the goal of maximizing the comprehensive prospect value. Finally, the simulation results show that the proposed model can make the VPP scheduling scheme more suitable for the actual electricity consumption behavior of users, and significantly improve the accuracy and practical value of VPP scheduling.