A combined approach of Latin hypercube sampling and K-means clustering is proposed in this study to address the uncertainty issue in wind and solar power output. Furthermore, the loads are categorized into three levels: primary load, secondary load, and tertiary load, each with distinct characteristics in terms of demand. Additionally, a load demand response characteristic model is developed by incorporating the dissatisfaction coefficient of electric and thermal loads, which is then integrated into the system’s operational costs. Moreover, an electricity−hydrogen−thermal power system is introduced, and a source-load coordination response mechanism is proposed based on the different levels of demand response characteristics. This mechanism enhances the interaction capability between the power sources and loads, thereby further improving the economic performance of the virtual power plant. Furthermore, the operation economy of the virtual power plant is enhanced by considering the participation of renewable energy sources in carbon capture devices and employing a tiered carbon-trading mechanism. Finally, the CPLEX algorithm is employed to solve the optimization model of the virtual power plant, thereby validating the effectiveness of the proposed models and algorithms.