In order to cope with the impacts of uncertainty factors on the reliability and economy of system operation, a two-stage robust reserve optimization model is proposed to take into account the real-time price day-ahead and intraday. The proposed model consists of two phases: the first phase is the day-ahead decision-making phase, which improves the flexibility of grid operation by establishing a demand response model based on the load classification under real-time price; and the second phase is the intraday decision-making phase, which takes into account the uncertainty of the wind - photovoltaic power and the failure of the transmission line in order to improve the robustness of the grid in coping with the uncertainties. The day-ahead decision-making is a deterministic optimization problem, which is solved by a linear programming problem, while the intra-day decision-making is an adaptive robust optimization problem, which is solved by using the Strong Dyadic Theory (SDT) method. By combining the power data of wind and photovoltaic fluctuation intervals and load demand of a regional power grid, a simulation model is constructed to verify that the proposed robust model is effective in optimizing the reserve capacity of the system operation under multiple uncertainties, reducing the scheduling cost of the system in the worst scenario, and ensuring the safe and reliable operation of the system.