[Objective] The increasing demand for green energy supply in data centers poses a critical challenge in matching the wind and photovoltaic （PV） power uncertainty with computational load requirements. To address this issue， this paper proposes an optimized two-stage stochastic scheduling method to coordinate wind and PV power uncertainty with computational flexibility. [Methods] First， considering the stochastic fluctuations and temporal coupling of wind and PV power outputs， a scenario generation model integrating first-order autoregressive process and Cholesky decomposition is developed to capture the temporal correlations and complementarity between wind and PV power outputs. Second， considering the heterogeneity of delay tolerance， a flexible computational response model is developed based on discrete-time task flows， in which the queue state equations quantify the time-dimensional migration capability and backlog constraints of workloads with different delay tolerances. Finally， in order to minimize the expected system operating cost， a two-stage stochastic optimization model incorporating computational load scheduling and multi-energy coordination is formulated to determine the optimal time-sequential operating strategy for computational tasks. [Results] Numerical case studies demonstrate that the proposed strategy shifts delay-tolerant workloads from peak price periods to off-peak periods with abundant wind and PV power. This coordination reduces the expected system operating cost by 4.1% compared with traditional rigid scheduling approaches. [Conclusions] The proposed method effectively leverages the demand response potential of computational loads to enable cost-effective data center operation.