计及风光不确定性与算力灵活性的数据中心两阶段随机优化OA

[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.