在数字经济背景下，数据中心产业飞速发展，其能耗占据社会电力消耗的相当比例，数据中心已经成为配电网中重要的组成部分。为了充分开发数据中心的灵活性资源，基于数据负载的时空可调特性，建立了数据负载调度模型，并将其接入综合能源站，以实现能源的高效利用。针对数据中心运行过程中数据负载处理能力与电能消耗量之间的复杂关系进行了深入分析，采用动态电压频率调节（dynamic voltage and frequency scaling，DVFS）技术对数据中心进行建模，并根据混合制排队论模型建立了不同类型数据负载的需求模型。为了平衡数据负载波动、提高整体效能和功耗比，提出了共享数据负载的数据中心联盟模式，并建立了联盟下数据负载调度模型。然后，以综合成本最小化为目标，建立了考虑多能互补特性与时空可调特性的多区域能源站协同调度模型。最后，采用集成数据中心的能源站进行仿真分析，分析考虑多能互补与时空可调特性对数据中心运行经济性与环保性的积极作用，验证了所提出调度策略的经济性收益。

Under the backdrop of the digital economy, the data center industry has rapidly developed, with its energy consumption accounting for a significant proportion of societal electricity usage. Data centers have become a crucial component of the power distribution network. To fully exploit the flexibility resources of data centers, a data load scheduling model is established based on the spatiotemporal adjustable characteristics of data loads, which is then integrated into integrated energy stations to achieve efficient energy utilization. An in-depth analysis is conducted on the complex relationship between data load processing capacity and electricity consumption during data center operations. Dynamic voltage and frequency scaling （DVFS） technology is employed to model the data centers, and demand models for different types of data loads are developed based on a mixed queuing theory model. To balance data load fluctuations and improve overall efficiency and power consumption ratios, a data center alliance model for shared data loads is proposed, and a data load scheduling model under the alliance is established. Subsequently, with the goal of minimizing comprehensive costs, a multi-regional energy station coordinated scheduling model is developed, considering the complementary characteristics of multiple energy sources and spatiotemporal adjustability. Finally, simulation analysis is conducted using energy stations integrated with data centers to analyze the positive effects of multi-energy complementarity and spatiotemporal adjustability on the economic and environmental performance of data center operations, validating the economic benefits of the proposed scheduling strategy.