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.