在当前数字化时代，互联网数据中心（internet data center，IDC）（以下简称“数据中心”）作为大型的电力消耗者已经成为电力网络中的重要组成部分。首先采用数据中心余热回收技术建立了数据中心等值热参数模型，并将其融入到微能源网中以实现冷热电多能互补。其次对数据中心运行过程中所需制冷量与余热之间的复杂关系进行了深入分析，采用溴化锂吸收式制冷机和电制冷机来满足数据中心的冷负荷需求。为了更有效地应对批处理负荷在时间维度上的变化特性，制定了高度灵活的数据负荷分配策略，并建立了双层规划模型。使用场景削减的典型日选择方法以应对源荷不确定性，并引入基于Tent映射的灰狼优化算法（Tent mapping grey wolf optimization algorithm，TMGWO）和CPLEX进行联合求解。最后，通过对某含数据中心的微能源网系统进行仿真分析，数值计算结果表明所提出的方法能够有效提高系统的经济性和环保性。

In the current digital age, Internet data centers （Internet data center, IDCs） have become important components of power grid as large power consumers. Firstly, IDC waste heat recovery technology is adopted to establish a IDC equivalent thermal parameter model, which is integrated into the micro-energy network to achieve multi-energy complementarity of cooling, heating, and power. Secondly, the complex relationship between the required cooling capacity and waste heat during the operation of IDCs is analyzed in depth, and lithium bromide absorption refrigeration machines and electric refrigeration machines are used to meet the cooling load requirements of IDCs. In order to more effectively cope with the changing characteristics of batch processing load in the time dimension, a highly flexible data load allocation strategy has been developed, and a two-layer programming model has been established. The typical day selection method for scenario reduction is used to address source load uncertainty, and the Tent mapping grey wolf optimization algorithm （TMGWO） based on Tent mapping and CPLEX are introduced for joint solution. Finally, through simulation analysis of a micro-energy network with a IDC, numerical results show that the proposed method can effectively improve the system's economy and environmental friendliness.