电力系统自动化2026，Vol.50Issue(17)：115-125,11.DOI:10.7500/AEPS20250926005
计及任务时序灵活性的数据中心算力-电力协同优化方法
Coordinated Optimization Method for Computing Power and Electric Power in Data Centers Considering Sequential Flexibility of Tasks
摘要
Abstract
With the rapid development of artificial intelligence technologies and the increasing demand for computing power,the energy consumption of data centers has surged dramatically,and high operating costs and emissions have become one of the bottlenecks restricting their development.Therefore,a coordinated optimization method for computing power and electric power in data centers considering sequential flexibility of computing power tasks is proposed.Firstly,by analyzing the dependency characteristics among computing power tasks,a decomposition model for computing power task sequences based on directed acyclic graph is proposed to precisely quantify the deferrable time for various computing power tasks.Secondly,by integrating Transformer and Kolmogorov-Arnold network,a computing power task-energy consumption mapping model is built to accurately characterize the nonlinear relationship between computing power task and electricity/cooling energy consumption under varying indoor and outdoor temperatures.Finally,with the objective of minimizing operating costs,a coordinated optimization model for computing power and electric power in data centers is constructed.Validation results of a publicly available dataset indicate that the proposed method achieves flexible time-shifting capabilities for computing power tasks suitable for practical scenarios,and can effectively reduce the total operating costs of the system.关键词
数据中心/综合能源系统/算力/依赖/能耗/协同优化/数据驱动Key words
data center/integrated energy system/computing power/dependency/energy consumption/coordinated optimization/data-driven引用本文复制引用
庞奥康,邵振国,陈昌铭,陈飞雄,程国扬..计及任务时序灵活性的数据中心算力-电力协同优化方法[J].电力系统自动化,2026,50(17):115-125,11.基金项目
国家自然科学基金资助项目(72501064) （72501064）
福建省自然科学基金资助项目(2025J08055).This work is supported by National Natural Science Foundation of China(No.72501064)and Fujian Provincial Natural Science Foundation of China(No.2025J08055). （2025J08055）
