综合智慧能源2026，Vol.48Issue(9)：56-67,12.DOI:10.3969/j.issn.2097-0706.2026.09.005
考虑数据中心柔性任务的综合能源系统能-算协同深度强化学习方法
Deep reinforcement learning method for energy-computing coordinated scheduling in integrated energy systems considering flexible data center tasks
摘要
Abstract
With the rapid development of the digital economy,artificial intelligence,and cloud computing,the electricity consumption of data centers continues to increase.Exploiting their regulation potential and achieving coordinated operation with integrated energy systems have become important challenges for improving energy utilization efficiency and operational economy.To address these issues,a joint dynamic economic scheduling model was established incorporating the utility grid,photovoltaic generation,combined heat and power units,gas boilers,electric boilers,battery energy storage,and data centers.Considering the differences between delay-sensitive and delay-tolerant tasks,a flexible task queue model based on the first-in-first-out(FIFO)principle was developed.The temporal evolution of flexible tasks was characterized by task arrivals,queue backlog,due tasks,and queue urgency.By further incorporating the number of active servers,server utilization,average response time,and dynamic voltage and frequency scaling(DVFS)characteristics,a mapping relationship between task execution and data center power consumption was established.The IES-DC joint scheduling problem was formulated as a Markov decision process,and the deep deterministic policy gradient(DDPG)algorithm was employed to learn a joint scheduling policy for energy equipment operation and flexible task processing.Case studies were conducted under a 24 h typical-day scenario,with SAC,PPO,and particle swarm optimization(PSO)adopted as comparison methods.The case study results showed that the proposed method could shift part of the delay-tolerant tasks from peak-price periods to flat-and valley-price periods while maintaining electric and thermal power balance,restoring the state of charge(SOC)of the energy storage system,completing all flexible tasks,and avoiding overdue tasks.Compared with the scenario in which all tasks were executed immediately upon arrival,DDPG-based flexible scheduling reduced the electricity purchasing cost from the utility grid by 2.17%.Under the same system model and fixed test scenario,the daily operating costs of SAC and PPO were 1.50%and 12.79%higher than that of DDPG,respectively.The daily operating cost of DDPG was only 0.50%higher than that of PSO,but the computation time for 24 h scheduling after training was only about 1/21 800 of that required by PSO.The findings demonstrate that flexible data center tasks can participate in energy-computing coordinated scheduling as adjustable loads of the integrated energy system,and that DDPG can effectively balance task delay constraints and system operating economy while exhibiting good overall scheduling performance in the case studies.关键词
算电协同/综合能源系统/数据中心/柔性任务/动态经济调度/深度强化学习/深度确定性策略梯度Key words
computing-electricity coordination/integrated energy system/data center/flexible task/dynamic economic dispatch/deep reinforcement learning/deep deterministic policy gradient分类
能源科技引用本文复制引用
曲鑫梅,钟俊杰,赵津津,孙康鸿,张振,曹一家..考虑数据中心柔性任务的综合能源系统能-算协同深度强化学习方法[J].综合智慧能源,2026,48(9):56-67,12.基金项目
国家自然科学基金项目(52407079) （52407079）
湖南省自然科学基金项目(2025JJ60372) National Natural Science Foundation of China(52407079) （2025JJ60372）
Natural Science Foundation of Hunan Province of China(2025JJ60372) （2025JJ60372）
