丁肇豪, 曹雨洁, 张素芳, 王鹏, 刘吉臻, 程明, 毛宏举. 能源互联网背景下数据中心与电力系统协同优化(一):数据中心能耗模型[J]. 中国电机工程学报, 2022, 42(9): 3161-3176. DOI: 10.13334/j.0258-8013.pcsee.210813
引用本文: 丁肇豪, 曹雨洁, 张素芳, 王鹏, 刘吉臻, 程明, 毛宏举. 能源互联网背景下数据中心与电力系统协同优化(一):数据中心能耗模型[J]. 中国电机工程学报, 2022, 42(9): 3161-3176. DOI: 10.13334/j.0258-8013.pcsee.210813
DING Zhaohao, CAO Yujie, ZHANG Sufang, WANG Peng, LIU Jizhen, CHENG Ming, MAO Hongju. Coordinated Operation for Data Center and Power System in the Context of Energy Internet (I): Energy Demand Management Model of Data Center[J]. Proceedings of the CSEE, 2022, 42(9): 3161-3176. DOI: 10.13334/j.0258-8013.pcsee.210813
Citation: DING Zhaohao, CAO Yujie, ZHANG Sufang, WANG Peng, LIU Jizhen, CHENG Ming, MAO Hongju. Coordinated Operation for Data Center and Power System in the Context of Energy Internet (I): Energy Demand Management Model of Data Center[J]. Proceedings of the CSEE, 2022, 42(9): 3161-3176. DOI: 10.13334/j.0258-8013.pcsee.210813

能源互联网背景下数据中心与电力系统协同优化(一):数据中心能耗模型

Coordinated Operation for Data Center and Power System in the Context of Energy Internet (I): Energy Demand Management Model of Data Center

  • 摘要: 随着云计算技术的发展和新基建战略的推进,数据中心正发展为数字信息网络和各行各业的纽带。在能源互联网背景下,探索数据中心内部的运行灵活性,是实现数据网络与能源网络深度融合,以数据赋能电网运行灵活性的重要研究方向。该系列论文在总结国内外相关研究成果的基础之上,将详细分析数据中心与电力系统协同优化的潜力和意义。作为系列论文的首篇,该文结合实际运行情况,探讨数据中心中两类工作负载的基本特征、运行特性和时空调度灵活性;从两个方面梳理国内外研究中所使用的数据中心能耗模型;以此为基础,进一步分析数据中心电力负荷在时间灵活性、空间灵活性和多能转换灵活性方面的潜力;最后,结合我国相关政策,分析数据中心在能耗管理方面的发展趋势。

     

    Abstract: With the development of cloud computing technology and the promotion of the new infrastructure strategy, data centers are acting as the linkage between digital & information networks and traditional industries. In the context of energy Internet, exploring internal operation flexibility of data centers is an important research field to accomplish the deep integration of data network and energy network, and thus boost system flexibility. On the basis of relevant literature hitherto, the potential and significance of collaborative optimization between data center and power system were analyzed in detail in this series of articles. In this first paper, the basic characteristics and spatio-temporal scheduling flexibility of two types of workloads in data center were analyzed based on the actual operation situation. The data center energy consumption models explored in existing literature were classified into two types. On this basis, the potential flexibility, including temporal flexibility, spatial flexibility and multi-energy conversion flexibility of data centers, was further analyzed. In addition, the trend of energy management in data centers was inferred and explained considering relevant policies at present.

     

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