Tie-line Power Fluctuation Smoothing Algorithm Based on Data Center Demand Response

被引：0
|
作者：
Yang T. [1 ]
Li Y. [1 ]
Pen H. [1 ]
Zhang Y. [1 ]
机构：
[1] Key Laboratory of Smart Grid of Ministry of Education (Tianjin University), Nankai District, Tianjin
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2017年 / 37卷 / 19期
基金：
中国国家自然科学基金;
关键词：
Controllable load; Data center; Demand response; Tie-line power control;
D O I：
10.13334/j.0258-8013.pcsee.162258
中图分类号：
学科分类号：
摘要：
The exploit of renewable energy can provide an effective solution to solve the problem of enormous energy consumption and operation cost in datacenters. While comparing with the traditional power supply mode, renewable energy has its unique characteristics, such as intermittency and randomness. Thus, when renewable energy supplies power to the datacenter industrial park, this kind of power supply mode not only has bad effects on the normal operation of equipment in datacenters and the power supply reliability of the industrial park, but also it would impact the stability of the main gird operation. After analyzing the characteristics of datacenter structure and different kinds of user requests, we acknowledge that the server cluster is a controllable workload. Furthermore, with the occupation of the redundant uninterruptible power system (UPS), a new data center tie-line power control method was proposed. A server cluster work load tuning model was proposed based on task delay mechanism. A demand side response controllable work load model of server clusters and a demand side response controllable workload model of UPS were established. Based on the models a novel datacenter industrial park tie-line power control method was proposed.. On the premise of ensuring service-level agreement (SLA) and power supply reliability, this method can regulate the tie-line power fluctuations between the datacenter industrial park and main grid effectively by dynamic adjusting the server cluster workload and the state of charge of UPS energy batteries. With the experiments, the proposed control method was evaluated; furthermore multiple parameters which would influence the effect of smoothing the power fluctuation in tie-line were analyzed as well. © 2017 Chin. Soc. for Elec. Eng.
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收藏
页码：5529 / 5540
页数：11
相关论文
共 25 条
  • [11] Ghamkhari M., Mohsenian-Rad H., Energy and performance management of green data centers: a profit maximization approach, IEEE Transactions on Smart Grid, 4, 2, pp. 1017-1025, (2013)
  • [12] Dou H., Qi Y., Wang P., Et al., Workload scheduling algorithm for minimizing electricity bills of green data centers, Journal of Software, 25, 7, pp. 1448-1458, (2014)
  • [13] Li C., Qouneh A., Li T., iSwitch: coordinating and optimizing renewable energy powered server clusters, Proceedings of the 39th IEEE Annual International Symposium on Computer Architecture (ISCA), pp. 512-523, (2012)
  • [14] Chen S., Shroff N.B., Sinha P., Heterogeneous delay tolerant task scheduling and energy management in the smart grid with renewable energy, IEEE Journal on Selected Areas in Communications, 31, 7, pp. 1258-1267, (2013)
  • [15] Yu L., Tang Q., Zhang J., Optimal operation for residential micro-grids based on load resources classification modelling and heuristic strategy, Power System Technology, 39, 8, pp. 2180-2187, (2015)
  • [16] Cho J., Kim Y., Improving energy efficiency of dedicated cooling system and its contribution towards meeting an energy-optimized data center, Applied Energy, 165, pp. 967-982, (2016)
  • [17] Katz R.H., Tech titans building boom, IEEE Spectrum, 46, 2, pp. 40-54, (2009)
  • [18] Mohsenian-Rad A.H., Leon-Garcia A., Coordination of cloud computing and smart power grids, Proceedings of the First IEEE International Conference on Smart Grid Communications, pp. 368-372, (2010)
  • [19] Goiri I., Le K., Nguyen T.D., Et al., GreenHadoop: leveraging green energy in data-processing frame-works, Proceedings of the 7th ACM European Conference on Computer Systems, pp. 57-70, (2012)
  • [20] Wang D., Liu X., A resource scheduling strategy for cloud computing platform of power system simulation based on dynamic migration of virtual machine, Automation of Electric Power Systems, 39, 12, pp. 97-105, (2015)
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