Bi-level Economic Dispatch Modeling Considering the Load Regulation Potential of Internet Data Centers

被引：0
|
作者：
Chen M. [1 ]
Gao C. [1 ]
Chen S. [2 ]
Li D. [2 ]
Liu Q. [3 ]
机构：
[1] School of Electrical Engineering, Southeast University, Nanjing, 210096, Jiangsu Province
[2] China Electric Power Research Institute, Haidian District, Beijing
[3] Zhejiang Electric Power Corporation, Hangzhou, 310007, Zhejiang Province
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2019年 / 39卷 / 05期
关键词：
Bi-level economic dispatch; Delay bound; Internet data centers; Regulation potential; Sensitivity analysis;
D O I：
10.13334/j.0258-8013.pcsee.180456
中图分类号：
学科分类号：
摘要：
With the rapid development of internet technology, the scale and quantity of the data centers is expanding rapidly, and the data centers have become a heavy load. Geographically distributed internet data centers (IDCs) can transfer power load by transferring data workload through data network. This paper established the encapsulated power consumption model of IDCs and analyzed their load regulation potential, based on which, a bi-level economic dispatch model considering the load regulation potential of IDCs was proposed. The sensitivity of the regulation potential of IDCs to the delay bound for processing data load was also analyzed in this paper. The case study showed the application of the proposed model. The simulation results showed that, the economic dispatch considering the load regulation potential of IDCs can reduce operation cost of the power system. Furthermore, the regulation potential of IDCs increases with the increase of the delay bound, and the sensitivity of their regulation potential to the delay bound decreases with the increase of the delay bound. © 2019 Chin. Soc. for Elec. Eng.
引用
收藏
页码：1301 / 1313
页数：12
相关论文
共 42 条
  • [1] Koomey J.G., Growth in data center electricity use 2005 to 2010, (2011)
  • [2] Gu L., Zhou F., Meng H., Research on energy consumption and energy efficiency of data center in our country, Energy of China, 32, 11, (2010)
  • [3] Wang H., Huang J., Lin X., Et al., Proactive demand response for data centers: a win-win solution, IEEE Transactions on Smart Grid, 7, 3, pp. 1584-1596, (2016)
  • [4] Gao C., Li Q., Li Y., Bi-level optimal dispatch and control strategy for air-conditioning load based on direct load control, Proceedings of the CSEE, 34, 10, pp. 1546-1555, (2014)
  • [5] Zhou L., Li Y., Gao C., Improvement of temperature adjusting method for aggregated air-conditioning loads and its control strategy, Proceedings of the CSEE, 34, 31, pp. 5579-5589, (2014)
  • [6] Gao C., Zhang L., Yang X., Research on load aggregation of central air conditioning and its participation in the operation of power system, Proceedings of the CSEE, 37, 11, (2017)
  • [7] Song M., Gao C., Yan H., Et al., Thermal battery modeling of inverter air conditioning for demand response, IEEE Transactions on Smart Grid, (2017)
  • [8] Xiang D., Hu Z., Song Y., Et al., Business model and day-ahead dispatch strategy to reduce wind power curtailment through vehicle-to-grid, Proceedings of the CSEE, 35, 24, pp. 6293-6303, (2015)
  • [9] Sun H., Shen Z., Zhou W., Et al., Congestion dispatch research of active distribution network with electric vehicle group response, Proceedings of the CSEE, 37, 19, (2017)
  • [10] Huang M., Yu A., Study on coordinated charging strategy for battery swapping station based on improved cuckoo search algorithm, Proceedings of the CSEE, 38, 4, (2018)
12345