Towards green data center microgrids by leveraging data center loads in providing frequency regulation

Document Type : Article


Department of Electrical and Computer Engineering, Clarkson University, Potsdam, 13699, NY, USA.



In an electricity grid, imbalance between generation and load need be corrected within seconds so that frequency deviations will not threaten the stability and security. This is especially important for a low inertia microgrid operated in islanded mode, which is equipped with a limited number of synchronous generators in regulating frequency. To this end, for data center microgrid with limited on-site generators and increased green energy, when isolated from the utility grid, frequency deviation due to generation-load imbalance could be potentially corrected by conventional generating units as well as data center loads. Focusing on high PV penetrated data center microgrid operated in islanded mode, this paper explores effective control strategies for data center loads to participate in primary frequency response. By analyzing unique operational characteristics of traditional and PV generation units, uninterruptible power supply (UPS) units, and power consumption characteristics of IT components and cooling systems, the proposed load control strategy design effectively utilizes primary FR capabilities while not compromising data center quality of service (QoS) requirements. Numerical simulations via MATLAB/Simulink illustrate effectiveness of the proposed load control strategy in enhancing renewable energy penetration without compromising the system stability and security, providing a viable solution for future green data centers.


1.Shehabi, A., Smith, S.J., Horner, N., et al., United  States Data Center Energy Usage Report, Lawrence  Berkeley National Laboratory, Berkeley, California,  LBNL-1005775, p. 4 (2016). 
2. Data Center Knowledge, 2014. [online] Available:  12/17/undertaking-challenge-reduce-data-centercarbon-  footprint/ 
3. Li, J. and Qi, W. Towards optimal operation of  internet data center microgrid", IEEE Transactions on  Smart Grid, 9(2), pp. 971-979 (2018). 
4. Yu, L., Tao, J., and Yulong, Z. Distributed realtime  energy management in data center microgrids",  IEEE Transactions on Smart Grid, 9(4), pp. 3748-3762  (2018).  5. Aksanli, B., Akyurek, A.S., and Rosing, T. Minimizing  the e_ects of data centers on microgrid stability",  Green Computing Conference and Sustainable Computing  Conference (IGSC), 2015 Sixth International,  pp. 1-9 (2015).  6. Chainer, T.J., Schultz, M.D., Parida, P.R., et al.  Improving data center energy e_ciency with advanced  thermal management", IEEE Trans. Compon.,  Packag., Manuf. Technol., 7(8), pp. 1228-1239 (2017).  7. Koch, B. and Slezak, D. Less energy, more e_-  ciency in server rooms and data centers", Computer  Science-Research and Development, 33(1-2), pp. 251-  252 (2018).  8. Femia, N., Petrone, G., Spagnuolo, G., et al. Optimization  of perturb and observe maximum power  3570 W. Qi and J. Li/Scientia Iranica, Transactions D: Computer Science & ... 26 (2019) 3559{3570  point tracking method", IEEE trans. Power Electron.,  20(4), pp. 963-973 (2005).  9. Senjyu, T., Datta, M., Yona, A., et al. A control  method for small utility connected large pv system  to reduce frequency deviation using a minimal-order  observer", IEEE Transactions on Energy Conversion,  24(2), pp. 520-528 (2009).  10. Han, H., Hou, X.C., Yang, J., et al. Review of power  sharing control strategies for islanding operation of ac  microgrids", IEEE Trans. Smart Grid, 7(1), pp. 200-  215 (2016).  11. Che, L. and Shahidehpour, M. DC microgrids: Economic  operation and enhancement of resilience by  hierarchical control", IEEE Trans. Smart Grid, 5(5),  pp. 2517-2526 (2014).  12. Zhao, Z., Yang, P., Xu, Z., et al. Control strategy  of energy storage system for frequency support of  autonomous microgrid", In Electrical Machines and  Systems (ICEMS), 2015 18th International Conference  on, pp. 422-426 (2015).  13. Molina-Garci__a, A., Bou_ard, F., Kirschen. D.S. Decentralized  demand side contribution to primary frequency  control", IEEE Trans. on Power Syst., 26(1),  pp. 411-419 (2011).  14. Pourmousavi, S. and Nehrir, M. Real-time central  demand response for primary frequency regulation in  microgrids", IEEE Transactions on Smart Grid, 3(4),  pp. 1988-1996 (2012).  15. Medina, J., Muller, N., and Roytelman, I. Demand  response and distribution grid operations: Opportunities  and challenges", IEEE Trans. on Smart Grid,  1(2), pp. 193-198 (2010).  16. Dayarathna, M., Wen, Y., and Fan, R. Data center  energy consumption modeling: A survey", IEEE Communications  Surveys & Tutorials, 18(1), pp. 732-794  (2015).  17. ABB. Review: data centers" (2013).  18. Sawyer, R., Calculating Total Power Requirements for  Data Centers, White Paper, American Power Conversion  (2004).  19. Chen, Z.,Wu, L., and Li, Z. Electric demand response  management for distributed largescale internet data  centers", IEEE Trans. on Smart Grid, 5(2), pp. 651-  661 (2014).  20. Meisner, D. andWenisch, T.F. Does low-power design  imply energy e_ciency for data centers?", in Proc.  17th IEEE/ACM ISLPED, pp. 109-114 (2011).  21. Pelley, S., Meisner, D., Wenisch, T.F., et al. Understanding  and abstracting total data center power", In  Workshop on Energy E_cient Design (2009).  22. Sawyer, R., Calculating Total Power Requirements for  Data Centers, White Paper, American Power Conversion  (2004).  23. Zhabelova, G., Yavarian, A., and Vyatkin, V. Data  center power dynamics within the settings of regional  power grid", Emerging Technologies & Factory Automation  (ETFA) 2015 IEEE 20th Conference on, pp.  1-5 (2015).  24. Bhattacharya, A.A., Culler, D., Kansal, A., et al.  The need for speed and stability in data center power  capping", in Sustainable Computing: Informatics and  Systems, 3(3), pp. 183-193 (2013).