Optimizing Data Centres Operation to Provide Ancillary Services On-Demand

  • Marcel Antal
  • Claudia Pop
  • Dan Valea
  • Tudor Cioara
  • Ionut Anghel
  • Ioan Salomie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9512)


In this paper a methodology for optimizing Data Centres (DCs) operation allowing them to provide various types of Ancillary Services on-demand is proposed. Energy flexibility models have been defined for hardware devices inside DCs aiming at optimizing energy demand profile by means of load time shifting, alternative usage of non-electrical cooling devices (e.g. thermal storage) or charging/discharging the electrical storage devices. As result DCs are able to shape their energy demand to provide additional load following reserve for large un-forecasted wind ramps, shed or shift energy demand over time to avoid an coincidental peak load and feed back in the grid the energy produced by turning on their backup fossil fuelled generators to maintain (local) reactive power balance under normal conditions. Experiments via numerical simulations based on real world traces of DC operation highlight the methodology potential for optimizing DC energy consumption to provide Ancillary Services.


Data centre Energy consumption optimization Ancillary Services Energy flexibility Demand shifting 



This work has been conducted within the GEYSER project Grant number 609211 [19], co-funded by the European Commission as part of the 7th Research Framework Programme (FP7-SMARTCITIES-2013).


  1. 1.
    Liu, Z., Wierman, A., Chen, Y., Razon, B., Chen, N.: Data center demand response: avoiding the coincident peak via workload shifting and local generation. In: ACM SIGMETRICS/International Conference on Measurement and Modeling of Computer Systems, pp. 341–342. ACM, New York (2013)Google Scholar
  2. 2.
    Ghatikar, G., Ganti, V., Matson, N., Piette, M.A.: Demand Response Opportunities and Enabling Technologies for Data Centers: Findings from Field Studies (2012).
  3. 3.
    Ghasemi-Gol, M., Wang, Y., Pedram, M., Hsieh, M.: An optimization framework for data centers to minimize electric bill under day-ahead dynamic energy prices while providing regulation services. In: Green Computing Conference (IGCC), Dallas, pp. 1–9. IEEE (2014)Google Scholar
  4. 4.
    Koomey, J.: Growth in data center electricity use 2005 to 2010. Analytics Press, Oakland, CA, 1 August 2010Google Scholar
  5. 5.
    Ma, O., Alkadi, N.: Demand response for ancillary services. IEEE Trans. Smart Grid (99) (2013). IEEEGoogle Scholar
  6. 6.
    Zhang, Y., Wang, Y., Wang, X.: TEStore: exploiting thermal and energy storage to cut the electricity bill for datacenter cooling. In: 8th International Conference and Workshop on Systems Virtualization Management, Las Vegas, pp. 19–27 (2012)Google Scholar
  7. 7.
    Tang, Q.: Sensor-based fast thermal evaluation model for energy efficient high-performance datacenters. Intel Corporation. In: Intelligent Sensing and Information Processing (ICISIP) 2006, Bangalore, pp. 203–208, IEEE (2006)Google Scholar
  8. 8.
    Sawyer, R.L.: Calculating Total Power Requirements for Data Centers, Whitepaper.
  9. 9.
    Li, L., Zheng, W., Wang, X.: Coordinating liquid and free air cooling with workload allocation for data center power minimization. In: 11th International Conference on Autonomic Computing (ICAC 2014), Philadelphia. USENIX (2014)Google Scholar
  10. 10.
    Urgaonkar, R., Urgaonkar, B., Neely, M., Sivasubramaniam, A.: Optimal power cost management using stored energy in data centers. In: ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, pp. 221–232, ACM, New York (2011)Google Scholar
  11. 11.
    Zheng, W., Ma, K., Wang, X.: Exploiting thermal energy storage to reduce data center capital and operating expenses. In: 20th International Symposium on High Performance Computer Architecture (HPCA), Orlando. IEEE (2014)Google Scholar
  12. 12.
    Govindan, S., Sivasubramaniam, A., Urgaonkar, B.: Benefits and limitations of tapping into stored energy for datacenters. In: Proceedings of the 38th Annual International Symposium on Computer Architecture (ISCA), San Jose, pp. 341–352. IEEE (2011)Google Scholar
  13. 13.
    Aksanli, B., Rosing, T.: Providing regulation services and managing data center peak power budgets. In: Design, Automation and Test in Europe Conference and Exhibition (DATE), Dresden, pp. 1–4. IEEE (2014)Google Scholar
  14. 14.
    Ghamkhari, M. Mohsenian-Rad, H.: Data centers to offer ancillary services. In: Proceedings of IEEE International Conference on Smart Grid Communications (SmartGridComm), Tainan City. IEEE (2012)Google Scholar
  15. 15.
    Li, Y., Chiu, D., Liu, C., Phan, L.T.X., Gill, T., Aggarwal, S., Zhang, Z., Thau Loo, B., Maier, D., McManus, B.: Towards dynamic pricing-based collaborative optimizations for green data centers. In: Data Engineering Workshops (ICDEW), Brisbane, pp. 272–278. IEEE (2013)Google Scholar
  16. 16.
    Janacek, S., Schomaker, G., Nebel, W.: Data center smart grid integration considering renewable energies and waste heat usage. In: Klingert, S., Hesselbach-Serra, X., Ortega, M.P., Giuliani, G. (eds.) E2DC 2013. LNCS, vol. 8343, pp. 99–109. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  17. 17.
    Chen, Y., et al.: The case for evaluating mapreduce performance using workload suites. In: Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), Singapore, pp. 390–399. IEEE (2011)Google Scholar
  18. 18.
  19. 19.
    GEYSER – Green Energy Data Centers as Energy Prosumers in Smart City.
  20. 20.
    LINDO™ Software for Integer Programming, Linear Programming, Nonlinear Programming, Stochastic Programming, Global Optimization.
  21. 21.
    Zheng, W., Ma, K., Wang, X.: Exploiting thermal energy storage to reduce data center capital and operating expenses.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marcel Antal
    • 1
  • Claudia Pop
    • 1
  • Dan Valea
    • 1
  • Tudor Cioara
    • 1
  • Ionut Anghel
    • 1
  • Ioan Salomie
    • 1
  1. 1.Technical University of Cluj-NapocaCluj-NapocaRomania

Personalised recommendations