Energy storage in datacenters: what, where, and how much?

  • Authors:

  • Di Wang;Chuangang Ren;Anand Sivasubramaniam;Bhuvan Urgaonkar;Hosam Fathy

  • Affiliations:

  • The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA;The Pennsylvania State University, University Park, PA, USA

  • Venue:
  • Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
  • Year:
  • 2012

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Abstract

Energy storage - in the form of UPS units - in a datacenter has been primarily used to fail-over to diesel generators upon power outages. There has been recent interest in using these Energy Storage Devices (ESDs) for demand-response (DR) to either shift peak demand away from high tariff periods, or to shave demand allowing aggressive under-provisioning of the power infrastructure. All such prior work has only considered a single/specific type of ESD (typically re-chargeable lead-acid batteries), and has only employed them at a single level of the power delivery network. Continuing technological advances have provided us a plethora of competitive ESD options ranging from ultra-capacitors, to different kinds of batteries, flywheels and even compressed air-based storage. These ESDs offer very different trade-offs between their power and energy costs, densities, lifetimes, and energy efficiency, among other factors, suggesting that employing hybrid combinations of these may allow more effective DR than with a single technology. Furthermore, ESDs can be placed at different, and possibly multiple, levels of the power delivery hierarchy with different associated trade-offs. To our knowledge, no prior work has studied the extensive design space involving multiple ESD technology provisioning and placement options. This paper intends to fill this critical void, by presenting a theoretical framework for capturing important characteristics of different ESD technologies, the trade-offs of placing them at different levels of the power hierarchy, and quantifying the resulting cost-benefit trade-offs as a function of workload properties.