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Leveraging thermal storage to cut the electricity bill for datacenter cooling

Published: 23 October 2011 Publication History

Abstract

The electricity cost of cooling systems can account for 30% of the total electricity bill of operating a data center. While many prior studies have tried to reduce the cooling energy in data centers, they cannot effectively utilize the time-varying power prices in the power market to cut the electricity bill of data center cooling. Thermal storage techniques have provided opportunities to store cooling energy in ice or water-based tanks or overcool the data center when the power price is relatively low. Consequently, when the power price is high, data centers can choose to use less electricity from power grid for cooling, resulting in a significantly reduced electricity bill.
In this paper, we design and evaluate TStore, a cooling strategy that leverages thermal storage to cut the electricity bill for cooling, without causing servers in a data center to overheat. TStore checks the low prices in the hourahead power market and overcools the thermal masses in the datacenter, which can then absorb heat when the power price increases later. On a longer time scale, TStore is integrated with auxiliary thermal storage tanks, which are recently adopted by some data-centers to store energy in the form of ice when the power price is low at night, such that the stored ice can be used to cool the datacenter in daytime. We model the impacts of TStore on server temperatures based on Computational Fluid Dynamics (CFD) to consider the realistic thermal dynamics in a data center with 1,120 servers. We then evaluate TStore using workload traces from real-world data centers and power price traces from a real power market. Our results show that TStore achieves the desired cooling performance with a 16.8% less electricity bill than the current practice.

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cover image ACM Conferences
HotPower '11: Proceedings of the 4th Workshop on Power-Aware Computing and Systems
October 2011
51 pages
ISBN:9781450309813
DOI:10.1145/2039252
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 October 2011

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Cited By

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  • (2022)Optimal Scheduling of Data Centers Considering Renewable Energy Consumption and Temporalspatial Load Characteristics2022 Power System and Green Energy Conference (PSGEC)10.1109/PSGEC54663.2022.9881170(283-288)Online publication date: Aug-2022
  • (2022)Market-Based Resource Allocation of Distributed Cloud Computing Services: Virtual Energy Storage SystemsIEEE Internet of Things Journal10.1109/JIOT.2022.31847509:22(22811-22821)Online publication date: 15-Nov-2022
  • (2021)Load Regulation Potential Model for Data Centers2021 IEEE Sustainable Power and Energy Conference (iSPEC)10.1109/iSPEC53008.2021.9735432(2307-2311)Online publication date: 23-Dec-2021
  • (2021)Internet Data Center Load Modeling for Demand Response Considering the Coupling of Multiple Regulation MethodsIEEE Transactions on Smart Grid10.1109/TSG.2020.304803212:3(2060-2076)Online publication date: May-2021
  • (2020)A Survey of Research on Datacenters Using Energy Storage Devices to Participate in Smart Grid Demand Response2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)10.1109/ICPICS50287.2020.9202343(22-26)Online publication date: Jul-2020
  • (2020)Demand Charges Minimization for Ontario Class-A Customers Based on the Optimization of Energy Storage System2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)10.1109/CCECE47787.2020.9255750(1-4)Online publication date: 30-Aug-2020
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  • (2020)State-of-the-art on thermal energy storage technologies in data centerEnergy and Buildings10.1016/j.enbuild.2020.110345226(110345)Online publication date: Nov-2020
  • (2016)How to cool internet-scale distributed networks on the cheapProceedings of the Seventh International Conference on Future Energy Systems10.1145/2934328.2934337(1-12)Online publication date: 21-Jun-2016
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