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Learning from Optimal: Energy Procurement Strategies for Data Centers

Published: 15 June 2019 Publication History

Abstract

Environmental concerns and rising grid prices have motivated data center owners to invest in on-site renewable energy sources. However, these sources present challenges as they are unreliable and intermittent. In an effort to mitigate these issues, data centers are incorporating energy storage systems. This introduces the opportunity for electricity bill reduction, as energy storage can be used for power market arbitrage.
We present two supervised learning-based algorithms, LearnBuy, that learns the amount to purchase, and LearnStore, that learns the amount to store, to solve this energy procurement problem. These algorithms utilize the idea of "learning from optimal" by using the values generated by the offline optimization as a label for training. We test our algorithms on a general case, considering buying and selling back to the grid, and a special case, considering only buying from the grid. In the general case, LearnStore achieves a 10--16% reduction compared to baseline heuristics, whereas in the special case, LearnBuy achieves a 7% reduction compared to prior art.

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cover image ACM Other conferences
e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems
June 2019
589 pages
ISBN:9781450366717
DOI:10.1145/3307772
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 the author(s) 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: 15 June 2019

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Cited By

View all
  • (2023)Adapting Datacenter Capacity for Greener Datacenters and GridProceedings of the 14th ACM International Conference on Future Energy Systems10.1145/3575813.3595197(200-213)Online publication date: 20-Jun-2023
  • (2023)Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity MarketsIEEE Transactions on Services Computing10.1109/TSC.2023.327092116:5(3385-3396)Online publication date: Sep-2023
  • (2023)Learning-Aided Framework for Storage Control Facing Renewable EnergyIEEE Systems Journal10.1109/JSYST.2022.315438917:1(652-663)Online publication date: Mar-2023
  • (2021)Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy SourcesBusiness & Information Systems Engineering10.1007/s12599-021-00686-zOnline publication date: 9-Mar-2021
  • (2020)Online Linear Optimization with Inventory Management ConstraintsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/33794824:1(1-29)Online publication date: 5-Jun-2020

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