research-article

Pricing data center demand response

Authors:

Adam WiermanAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 42, Issue 1

Pages 111 - 123

https://doi.org/10.1145/2637364.2592004

Published: 16 June 2014 Publication History

Abstract

Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, but they can provide as much (or possibly more) flexibility as large-scale storage if given the proper incentives. However, due to the market power most data centers maintain, it is difficult to design programs that are efficient for data center demand response. To that end, we propose that prediction-based pricing is an appealing market design, and show that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue. However, prediction-based pricing may be inefficient when predictions are inaccurate, and so we provide analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction-based pricing. These bounds hold even when network constraints are considered, and highlight that prediction-based pricing is surprisingly robust to prediction error.

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Index Terms

Pricing data center demand response
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 42, Issue 1

Performance evaluation review

June 2014

569 pages

ISSN:0163-5999

DOI:10.1145/2637364

Editors:
Derek Eager
University of Saskatchewan
,
Carey Williamson
University of Calgary

Issue’s Table of Contents

SIGMETRICS '14: The 2014 ACM international conference on Measurement and modeling of computer systems
June 2014
614 pages
ISBN:9781450327893
DOI:10.1145/2591971
General Chairs:
Sujay Sanghavi
The University of Texas at Austin
,
Sanjay Shakkottai
The University of Texas at Austin
,
Program Chairs:
Marc Lelarge
INRIA, France
,
Bianca Schroeder
University of Toronto

Copyright © 2014 ACM.

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Publication History

Published: 16 June 2014

Published in SIGMETRICS Volume 42, Issue 1

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Cited By

Sun JGong ZAgarwal ANoghabi SChandra RSnir MHuang J(2024)Exploring the Efficiency of Renewable Energy-based Modular Data Centers at ScaleProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698544(552-569)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698544
Shi MLin XJiao L(2024)Combining Regularization With Look-Ahead for Competitive Online Convex OptimizationIEEE/ACM Transactions on Networking10.1109/TNET.2024.335099032:3(2391-2405)Online publication date: Jul-2024
https://doi.org/10.1109/TNET.2024.3350990
Hou YZhao JZhu SGu HPan CZhang Y(2024)Analysis of DC Building Flexible Capacity Optimization Configuration Based on PEDF Microgrid2024 13th International Conference on Renewable Energy Research and Applications (ICRERA)10.1109/ICRERA62673.2024.10815217(661-666)Online publication date: 9-Nov-2024
https://doi.org/10.1109/ICRERA62673.2024.10815217
Xie XJiao ZZhang YGuo YYe H(2024)Integrating State-Dependent M/M/c Queue Analysis into Optimization Models for Energy-Aware Multi-Server Data Centers2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)10.1109/DOCS63458.2024.10704409(773-780)Online publication date: 16-Aug-2024
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Li JMotoki MZhang B(2024)Socially optimal energy usage via adaptive pricingElectric Power Systems Research10.1016/j.epsr.2024.110640235(110640)Online publication date: Oct-2024
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Weng YLiu YLim RNguyen H(2023)Distributed Energy Resource Exploitation through Co-Optimization of Power System and Data Centers with Uncertainties during Demand ResponseSustainability10.3390/su15141099515:14(10995)Online publication date: 13-Jul-2023
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Biemann MGunkel PScheller FHuang LLiu X(2023)Data Center HVAC Control Harnessing Flexibility Potential via Real-Time Pricing Cost Optimization Using Reinforcement LearningIEEE Internet of Things Journal10.1109/JIOT.2023.326326110:15(13876-13894)Online publication date: 1-Aug-2023
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