A Caltech Library Service

Data center demand response: Avoiding the coincident peak via workload shifting and local generation

Liu, Zhenhua and Wierman, Adam and Cheng, Yuan and Razon, Benjamin and Chen, Niangjun (2013) Data center demand response: Avoiding the coincident peak via workload shifting and local generation. Performance Evaluation, 70 (10). pp. 770-791. ISSN 0166-5316. doi:10.1016/j.peva.2013.08.014.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers’ participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center’s peak loads and energy expenditure: workload shifting and the use of local power generation. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and local power generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern, workload demand and renewable generation prediction error distributions. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with local generation can provide significant cost savings (up to 40% under the Fort Collins Utilities charging scheme) compared to either alone.

Item Type:Article
Related URLs:
URLURL TypeDescription
Chen, Niangjun0000-0002-2289-9737
Additional Information:© 2013 Elsevier B.V. All rights reserved. Available online 29 August 2013. This work was supported by NSF grants CNS-0846025, CNS-1319820, DoE grant DE-EE0002890, and HP Labs. We are also grateful to Pablo Bauleo from Fort Collins Utilities for his comments and insights.
Funding AgencyGrant Number
Department of Energy (DOE)DE-EE0002890
Subject Keywords: Demand response; Data center; Renewable penetration; Workload management; Online algorithm; Prediction error
Issue or Number:10
Record Number:CaltechAUTHORS:20131114-120720977
Persistent URL:
Official Citation:Zhenhua Liu, Adam Wierman, Yuan Chen, Benjamin Razon, Niangjun Chen, Data center demand response: Avoiding the coincident peak via workload shifting and local generation, Performance Evaluation, Volume 70, Issue 10, October 2013, Pages 770-791, ISSN 0166-5316, (
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:42458
Deposited By: Ruth Sustaita
Deposited On:15 Nov 2013 15:52
Last Modified:10 Nov 2021 16:23

Repository Staff Only: item control page