Predictive Electricity Cost Minimization Through Energy Buffering in Data Centers

More and more cloud computing services are handled by different Internet operators in distributed Internet data centers (IDCs), which incurs massive electricity costs. Today, the power usage of data centers contributes to more than 1.5% market share of electricity consumption across the United State...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2014-01, Vol.5 (1), p.230-238
Main Authors: Yao, Jianguo, Liu, Xue, Zhang, Chen
Format: Article
Language:English
Subjects:
Batteries
Data center
Electricity
electricity cost
energy buffering
Internet
Power demand
Servers
smart grid
Smart grids
Uninterruptible power systems
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Summary:More and more cloud computing services are handled by different Internet operators in distributed Internet data centers (IDCs), which incurs massive electricity costs. Today, the power usage of data centers contributes to more than 1.5% market share of electricity consumption across the United States. Minimization of these costs benefits cloud computing operators, and attracts increasing attentions from many research groups and industrial sectors. Along with the deployment of smart grid, the electrical real-time pricing policy promotes power consumers to adaptively schedule their electricity utilization for lower operational costs. This paper proposes a novel approach to enable electrical energy buffering in batteries to predictively minimize IDC electricity costs in smart grid. Batteries are charged when electricity price is low and discharged to power servers when electricity price is high. A power management controller is used per battery to arbitrate the charging and discharging actions of the battery. The controller is designed as a MPC-based (model predictive control) controller. To this end, an MPC power minimization problem is formulated based on a discrete state-space model with states of battery power level and cost. Extensive simulation results demonstrate the effectiveness of our approach based on real-life electricity prices in smart grid.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2013.2274525