4.6 Article

Real-Time Energy Management for Cloud Data Centers in Smart Microgrids

Journal

IEEE ACCESS
Volume 4, Issue -, Pages 941-950

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2016.2539369

Keywords

Cloud data centers; smart microgrids; energy cost; uncertainty

Funding

  1. National Natural Science Foundation of China [61502252, 61522109, 61471177, 61471163, 61428104]
  2. Natural Science Foundation of Jiangsu Province [BK20150869, BK20150040, BK20140883]
  3. Joint Specialized Research Fund for the Doctoral Program of Higher Education
  4. Research Grants Council Earmarked Research Grants [20130142140002]
  5. General Program for Natural Science Research of Higher Education Institute of Jiangsu Province [15KJB110017]
  6. Scientific Research Foundation of Nanjing University of Posts and Telecommunications [NY214187]

Ask authors/readers for more resources

Cloud service providers are typically faced with three significant problems when running their cloud data centers, i.e., rising electricity bills, growing carbon footprints, and unexpected power outages. To mitigate these issues, running cloud data centers in smart microgrids (SMGs) is a good choice, since SMGs can enhance the energy efficiency, sustainability, and reliability of electrical services. Thus, in this paper, we investigate the problem of energy management for cloud data centers in SMGs. To be specific, we would minimize the time average expected energy cost (including electricity bill, battery depreciation cost, the total generation cost of conventional generators, and revenue loss due to the unfinished workloads) with the consideration of three practical factors, i.e., the ramping constraints of backup generators, the charging and discharging efficiency parameters of batteries, and two kinds of data center workloads. A stochastic programming is formulated by integrating the constraints associated with workload allocation, electricity buying/selling, battery management, backup generators, and power balancing. To solve the stochastic programming problem, an online algorithm is designed, and the algorithmic performance is analyzed. Simulation results show the advantages of the designed algorithm over other baselines.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available