Energy Management Optimization for Cellular Networks under Renewable Energy Generation Uncertainty
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Electrical Engineering Program
Online Publication Date2017-03-28
Print Publication Date2017-06
Permanent link to this recordhttp://hdl.handle.net/10754/623083
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AbstractThe integration of renewable energy (RE) as an alternative power source for cellular networks has been deeply investigated in literature. However, RE generation is often assumed to be deterministic; an impractical assumption for realistic scenarios. In this paper, an efficient energy procurement strategy for cellular networks powered simultaneously by the smart grid (SG) and locally deployed RE sources characterized by uncertain processes is proposed. For a one-day operation cycle, the mobile operator aims to reduce its total energy cost by optimizing the amounts of energy to be procured from the local RE sources and SG at each time period. Additionally, it aims to determine the amount of extra generated RE to be sold back to SG. A chance constrained optimization is first proposed to deal with the RE generation uncertainty. Then, two convex approximation approaches: Chernoff and Chebyshev methods, characterized by different levels of knowledge about the RE generation, are developed to determine the energy procurement strategy for different risk levels. In addition, their performances are analyzed for various daily scenarios through selected simulation results. It is shown that the higher complex Chernoff method outperforms the Chebyshev one for different risk levels set by the operator.
CitationBen Rached N, Ghazzai H, Kadri A, Alouini M-S (2017) Energy Management Optimization for Cellular Networks under Renewable Energy Generation Uncertainty. IEEE Transactions on Green Communications and Networking: 1–1. Available: http://dx.doi.org/10.1109/TGCN.2017.2688424.
SponsorsThis work was made possible by grant NPRP # 6-001-2-001 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.