Energy Management Optimization for Cellular Networks under Renewable Energy Generation Uncertainty
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionApplied Mathematics and Computational Science Program
Electrical Engineering Program
Date
2017-03-28Online Publication Date
2017-03-28Print Publication Date
2017-06Permanent link to this record
http://hdl.handle.net/10754/623083
Metadata
Show full item recordAbstract
The 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.Citation
Ben 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.Sponsors
This 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.Additional Links
http://ieeexplore.ieee.org/document/7888489/ae974a485f413a2113503eed53cd6c53
10.1109/TGCN.2017.2688424