Optimized Energy Procurement for Cellular Networks with Uncertain Renewable Energy Generation
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionApplied Mathematics and Computational Science Program
Electrical Engineering Program
Date
2017-02-07Online Publication Date
2017-02-07Print Publication Date
2016-12Permanent link to this record
http://hdl.handle.net/10754/623884
Metadata
Show full item recordAbstract
Renewable energy (RE) is an emerging solution for reducing carbon dioxide (CO2) emissions from cellular networks. One of the challenges of using RE sources is to handle its inherent uncertainty. In this paper, a RE powered cellular network is investigated. For a one-day operation cycle, the cellular network aims to reduce energy procurement costs from the smart grid by optimizing the amounts of energy procured from their locally deployed RE sources as well as from the smart grid. In addition to that, it aims to determine the extra amount of energy to be sold to the electrical grid at each time period. Chance constrained optimization is first proposed to deal with the randomness in the RE generation. Then, to make the optimization problem tractable, two well- know convex approximation methods, namely; Chernoff and Chebyshev based-approaches, are analyzed in details. Numerical results investigate the optimized energy procurement for various daily scenarios and compare between the performances of the employed convex approximation approaches.Citation
Ben Rached N, Ghazzai H, Kadri A, Alouini M-S (2016) Optimized Energy Procurement for Cellular Networks with Uncertain Renewable Energy Generation. 2016 IEEE Global Communications Conference (GLOBECOM). Available: http://dx.doi.org/10.1109/glocom.2016.7842105.Sponsors
This work was made possible by NPRP grant # 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/7842105/ae974a485f413a2113503eed53cd6c53
10.1109/glocom.2016.7842105