Ben Rached, N, Ghazzai, H, Kadri, A et al. (1 more author) (2017) Energy Management Optimization for Cellular Networks Under Renewable Energy Generation Uncertainty. IEEE Transactions on Green Communications and Networking, 1 (2). pp. 158-166.
Abstract
The integration of renewable energy (RE) as an alternative power source for cellular networks has been deeply investigated in the 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: 1) Chernoff and 2) 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Cellular networks; chance constrained optimization; convex approximation; renewable energy generation uncertainty |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Nov 2023 15:49 |
Last Modified: | 27 Nov 2023 15:49 |
Published Version: | http://dx.doi.org/10.1109/tgcn.2017.2688424 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/tgcn.2017.2688424 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:195471 |