Energy Management Optimization for Cellular Networks Under Renewable Energy Generation Uncertainty

Abstract

Abstract-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.

Renewable Energy-Enabled Cellular Networks

“…The authors [94] aimed to maximize the profit of a network operator using the optimal amount of energy drawn from a smart grid, and RE generator for each BS and each time slot considering the uncertainty of RE and the variation in the smart grid price. Additionally, since the network is powered by the smart grid, the authors optimized the amount of excess RE to be sold back to the smart grid and included it in the profit.…”

Renewable Energy-Enabled Cellular Networks

“…10. This RE allocation across time slots has been addressed jointly with energy sharing [94], user association [100], [103], cell size optimization [95], and transmit power control [57], [58].…”

Optimization of Mixed Energy Supply of IoT Network Based on Matching Game and Convex Optimization

“…Equation ( 19) is the value range of binary parameters for "switch" of the TEs, and Equation (20) indicates that each energy supplier has an upper limit of energy generation. Equation (21) demonstrates the relationship between energy quantity that SBS must purchase from the smart grid and energy consumption of the SBS.…”

“…With the continuous development of a smart grid, the renewable energy generation unit of BSs provides an opportunity to decrease the energy expense cost of a communication system [ 20 ]. Rached et al [ 21 ] proposed an energy purchasing strategy of the cellular network to solve the uncertainty in renewable energy supply. This strategy involves simultaneous power supplies of the smart grid and uncertain local renewable energy sources.…”