REAL-TIME ENERGY TRADING AND FUTURE PLANNING FOR FIFTH GENERATION WIRELESS COMMUNICATIONS

被引：32

作者：

Chen, Xiaojing ^{[1
,3
]}

Ni, Wei ^{[6
]}

Chen, Tianyi ^{[7
]}

Collings, Iain B. ^{[4
]}

Wang, Xin ^{[2
,5
]}

Giannakis, Georgios B. ^{[8
]}

机构：

[1] Fudan Univ, Shanghai, Peoples R China

[2] Fudan Univ, Dept Commun Sci & Engn, Shanghai, Peoples R China

[3] Macquarie Univ, N Ryde, NSW, Australia

[4] Macquarie Univ, Engn, N Ryde, NSW, Australia

[5] Florida Atlantic Univ, Dept Comp & Elect Engn & Comp Sci, Boca Raton, FL 33431 USA

[6] CSIRO, Digital Prod ity & Serv Flagship, Canberra, ACT, Australia

[7] Univ Minnesota, Minneapolis, MN 55455 USA

[8] Univ Minnesota, Wireless Telecommun, Minneapolis, MN 55455 USA

来源：

IEEE WIRELESS COMMUNICATIONS | 2017年 / 24卷 / 04期

基金：

美国国家科学基金会; 中国国家自然科学基金;

关键词：

NETWORKS;

D O I：

10.1109/MWC.2017.1600344

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Future 5G cellular networks, equipped with energy harvesting devices, are uniquely positioned to interoperate with smart grid, due to their resemblance in scale and ubiquity. New interoperable functionalities, such as real-time energy trading and future planning, are of particular interest to improve productivity, but extremely challenging due to the physical characteristics of wireless channels and renewable energy sources, as well as time-varying energy prices. Particularly, a priori knowledge on future wireless channels, energy harvesting, and pricing is unavailable in practice. In this scenario, simple but efficient Lyapunov control theory can be applied to stochastically optimize energy trading and planning. Simulations demonstrate that Lyapunov control can approach the offline optimum which is obtained under the ideal assumption of full a priori knowledge, leading to 65 percent reduction of the operational expenditure of 5G on energy over existing alternatives.

引用

页码：24 / 30

页数：7

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