Real-Time Energy Trading and Future Planning for Fifth Generation Wireless Communications

Chen, X; Ni, W; Chen, T; Collings, IB; Wang, X; Guanacos, GB

Real-Time Energy Trading and Future Planning for Fifth Generation Wireless Communications

Chen, X Ni, W

Chen, T Collings, IB Wang, X Guanacos, GB

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Publication Type:: Journal Article
Citation:: IEEE Wireless Communications, 2017, 24 (4), pp. 24 - 30
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Chen, X	en_US
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X	en_US
dc.contributor.author	Chen, T	en_US
dc.contributor.author	Collings, IB	en_US
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Guanacos, GB	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	IEEE Wireless Communications, 2017, 24 (4), pp. 24 - 30	en_US
dc.identifier.issn	1536-1284	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126851
dc.description.abstract	© 2017 IEEE. 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.	en_US
dc.relation.ispartof	IEEE Wireless Communications	en_US
dc.relation.isbasedon	10.1109/MWC.2017.1600344	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Real-Time Energy Trading and Future Planning for Fifth Generation Wireless Communications	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	24	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

© 2017 IEEE. 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.

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http://hdl.handle.net/10453/126851