Joint Optimization of Internet of Things and Smart Grid for Energy Generation, Battery (Dis)charging, and Information Delivery

Qi, L; Wu, B; Chen, X; Zhou, L; Ni, W; Jamalipour, A

Joint Optimization of Internet of Things and Smart Grid for Energy Generation, Battery (Dis)charging, and Information Delivery

Qi, L Wu, B Chen, X Zhou, L Ni, W

Jamalipour, A

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2024, 11, (12), pp. 21647-21658
Issue Date:: 2024-06-15

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Field	Value	Language
dc.contributor.author	Qi, L
dc.contributor.author	Wu, B
dc.contributor.author	Chen, X
dc.contributor.author	Zhou, L
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Jamalipour, A
dc.date.accessioned	2024-08-07T05:07:05Z
dc.date.available	2024-08-07T05:07:05Z
dc.date.issued	2024-06-15
dc.identifier.citation	IEEE Internet of Things Journal, 2024, 11, (12), pp. 21647-21658
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/180272
dc.description.abstract	This article studies the potential of tightly coupling the Internet of Things (IoT) and smart grids for effective management of energy. A new approach is presented to minimize energy costs for IoT devices and edge servers, and reduce reliance on nonrenewable energy by diversifying power supply. Rechargeable batteries at end devices are considered for holistic energy management of the system. We jointly optimize the transmit powers and battery (dis)charging decisions of the devices, the receive beamformer of the edge servers, and the dynamic generation of different energy types. The alternating direction method of multipliers (ADMMs) is applied to support distributed optimization of (dis)charging decisions at individual devices. The Karush-Kuhn-Tucker (KKT) conditions are applied to deliver semi-closed-form power control of the devices. Simulations demonstrate significant improvement of the algorithm in renewable energy utilization and cost saving, compared to the existing techniques.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2024.3377451
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Joint Optimization of Internet of Things and Smart Grid for Energy Generation, Battery (Dis)charging, and Information Delivery
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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	*
dc.date.updated	2024-08-07T05:07:02Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	12

Abstract:

This article studies the potential of tightly coupling the Internet of Things (IoT) and smart grids for effective management of energy. A new approach is presented to minimize energy costs for IoT devices and edge servers, and reduce reliance on nonrenewable energy by diversifying power supply. Rechargeable batteries at end devices are considered for holistic energy management of the system. We jointly optimize the transmit powers and battery (dis)charging decisions of the devices, the receive beamformer of the edge servers, and the dynamic generation of different energy types. The alternating direction method of multipliers (ADMMs) is applied to support distributed optimization of (dis)charging decisions at individual devices. The Karush-Kuhn-Tucker (KKT) conditions are applied to deliver semi-closed-form power control of the devices. Simulations demonstrate significant improvement of the algorithm in renewable energy utilization and cost saving, compared to the existing techniques.

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http://hdl.handle.net/10453/180272