A hybrid governance framework for adaptive and sustainable urban energy management

Wang, R; Qiu, H; Liu, R; Huo, H; Cheng, X; Liu, X

A hybrid governance framework for adaptive and sustainable urban energy management

Wang, R Qiu, H Liu, R Huo, H

Cheng, X Liu, X

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Sustainable Cities and Society, 2025, 130
Issue Date:: 2025-07-15

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Field	Value	Language
dc.contributor.author	Wang, R
dc.contributor.author	Qiu, H
dc.contributor.author	Liu, R
dc.contributor.author	Huo, H https://orcid.org/0000-0003-2440-714X
dc.contributor.author	Cheng, X
dc.contributor.author	Liu, X
dc.date.accessioned	2025-10-09T06:22:47Z
dc.date.available	2025-10-09T06:22:47Z
dc.date.issued	2025-07-15
dc.identifier.citation	Sustainable Cities and Society, 2025, 130
dc.identifier.issn	2210-6707
dc.identifier.issn	2210-6715
dc.identifier.uri	http://hdl.handle.net/10453/190349
dc.description.abstract	Rapid urbanization and escalating energy demands necessitate innovative solutions for sustainable and efficient energy management in smart cities. This paper presents a novel hybrid urban energy governance framework, distinguished by its unique architectural design that orchestrates a synergistic interplay of Multi-Agent Systems (MAS), Internet of Things (IoT), Cloud Computing, and Federated Learning. The framework's core technical novelty lies in its adaptive feedback loop: MAS facilitate decentralized, game-theoretic negotiations among urban areas for resource allocation, informed by real-time IoT data and privacy-preserving demand forecasts generated by Federated Learning. This local intelligence is then dynamically integrated with a cloud-based platform that performs multi-objective optimization (MOO) using evolutionary algorithms to achieve system-wide Pareto-optimal solutions for cost, environmental impact, and energy security. This continuous two-tiered decision-making, balancing local autonomy with global sustainability targets, creates a truly adaptive and resilient governance paradigm. Extensive simulations, using real-world datasets to represent diverse urban scenarios including peak demand and infrastructure challenges, demonstrate the framework's effectiveness in minimizing energy transaction costs, improving forecasting accuracy while ensuring data privacy, and promoting environmental sustainability. This decentralized, adaptive, and secure approach offers a promising pathway for efficient and resilient urban energy management, directly contributing to the development of sustainable smart cities by enhancing energy equity, supporting policy implementation, and optimizing resource use for long-term urban well-being.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Sustainable Cities and Society
dc.relation.isbasedon	10.1016/j.scs.2025.106638
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0502 Environmental Science and Management, 1205 Urban and Regional Planning
dc.subject.classification	3302 Building
dc.subject.classification	3304 Urban and regional planning
dc.subject.classification	4406 Human geography
dc.title	A hybrid governance framework for adaptive and sustainable urban energy management
dc.type	Journal Article
utslib.citation.volume	130
utslib.for	0502 Environmental Science and Management
utslib.for	1205 Urban and Regional Planning
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 Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Cyber Security and Privacy (CCSP)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-10-09T06:22:45Z
pubs.publication-status	Published
pubs.volume	130

Abstract:

Rapid urbanization and escalating energy demands necessitate innovative solutions for sustainable and efficient energy management in smart cities. This paper presents a novel hybrid urban energy governance framework, distinguished by its unique architectural design that orchestrates a synergistic interplay of Multi-Agent Systems (MAS), Internet of Things (IoT), Cloud Computing, and Federated Learning. The framework's core technical novelty lies in its adaptive feedback loop: MAS facilitate decentralized, game-theoretic negotiations among urban areas for resource allocation, informed by real-time IoT data and privacy-preserving demand forecasts generated by Federated Learning. This local intelligence is then dynamically integrated with a cloud-based platform that performs multi-objective optimization (MOO) using evolutionary algorithms to achieve system-wide Pareto-optimal solutions for cost, environmental impact, and energy security. This continuous two-tiered decision-making, balancing local autonomy with global sustainability targets, creates a truly adaptive and resilient governance paradigm. Extensive simulations, using real-world datasets to represent diverse urban scenarios including peak demand and infrastructure challenges, demonstrate the framework's effectiveness in minimizing energy transaction costs, improving forecasting accuracy while ensuring data privacy, and promoting environmental sustainability. This decentralized, adaptive, and secure approach offers a promising pathway for efficient and resilient urban energy management, directly contributing to the development of sustainable smart cities by enhancing energy equity, supporting policy implementation, and optimizing resource use for long-term urban well-being.

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http://hdl.handle.net/10453/190349