A hybrid governance framework for adaptive and sustainable urban energy management

Bibliographic Data

ID21233907
AuthorsRenfang Wang (0000-0002-8239-8248, Zhejiang Wanli University), Hong Qiu (0000-0002-4960-8661, Zhejiang Wanli University), Ruyu Liu (0000-0003-2646-7349, Technical University of Denmark, corresponding author), Huan Huo (0000-0003-2440-714X, University of Technology Sydney), Xu Cheng (0009-0008-2222-735X, Technical University of Denmark), Xiufeng Liu (0000-0003-2264-9882, Technical University of Denmark, corresponding author)
Year2025
Volume130
Pages106638
Publication date2025-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSustainable Cities and Society (JOURNAL)
Journal identifiersISSN: 2210-6707 • E-ISSN: 2210-6715
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.scs.2025.106638
OpenAlexW4412627711
LanguageEN
Citations received3
References cited61

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

Adaptive Management · Business · Corporate governance · Economics · Environmental economics · Environmental planning · Environmental resource management · Energy Efficiency and Management · Environmental Science · Smart Cities and Technologies · Smart Grid Energy Management