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Title	Integrated Management Of Urban Resources Toward Net-Zero Smart Cities Considering Renewable Energies Uncertainty And Modeling In Digital Twin
ID_Doc	31862
Authors	Zhao X.; Zhang Y.
Year	2024
Published	Sustainable Energy Technologies and Assessments, 64
DOI	http://dx.doi.org/10.1016/j.seta.2024.103656
Abstract	This research introduces a groundbreaking strategy for urban microgrid (MG) management and social economics, focusing on enhancing energy efficiency, reliability, and steering toward net-zero emissions. Leveraging the Multi-MG (MMG) architecture in renewable energy resources (RERs), our study integrates advanced techniques like the Covariance Matrix Adaptation algorithm and Long Short-Term Memory Recurrent Neural Networks for precise forecasting. The incorporation of the unscented transform models market expenses and RER generation accurately. Real-time pricing (RTP) and time-of-use (TOU) plans are optimized using a hybrid genetic algorithm, addressing uncertainties in both RERs and load variations effectively. Additionally, our research explores Digital Twin integration with Non-Intrusive Load Monitoring (NILM) and smart metering devices for refined Demand Response (DR). This synergy not only elevates MG efficiency but also bolsters cybersecurity, offering personalized insights into energy consumption patterns. Our approach represents a significant leap in MG management, ushering in smarter, more adaptive energy grids in urban settings. By fostering sustainability and resilience, it signifies a pivotal step toward a greener and energy-efficient future, aligning seamlessly with the goals of net-zero emission control initiatives. © 2024 Elsevier Ltd
Author Keywords	Digital Twin; Reliability; Renewable Energy; Smart Cities; Urban Systems

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