Abstract

The rapid growth of smart cities requires the implementation of advanced technologies to effectively manage and secure distributed energy systems. As energy forms become increasingly interconnected due to urbanization, the systems in place become more complex, raising concerns about security and functionality. This research aims to address these challenges by proposing adaptive AI architectures specifically designed to protect and enhance distributed energy systems in smart urban environments. The main concern is the vulnerability of energy networks to cyberthreats and challenges, which consequently poses a threat to the reliability of energy delivery as well as the stability of urban infrastructures. This study focuses on developing innovative artificial intelligence (AI)-based techniques to mitigate these risks and enhance energy networks. Our approach involves analyzing current energy network management processes and designing new architectural models that incorporate adaptive AI features. The proposed framework, which leverages real-time data, anomaly detection, and predictive maintenance, aims to strengthen both security and operational efficiency. To ensure the framework's stability and alignment with best practices in addressing cyberthreats, it is modeled after the Cyber Assessment Framework provided by the United States National Cyber Security Centre. Future research will focus on refining these AI frameworks through simulations and pilot programs, tailoring them to different cities and ensuring compliance with evolving security protocols. Additionally, ongoing efforts aim to create a viable and flexible strategy for managing and safeguarding distributed energy systems (DERs) in smart cities.

Keywords: Smart Grid; Distributed Energy Network (DENs); Cyber Security and Privacy; Consequence-driven Cyber-Informed Engineering (CCE); Adaptive AI frameworks

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Citation

Jovita Nsoh. “Adaptive AI Frameworks to Secure and Manage Distributed Energy Networks in Smart Urban Environments". Clareus Scientific Science and Engineering 2.7 (2025): 16-52.

Copyright

© 2025 Jovita Nsoh. Licensee Clareus Scientific Publications. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.