Browsing by Author "Alhazmi, Mohannad"

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    Psychological modeling for community energy systems
    (Elsevier, 2025-06-01) Zhao, Alexis Pengfei; Alhazmi, Mohannad; Huo, Da; Li, Wanzi
    This paper introduces a novel framework for community energy system (CES) optimization that integrates Social Cognitive Theory (SCT) with Distributionally Robust Optimization (DRO) to address both behavioral and technical challenges. The rapid integration of renewable energy resources and the proliferation of peer-to-peer (P2P) trading platforms necessitate solutions that balance economic efficiency, environmental sustainability, and user engagement under uncertainty. While existing studies focus predominantly on technical optimization, they often neglect the significant influence of behavioral dynamics on community energy systems. This research bridges the gap by explicitly incorporating peer influence, observational learning, and engagement incentives into a robust optimization framework. The proposed methodology models user behavior through SCT, enabling dynamic adjustments to trading patterns and energy-sharing decisions. A DRO model is employed to handle uncertainties in renewable energy generation and demand, ensuring system reliability and resilience. To enhance computational efficiency, a primal–dual algorithm is developed, offering faster convergence compared to traditional DRO methods. The framework is validated through a comprehensive case study on a 50-household microgrid equipped with solar, wind, and storage systems, alongside a P2P trading platform. Results demonstrate the framework's ability to reduce carbon emissions by up to 30%, improve renewable energy utilization to over 90%, and increase trading participation by 25%, compared to baseline scenarios. This study makes four key contributions: (1) introducing SCT-based behavioral modeling to enhance user engagement in CES operations, (2) leveraging DRO to address uncertainties in renewable energy and demand profiles, (3) proposing a primal–dual algorithm for scalable and efficient optimization, and (4) presenting a unified framework that balances economic, environmental, and social objectives. The findings highlight the transformative potential of integrating behavioral and technical approaches for sustainable and resilient community energy management.
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    Unmanned aerial vehicles versus smart grids
    (Institution of Engineering and Technology (IET), 2025-01) Pengfei Zhao, Alexis; Li, Shuangqi; Huo, Da; Alhazmi, Mohannad
    The increasing threat of unmanned aerial vehicles (UAVs) to smart grid infrastructures poses critical challenges to energy systems security. This study examines smart grid vulnerabilities to UAV‐based attacks and proposes a novel optimisation framework to enhance grid resilience. Employing a multi‐objective optimisation approach using the Non‐dominated Sorting Genetic Algorithm III (NSGA‐III) and a game‐theoretic Stackelberg model, the research captures the strategic interplay between UAV operators and grid defenders. Key contributions include the development of a multi‐objective optimisation framework, integration of adversarial game theory, incorporation of dynamic environmental conditions, and generation of Pareto‐optimal solutions for strategic defence planning. This research makes four pivotal contributions: (a) the design of a comprehensive multi‐objective optimisation framework tailored for UAV strike optimisation, (b) the integration of game‐theoretic principles to model adversarial behaviours, (c) the inclusion of dynamic environmental factors to improve solution robustness, and (d) the application of NSGA‐III to generate trade‐off solutions, equipping decision‐makers with diverse strategies to enhance grid resilience. By addressing an urgent and timely challenge, this work offers practical guidance for fortifying smart grid infrastructures against emerging UAV threats in increasingly complex operational environments.