Pengfei Zhao, AlexisLi, ShuangqiHuo, DaAlhazmi, Mohannad2025-03-032025-03-032025-01Pengfei Zhao A, Li S, Huo D, Alhazmi M. (2025) Unmanned aerial vehicles versus smart grids. IET Smart Grid, Volume 8, Issue 1, January/December 2025, Article number e700002515-2947https://doi.org/10.1049/stg2.70000https://dspace.lib.cranfield.ac.uk/handle/1826/23555The 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/40 Engineering4008 Electrical Engineering4009 Electronics, Sensors and Digital Hardware7 Affordable and Clean Energy11 Sustainable Cities and Communitiesfault diagnosisoptimisationpower system managementArticle2515-2947565568e7000081