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<p>This paper presents a comprehensive analysis of dynamic risk assessment (DRA) methods in smart grid environments, focusing on emerging approaches in artificial intelligence, mathematical modeling, and hybrid frameworks. We evaluate 44 recent publications (2016–2025) and analyze their primary analysis methods, application areas, risk assessment techniques, impact measurements, evaluation approaches, and implementation requirements. Our analysis reveals three primary methodological categories: Artificial Intelligence and Machine Learning (AI/ML)-based approaches (55%), mathematical model-based methods (20%), and hybrid approaches (25%). A clear trend is observed towards integrated methodologies that combine multiple techniques while maintaining real-time assessment capabilities. Using a multidimensional analysis framework, we examine underlying methods, application domains, risk analysis techniques, impact measurements, and evaluation approaches, revealing significant patterns in implementation strategies across different smart grid security contexts. This survey fills a critical gap in the existing literature, which typically addresses cybersecurity in general without focusing on the dynamic aspects required for critical infrastructure protection in smart grids.</p>

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From voltage to vulnerability: A comprehensive survey of dynamic security risk assessment techniques in smart grids

  • 期刊论文

  • 发表日期：

    2025年12月11日

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  • Mostafa Arjmand,
  • Behzad Akbari,
  • Hamid Reza Ghaeini

摘要

This paper presents a comprehensive analysis of dynamic risk assessment (DRA) methods in smart grid environments, focusing on emerging approaches in artificial intelligence, mathematical modeling, and hybrid frameworks. We evaluate 44 recent publications (2016–2025) and analyze their primary analysis methods, application areas, risk assessment techniques, impact measurements, evaluation approaches, and implementation requirements. Our analysis reveals three primary methodological categories: Artificial Intelligence and Machine Learning (AI/ML)-based approaches (55%), mathematical model-based methods (20%), and hybrid approaches (25%). A clear trend is observed towards integrated methodologies that combine multiple techniques while maintaining real-time assessment capabilities. Using a multidimensional analysis framework, we examine underlying methods, application domains, risk analysis techniques, impact measurements, and evaluation approaches, revealing significant patterns in implementation strategies across different smart grid security contexts. This survey fills a critical gap in the existing literature, which typically addresses cybersecurity in general without focusing on the dynamic aspects required for critical infrastructure protection in smart grids.