From Voltage to Vulnerability: A Survey of Dynamic Security Risk Assessment Techniques in Smart Grids

This paper presents a comprehensive analysis of dynamic risk assessment methods used in smart grid environments, with a particular focus 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. The 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%), with a clear trend towards integrated approaches that combine multiple methods while maintaining real-time assessment capabilities. Our multidimensional analysis framework examines the underlying methods, application domains, risk analysis techniques, impact measurements and evaluation approaches and reveals significant patterns in implementation strategies in different smart grid security contexts. The analysis of DRA methods for smart grid environments is a novel contribution that fills a gap in the existing literature, which typically deals with cybersecurity in general and does not focus on the dynamic aspects required for critical infrastructure protection.