TAHNIAH PROFESOR MADYA IR. TS. DR. TEH JIASHEN TAJAAN MEGAGO TECHNOLOGY CO. LTD.

Dynamic Thermal Rating (DTR) systems enhance power grid efficiency by dynamically adjusting transmission line capacity based on real-time conditions, facilitating renewable energy integration and reducing congestion. However, their reliance on cyber-physical components, including sensor networks and communication infrastructure, introduces reliability and cybersecurity concerns that may impact grid stability.

This study examines the cyber-physical reliability impacts of DTR systems in smart grids, focusing on their role in power system resilience, cyber vulnerability, and operational risks. A multi-layer modeling framework is developed to assess interactions between physical power components, cyber networks, and environmental conditions. The research aims to: (1) quantify DTR’s impact on grid reliability under normal and contingency conditions, (2) identify cybersecurity risks associated with DTR deployment, and (3) propose a resilient optimization framework to mitigate cyber threats while maximizing grid efficiency.

The methodology integrates probabilistic risk assessment, machine learning-based anomaly detection, and networked control strategies to model potential DTR failures and cyber intrusions. Large-scale transmission network case studies validate the framework using real-world data and industry-standard simulation tools.

Expected outcomes include improved cyber-physical reliability of DTR-equipped grids, enhanced security through cyber-aware DTR deployment, and policy recommendations for robust implementation. These findings provide actionable insights for utilities, policymakers, and researchers to ensure secure, resilient, and efficient smart grid operations in an increasingly digitalized power sector.