A digital twin framework for efficient electric power restoration and resilient recovery in the aftermath of hurricanes considering the interdependencies with road network and essential facilities

Abstract

The community's resilience in the face of natural hazards relies heavily on the rapid and efficient restoration of electric power networks, which plays a critical role in emergency response, economic recovery, and the functionality of essential lifeline and social infrastructure systems. Leveraging the recent data revolution, the digital twin (DT) concept emerges as a promising tool to enhance the effectiveness of post-disaster recovery efforts. This paper introduces a novel framework for post-hurricane electric power restoration using a hybrid DT approach that combines physics-based and data-driven models by utilizing a dynamic Bayesian network. By capturing the complexities of power system dynamics and incorporating the road network's influence, the framework offers a comprehensive methodology to guide real-time power restoration efforts in post-disaster scenarios. A discrete event simulation is conducted to demonstrate the proposed framework's efficacy. The study showcases how the electric power restoration DT can be monitored and updated in real-time, reflecting changing conditions and facilitating adaptive decision-making. Furthermore, it demonstrates the framework's flexibility to allow decision-makers to prioritize essential, residential, and business facilities and compare different restoration plans and their potential effect on the community.