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.

Keywords

Community resilience / Digital twin / Disaster recovery strategies / Electric power restoration / Hurricanes / Road network

Relevance to resilience

This paper presents a framework for post-hurricane electric power restoration using a DT approach, focusing on the restoration of the EPN and RN. By prioritizing repairs and optimizing resource allocation, the framework enhances infrastructure resilience, ensuring efficient restoration of essential services crucial for maintaining community functionality and well-being after a disaster. Through adaptive decision-making and data-driven methodologies, the framework contributes to building more resilient communities and enabling real-time updates based on accurate information. Overall, the study underscores the importance of leveraging DT technology to enhance post-disaster recovery efforts, ultimately contributing to community resilience.

CRediT authorship contribution statement

Abdullah M. Braik: Writing - review & editing, Writing - original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Maria Koliou: Writing - review & editing, Writing - original draft, Supervision, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Financial support for this work was provided by the US National Science Foundation (NSF) under Award Number 2052930. This financial support is gratefully acknowledged. Any opinions, findings, conclusions, and recommendations presented in this paper are those of the authors and do not necessarily reflect the views of NSF.

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