Resilience-based post-earthquake restoration scheduling for urban interdependent transportation-electric power network

Abstract

As critical lifeline systems, transportation network (TN) and electric power network (EPN) are highly susceptible to natural hazards, such as earthquakes during their service life. At the same time, restoration of damaged TN and EPN is essential to support the post-earthquake reconstruction and emergency rescue in affected areas. Restoration strategies were traditionally developed for TN or EPN separately. However, neglecting the potential interconnection between these two networks in the recovery phase may lead to detrimental consequences, as in real-world scenarios, the obtained strategy may be less efficient or even unfeasible given that recovery of one system is usually dependent on the others for service provision. Accordingly, this paper presents a resilience-based framework for post-earthquake restoration of interdependent transportation-electric power networks. In this framework, restoration independencies and functionality dependencies are introduced to represent the interaction between TN and EPN. Then, a bi-level optimization model with the objective of maximizing seismic resilience is established to characterize the network recovery problem. Furthermore, a solution algorithm that incorporates a genetic algorithm and a chromosome validity test operator is designed to obtain the near-optimal solution. Finally, the proposed framework is illustrated through two numerical examples.

Keywords:

• Decision optimization
• electric power network
• restoration scheduling
• seismic resilience
• transportation network

Acknowledgements

The opinions, results and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant No. 52338010 and National Key Research and Development Program of China under Grant No. 2022YFC3003603.