Enhancing Buildings' Energy Resilience by Dynamic Seismic Emergency Inspection and Restoration Scheduling in Multiple Systems

Buildings' energy resilience in natural disasters is reliant on the support of the functionalities of critical infrastructure that the buildings connect to, such as highway-bridge and electric power systems. Meanwhile, as critical infrastructure systems have increasingly become interconnected and interdependent, they are more susceptible to natural hazards and less able to withstand their effects. Insufficient research has been conducted regarding computational models of effectively representing the interdependencies and interactions involved in the restoration scheduling of post-disaster critical infrastructure systems. To address this research gap, this study proposes integer programs, integrating hybrid genetic algorithms, to explicitly investigate the impact of interactions and interdependencies between electric power systems (EPSs) and highway-bridge systems (HBSs) on the energy-recovery processes of buildings. The objective is to dynamically prioritize the restoration scheduling for EPSs and HBSs while considering inspection and restoration activities. A case study based on the 2008 Wenchuan Earthquake in Sichuan province, China, is employed to validate the efficacy of the proposed method. The results of the analysis reveal that the dynamic model exhibits a substantial 6.4% improvement in building energy resilience at the seven-day mark, compared to the static model. Moreover, the proposed coupled EPS-HBS inspection-restoration joint model outperforms a disjoint EPS inspection-restoration scheduling model, yielding a remarkable 11.4% enhancement in system resilience at the seven-day mark. These findings underscore the significance of considering interdependencies and interactions within critical infrastructure systems to enhance the energy resilience of buildings in earthquake-affected areas.

Automated summary

This study proposes an integer programming method combined with hybrid genetic algorithms to investigate the impact of interactions between highway-bridge systems and electric power systems on the energy-recovery processes of buildings. The findings demonstrate that the dynamic model outperforms the static model in improving building energy resilience.