Risk Assessment and Vulnerability Identification of Energy-Transportation Infrastructure Systems to Extreme Weather
This work addresses risk management for urban infrastructure systems, but it appears incremental as it builds on existing network and surrogate modeling approaches.
The paper tackles the problem of assessing risks and identifying vulnerabilities in interdependent energy-transportation infrastructure systems during extreme weather events, proposing a unified network flow model and neural network surrogates that balance accuracy and speed in numerical experiments.
The interaction between extreme weather events and interdependent critical infrastructure systems involves complex spatiotemporal dynamics. Multi-type emergency decisions within energy-transportation infrastructures significantly influence system performance throughout the extreme weather process. A comprehensive assessment of these factors faces challenges in model complexity, heterogeneous differences between energy and transportation systems, and cross-sector privacy. This paper proposes a risk assessment framework that integrates the heterogeneous energy and transportation systems in the form of a unified network flow model, which enables full accommodation of multiple types of energy-transportation emergency decisions while capturing the compound spatiotemporal impacts of extreme weather on both systems simultaneously. Based on this framework, a targeted method for identifying system vulnerabilities is further developed. This method employs neural network surrogates to achieve privacy protection and accelerated identification while maintaining consideration of system interdependencies. Numerical experiments demonstrate that the proposed framework and method can reveal the risk levels faced by urban infrastructure systems, identify vulnerabilities that should be prioritized for reinforcement, and strike a balance between accuracy and speed.