Risk and Vulnerability Assessment of Energy-Transportation Infrastructure Systems to Extreme Weather

TL;DR

This paper addresses risk and vulnerability assessment of interdependent energy-transportation infrastructures under extreme weather by modeling electricity, heating, and transportation as a unified system. It develops a Monte Carlo framework based on Latin hypercube sampling $LHS$ and a time-expanded, spatiotemporal network-flow representation to simulate damage propagation and quantify performance across coupled networks with multiple emergency decisions, using objective functions such as $F^{\text{PN}}$, $F^{\text{HN}}$, and $F^{\text{TN}}$. To enable scalable and privacy-preserving analysis, the authors introduce a direct vulnerability metric with reinforcement utility $U^{\text{Abs}}_{b}$ and $U^{\text{Rel}}_{b}$, and a neural network surrogate approach that trains sub-networks on boundary variables and embeds surrogates into the full model for rapid, privacy-protected evaluation. Numerical experiments on a 33/27/33-node testbed under a rainstorm/typhoon scenario demonstrate meaningful risk reduction through multi-type emergency decisions and show that the surrogate-based SP method achieves substantial speedups while closely matching the baseline Monte Carlo results.

Abstract

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 and heterogeneity between energy and transportation systems. This paper proposes an assessment framework that accommodates multiple types of emergency decisions. It integrates the heterogeneous energy and transportation infrastructures in the form of a network flow model to simulate and quantify the impact of extreme weather events on the energy-transportation infrastructure system. Based on this framework, a targeted method for identifying system vulnerabilities is further introduced, utilizing a neural network surrogate that achieves privacy protection and evaluation acceleration 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 weak points that should be prioritized for reinforcement, and strike a balance between accuracy and evaluation speed.

Figures (7)

• Figure 1: A framework for risk assessment under extreme weather based on the power-heat-transportation system model in the form of network flow.
• Figure 2: Surrogate-based approach for system vulnerability identification and quantification.
• Figure 3: System indicator curves of all cases.
• Figure 4: Vehicle behaviors in tranportation system (Case#8) - scenario average
• Figure 5: Contribution of different emergency decisions in reducing extreme weather losses.