With the marked increase in the frequency and intensity of extreme rainfall events, the growing likelihood of substation and road flooding poses a severe threat to the operation of power-traffic coupled networks. Existing studies have failed to fully quantify the impact of urban waterlogging disasters on power-traffic networks, and the interactive influence between the two networks under disaster scenarios remains poorly understood. To accurately identify critical nodes in the power-traffic coupled network and clarify key fault propagation links, a vulnerability assessment method that comprehensively integrates multidimensional influencing factors and the bidirectional influence mechanism between the power grid and the traffic network must be developed. Accordingly, this study proposes a substation-focused vulnerability assessment method for power nodes in a power-traffic coupled network, providing guidance for the planning and dispatch of power systems and traffic networks, as well as the allocation of disaster prevention materials.

A research framework comprising "urban waterlogging modeling-coupling mechanism analysis-key node identification" is established. First, weather and geographic data were integrated into a two-dimensional hydrodynamic model, which incorporated the D8 single flow direction algorithm to establish an urban rainstorm waterlogging model. This model was used to map rainfall parameters to multi-period gridded urban waterlogging depths. Second, the power node failure mechanism was analyzed across different waterlogging depths, using Monte Carlo simulations to sample all grid nodes and obtain the operational state of the distribution network. Then, using traffic network parameters and electric vehicle charging models, the origin-destination analysis method and the Floyd algorithm were used to investigate traffic flow redistribution and charging loads in the urban traffic network, revealing the bidirectional influence mechanism under waterlogging conditions. An iterative "fault-diversion-redispatch-assessment" simulation was constructed to dynamically model the operating state of the coupled system under disaster scenarios. Finally, risk indicators, such as road and node saturation risks, were proposed from an operational perspective. By mapping the traffic network saturation risk to grid nodes and combining network topology with electrical indicators, a comprehensive evaluation method for identifying key nodes in the coupled system was developed based on the analytic hierarchy process, achieving accurate identification of weak links in the coupled system.

A case study involving a modified IEEE 33 bus system and a 32 nodes traffic network was conducted, which intuitively displayed the dynamic cascading failures within power-traffic coupled systems under urban waterlogging scenarios. The results showed that the proposed method accurately identified key nodes in the integrated network, fully verified the necessity of component-level identification from a coupling perspective.

Based on an in-depth analysis of the fault propagation mechanisms of the power-traffic coupled network under urban waterlogging conditions, this study provides a new method for the vulnerability assessment of urban power-traffic coupled networks under extreme rainfall disasters. Future research will optimize the allocation of disaster prevention materials to more efficiently cope with possible waterlogging disasters.