极端降雨对城市电力和交通系统均有严重影响, 该影响在电力-交通耦合网络中呈双向传播特征。具体而言, 内涝会导致变电站水淹失效, 影响充电站的服务能力, 进而导致交通流和充电负荷再分布, 在耦合网络中产生复杂的交互作用。该文考虑极端降雨场景, 提出一种耦合交通网络影响的电网节点脆弱性评估方法。首先, 基于天气和地理数据搭建城市暴雨内涝模型, 实现了极端降雨预报数据到多时段城市网格化积水深度的解析映射, 并进一步研究了不同积水深度的电网节点失效机理, 评估了电网运行状态; 其次, 采用起讫点分析法和Floyd法模拟城市交通流和充电负荷的再分布过程, 提出了道路饱和风险和节点饱和风险指标, 并考虑电力-交通耦合网络运行影响, 采用改进层次分析法评估了电网节点的综合脆弱性; 最后, 结合改进IEEE-33节点和32交通节点耦合网络进行了算例分析。结果表明: 该文所提方法能够精准辨识电力-交通耦合网络的关键节点。该文研究结果可为防灾物资调配, 制定灾前、灾中和灾后决策方案提供参考。

Objective: 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. Methods: 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. Results: 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. Conclusions: 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.