评估气候变化下城市轨道交通的洪水条件电力依赖及其对韧性的影响
Assessing flood-conditioned power dependency of urban rail transit and its effects on resilience under climate change
- ETH Zurich（苏黎世联邦理工学院）
- Princeton University（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究首次定量评估城市轨道交通的洪水条件电力依赖，通过双层网络模型和依赖指数，发现牵引供电冗余显著影响级联损失，为韧性规划提供代理指标。
AI中文摘要：
城市轨道交通（URT）在依赖牵引供电系统的同时面临日益增长的洪水风险，而牵引供电系统的故障可能引发级联中断。现有关于URT洪水韧性的研究往往通过忽视牵引供电冗余来过度简化电力依赖，并且很少考虑洪水条件下的级联影响，从而对洪水引发的牵引供电故障如何通过运营传播并影响系统韧性提供的见解有限。本研究首次对URT的洪水条件电力依赖及其对系统韧性的影响进行了定量评估。该方法整合了双层URT网络模型、空间洪水暴露评估、服务中断模拟、洪水条件电力依赖指数以及基于行程的性能指标，该指标捕捉了鲁棒性和冗余性的韧性属性。该方法应用于伦敦轨道交通网络，考虑了地表水洪水情景下的当前和RCP8.5气候条件、两种牵引供电馈电机制以及四个变电站洪水故障阈值。结果表明，牵引供电冗余从根本上决定了级联影响的严重程度。在双端馈电下，电力导致的性能损失保持最小，而单端馈电导致显著更大的损失，并且对变电站故障阈值高度敏感。所提出的依赖指数与依赖相关的性能损失强相关，支持其作为战略规划中依赖引起的额外后果的代理指标的使用。
英文摘要：
Urban rail transit (URT) faces increasing flood risk while relying on traction power systems whose failures can trigger cascading disruption. Existing studies on URT flood resilience often oversimplify power dependency by overlooking traction power redundancy and rarely consider flood-conditioned cascading impacts, providing limited insight into how flood-induced traction power failures propagate through operations and affect system resilience. This study marks the first quantitative assessment of flood-conditioned power dependency of URT and its effects on system resilience. The methodology integrates a double-layer URT network model, spatial flood exposure assessment, service disruption simulation, a flood-conditioned power dependency index, and a journey-based performance metric that captures resilience properties of robustness and redundancy. It is applied to the London rail transit network under surface water flood scenarios across current and RCP8.5 climate conditions, two traction power feeding mechanisms, and four substation flood-failure thresholds. Results show that traction power redundancy fundamentally shapes the severity of cascading impacts. Under double-end feeding, power-attributable performance loss remains minimal, whereas single-end feeding leads to substantially greater loss and is highly sensitive to substation failure thresholds. The proposed dependency index is strongly correlated with dependency-related performance loss, supporting its use as a proxy for dependency-induced additional consequences in strategic planning.
