供水管网是保证城市正常运行的重要基础设施之一，维持良好的管网运行状态是保障供水安全的关键。目前我国供水系统面临着许多外部压力及内部变化的冲击，包括人口增长、管道老化及二次供水模式变化等。供水管网抵御这些威胁并确保供水水质、水量和水压的能力称为韧性，提高管网韧性通常意味着供水能耗的增加。识别供水系统面临的供水安全影响因素，评估管网韧性，分析供水能耗及管网韧性的关系，有助于保障供水安全，提高居民生活质量。针对目前对韧性响应机制和评价方法尚缺乏系统分析的问题，本研究以情景设计及模拟分析为研究方法，构建了在役供水管网韧性评估框架，在非结构性变化及结构性变化情景下进行韧性评估，分别计算Todini指数，供水保证率，剩余供水能力，管线故障后节点能量变化等韧性指标，并探究了供水能耗与韧性的关系。两个案例管网的研究结果阐释了结构性和非结构性变化下的系统韧性响应机制。在非结构性变化中，人口增长、管道腐蚀和气候变化都对系统的韧性有负面影响，其中管道腐蚀的影响通常在规划阶段被忽略。在结构性变化中，供水管网韧性评估框架解决了关键约束管线的识别问题，为制定管道维护管理计划提供支持。 管网能耗分析揭示了供水能耗足迹和城市空间布局的响应机制，发现供水总能耗受最小服务水头和建筑层高双重影响，适中集聚式的建筑布局比高密度集聚式的建筑布局更节能，各城市应根据自身实际情况确定供水服务水头。管网能耗与韧性的协同分析研究结果明确了供水模式对能耗和管网韧性有双重约束效应，从水箱均匀供水切换成叠压供水，节能6.30%，但Todini指数下降4.81%，揭示了制约管网能耗及韧性的系统性约束问题。叠压供水模式影响系统韧性的根本原因是供水模式的突变和系统缓冲空间的降低。配水池供水模式与集中供水模式相比，前者的剩余供水能力比后者增加了1.6%，但同时能耗也增加了77.2%，管网优化设计中应进行能耗及韧性的协同控制，优化配水池的位置及容积。
The water distribution system is one of the important infrastructures to ensure the normal operation of the city. Maintaining a good system operating state is the key to ensuring the safety of water supply. At present, China's water distribution systems are facing many external pressures and internal changes, including population growth, aging pipelines, and changes in secondary water supply modes. The ability of the water distribution system to resist these risks and ensure the quality, quantity, and pressure of the water supply is called resilience. Increasing the resilience of the network usually means an increase in the energy consumption of the water supply. Identifying the risks, assessing the resilience of the water distribution system, and analyzing the relationship between energy consumption and resilience can help ensure the safety of the water supply and improve the life quality of the residents.In view of the current lack of systematic analysis of the resilience response mechanism and comprehensive assessment methods for network resilience, this study uses scenario design and simulation analysis as research methods and construct a water distribution system resilience assessment framework. The framework assesses network resilience under non-structural and structural change scenarios, and calculate the Todini index, water demand satisfaction, surplus water supply capacity, node energy loss after pipe failure. Two networks are selected as case studies to analyze the resilience in different scenarios and explore the relationship between water supply energy consumption and resilience.The results explain the system resilience response mechanism under both structural and non-structural changes. Among non-structural changes, population growth, pipeline corrosion, and climate change have a negative impact on system resilience, where the impact of pipeline corrosion is usually ignored during water supply planning. For the structural changes, the resilience assessment framework solves the problem of the identification of key constrained pipelines and provides support for the development of pipeline maintenance management plans.The energy consumption analysis reveals the response mechanism of water supply energy footprint and urban spatial layout. It is found that the total energy consumption of water supply is affected by the minimum service head and the height of the building. A moderately clustered building layout is more energy-efficient than a high-density clustered building layout. Each city should determine the water supply head according to its actual situation. The results clarify that the water supply mode has a dual constraint effect on energy consumption and resilience. When conventional tank mode is switched to pressure-superposed water supply, it saves 6.30% energy, but the Todini index drops by 4.81%. The fundamental reason for resilience decrease is the sudden change of the water supply pattern and the reduction of the system buffer. Compared with the centralized water supply mode, the surplus water supply capacity of decentralized water supply mode increases by 1.6%, but energy consumption also increases by 77.2%. During the design of water distribution systems, the location and volume of service tanks should be optimized to improve energy consumption and system resilience.
