天然气管网系统韧性研究现状与应用展望

大V快跑 行业前沿来源:油气储运评论6,647阅读模式

管网系统作为天然气运输的主要途径,其安全、稳定运营在国民经济生命线工程与能源供应链中具有重要地位[1-2]。中国“十四·五”规划对能源系统安全及其现代化提出了更高要求[3]天然气管网安全保供日益重要的同时也面临着前所未有的挑战。

天然气管网系统(Natural Gas Pipeline Network System,NGPNS)作为多层面的工业运输体系[4],其内部属性与外部环境具有高度的复杂性,具体表现为:NGPNS作为上游气源与下游用户的连接纽带,承载着复杂多变的供需关系[5];供需关系与气源、用户地理位置分布的不均衡性,导致管网系统的拓扑结构愈发复杂[6],因而增大了系统结构层面的复杂性;复杂的拓扑结构内包含数量繁多的压缩机、管道、管件及信号检测与传输设备,同时融合了气田集输、储气库注采等多种复杂工艺[7],进一步增大了物理系统层面的复杂性;天然气的可压缩性及管存气的持续波动,造成NGPNS兼具慢瞬变与时滞性的特点[8],因而增大了系统运行层面的复杂性。此外,管网系统在融合多层面复杂性的同时,不同层面所对应的潜在扰动事件将管网系统的安全保供推向更加未知的“深水区”。文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

基于天然气管网的上述复杂性,在各类扰动事件情况下如何实现大型NGPNS供气保障,是一项艰巨的工程任务。这背后的科学问题是:具备上述复杂特性的工业运输系统在广义扰动下的灾变,以及系统与扰动的耦合作用机制、系统特性变化机理,即扰动后如何变化、变化之后如何处理。目前已开展的管道完整性研究[9]、管网可靠性研究[10-11]在概念范畴与研究方法上已无法满足管网安全保供需求[12-13],主要体现在:①可靠性评价常用正态分布进行假设,但此类通用概率模型容易稀释“小概率、高后果”的极端事件;②可靠性研究的对象为正常工况下管网系统所呈现的“大概率”工作状态,侧重维持正常工作的能力评估,不涉及扰动之后系统的脆弱性、鲁棒性、恢复力;③多数NGPNS研究的视角仍局限在单体与局部区域,可靠度的评价结果会随着分析对象复杂程度与数量的增加而被快速稀释;④管道完整性研究主要围绕管道系统本体安全,在供气保障方面涉及甚少。因此,管网在经受扰动之后的响应与恢复特性,成为新的研究需求。文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

1973年,系统韧性的概念首次被引入生态系统,旨在研究种群关系在经受扰动后的稳定性[14]。自此,以系统在经受扰动后的响应、恢复等能力为主体概念的系统韧性,逐渐成为全球系统工程领域的研究热点。“韧性”思想也快速、全面拓展应用于经济、社会等众多领域[15],中国科协发布的2022年10个前沿科学问题中,就包含了“如何全方位精准评价城市综合交通系统及基础设施韧性”[16]。天然气管网是高度复杂的系统,适于运用韧性的思想、理论、方法加以研究。文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

1 NGPNS韧性的概念

“韧性”一词源于拉丁语“resiliere”,本意为反弹、复原。区别于材料韧性的概念,系统韧性保留了原词中扰动、恢复两个特征阶段,将研究目标转变为系统对于扰动的抵御、响应、恢复等能力。系统韧性概念提出后,社会系统[17]、经济系统[18]、医疗系统[19]、交通系统[20]、能源系统[21]等领域分别提出基于各自特性的韧性概念,共性是围绕系统在遭遇扰动后的性能损失、能力剩余、恢复程度,即脆弱性、鲁棒性(可靠性)、恢复力[22]。对于NGPNS,现阶段多以供气韧性研究为主,其定义是:NGPNS在遭受各类扰动之后的供气性能损失(脆弱性)、剩余供气能力(鲁棒性)、供气能力恢复(恢复力)的研究。此外,NGPNS韧性概念还包括管网系统运行韧性、物理管网韧性等:运行韧性研究主要基于站场动设备进行运行状态的韧性分析;物理管网韧性研究通过网络结构完整性、连通性等指标对管网结构进行韧性评价。文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

系统韧性的概念由灾后主观意识而生,逐渐发展为工程思想与研究方向,对应研究由定性认知向定量评价发展。NGPNS韧性概念涉及运营者、研究者及用户,作为安全运行的重要思想,系统韧性是管网中各类保供举措制定的基础;作为管网系统的灾变行为表征,系统韧性是相关研究中建模、优化的核心。因此,基于不同对象与视角,NGPNS韧性既是思想认知,又是系统属性(图1)。文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

IMG_257文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

图1 NGPNS韧性概念关系表征图文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

Fig. 1 Characterization of conceptual relation of  NGPNS resilience文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

此外,提及韧性离不开“扰动”的概念。对于NGPNS,常见扰动主要包括:自然灾害(地震、滑坡、泥石流等)[23-24],人因事故(施工误伤、蓄意破坏、人为操作等)[25-26],设备损坏(压缩机故障、传感器故障等)。根据受扰动的对象,可将扰动分为供需关系扰动、管网实体扰动;根据扰动的可预见性,可将扰动分为确定性扰动(季节变化、法定假期等)、不确定性扰动(疾病封控、气价波动、腐蚀泄漏等)(图2)。随着能源结构的变化,NGPNS需要与风电、光电等新能源系统进行对接,起“兜底”作用。在综合能源系统背景下,新能源的不确定波动也将成为管网系统面临的扰动。因此,对“扰动”概念的理解,应从供需关系与管网实体的视角,结合确定性与不确定性的扰动种类,以综合角度分析管网系统发生变化的因素。文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

IMG_258文章源自云智设计-https://www.cidrg.com/news/forward/21137.html

图2 NGPNS扰动分类图

Fig. 2 NGPNS disturbance classification

综上,与NGPNS可靠性、完整性相比,韧性弥补了可靠性与完整性的不足。在概念上,韧性补充了系统受扰动后响应行为的空白;在研究内容上,韧性涵盖受扰-响应-恢复的全过程。因此,NGPNS韧性概念涵盖了更广的研究时域与范围,以更高的视角丰富了管网系统安全保供的相关研究。

2 NGPNS韧性建模与求解

韧性度量是NGPNS韧性研究的主要内容之一,也是韧性优化的基础。系统韧性度量取决于对系统的认知程度,包括定性、半定量及定量度量,相关工作包括指标构建、模型搭建和模型求解。

2.1 指标构建

指标构建是基于对系统的整体认知,搭建系统韧性评价的主体架构,为韧性定量化分析建立基础。目前,生态系统[27]、社会人口系统[28]、国土安全系统[29]、组织管理系统[30]、供电系统[31]、交通系统[32]均已形成较公认的韧性评价指标框架。现阶段,NGPNS韧性评价主要基于系统供气功能,以脆弱性-鲁棒性(可靠性)-恢复力为视角构建韧性评价指标[11,33]

可靠性相关的评价指标构建,目前发展较成熟的是电力网络系统。电网与天然气管网在系统特征上具有一定的相似性。电力系统通常以失效率、维修率、可用度、平均失效时间作为供电可靠性的评价指标。对于NGPNS,已有较多研究建立了相关评价指标,包括将机械可靠性、水力可靠性、供气可靠性相结合作为管网功能可靠性的评价指标[12];将单元可靠性、网络可靠性相结合作为管网综合可靠性的评价指标[34];基于可靠度定义的评价指标,也可对管网系统的部分组件进行可靠性评价。

脆弱性的相关评价指标主要有两种构建方法,一是基于管网整体,定性观察管网系统在多场景、多层次扰动前后的变化,有针对性地建立脆弱性评价指标。如遍历NGPNS的点位来模拟潜在扰动,将所有的遍历结果汇总,构建管网系统的脆弱性评价指标[35];欧盟能源联合研究中心以乌克兰供气中断为例,通过冬季供气损失来评价欧盟及其成员国天然气供应链的脆弱性[36];欧洲天然气管网的脆弱性分析将水力要素与经济要素相结合,采用情景分析法进行供气脆弱性的指标构建[37]。二是从单元相互作用出发,针对管网系统内的局部脆弱性,结合系统工程与统计物理学理论,提出局部脆弱性评价指标,涉及的方法包括多智能体建模仿真[38-41]、系统条件熵计算[42]及逾渗理论[43]等。

恢复力的评价指标以恢复速率与恢复时间为基础,这也是现阶段管网系统韧性研究的难点之一。现阶段系统恢复力的评价方法主要有:①分段评价法,即对受扰后的NGPNS分阶段制订评价指标,这是因为对于复杂的NGPNS而言,在其多部件受扰动的情况下,系统整体恢复过程难以用单一过程指标进行描述[17];②网络结构法,从NGPNS的拓扑结构出发,用网络连通度、单元贡献度、功能上升度、恢复率等指标评价系统的恢复力[44];③管理评价法,基于系统的管理架构、员工培训、责任意识等方面构建系统的恢复力指标[45]

半定量评价以专家打分法与层次分析法、主成分分析法相结合为主,如根据专家建议构建系统冗余度、资源丰富度、鲁棒性等一系列指标[46]。对供应链系统,由于系统自身的复杂性以及相关历史数据较少,难以评价其脆弱程度,因此采用专家打分确定各类变量权重,最终形成综合评价指标[47]。由于该方法主观性较强,对专家的权威性、认知程度、从业年限、问题设计、数量均有较高要求,目前在NGPNS的韧性指标构建中应用较少,仅在部分天然气供应链、城市燃气管网系统的宏观研究中进行因果关系与变量权重的确定[48-49]

2.2 模型搭建

模型用于描述管网系统在扰动前后性能的变化过程,是系统韧性度量由定性提升至定量的载体与核心。针对扰动事件的特点,NGPNS的韧性度量模型可分为确定性模型、不确定性模型。确定性模型通常用于气候/季节变化、已经发生的扰动、具有规律的节假日等,不确定性模型通常针对突发的自然灾害、供需侧波动、管道的腐蚀损伤以及其他突发事件等。基于模型中涵盖的时域范围,可分为包括事前准备阶段[50-52]、不包括事前准备阶段[53-57]两类。对于时域范围的划分,美国阿贡实验室已经有了较细致的论述[58]。尚处于初期阶段的NGPNS韧性研究,应重点关注韧性对管网系统安全保供的指导与应用。

韧性曲线(又称浴盆曲线)是对系统性能在扰动时域中变化规律的表征,是系统韧性度量向定量化、可视化发展的体现(图3),可以展现从扰动发生到结束,管网系统响应-恢复的全过程,曲线的下降程度、上升程度及过程时间分别反映管网系统的脆弱性、恢复力及鲁棒性,通过与系统功能基线对比可以评价扰动产生的影响。NGPNS的韧性曲线发展过程,可归纳为4个阶段[59]:第1阶段仅考虑了系统经受扰动后的衰减和恢复过程(图4a),且多数模型中假设衰减过程是瞬时的,通过线性过程或定性拟合来描述恢复过程,该阶段的研究对象多为设备较少的简单系统[60];第2阶段在第1阶段的基础上考虑了备用组件,如备用压缩机、备用管道等[61](图4b);第3阶段则将假设的理想瞬时衰减过程转变为线性衰减过程[62](图4c);第4阶段考虑了管存气的存在、管内压力衰减等,结合管网系统的修复过程特性,得到了更符合管网生产实际的韧性曲线[63](图4d)。

IMG_259

图3 系统韧性曲线示例图

Fig. 3 System resilience curves

IMG_260

(a)第1阶段

IMG_261

(b)第2阶段

IMG_262

(c)第3阶段

IMG_263

(d)第4阶段

图4 NGPNS的韧性曲线发展过程示例图

Fig. 4 The development process of resilience curves in NGPNS

除韧性曲线之外,韧性评价函数也是模型搭建的关键。由于韧性曲线与时间的积分具有实际工程意义,韧性曲线的积分常被用作重要的韧性评价函数(表1),其主要分为3类。第1类是简单积分法,即评价扰动前后系统效益,并不考虑实际生产效率的变化[64-67]。此类评价基于理想的假设与简化,从生产总量的角度来评估系统韧性,适用于缺乏衰减与恢复数据的大型复杂系统建模。第2类是时间积分法,即建模过程中考虑系统的生产效率、衰减速率及恢复速率[62,68-70],该类方法的关键是结合系统实际定量描述其衰减过程与恢复过程。对于无法描述的过程,则采用线性假设法或关键点法进行简化描述。第3类是综合评价法,即多维度构建模型函数,主要分为多阶段综合评价法、多指标综合评价法,例如考虑临界时间的分段评价[71],以及从时间韧性、阈值韧性、全局韧性对天然气管网的供气韧性进行综合度量[63]

表1 系统韧性评价函数汇总表

Table 1 Summary of system resilience evaluation functions

IMG_264

2.3 模型求解

根据评价函数、度量指标、研究目标,NGPNS韧性度量模型主要通过水力仿真、网络理论、概率抽样、系统动力学等方法求解。区别于电力、交通等系统,NGPNS韧性模型的求解需要着重关注流量与压降的变化,以识别供给安全与流动安全。此外,天然气的可压缩性使天然气管网参数具有慢瞬变的特性,因而相应的时滞性分析以压力、流速变化的求解为基础,对大型天然气管网的水力特性进行快速、准确的计算。当前,以北美、欧洲等地区为代表,已经将水力计算应用到NGPNS韧性模型的求解中[72]

大型天然气管网水力计算耗时长,算力要求高,当水力参数难以求解时,可将管网抽象为一个具有权重、方向的网络系统,实现网络化的管网由节点、边组成,管内的流量、压力等参数转化为性能流。复杂网络理论可以有效提升管网系统韧性模型求解的速度[73],根据求解对象可分为管网系统的结构韧性求解、性能韧性求解。前者基于网络直径、平均最短路径、聚类系数等拓扑结构参数,研究扰动前后的管网拓扑结构变化;后者融合最小路径、最大流等算法,研究扰动前后的流量分配、路径优化等问题。如欧盟分析各个成员国天然气的供应韧性时,采用复杂网络理论的方法,对各国天然气供给量、用户满意度、缺气严重度进行求解[74]

NGPNS面对大量发生时间、位置不确定的扰动时,模型求解主要采用随机模拟[75-76]。随机模拟涉及两个关键环节:①切实、准确构建概率模型;②高效、充足实现样本抽取。在概率模型构建方面,主要有历史数据统计、概率推断两种方法,通过历史数据构建概率模型多用于腐蚀失效场景[77-78],但仍依赖理想假设;概率推断多以发展最为成熟的贝叶斯推断为主[79]。在样本抽取方面,蒙特卡洛算法应用最为广泛,但因所需样本量、抽样次数十分巨大,计算耗时较长,解决方法主要有抽样方法优化、样本空间优化:抽样方法优化主要采用融合马尔科夫过程的马尔科夫蒙特卡洛(Markov Chain Monte Carlo,MCMC)抽样算法[80-81]、拉丁超立方算法[82];优化样本空间主要通过管网系统的历史数据、专家经验、先验知识等缩小样本数量,或通过事件树剪枝的方法减少事件数量[83]

对于功能与结构明确、扰动事件因果清晰的NGPNS,可采用系统工程中较成熟的系统动力学方法实现韧性模型求解,现阶段系统动力学模拟软件主要有DYNAMO、Powersim、Vensim等。该方法应用于管网系统在供需波动情景下的韧性模型求解,具有高效便捷的优势[84]

3 NGPNS韧性优化与提升

基于NGPNS韧性的优化方式,围绕基于韧性建模的优化、基于韧性思想的管理两方面展开探讨。

3.1 基于韧性模型的优化

由于NGPNS的高度复杂性,实现整体韧性优化较难且相关研究较少,因此目前管网系统韧性优化主要针对单一功能或单一层级[85]。韧性优化主要围绕面临设备数量不足、供需关系变化、价格波动等各类扰动,采取较优方案实现安全、经济供气。基于韧性模型的优化以韧性评价指标为优化目标[33,86-88],为提升管网系统整体效率、降低运行成本[89-90]提供指导。如以供气韧性为目标函数,采用网络流算法[91]指导天然气管道线路、阀门、压缩机、储气设备的调控,实现系统供气韧性提升[92];以输气成本为目标函数,转换为优化问题求解后,指导供气分配以及相关措施的制定与实施[93];基于供气韧性评价指标,建立气体流量优化模型[94],以指导压缩机、阀门及管道选型;针对压缩机组的韧性评价指标,通过模拟各种条件下的压力、流量参数优化压缩机组的性能[95]

NGPNS与市场、应急、控制、环保等领域相结合的韧性优化研究开始受到重视。如基于环保法规约束,优化压缩机排污系统的相关指标,从环保视角提升管网系统韧性[96]。由此可见,NGPNS与环境可持续发展开始逐渐融合[96-97],并将环境污染、碳排放等纳入管网系统韧性优化的约束条件[98]。NGPNS涉及“产、运、储、销、贸”多种业务,除流动和供应安全之外,环保可持续性、经济可持续性亦应考虑在内[99-100]

管网系统韧性优化问题的求解主要基于直接求解法[101]、仿真求解法[102-106],直接求解法适用于可转化为传统优化问题的韧性模型,仿真求解法适用于动态求解或无法转换为传统优化问题的模型(表2)。如针对韧性模型评价指标,采用混合整数非线性规划优化了天然气管道扩建方案[107];利用凸优化和非凸优化结合的方法减少压降引起的能量消耗[108],从而优化管网系统的运行韧性。在复杂优化问题中不同模型与方法的优化效果存在显著差异[109-110]。对于直接求解法[85,87,111],有研究提出该方法多基于静态求解且结果与工程实际存在一定差异[112-114]。NGPNS的高度复杂性,导致精确的数学建模与动态求解极为困难,需结合仿真求解法[115],以获得管网系统韧性的实时动态监测[116]。NGPNS韧性优化问题目前尚未提出一套公认最优的方法论,需针对具体问题,将优化方法与动态仿真有机结合[117]

表2 天然气管网系统韧性模型优化求解方法汇总表

Table 2 Optimization and solution methods of NGPNS resilience models

IMG_265

3.2 基于韧性思想的管理

基于韧性思想的管理措施既是韧性思想与生产实际的结合,也证明在相关研究兴起之前,韧性思想已经在生产实际中得以体现。根据管理措施的时间节点与目标,可分为缓解型举措、恢复型举措。

缓解型(又称预防型)举措旨在扰动发生之前降低系统脆弱性或提升系统鲁棒性,如提升供气量、增设替代管道、提升储气库存、监督管理及实行预警等,同时管道完整性的相关预防、监测方案也可视作缓解型举措的一部分。给排水领域基于韧性思想引入了拓扑冗余的理念,通过增设旁通管道及闭合回路,实现系统在扰动时可通过备选路径保供[128]。生物质能运输领域采用库存管理[129-130]、在局部区域设置预处理工厂来提升系统韧性[104]。电力系统通过增设局部微电网提升面对大型扰动时的韧性[131]。对于NGPNS,目前多通过储气库增储实现保供[91],并且针对供气中断或级联失效问题,在管道上设置紧急切断阀[62]

恢复型策略旨在扰动发生后采取措施促进系统功能尽快恢复,因此,对于扰动影响和恢复过程的认知是该策略的关键,损失评估函数可定量评估扰动后的系统功能缺失[109],辅助制定相应恢复策略。在NGPNS中,如果输气因故中断,可采用输气调控、市场干预、政策调控等方式,提升管网系统的恢复力[72]。虽然目前NGPNS韧性的研究处于初期阶段,但已经基于韧性思想制定出一系列韧性提升举措(表3)[132-135]。不同的恢复型措施在管网系统的韧性曲线中进一步体现[64],可用于系统恢复能力的计算[136],也是韧性曲线向工程实际的进一步发展。图4中从最初的线性恢复发展到更符合实际的非线性恢复(图4a),再到图4b中具有备用组件的恢复过程。图4b中存在备用组件与原组件状态/功能相同的交点,此时应考虑继续使用备用组件还是重新使用系统原有组件。图4c、图4d刻画了多种恢复型措施顺序实施后导致的“阶梯效应”,标志着更多恢复举措融入韧性曲线。若管道遭受重大灾害或事故,当系统功能无法恢复或恢复成本过大而不具备恢复意义时,将采取被动接受策略,如图4d中红色实线所示。

表3 基于韧性思想的NGPNS管理措施表

Table 3 NGPNS management measures based on resilience

IMG_266

4 NGPNS韧性研究的应用基础

相较完整性与可靠性研究,NGPNS韧性研究的落地应用更加任重道远。NGPNS韧性研究是对系统整体-局部鲁棒性、脆弱性、恢复力的综合研判,同时需要流体力学、系统工程、统计学、概率论、博弈论、运筹学等诸多学科协同合作[137-138],其进一步落地应用需重点依托以下基础。

4.1 技术基础

NGPNS的水力仿真是系统韧性研究中水力参数求解的技术基础,尤其对于水力特性变化明显的特殊时段,可对以下研究提供技术支持:①在脆弱性与鲁棒性分析中,对扰动之后管内流量、压力的临界状态识别,需要准确、快速的水力仿真技术;在恢复力分析中,需要对管存气、储气库/LNG储库的水力参数变化进行瞬态水力计算。②对用户缺气量与应急供气量的高效计算,可为系统韧性应用中的应急保供措施(如“压非保民”)提供定量化指导。③天然气管网时滞性随管网规模显著增大,时滞性的高效计算可以有效助力事故后果评价与应急方案优化。因此,发展成熟的NGPNS水力仿真技术是韧性研究实现进一步落地应用的有力保障。

4.2 数据基础

管网系统的历史数据是保障韧性分析中概率推断、后果量化及临界性分析准确性的关键,也是韧性评价指标设立与检验的依据,完善相关数据库是韧性研究进一步落地应用的必备条件。对NGPNS韧性研究应用较为成熟国家与地区[36,139]的相关数据库进行梳理,发现以下3类数据库对管网系统韧性研究的应用具有重要作用:①管网结构与运营数据库。结构数据包含管道结构参数、设备参数、管网拓扑结构参数等;运营数据指各类扰动前后的全时段运营监测数据,包括操作数据、设备运行数据等。目前以EGIG[140]、ENTSO-G[141]等数据库为代表。②事故类型与损失量化数据库。事故类型包括但不限于自然灾害、人为破坏、腐蚀损伤等,损失量化包括人员伤亡量化、财产损失量化、功能损失量化等,代表数据库有AGSI+and ALSI Transparency Platforms[142]、Eurostat[143]等。

③市场交易与供需关系数据库。该类数据库应涵盖不同类型天然气交易价格、管网上游与下游供需关系、国际/国内天然气市场数据等,现阶段以ICIS[144]、BOD[145]、IHS[146]等数据库为代表。

4.3 理论基础

复杂网络理论与数据科学可以有效简化韧性模型求解耗时长、效率低的问题。前者将管网系统简化为具有性能流的结构化网络,后者从历史数据出发评估管网工作特性,可有效节省所需算力与时间。在多目标韧性评价问题中,数据科学中的机器学习、深度学习方法开始与网络理论相结合,如将图论应用到随机神经网络中求解油气管网韧性模型[147]。上述两种理论存在其固有的局限性,对于复杂网络理论,目前缺少时滞性传播的模型,无法表征天然气管网的慢瞬变特性,需结合管内压力传播特性及管存气波动特性,对传统的复杂网络理论加以改进与完善。数据科学对管网系统数据质量、数量要求较高,若相关数据缺失,其对应的小样本学习尚属该领域的难点。因此,复杂网络理论、数据科学在推动韧性研究落地应用的同时,还需要进一步完善水力仿真技术与相关数据库。

5 NGPNS韧性研究的应用展望

随着NGPNS韧性研究的发展,其工程应用可概括为完善目标导向、指导相关预防与应急举措、辅助工程设计与运营等。德国经济研究所搭建了欧洲天然气供应战略模型[148],主要功能包括:发现影响天然气供需平衡的主要因素,指导天然气交易市场的决策与优化,以提升供气韧性;科隆大学能源经济研究所研发了天然气基础设施评估模型[149],主要功能包括:供气能力分析、管网输配方案优化,以提升天然气供应链系统韧性,现已应用于俄罗斯、德国等地的管网系统;荷兰能源研究中心基于韧性评价指标开发了欧洲天然气市场仿真与风险评价软件[150],以供气风险评价、供气脆弱性分析来评价欧洲天然气市场韧性,现已应用于欧盟多个国家或地区;欧盟能源委员会开发了天然气供应系统风险分析模型[74],通过随机模拟来评价NGPNS在各种供需场景下的保供能力。由此可见,NGPNS韧性研究在全球范围内已实现区域化应用,对管网系统的风险评估、韧性提升具有指导作用。结合现阶段相关研究的应用范例,NGPNS韧性研究未来可重点应用于以下领域。

5.1 NGPNS预警

NGPNS韧性概念与方法论为管网系统预警提供了新的思路。传统的预警思想重在预防扰动事件发生,可理解为面对扰动的“堵”;系统韧性在分析脆弱性、鲁棒性、恢复力时,将问题转化为如何应对此类扰动、如何实现损失最小、如何恢复得更快更好,可理解为面对扰动的“疏”。事实证明单方面避免扰动事件发生是片面的,且后果往往比事前预估更加严重[151]。新西兰可持续发展中心已经将韧性预警作为环境领域预警的第四类方法论[152];荷兰代尔夫特大学发现在新型冠状病毒感染的抗击过程中,传统预警方法已经无法充分应对此类突发事件,应将韧性方法论融入预警系统[153];在墨西哥湾事件与Piper Alpha的钻井平台事件之后,荷兰、加拿大、英国相继建立融合韧性的预警系统,指导股票受到扰动影响之前制订应对举措[154]。作为传统预警方法在目标与方法上的扩充,NGPNS韧性研究对管网系统的预警具有补充、指导作用。

5.2 NGPNS优化

相较于传统NGPNS的优化目标多集中于输气调度、人力与物资分配、运营收益等,融合韧性理念的优化具有以下优势:①将全时域的韧性过程加入优化目标,包括脆弱性、鲁棒性、恢复力的优化,且各阶段的优化结果可相互指导,如面向恢复过程的准备阶段优化,或基于损失过程的恢复策略优化[155]。②避免单一追求正常工况的最大效益,而是将“居安思危”的思想贯穿始终,弱化因忽略不确定性扰动而造成始料未及的巨大损失[156]。③韧性评价指标可以补充约束条件,包括基于脆弱性、鲁棒性、恢复力的约束[157]。现阶段欧盟各国已经在互联网系统、地理信息系统、电力系统、交通道路系统的优化运行中融合相关韧性分析,以获得更综合的全时域优化结果。

5.3 资产管理

与资产管理相结合的系统韧性研究在欧盟较为成熟,被欧盟结构安全委员会列入工程风险评价准则,这标志着与资产管理相结合的韧性分析方法成为推荐方法论之一[158]。该方法通过系统流动资产与固定资产的变化特性评价系统韧性,其优势在于将韧性评价结果从经济角度量化,更加符合企业需求。以丹麦、瑞士、意大利为代表的欧盟国家将综合能源系统韧性评价与电力输送系统、海上风机系统等子系统的资产管理结合,考虑了扰动带来经济效益、资产损失、维修费用的变化,并从管理决策、外部环境、基础设施、经济效益等角度实现综合能源系统的韧性评价[159]。韧性研究的相关方法论从受灾、应灾、恢复的角度对系统资产实现多维评估,可推动全生命周期、涵盖不确定性的资产管理。

6 结论

继电网、水网、综合能源系统、城市生命线系统韧性研究快速发展之后,NGPNS的韧性研究随之兴起,从概念、研究方法上补充了可靠性与完整性研究的不足,当前处于起步阶段。由于管网系统与相应扰动的高度复杂性,NGPNS韧性研究需要进一步深化,以实现全时域、多层次、复合维度的NGPNS供气安全保障。

(1)理解系统韧性概念,应从韧性思想、系统特性两方面展开。对于NGPNS韧性研究,广义上的扰动更加适应未来趋势,包括但不限于自然灾害、人为事故、市场波动等。

(2)NGPNS韧性研究主要分为韧性度量与优化。NGPNS韧性的度量方式由定性向定量发展,围绕系统脆弱性-鲁棒性、可靠性-恢复力进行框架搭建与指标设定。韧性曲线是韧性建模可视化的体现,由此衍生3类韧性评价方法:简单积分法、时间积分法、综合评价法。韧性评价模型求解方法主要有:水力仿真、网络理论、概率抽样、系统动力学等。管网系统韧性优化主要包括基于韧性模型的优化,以及韧性思想指导下的韧性提升举措。相关研究成果证明了韧性研究对NGPNS的设计、运营、应急等环节具有指导作用。

(3)NGPNS韧性研究需要多学科交叉融合,以及决策、生产、研究等多方人员协同合作。相关研究在管网系统的进一步落地应用,需要发展管网水力仿真技术作为保障,以实现对临界状态的精准识别,对水力参数变化的高效计算;需要完善管网系统相关数据库作为条件,包括管网结构与运营数据库、事故类型与损失量化数据库、市场交易与供需关系数据库,以实现更加精准的概率推断、后果量化及临界性分析;需要融合复杂网络理论、数据科学的相关算法作为推动力,突破复杂大型NGPNS计算耗时长、算力要求高的技术壁垒。

(4)欧洲、北美地区率先开展NGPNS韧性研究。欧盟各国基于NGPNS韧性研究开发了具有管网风险评价、性能分析、灾前预警、灾后恢复指导等功能的算法与模型。结合本领域韧性研究的应用案例,NGPNS韧性研究未来可重点应用于管网预警、管网优化、资产管理等场景,并与管道完整性与管网可靠性共同形成综合、实用、高效的管网系统安全评价与优化体系。

参考文献:

[1] 黄维和,梅应丹,吴丽丽,梁赟玲,赵晓丽.能源革命与中国能源经济安全保障探析[J].中国工程科学,2021,23(1):124-132.DOI:10.15302/J-SSCAE-2021.01.018.

HUANG W H, MEI Y D, WU L L, LIANG Y L, ZHAO X L. Energy revolution and security guarantee of China’s energy economy[J]. Strategic Study of CAE, 2021, 23(1): 124-132.

[2]黄维和,韩景宽,王玉生,沈珏新,程蕾.我国能源安全战略与对策探讨[J].中国工程科学,2021,23(1):112-117. DOI:10.15302/J-SSCAE-2021.01.017.

HUANG W H, HAN J K, WANG Y S, SHEN J X, CHENG L. Strategies and countermeasures for ensuring energy security in China[J]. Strategic Study of CAE, 2021, 23(1): 112-117.

[3]杨兆铭,张劲军,苏怀,左丽丽,江璐鑫.天然气管网系统韧性及其研究进展[C].北京:中国国际管道会议,2021:1-14.

YANG Z M, ZHANG J J, SU H, ZUO L L, JIANG L X. A review on research of natural gas pipeline system resilience[C]. Beijing: China International Pipeline Conference, 2021: 1-14.

[4]黄维和,郑洪龙,李明菲.中国油气储运行业发展历程及展望[J].油气储运,2019,38(1):1-11. DOI:10.6047/j.issn.1000-8241. 2019.01.001.

HUANG W H, ZHENG H L, LI M F. Development history and prospect of oil & gas storage and transportation industry in China[J]. Oil & Gas Storage and Transportation, 2019, 38(1): 1-11.

[5]虞维超.基于需求侧分析的天然气管网供气可靠性研究[D].北京:中国石油大学(北京),2019.

YU W C. Study on the gas supply reliability of natural gas pipeline networks based on demand side analysis[D]. Beijing: China University of Petroleum (Beijing), 2019.

[6]LI X Y, SU H, ZHANG J J. A systematic assessment method of supply resilience for natural gas supply systems[J]. Chemical Engineering Research and Design, 2022, 182: 207-215. DOI: 10.1016/j.cherd.2022.03.057.

[7]苏怀.天然气管网供气可靠性评价方法研究[D].北京:中国石油大学(北京),2018.

SU H. Research on assessment method of supply reliability of natural gas pipeline networks[D]. Beijing: China University of Petroleum (Beijing), 2018.

[8]黄维和.大型天然气管网系统可靠性[J].石油学报,2013,34(2):401-404. DOI:10.7623/syxb201302026.

HUANG W H. Reliability of large-scale natural gas pipeline network[J]. Acta Petrolei Sinica, 2013, 34(2): 401-404.

[9]燕冰川,陈朋超,冯庆善,王婷,陈健,王富祥,等.油气管道完整性评价技术规范:GB/T 42033—2022[S].北京:中国标准出版社,2022.

YAN B C, CHEN P C, FENG Q S, WANG T, CHEN J, WANG F X, et al. Specification for oil and gas pipeline integrity assessment: GB/T 42033—2022[S]. Beijing: Standards Press of China, 2022.

[10]范慕炜,宫敬,伍阳,孔文慧.陕京天然气管网系统供气可靠度的敏感性[J].油气储运,2018,37(4):378-384. DOI:10.6047/j.issn.1000-8241.2018.04.003.

FAN M W, GONG J, WU Y, KONG W H. Sensitivity analysis on the gas supply reliability of Shaanxi-Beijing natural gas pipeline network system[J]. Oil & Gas Storage and Transportation, 2018, 37(4): 378-384.

[11]张劲军,苏怀,高鹏.天然气管网韧性保供问题及其研究展望[J].石油学报,2020,41(12):1665-1678. DOI:10.7623/syxb202012019.

ZHANG J J, SU H, GAO P. Resilience-based supply assurance of natural gas pipeline networks and its research prospects[J]. Acta Petrolei Sinica, 2020, 41(12): 1665-1678.

[12]ZIO E. Challenges in the vulnerability and risk analysis of critical infrastructures[J]. Reliability Engineering & System Safety, 2016, 152: 137-150. DOI: 10.1016/j.ress.2016.02.009.

[13]JOHANSSON J, HASSEL H, ZIO E. Reliability and vulnerability analyses of critical infrastructures: Comparing two approaches in the context of power systems[J]. Reliability Engineering & System Safety, 2013, 120: 27-38. DOI: 10.1016/j.ress.2013.02.027.

[14]FIERING M B, HOLLING C S. Management and standards for perturbed ecosystems[J]. Agro-Ecosystems, 1974, 1: 301-321. DOI: 10.1016/0304-3746(74)90040-7.

[15]陈海波,赵嘉伟.这些问题,关乎我们的未来[N].光明日报,2022-06-28(11).

CHEN H B, ZHAO J W. These are the issues that concern our future[N]. Guangming Daily, 2022-06-28(11).

[16]万玉航.中国科协发布2022年重大科学问题[EB/OL].(2022-06-30)[2023-02-06]. http://tech.cnr.cn/techyw/technews/ 20220630/t20220630_525890177.shtml.

WAN Y H. CAST releases major scientific issues for    2022[EB/OL]. (2022-06-30)[2023-02-06]. http://tech.cnr.cn/techyw/technews/20220630/t20220630_525890177.shtml.

[17]HOSSEINI S, BARKER K, RAMIREZ-MARQUEZ J E. A review of definitions and measures of system resilience[J]. Reliability Engineering & System Safety, 2016, 145: 47-61. DOI: 10.1016/j.ress.2015.08.006.

[18]REGGIANI A, DE GRAAFF T, NIJKAMP P. Resilience: an evolutionary approach to spatial economic systems[J]. Networks and Spatial Economics, 2002, 2(2): 211-229. DOI: 10.1023/A:1015377515690.

[19]HALDANE V, DE FOO C, ABDALLA S M, JUNG A S, TAN M, WU S S, et al. Health systems resilience in managing the COVID-19 pandemic: lessons from 28 countries[J]. Nature Medicine, 2021, 27(6): 964-980. DOI: 10.1038/s41591-021-01381-y.

[20]DE CARVALHO P V R. The use of Functional Resonance Analysis Method (FRAM) in a mid-air collision to understand some characteristics of the air traffic management system resilience[J]. Reliability Engineering & System Safety, 2011, 96(11): 1482-1498. DOI: 10.1016/j.ress.2011.05.009.

[21]JASIUNAS J, LUND P D, MIKKOLA J. Energy system resilience--a review[J]. Renewable and Sustainable Energy Reviews, 2021, 150: 111476. DOI: 10.1016/j.rser.2021.111476.

[22]AHMADI S, SABOOHI Y, VAKILI A. Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: a review[J]. Renewable and Sustainable Energy Reviews, 2021, 144: 110988. DOI: 10.1016/j.rser.2021.110988.

[23]HE C, DAI C X, WU L, LIU T Q. Robust network hardening strategy for enhancing resilience of integrated electricity and natural gas distribution systems against natural disasters[J]. IEEE Transactions on Power Systems, 2018, 33(5): 5787-5798. DOI: 10.1109/TPWRS.2018.2820383.

[24]HARRISON C G, WILLIAMS P R. A systems approach to natural disaster resilience[J]. Simulation Modelling Practice and Theory, 2016, 65: 11-31. DOI: 10.1016/j.simpat.2016.02.008.

[25]CHEN C, LI C J, RENIERS G, YANG F Q. Safety and security of oil and gas pipeline transportation: a systematic analysis of research trends and future needs using WoS[J]. Journal of Cleaner Production, 2021, 279: 123583. DOI: 10.1016/j.jclepro. 2020.123583.

[26]WANG J, ZUO W D, RHODE-BARBARIGOS L, LU X, WANG J H, LIN Y L. Literature review on modeling and simulation of energy infrastructures from a resilience perspective[J]. Reliability Engineering & System Safety, 2019, 183: 360-373. DOI: 10.1016/j.ress.2018.11.029.

[27]GUNDERSON  L, KINZIG A, QUINLAN A, WALKER B, CUNDHILL G, BEIER C, et al. Assessing resilience in social-ecological systems: workbook for practitioners. Version 2.0[M]. Wolfville: Resilience Alliance, 2010: 1-54.

[28]IFEJIKA SPERANZA C, WIESMANN U, RIST S. An indicator framework for assessing livelihood resilience in the context of social-ecological dynamics[J]. Global Environmental Change, 2014, 28: 109-119. DOI: 10.1016/j.gloenvcha.2014. 06.005.

[29]KAHAN J, ALLEN A, GEORGE J, THOMPSON G. Concept development: an operational framework for resilience: RP09-01.03.02.12-1[R]. Arlington: Homeland Security Studies and Analysis Institute, 2009: 1-44.

[30]LABAKA L, HERNANTES J, SARRIEGI J M. Resilience framework for critical infrastructures: an empirical study in a nuclear plant[J]. Reliability Engineering & System Safety, 2015, 141: 92-105. DOI: 10.1016/j.ress.2015.03.009.

[31]VLACHEAS P, STAVROULAKI V, DEMESTICHAS P, CADZOW S, IKONOMOU D, GORNIAK S. Towards end-to-end network resilience[J]. International Journal of Critical Infrastructure Protection, 2013, 6(3/4): 159-178. DOI: 10.1016/j.ijcip.2013.08.004.

[32]BRUYELLE J L, O’NEILL C, EL-KOURSI E M, HAMELIN F, SARTORI N, KHOUDOUR L. Improving the resilience of metro vehicle and passengers for an effective emergency response to terrorist attacks[J]. Safety Science, 2014, 62: 37-45. DOI: 10.1016/j.ssci.2013.07.022.

[33]SU H, ZHANG J J, ZIO E, YANG N, LI X Y, ZHANG Z J. An integrated systemic method for supply reliability assessment of natural gas pipeline networks[J]. Applied Energy, 2018, 209: 489-501. DOI: 10.1016/j.apenergy.2017.10.108.

[34]薛鲁宁,郑洪龙,周利剑,李明菲,李在蓉,程万洲.油气管网可靠性计算方法评述[J].油气储运,2016,35(2):124-129. DOI:10.6047/j.issn.1000-8241.2016.02.002.

XUE L N, ZHENG H L, ZHOU L J, LI M F, LI Z R, CHENG W Z. Reliability calculation methods for oil and gas pipeline network[J]. Oil & Gas Storage and Transportation, 2016, 35(2): 124-129.

[35]SU H, ZIO E, ZHANG J J, LI X Y. A systematic framework of vulnerability analysis of a natural gas pipeline network[J]. Reliability Engineering & System Safety, 2018, 175: 79-91. DOI: 10.1016/j.ress.2018.03.006.

[36]SESINI M, GIAROLA S, HAWKES A D. The impact of liquefied natural gas and storage on the EU natural gas infrastructure resilience[J]. Energy, 2020, 209: 118367. DOI: 10.1016/j.energy.2020.118367.

[37]DIECKHÖNER C, LOCHNER S, LINDENBERGER D. European natural gas infrastructure: the impact of market developments on gas flows and physical market integration[J]. Applied Energy, 2013, 102: 994-1003. DOI: 10.1016/j.apenergy. 2012.06.021.

[38]AFRASIABI M, MOHAMMADI M, RASTEGAR M, KARGARIAN A. Multi-agent microgrid energy management based on deep learning forecaster[J]. Energy, 2019, 186: 115873. DOI: 10.1016/j.energy.2019.115873.

[39]BEN OTHMAN S, ZGAYA H, DOTOLI M, HAMMADI S. An agent-based decision support system for resources’ scheduling in emergency supply chains[J]. Control Engineering Practice, 2017, 59: 27-43. DOI: 10.1016/j.conengprac.2016.11.014.

[40]ALIABADI D E, KAYA M, AHIN G. An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms[J]. Energy Policy, 2017, 100: 191-205. DOI: 10.1016/j.enpol.2016.09.063.

[41]MARI S I, LEE Y H, MEMON M S, PARK Y S, KIM M. Adaptivity of complex network topologies for designing resilient supply chain networks[J]. International Journal of Industrial Engineering: Theory, Applications and Practice, 2015, 22(1): 102-116. DOI: 10.23055/ijietap.2015.22.1.1326.

[42]SU M R, ZHANG M Q, LU W W, CHANG X, CHEN B, LIU G Y, et al. ENA-based evaluation of energy supply security: Comparison between the Chinese crude oil and natural gas supply systems[J]. Renewable and Sustainable Energy Reviews, 2017, 72: 888-899. DOI: 10.1016/j.rser.2017.01.131.

[43]STAUFFER D, AHARONY A. Introduction to percolation theory[M]. 2nd ed. London: Taylor & Francis, 1994: 1-192.

[44]SHAIKH F, JI Q, FAN Y. Evaluating China’s natural gas supply security based on ecological network analysis[J]. Journal of Cleaner Production, 2016, 139: 1196-1206. DOI: 10.1016/j. jclepro.2016.09.002.

[45]SHIRALI G H A, MOHAMMADFAM I, MOTAMEDZADE M, EBRAHIMIPOUR V, MOGHIMBEIGI A. Assessing resilience engineering based on safety culture and managerial factors[J]. Process Safety Progress, 2012, 31(1): 17-18. DOI: 10.1002/prs.10485.

[46]CUTTER S L, BARNES L, BERRY M, BURTON C, EVANS E, TATE E, et al. A place-based model for understanding community resilience to natural disasters[J]. Global Environmental Change, 2008, 18(4): 598-606. DOI: 10.1016/j. gloenvcha.2008.07.013.

[47]PETTIT T J, FIKSEL J, CROXTON K L. Ensuring supply chain resilience: development of a conceptual framework[J]. Journal of Business Logistics, 2010, 31(1): 1-21. DOI: 10.1002/j. 2158-1592.2010.tb00125.x.

[48]SHIRALI G A, MOHAMMADFAM I, EBRAHIMIPOUR V. A new method for quantitative assessment of resilience engineering by PCA and NT approach: a case study in a process industry[J]. Reliability Engineering & System Safety, 2013, 119: 88-94. DOI: 10.1016/j.ress.2013.05.003.

[49]ZHANG Y, WENG W G. Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference[J]. Reliability Engineering & System Safety, 2020, 203: 107089. DOI: 10.1016/j.ress.2020.107089.

[50]MARINO A, ZIO E. A framework for the resilience analysis of complex natural gas pipeline networks from a cyber-physical system perspective[J]. Computers & Industrial Engineering, 2021, 162: 107727. DOI: 10.1016/j.cie.2021.107727.

[51]KAHAN J H, ALLEN A C, GEORGE J K. An operational framework for resilience[J]. Journal of Homeland Security and Emergency Management, 2009, 6(1): 83. DOI: 10.2202/1547-7355.1675.

[52]Tierney K J. Conceptualizing and measuring organizational and community resilience: lessons from the emergency response following the September 11, 2001 attack on the world trade center[R]. Newark: University of Delaware, Disaster Research Center, 2003:1-7.

[53]LONGSTAFF P H, ARMSTRONG N J, PERRIN K, PARKER W M, HIDEK M A. Building resilient communities: a preliminary framework for assessment[J]. Homeland Security Affairs, 2010, 6(3): 1-23.

[54]NORRIS F H, STEVENS S P, PFEFFERBAUM B, WYCHE K F, PFEFFERBAUM R L. Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness[J]. American Journal of Community Psychology, 2008, 41(1): 127-150. DOI: 10.1007/s10464-007-9156-6.

[55]ALLENBY B, FINK J. Toward inherently secure and resilient societies[J]. Science, 2005, 309(5737): 1034-1036. DOI: 10. 1126/science.1111534.

[56]FIKSEL J. Sustainability and resilience: toward a systems approach[J]. Sustainability: Science, Practice and Policy, 2006, 2(2): 14-21. DOI: 10.1080/15487733.2006.11907980.

[57]HOLLING C S. Resilience and stability of ecological systems[J]. Annual Review of Ecology and Systematics, 1973, 4(1): 1-23. DOI: 10.1146/annurev.es.04.110173.000245.

[58]FISHER R E, BASSETT G W, BUEHRING W A, COLLINS M J, DICKINSON D C, EATON L K, et al. Constructing a resilience index for the enhanced critical infrastructure protection program: ANL/DIS-10-9[R]. Washington, D.C.: United States Department of Homeland Security, 2010: 1-34.

[59]LIN Y L, BIE Z H. Study on the resilience of the integrated energy system[J]. Energy Procedia, 2016, 103: 171-176. DOI: 10.1016/j.egypro.2016.11.268.

[60]TIERNEY K, BRUNEAU M. Conceptualizing and measuring resilience: a key to disaster loss reduction[J]. TR News, 2009, 250: 14-15, 17.

[61]AHMADIAN N, LIM G J, CHO J, BORA S. A quantitative approach for assessment and improvement of network resilience[J]. Reliability Engineering & System Safety, 2020, 200: 106977. DOI: 10.1016/j.ress.2020.106977.

[62]CIMELLARO G P, VILLA O, BRUNEAU M. Resilience-based design of natural gas distribution networks[J]. Journal of Infrastructure Systems, 2015, 21(1): 05014005. DOI: 10.1061/(ASCE)IS.1943-555X.0000204.

[63]YANG Z M, SU H, DU X K, ZIO E, XIANG Q, PENG S L, et al. Supply resilience assessment of natural gas pipeline network systems[J]. Journal of Cleaner Production, 2023, 385: 135654. DOI: 10.1016/j.jclepro.2022.135654.

[64]BRUNEAU M, CHANG S E, EGUCHI R T, LEE G C, O'ROURKE T D, REINHORN A M, et al. A framework to quantitatively assess and enhance the seismic resilience of communities[J]. Earthquake Spectra, 2003, 19(4): 733-752. DOI: 10.1193/1.1623497.

[65]CIMELLARO G P, REINHORN A M, BRUNEAU M. Seismic resilience of a hospital system[J]. Structure and Infrastructure Engineering, 2010, 6(1/2): 127-144. DOI: 10.1080/15732470802663847.

[66]JIN C, LI R Y, KANG R. Maximum flow-based resilience analysis: from component to system[J]. PLoS One, 2017, 12(5): e0177668. DOI: 10.1371/journal.pone.0177668.

[67]ZHANG X G, MAHADEVAN S, SANKARARAMAN S, GOEBEL K. Resilience-based network design under uncertainty[J]. Reliability Engineering & System Safety, 2018, 169: 364-379. DOI: 10.1016/j.ress.2017.09.009.

[68]OUYANG M, DUEÑAS-OSORIO L, MIN X. A three-stage resilience analysis framework for urban infrastructure systems[J]. Structural Safety, 2012, 36/37: 23-31. DOI: 10.1016/j.strusafe.2011.12.004.

[69]REED D A, KAPUR K C, CHRISTIE R D. Methodology for assessing the resilience of networked infrastructure[J]. IEEE Systems Journal, 2009, 3(2): 174-180. DOI: 10.1109/JSYST.2009.2017396.

[70]LI R Y, DONG Q, JIN C, KANG R. A new resilience measure for supply chain networks[J]. Sustainability, 2017, 9(1): 144. DOI: 10.3390/su9010144.

[71]NAJARIAN M, LIM G J. Design and assessment methodology for system resilience metrics[J]. Risk Analysis, 2019, 39(9): 1885-1898. DOI: 10.1111/risa.13274.

[72]CARVALHO R, BUZNA L, BONO F, MASERA M, ARROWSMITH D K, HELBING D. Resilience of natural gas networks during conflicts, crises and disruptions[J]. PLoS One, 2014, 9(3): e90265. DOI: 10.1371/journal.pone.0090265.

[73]叶恒,高振宇,张轶,滕飞,黄柏麟,邰军.复杂网络理论的应用进展及对天然气管网的启示[J].油气储运,2022,41(5):515-524. DOI:10.6047/j.issn.1000-8241.2022.05.003.

YE H, GAO Z Y, ZHANG Y, TENG F, HUANG B L, TAI J. Advance in application of complex network theory and implications for natural gas pipeline networks[J]. Oil & Gas Storage and Transportation, 2022, 41(5): 515-524.

[74]MONFORTI F, SZIKSZAI A. A MonteCarlo approach for assessing the adequacy of the European gas transmission system under supply crisis conditions[J]. Energy Policy, 2010, 38(5): 2486-2498. DOI: 10.1016/j.enpol.2009.12.043.

[75]RYAKHOVSKIKH I V, BOGDANOV R I. Model of stress corrosion cracking and practical guidelines for pipelines operation[J]. Engineering Failure Analysis, 2021, 121: 105134. DOI: 10.1016/j.engfailanal.2020.105134.

[76]MO H D, LI Y F, ZIO E. A system-of-systems framework for the reliability analysis of distributed generation systems accounting for the impact of degraded communication networks[J]. Applied Energy, 2016, 183: 805-822. DOI: 10.1016/j.apenergy.2016.09.041.

[77]GUO Y B, MENG X L, WANG D G, MENG T, LIU S H, HE R Y. Comprehensive risk evaluation of long-distance oil and gas transportation pipelines using a fuzzy Petri net model[J]. Journal of Natural Gas Science and Engineering, 2016, 33: 18-29. DOI: 10.1016/j.jngse.2016.04.052.

[78]MIRAN S A, HUANG Q D, CASTANEDA H. Time-dependent reliability analysis of corroded buried pipelines considering external defects[J]. Journal of Infrastructure Systems, 2016, 22(3): 04016019. DOI: 10.1061/(ASCE)IS.1943-555X.0000307.

[79]SHABARCHIN O, TESFAMARIAM S. Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model[J]. Journal of Loss Prevention in the Process Industries, 2016, 40: 479-495. DOI: 10.1016/j.jlp.2016.02.001.

[80]XIE S S, HE H W, PENG J K. An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses[J]. Applied Energy, 2017, 196: 279-288. DOI: 10.1016/j.apenergy.2016.12.112.

[81]TURATI P, PEDRONI N, ZIO E. Simulation-based exploration of high-dimensional system models for identifying unexpected events[J]. Reliability Engineering & System Safety, 2017, 165: 317-330. DOI: 10.1016/j.ress.2017.04.004.

[82]KABIR M N, MISHRA Y, BANSAL R C. Probabilistic load flow for distribution systems with uncertain PV generation[J]. Applied Energy, 2016, 163: 343-351. DOI: 10.1016/j.apenergy. 2015.11.003.

[83]DI MAIO F, PICOCO C, ZIO E, RYCHKOV V. Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment[J]. Reliability Engineering & System Safety, 2017, 162: 122-138. DOI: 10.1016/j.ress.2017. 01.020.

[84]DING Y T, CHEN S, ZHENG Y L, CHAI S L, NIE R. Resilience assessment of China’s natural gas system under supply shortages: a system dynamics approach[J]. Energy, 2022, 247: 123518. DOI: 10.1016/j.energy.2022.123518.

[85]AZADEH A, RAOOFI Z, ZARRIN M. A multi-objective fuzzy linear programming model for optimization of natural gas supply chain through a greenhouse gas reduction approach[J]. Journal of Natural Gas Science and Engineering, 2015, 26: 702-710. DOI: 10.1016/j.jngse.2015.05.039.

[86]TOMASGARD A, RØMO F, FODSTAD M, MIDTHUN K. Optimization models for the natural gas value chain[M]//HASLE G, LIE K A, QUAK E. Geometric Modelling, Numerical Simulation, and Optimization. Berlin: Springer, 2007: 521-558.

[87]HAMEDI M, ZANJIRANI FARAHANI R, HUSSEINI M M, ESMAEILIAN G R. A distribution planning model for natural gas supply chain: a case study[J]. Energy Policy, 2009, 37(3): 799-812. DOI: 10.1016/j.enpol.2008.10.030.

[88]CONTESSE L, FERRER J C, MATURANA S. A mixed-integer programming model for gas purchase and transportation[J]. Annals of Operations Research, 2005, 139(1): 39-63. DOI: 10.1007/s10479-005-3443-0.

[89]GARCIA D J, YOU F Q. Supply chain design and optimization: challenges and opportunities[J]. Computers & Chemical Engineering, 2015, 81: 153-170. DOI: 10.1016/j.compchemeng. 2015.03.015.

[90]PAPAGEORGIOU L G. Supply chain optimisation for the process industries: advances and opportunities[J]. Computers & Chemical Engineering, 2009, 33(12): 1931-1938. DOI: 10.1016/j.compchemeng.2009.06.014.

[91]BOPP A E, KANNAN V R, PALOCSAY S W, STEVENS S P. An optimization model for planning natural gas purchases, transportation, storage and deliverability[J]. Omega, 1996, 24(5): 511-522. DOI: 10.1016/0305-0483(96)00025-4.

[92]URCIUOLI L, MOHANTY S, HINTSA J, GERINE BOEKESTEIJN E. The resilience of energy supply chains: a multiple case study approach on oil and gas supply chains to Europe[J]. Supply Chain Management, 2014, 19(1): 46-63. DOI: 10.1108/SCM-09-2012-0307.

[93]RÍOS-MERCADO R Z, BORRAZ-SÁNCHEZ C. Optimization problems in natural gas transportation systems: a state-of-the-art review[J]. Applied Energy, 2015, 147: 536-555. DOI: 10.1016/j.apenergy.2015.03.017.

[94]HANTE F M, LEUGERING G, MARTIN A, SCHEWE L, SCHMIDT M. Challenges in optimal control problems for gas and fluid flow in networks of pipes and canals: from modeling to industrial applications[M]//MANCHANDA P, LOZI R, SIDDIQI A H. Industrial mathematics and complex systems. Singapore: Springer, 2017: 77-122.

[95]WOLDEYOHANNES A D, MAJID M A A. Simulation model for natural gas transmission pipeline network system[J]. Simulation Modelling Practice and Theory, 2011, 19(1): 196-212. DOI: 10.1016/j.simpat.2010.06.006.

[96]EPA. Reducing emissions when taking compressors off-line[M]. Washington, D.C.: United States Environmental Protection Agency, 2006:1-11.

[97]SEURING S. A review of modeling approaches for sustainable supply chain management[J]. Decision Support Systems, 2013, 54(4): 1513-1520. DOI: 10.1016/j.dss.2012.05.053.

[98]BARBOSA-PÓVOA A P. Progresses and challenges in process industry supply chains optimization[J]. Current Opinion in Chemical Engineering, 2012, 1(4): 446-452. DOI: 10.1016/j.coche.2012.09.006.

[99]TÜRKAY M, ORUÇ C, FUJITA K, ASAKURA T. Multi-company collaborative supply chain management with economical and environmental considerations[J]. Computers & Chemical Engineering, 2004, 28(6/7): 985-992. DOI: 10.1016/j.compchemeng.2003.09.005.

[100]HEPBASLI A. A key review on exergetic analysis and assessment of renewable energy resources for a sustainable future[J]. Renewable and Sustainable Energy Reviews, 2008, 12(3): 593-661. DOI: 10.1016/j.rser.2006.10.001.

[101]AZADEH A, SHABANPOUR N, SOLTANPOUR GHARIBDOUSTI M, NASIRIAN B. Optimization of supply chain based on macro ergonomics criteria: a case study in gas transmission unit[J]. Journal of Loss Prevention in the Process Industries, 2016, 43: 332-351. DOI: 10.1016/j.jlp.2016. 05.024.

[102]CARVALHO H, BARROSO A P, MACHADO V H, AZEVEDO S, CRUZ-MACHADO V. Supply chain redesign for resilience using simulation[J]. Computers & Industrial Engineering, 2012, 62(1): 329-341. DOI: 10.1016/j.cie.2011.10.003.

[103]SCHMITT A J, SINGH M. A quantitative analysis of disruption risk in a multi-echelon supply chain[J]. International Journal of Production Economics, 2012, 139(1): 22-32. DOI: 10.1016/j.ijpe.2012.01.004.

[104]MAHESHWARI P, SINGLA S, SHASTRI Y. Resiliency optimization of biomass to biofuel supply chain incorporating regional biomass pre-processing depots[J]. Biomass and Bioenergy, 2017, 97: 116-131. DOI: 10.1016/j.biombioe. 2016. 12.015.

[105]FARZANEH-GORD M, RAHBARI H R. Unsteady natural gas flow within pipeline network, an analytical approach[J]. Journal of Natural Gas Science and Engineering, 2016, 28: 397-409. DOI: 10.1016/j.jngse.2015.12.017.

[106]MOSLEHI S, REDDY T A. Sustainability of integrated energy systems: a performance-based resilience assessment methodology[J]. Applied Energy, 2018, 228: 487-498. DOI: 10.1016/j.apenergy.2018.06.075.

[107]SCHWEIGER J, LIERS F. A decomposition approach for optimal gas network extension with a finite set of demand scenarios[J]. Optimization and Engineering, 2018, 19(2): 297-326. DOI: 10.1007/s11081-017-9371-4.

[108]LIANG Y Z, HUI C W. Convexification for natural gas transmission networks optimization[J]. Energy, 2018, 158: 1001-1016. DOI: 10.1016/j.energy.2018.06.107.

[109]ALMOGHATHAWI Y, BARKER K, ALBERT L A. Resilience-driven restoration model for interdependent infrastructure networks[J]. Reliability Engineering & System Safety, 2019, 185: 12-23. DOI: 10.1016/j.ress.2018.12.006.

[110]POE W A, MOKHATAB S. Modeling, control, and optimization of natural gas processing plants[M]. Boston: Gulf Professional Publishing, 2017: 173-213.

[111]VASCONCELOS C D, LOURENÇO S R, GRACIAS A C, CASSIANO D A. Network flows modeling applied to the natural gas pipeline in Brazil[J]. Journal of Natural Gas Science and Engineering, 2013, 14: 211-224. DOI: 10.1016/j. jngse.2013.07.001.

[112]FAHIMNIA B, FARAHANI R Z, MARIAN R, LUONG L. A review and critique on integrated production-distribution planning models and techniques[J]. Journal of Manufacturing Systems, 2013, 32(1): 1-19. DOI: 10.1016/j.jmsy.2012.07.005.

[113]BYRNE M D, BAKIR M A. Production planning using a hybrid simulation-analytical approach[J]. International Journal of Production Economics, 1999, 59(1/2/3): 305-311. DOI: 10.1016/S0925-5273(98)00104-2.

[114]LEE Y H, KIM S H. Production-distribution planning in supply chain considering capacity constraints[J]. Computers & Industrial Engineering, 2002, 43(1/2): 169-190. DOI: 10.1016/S0360-8352(02)00063-3.

[115]BAZMI A A, ZAHEDI G. Sustainable energy systems: Role of optimization modeling techniques in power generation and supply--a review[J]. Renewable and Sustainable Energy Reviews, 2011, 15(8): 3480-3500. DOI: 10.1016/j.rser.2011. 05.003.

[116]KBAH Z, ERDIL N O, AQLAN F. Analysis of oil and gas supply chain using continuous-time discrete-event simulation[C]. Anaheim: 2016 Industrial and Systems Engineering Research Conference, 2016: 213-218.

[117]CHRISTOPHER, M, PECK H. Building the resilient supply chain[J]. The International Journal of Logistics Management, 2004, 15(2): 1-14. DOI: 10.1108/09574090410700275.

[118]RYU J H, DUA V, PISTIKOPOULOS E N. A bilevel programming framework for enterprise-wide process networks under uncertainty[J]. Computers & Chemical Engineering, 2004, 28(6/7): 1121-1129. DOI: 10.1016/j.compchemeng. 2003.09.021.

[119]JUNG H, JEONG B, LEE C G. An order quantity negotiation model for distributor-driven supply chains[J]. International Journal of Production Economics, 2008, 111(1): 147-158. DOI: 10.1016/j.ijpe.2006.12.054.

[120]CHEN M Y, WANG W M. A linear programming model for integrated steel production and distribution planning[J]. International Journal of Operations & Production Management, 1997, 17(6): 592-610. DOI: 10.1108/ 01443579710167276.

[121]KANYALKAR A P, ADIL G K. An integrated aggregate and detailed planning in a multi-site production environment using linear programming[J]. International Journal of Production Research, 2005, 43(20): 4431-4454. DOI: 10.1080/ 00207540500142332.

[122]VAN DYKEN S, BAKKEN B H, SKJELBRED H I. Linear mixed-integer models for biomass supply chains with transport, storage and processing[J]. Energy, 2010, 35(3): 1338-1350. DOI: 10.1016/j.energy.2009.11.017.

[123]INCEKARA C O, OGULATA S N. Turkey’s energy planning considering global environmental concerns[J]. Ecological Engineering, 2017, 102: 589-595. DOI: 10.1016/j.ecoleng. 2017.02.033.

[124]KABIRIAN A, HEMMATI M R. A strategic planning model for natural gas transmission networks[J]. Energy Policy, 2007, 35(11): 5656-5670. DOI: 10.1016/j.enpol.2007.05.022.

[125]SUKHAREV M G, KOSOVA K O. A parameter identification method for natural gas supply systems under unsteady gas flow[J]. Automation and Remote Control, 2017, 78(5): 882-890. DOI: 10.1134/S0005117917050101.

[126]SABRI E H, BEAMON B M. A multi-objective approach to simultaneous strategic and operational planning in supply chain design[J]. Omega, 2000, 28(5): 581-598. DOI: 10.1016/S0305-0483(99)00080-8.

[127]LIANG T F, CHENG H W. Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains[J]. Expert Systems with Applications, 2009, 36(2): 3367-3377. DOI: 10.1016/j.eswa.2008.01.002.

[128]TODINI E. Looped water distribution networks design using a resilience index based heuristic approach[J]. Urban Water, 2000, 2(2): 115-122. DOI: 10.1016/S1462-0758(00)00049-2.

[129]MAFAKHERI F, NASIRI F. Modeling of biomass-to-energy supply chain operations: applications, challenges and research directions[J]. Energy Policy, 2014, 67: 116-126. DOI: 10.1016/j.enpol.2013.11.071.

[130]IAKOVOU E, KARAGIANNIDIS A, VLACHOS D, TOKA A, MALAMAKIS A. Waste biomass-to-energy supply chain management: a critical synthesis[J]. Waste Management, 2010, 30(10): 1860-1870. DOI: 10.1016/j.wasman.2010.02.030.

[131]HUSSAIN A, BUI V H, KIM H M. Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience[J]. Applied Energy, 2019, 240: 56-72. DOI: 10.1016/j.apenergy.2019.02.055.

[132]广西壮族自治区发展和改革委员会.《广西壮族自治区加强天然气管网设施运行调度与应急保供实施细则》政策解读[EB/OL]. (2022-12-05)[2023-02-10]. http://fgw.chongzuo.gov.cn/xxgk/fdzdgknr/zcjd/t14108804.shtml.

Development and Reform Commission of Guangxi Zhuang Autonomous Region. Policy interpretation of the implementation rules on strengthening the operation and dispatch of natural gas pipeline network facilities and emergency supply assurance in Guangxi Zhuang autonomous region[EB/OL]. (2022-12-05)[2023-02-10]. http://fgw.chongzuo.gov.cn/xxgk/fdzdgknr/zcjd/t14108804.shtml.

[133]中华人民共和国国家发展和改革委员会.天然气管网设施运行调度与应急保供管理办法(试行)(征求意见稿)[EB/OL].(2021-07-15)[2023-02-10]. https://www.ndrc.gov.cn/yjzxDownload/ 20210715fj1.pdf.

National Development and Reform Commission. Measures for the management of operation and dispatch of natural gas pipeline network facilities and emergency supply  assurance[EB/OL]. (2021-07-15)[2023-02-10]. https://www.ndrc.gov.cn/yjzxDownload/20210715fj1.pdf.

[134]彭强. “三油一网”多措并举,推动今冬明春天然气保供[EB/OL].(2022-11-22)[2023-02-10]. http://www.21jingji.com/article/20221122/herald/3bd8fa32162f2221ec6f82b2306d0c1a.html.

PENG Q. ‘Three oil and one network’ take multiple measures to promote natural gas supply this winter and spring[EB/OL]. (2022-11-22)[2023-02-10]. http://www.21jingji.com/article/20221122/herald/3bd8fa32162f2221ec6f82b2306d0c1a.html.

[135]樊永涛.中国石油青海油田:抓早设备维护 抓好冬季保供[EB/OL].(2021-11-05)[2023-02-10]. https://baijiahao.baidu.com/s?id=1715578383454715321&wfr=spider&for=pc.

FAN Y T. China national petroleum Qinghai oilfield: grasp early equipment maintenance, grasp the winter to ensure supply[EB/OL]. (2021-11-05)[2023-02-10]. https://baijiahao.baidu.com/s?id=1715578383454715321&wfr=spider&for=pc.

[136]JUFRI F H, WIDIPUTRA V, JUNG J. State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies[J]. Applied Energy, 2019, 239: 1049-1065. DOI: 10.1016/j.apenergy.2019. 02.017.

[137]FREEDMAN D A. Statistical models: theory and practice[M]. Cambridge: Cambridge University Press, 2009: 115-218.

[138]LOÈVE M. Probability theory[M]. New York: Courier Dover Publications, 2017:149-232.

[139]ZENIEWSKI P, BOLADO-LAVIN R. A review of National gas emergency plans in the European Union[J]. Energy Policy, 2012, 49: 652-662. DOI: 10.1016/j.enpol.2012.07.005.

[140]EGIG. About EGIG[EB/OL]. [2023-02-01]. https://www.egig.eu/.

[141]ENTSOG. European network of transmission system operators for gas[EB/OL]. (2009-02-08)[2023-02-08]. https://www.entsog.eu/.

[142]GIE. AGSI+ and ALSI transparency platforms[EB/OL]. [2023-02-08]. https://www.gie.eu/agsi-and-alsi-transparency-platforms/.

[143]Eurostat. Welcome to Eurostat[EB/OL]. [2023-02-08]. https://ec.europa.eu/eurostat.

[144]ICIS. Shaping tomorrow together[EB/OL]. [2023-02-08]. https://www.icis.com/explore/.

[145]BOD. Transportation[EB/OL]. [2023-02-08]. https://www.snam.it/en/transportation/operational-data-business/.

[146]Accuris. Welcome to the Accuris standards store[EB/OL]. [2023-02-08]. https://global.ihs.com/.

[147]LIU Z, PAN B. Fuzzy reliability analysis for seabed oil-gas pipeline networks under earthquakes[J]. China Ocean Engineering, 2003, 17(1): 83-92. DOI: 10.3321/j.issn:0890- 5487.2003.01.008.

[148]HOLZ F, VON HIRSCHHAUSEN C, KEMFERT C. A strategic model of European gas supply (GASMOD)[J]. Energy Economics, 2008, 30(3): 766-788. DOI: 10.1016/j.eneco.2007.01.018.

[149]LOCHNER S, BOTHE D. From russia with gas an analysis of the Nord Stream pipeline’s impact on the European gas transmission system with the tiger-model: No 07.02[R]. Cologne: Institute of Economics at the University of Cologne, 2007: 1-16.

[150]LISE W, HOBBS B F, VAN OOSTVOORN F. Natural gas corridors between the EU and its main suppliers: Simulation results with the dynamic GASTALE model[J]. Energy Policy, 2008, 36(6): 1890-1906. DOI: 10.1016/j.enpol.2008.01.042.

[151]MARCHANT G E, STEVENS Y A. Resilience: a new tool in the risk governance toolbox for emerging technologies[J]. U.C. Davis Law Review, 2017, 51(1): 233-271.

[152]AKINS A, LYVER P O B, ALRØE H F, MOLLER H. The universal precautionary principle: new pillars and pathways for environmental, sociocultural, and economic resilience[J]. Sustainability, 2019, 11(8): 2357. DOI: 10.3390/su11082357.

[153]ALE B J M, HARTFORD D N D, SLATER D H. Precaution, resilience, faith, and COVID-19[J]. Medical Research Archives, 2020, 8(8): 1-17. DOI: 10.18103/mra.v8i8.2197.

[154]ALE B J M, HARTFORD D N D, SLATER D H. Prevention, precaution and resilience: Are they worth the cost?[J]. Safety Science, 2021, 140: 105271. DOI: 10.1016/j.ssci.2021.105271.

[155]LIAO T Y, HU T Y, KO Y N. A resilience optimization model for transportation networks under disasters[J]. Natural Hazards, 2018, 93(1): 469-489. DOI: 10.1007/s11069-018-3310-3.

[156]REN F C, ZHAO T D, JIAO J, HU Y Q. Resilience optimization for complex engineered systems based on the Multi-Dimensional resilience concept[J]. IEEE Access, 2017, 5: 19352-19362. DOI: 10.1109/ACCESS.2017.2755043.

[157]SHARKEY T C, NURRE PINKLEY S G, EISENBERG D A, ALDERSON D L. In search of network resilience: an optimization-based view[J]. Networks, 2021, 77(2): 225-254. DOI: 10.1002/net.21996.

[158]FABER M H. Principles, system representation & risk criteria[M]. Zurich: Joint Committee on Structural Safety, 2008: 1-35.

[159]LIU M, QIN J J, LU D G, ZHANG W H, ZHU J S, FABER M H. Towards resilience of offshore wind farms: a framework and application to asset integrity management[J]. Applied Energy, 2022, 322: 119429. DOI: 10.1016/j.apenergy. 2022.119429.

作者简介

杨兆铭,男,1993年生,在读博士生,2019年硕士毕业于中国石油大学(华东)油气储运工程专业,现主要从事油气管网系统韧性、韧性城市、多相流理论与相分离技术、油气储运系统数据分析等方面的研究工作。

地址:北京市昌平区府学路18号,102249。

电话:15092188428。

Email:yangzhaoming.upc@foxmail.com

客服微信
扫一扫
weinxin
EngGPT
云智微信公众号
扫一扫
weinxin
cidrg_com
 
大V快跑
  • 版权声明 本文源自 油气储运, 大V快跑 整理 发表于 2023年11月4日03:27:39
  • 本站原创文章转载请注明文章出处及链接,谢谢合作!
  • 天然气处理
  • 天然气管道
  • 风险分析
  • 风险评估
  • OLI
  • 天然气管网
匿名

发表评论

匿名网友
:?: :razz: :sad: :evil: :!: :smile: :oops: :grin: :eek: :shock: :???: :cool: :lol: :mad: :twisted: :roll: :wink: :idea: :arrow: :neutral: :cry: :mrgreen:
确定

拖动滑块以完成验证