Optimizing resilience decision-support for natural gas networks under uncertainty

2019 Summer.Includes bibliographical references.Community resilience in the aftermath of a hazard requires the functionality of complex, interdependent infrastructure systems become operational in a timely manner to support social and economic institutions. In the context of risk management and community resilience, critical decisions should be made not only in the aftermath of a disaster in order to immediately respond to the destructive event and properly repair the damage, but preventive decisions should to be made in order to mitigate the adverse impacts of hazards prior to their occurrence. This involves significant uncertainty about the basic notion of the hazard itself, and usually involves mitigation strategies such as strengthening components or preparing required resources for post-event repairs. In essence, instances of risk management problems that encourage a framework for coupled decisions before and after events include modeling how to allocate resources before the disruptive event so as to maximize the efficiency for their distribution to repair in the aftermath of the event, and how to determine which network components require preventive investments in order to enhance their performance in case of an event. In this dissertation, a methodology is presented for optimal decision making for resilience assessment, seismic risk mitigation, and recovery of natural gas networks, taking into account their interdependency with some of the other systems within the community. In this regard, the natural gas and electric power networks of a virtual community were modeled with enough detail such that it enables assessment of natural gas network supply at the community level. The effect of the industrial makeup of a community on its natural gas recovery following an earthquake, as well as the effect of replacing conventional steel pipes with ductile HDPE pipelines as an effective mitigation strategy against seismic hazard are investigated. In addition, a multi objective optimization framework that integrates probabilistic seismic risk assessment of coupled infrastructure systems and evolutionary algorithms is proposed in order to determine cost-optimal decisions before and after a seismic event, with the objective of making the natural gas network recover more rapidly, and thus the community more resilient. Including bi-directional interdependencies between the natural gas and electric power network, strategic decisions are pursued regarding which distribution pipelines in the gas network should be retrofitted under budget constraints, with the objectives to minimizing the number of people without natural gas in the residential sector and business losses due to the lack of natural gas in non-residential sectors. Monte Carlo Simulation (MCS) is used in order to propagate uncertainties and Probabilistic Seismic Hazard Assessment (PSHA) is adopted in order to capture uncertainties in the seismic hazard with an approach to preserve spatial correlation. A non-dominated sorting genetic algorithm (NSGA-II) approach is utilized to solve the multi-objective optimization problem under study. The results prove the potential of the developed methodology to provide risk-informed decision support, while being able to deal with large-scale, interdependent complex infrastructure considering probabilistic seismic hazard scenarios

Methodologies for Simplified Lifeline System Risk Assessments

Natural hazards are a growing risk across the globe. As regions have urbanized, single events impact greater proportions of the population, and the populations within those regions have become more dependent on infrastructure systems. Regional resilience has become closely tied to the performance of infrastructure. For a comprehensive risk assessment losses caused by lifeline outage must be considered alongside structural and nonstructural risks. Many well developed techniques quantify structural and nonstructural risk; however, there are insufficient procedures to determine the likelihood of lifeline outages. Including lifelines in seismic assessments will provide a comprehensive risk, improving a decision maker’s capacity to efficiently balance mitigation against the full spectrum of risks. An ideal lifeline risk assessment is infeasible due to the large geographic scale of lifeline systems and their system structure; these same characteristics also make them vulnerable to disruption in hazard events. Probabilistic methods provide solutions for their analysis, but many of the necessary analysis variables remain unknown. Continued research and increased collection of infrastructure data may improve the ability of advanced probabilistic methods to study and forecast performance of lifelines, but many inputs for a complete probabilistic model are likely to remain unknown. This thesis recognizes these barriers to assessment and proposes a methodology that uses consequences to simplify analysis of lifeline systems. Risk is often defined as the product of probability of failure and consequence. Many assessments study the probability of failure and then consider the consequence. This thesis proposes the opposite, studying consequence first. In a theoretical model where all information is available the difference in approach is irrelevant; the results are the same regardless of order. In the real world however, studying consequence first provides an opportunity to simplify the system assessment. The proposed methodology starts with stakeholders defining consequences that constitute ruin, and then the lifeline system is examined and simplified to components that can produce such consequences. Previously large and expansive systems can be greatly simplified and made more approachable systems to study. The simplified methodology does not result in a comprehensive risk assessment, rather it provides an abbreviated risk profile of catastrophic risk; risk that constitutes ruin. By providing an assessment of only catastrophic lifeline risk, the risk of greatest importance is measured, while smaller recoverable risk remains unknown. This methodology aligns itself with the principle of resilience, the ability to withstand shocks and rebound. Assessments can be used directly to consider mitigation options that directly address stakeholder resilience. Many of the same probabilistic issues remain, but by simplifying the process, abbreviated lifelines assessments are more feasible providing stakeholders with information to make decisions in an environment that currently is largely unknown

Reliability of Critical Infrastructure Networks: Challenges

Critical infrastructures form a technological skeleton of our world by providing us with water, food, electricity, gas, transportation, communication, banking, and finance. Moreover, as urban population increases, the role of infrastructures become more vital. In this paper, we adopt a network perspective and discuss the ever growing need for fundamental interdisciplinary study of critical infrastructure networks, efficient methods for estimating their reliability, and cost-effective strategies for enhancing their resiliency. We also highlight some of the main challenges arising on this way, including cascading failures, feedback loops, and cross-sector interdependencies.Comment: 12 pages, 3 figures, submitted for publication in the ASCE (American Society of Civil Engineers) technical repor

A survey on multilayer networks modelled to assess robustness in infrastructure systems

The development of modern societies places particular demands on the consistent performance of infrastructure systems. Because multilayer network models are capable of representing the interdependencies between infrastructure components, they have been widely used to analyse the robustness of infrastructure systems. This present study is a systematic review of literature, published since 2010. It aims to investigate how multilayer network models have been used in analysing the robustness of infrastructure systems. According to findings, percolation theory was the most popular method used in about 57% of papers. Regarding the properties, coupling strength and node degree were the most common while directed links and feedback conditions were the least common. The following gaps were identified which provide opportunities for further research. These include the absence of models based on real-world data and the need for models that make fewer simplifying assumptions about complex systems. No papers considered all potential properties, and their effect on boosting or weakening each other’s effect. By considering all properties, the importance of different properties on the robustness of infrastructure systems can be quantified and compared in future studies

相互依存性を有するクリティカルインフラストラクチャーの地震時性能と地震災害マネジメントに関する研究

京都大学0048新制・課程博士博士(工学)甲第17142号工博第3632号新制||工||1551(附属図書館)29881京都大学大学院工学研究科都市社会工学専攻(主査）教授 清野 純史, 教授 小池 武, 准教授 古川 愛子学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

Probabilistic Fragility of Interdependent Urban Systems Subjected to Seismic Hazards

Urban service networks have come under increased pressure due to expansion of urban population, decrease of capital investment, growing interdependence, and man-made and natural hazards. This thesis introduces a simulation-based methodology for the estimation of the fragility of urban networks subjected to earthquake perturbation. The proposed Interdependent Fragility Assessment (IFA) algorithm abstracts the steps required for perturbation-induced damage propagation within and between networks through internal and interdependent links, respectively. Damage propagation uncertainty is accounted by considering conditional probabilities of failure for components and interdependent strengths measuring the likelihood of intersystemic failure propagation. The IFA algorithm is used in four applications. The first application subjected two simplified models of real interdependent urban power and water networks to selected seismic scenarios. Test results showed that interdependence presence worsens systemic fragility, but that the features of interdependence effects were jointly influenced by local fragility properties and interdependence strengths. A second application examined the role of cascading failures caused by component overloading in systemic fragility. The results showed that cascading failures worsen interdependence fragility, and that mitigation actions improving local component capacity have limited effect on controlling interdependent-induced fragility. Two additional conceptual mitigation measures, component fragility reduction ( CFR ) and interdependence redundancy enhancement ( IRE ), were explored. CFR , decreases component seismic fragilities while IRE adds interdependence links to dependent nodes. Test results showed that CFR outperforms IRE ; however, their combination achieved comparable fragility reductions. This outcome highlights the potential of synergistic mitigation policies in controlling interdependent systemic fragility. Finally, the IFA methodology was adapted to use a probabilistic seismic description for the estimation of unconditional systemic fragilities. The hazard description was obtained following an existing approach that uses importance sampling for the generation of intensity maps. The value of the hybrid methodology rests on its capacity to generate unconditional fragility estimates for direct use in risk assessment. Topics for future work include the development of more sophisticated models of cascading failure, the analysis of optimal mitigation actions using mitigation cost-structures and life-cycle costs, the extension of the IFA methodology for perturbation such as hurricanes and flooding, and interdependent fragility studies of theoretical network models

Socially-integrated resilience in building-level water networks using smart microgrid+net

Environmental change and natural events can impact on multiple dimensions of human life; economic, social, political, physical (built) and natural (ecosystems) environments. Water distribution networks cover both the built and natural realms and are as such inherently vulnerable to accidental or deliberate physical, natural, chemical, or biological threats. An example of such threats include flooding. The damage to water networks from flooding at the building level can include disrupted supply, pipe damage, sink and sewer overflows, fittings and appliance malfunctions etc. as well as the consequential socio-economic loss and distress. It has also been highlighted that the cost of damage caused by disasters including flooding can be correlated to the warning-time given before it occurs. Therefore, contiguous and continuous preparedness is essential to sustain disaster resilience. This paper presents an early stage review to: 1. Understand the challenges and opportunities posed by disaster risks to critical infrastructure at the building level. 2. Examine the role and importance of early warnings within the smart systems context to promote anticipatory preparedness and reduce physical, economic, environmental and social vulnerability 3. Review the opportunities provided by smart water microgrid/net to deliver such an early warning system and 4. Define the basis for a socially-integrated framework for resilience in building water networks based on smart water micro grids and micronets. The objective is to establish the theoretical approach for smart system integration for risk mitigation in water networks at the building level. Also, to explore the importance and scope integration of other social-political dimensions within such framework and associated solutions. The findings will inform further studies to address the gaps in understanding the disaster risks in micro water infrastructure e.g. flooding, and; to develop strategies and systems to strengthen disaster preparedness for effective response and anticipatory action for such risks

Enabling Resilience in Cyber-Physical-Human Water Infrastructures

Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments

Resilience-Driven Post-Disruption Restoration of Interdependent Critical Infrastructure Systems Under Uncertainty: Modeling, Risk-Averse Optimization, and Solution Approaches

Critical infrastructure networks (CINs) are the backbone of modern societies, which depend on their continuous and proper functioning. Such infrastructure networks are subjected to different types of inevitable disruptive events which could affect their performance unpredictably and have direct socioeconomic consequences. Therefore, planning for disruptions to CINs has recently shifted from emphasizing pre-disruption phases of prevention and protection to post-disruption studies investigating the ability of critical infrastructures (CIs) to withstand disruptions and recover timely from them. However, post-disruption restoration planning often faces uncertainties associated with the required repair tasks and the accessibility of the underlying transportation network. Such challenges are often overlooked in the CIs resilience literature. Furthermore, CIs are not isolated from each other, but instead, most of them rely on one another for their proper functioning. Hence, the occurrence of a disruption in one CIN could affect other dependent CINs, leading to a more significant adverse impact on communities. Therefore, interdependencies among CINs increase the complexity associated with recovery planning after a disruptive event, making it a more challenging task for decision makers. Recognizing the inevitability of large-scale disruptions to CIs and their impacts on societies, the research objective of this work is to study the recovery of CINs following a disruptive event. Accordingly, the main contributions of the following two research components are to develop: (i) resilience-based post-disruption stochastic restoration optimization models that respect the spatial nature of CIs, (ii) a general framework for scenario-based stochastic models covering scenario generation, selection, and reduction for resilience applications, (iii) stochastic risk-related cost-based restoration modeling approaches to minimize restoration costs of a system of interdependent critical infrastructure networks (ICINs), (iv) flexible restoration strategies of ICINs under uncertainty, and (v) effective solution approaches to the proposed optimization models. The first research component considers developing two-stage risk-related stochastic programming models to schedule repair activities for a disrupted CIN to maximize the system resilience. The stochastic models are developed using a scenario-based optimization technique accounting for the uncertainties of the repair time and travel time spent on the underlying transportation network. To assess the risks associated with post-disruption scheduling plans, a conditional value-at-risk metric is incorporated into the optimization models through the scenario reduction algorithm. The proposed restoration framework is illustrated using the French RTE electric power network. The second research component studies the restoration problem for a system of ICINs following a disruptive event under uncertainty. A two-stage mean-risk stochastic restoration model is proposed to minimize the total cost associated with ICINs unsatisfied demands, repair tasks, and flow. The model assigns and schedules repair tasks to network-specific work crews with consideration of limited time and resources availability. Additionally, the model features flexible restoration strategies including a multicrew assignment for a single component and a multimodal repair setting along with the consideration of full and partial functioning and dependencies between the multi-network components. The proposed model is illustrated using the power and water networks in Shelby County, Tennessee, United States, under two hypothetical earthquakes. Finally, some other topics are discussed for possible future work