Leonardo Dueñas-Osorio

Performance assessment of topologically diverse power systems subjected to hurricane events

Large tropical cyclones cause severe damage to major cities along the United States Gulf Coast annually. A diverse collection of engineering and statistical models are currently used to estimate the geographical distribution of power outage probabilities stemming from these hurricanes to aid in storm preparedness and recovery efforts. Graph theoretic studies of power networks have separately attempted to link abstract network topology to transmission and distribution system reliability. However, few works have employed both techniques to unravel the intimate connection between network damage arising from storms, topology, and system reliability. This investigation presents a new methodology combining hurricane damage predictions and topological assessment to characterize the impact of hurricanes upon power system reliability. Component fragility models are applied to predict failure probability for individual transmission and distribution power network elements simultaneously. The damage model is calibrated using power network component failure data for Harris County, TX, USA caused by Hurricane Ike in September of 2008, resulting in a mean outage prediction error of 15.59% and low standard deviation. Simulated hurricane events are then applied to measure the hurricane reliability of three topologically distinct transmission networks. The rate of system performance decline is shown to depend on their topological structure. Reliability is found to correlate directly with topological features, such as network meshedness, centrality, and clustering, and the compact irregular ring mesh topology is identified as particularly favorable, which can influence regional lifeline policy for retrofit and hardening activities to withstand hurricane events.

An approach to design interface topologies across interdependent urban infrastructure systems

This paper proposes an approach to design or retrofit interface topologies to minimize cascading failures across urban infrastructure systems. Four types of interface design strategies are formulated based on maximum network component degree, maximum component betweenness, minimum Euclidean distance across components and component reliability rankings. To compute and compare strategy effectiveness under multiple hazard types, this paper introduces a global annual cascading failure effect (GACFE) metric as well as a GACFE-based cost improvement (GACI) metric. The GACI metric quantifies the improvement of the strategy effectiveness per kilometer increment of interdependent link length (ILL) relative to a reference strategy with minimum ILL. Taking as examples the power and gas transmission systems in Harris County, Texas, USA, optimum interface designs under random and hurricane hazards are discussed. Findings include that the strategy based on reliability rankings minimizes the GACFE metric, and decreases the GACI value relative to a reference practical strategy by 10–15% under different power grid safety margins. Such metrics will contribute to coupled utility system design or retrofit given that current guidelines or recommended practices in the utility industry mostly rely on minimum Euclidean distances and are yet to include interdependent effects in their provisions.

Probabilistic study of cascading failures in complex interdependent lifeline systems

The internal complexity of lifeline systems and their standing interdependencies can operate in conjunction to amplify the negative effects of external disruptions. This paper introduces a simulation-based methodology to evaluate the joint impact of interdependence, component fragilities, and cascading failures in systemic fragility estimates. The proposed strategy uses a graph model of interdependent networks, an enhanced betweenness centrality for cascading failures approximation, and an interdependence model accounting for coupling uncertainty in the simulation of damage propagation for probabilistic performance assessment. This methodology is illustrated through its application to a realistic set of power and water networks subjected to earthquake scenarios and random failures. Test case results reveal two key insights: (1) the intensity of a perturbation influences interdependent systemic fragility by shaping the magnitudes of initial component damage and, sometimes counter-intuitively, the subsequent interdependence effects and (2) increasing local redundancy mitigates the effects of interdependence on systemic performance, but such intervention is incapable of eliminating interdependent effects completely. The previous insights provide basic guidelines for the design of systemic retrofitting policies. Additionally, the limitations of local capacity redundancy as a fragility control measure highlight the need for a critical assessment of intervention strategies in distributed infrastructure networks. Future work will assess the fragility-reduction efficiency of strategies involving informed manipulation of individual systemic topologies and the interdependence interfaces connecting them.

Reliability Assessment of Lifeline Systems with Radial Topology

The increased susceptibility of lifeline systems to failure due to aging and external hazards requires efficient methods to quantify their reliability and related uncertainty. Monte Carlo simulation techniques for network-level reliability and uncertainty assessment usually require large computational experiments. Also, available analytical approaches apply mainly to simple network topologies, and are limited to providing average values, low order moments, or confidence bounds of reliability metrics. This study introduces a closed form technique to obtain the entire probability distribution of a reliability metric of customer service availability (CSA) for generic radial lifeline systems. A special case of this general formulation reduces to a simple sum of products equation, for which a recursive algorithm that exploits its structure is presented. This special-case algorithm computes the probability mass function (PMF) of CSA for systems with M elements in O(M 3 ) operations, relative to conventional O(2 M ) operations, and opens the possibility of finding recursive algorithms for the general radial case. Parametric models that approximate the CSA metric are also explored and their errors quantified. The proposed radial topology reliability assessment tools and resulting probability distributions provide infrastructure owners with critical insights for informed operation and maintenance decision making under uncertainty.

Quantification of Lifeline System Interdependencies after the 27 February 2010 Mw 8.8 Offshore Maule, Chile, Earthquake

Data on lifeline system service restoration is seldom exploited for the calibration of performance prediction models or for response comparisons across systems and events. This study explores utility restoration curves after the 2010 Chilean earthquake through a time series method to quantify coupling strengths across lifeline systems. When consistent with field information, cross-correlations from restoration curves without significant lag times quantify operational interdependence, whereas those with significant lags reveal logistical interdependence. Synthesized coupling strengths are also proposed to incorporate cross-correlations and lag times at once. In the Chilean earthquake, coupling across fixed and mobile phones was the strongest per region followed by coupling within and across telecommunication and power systems in adjacent regions. Unapparent couplings were also revealed among telecommunication and power systems with water networks. The proposed methodology can steer new protocols for post-disaster data collection, including anecdotal information to evaluate causality, and inform infrastructure interdependence effect prediction models.

Interface Network Models for Complex Urban Infrastructure Systems

The reliability assessment of infrastructure systems providing power, natural gas, and potable water is an integral part of societal preparedness to unforeseen hazards. The topological properties of interface networks connecting electric substations to water pumping stations and natural gas compressors have received little attention, despite the key role these connections play in operation and failure propagation. This work introduces a performance assessment methodology for coupled infrastructures that links physical fragility modeling with the topology of realistic and ideal connecting interfaces. Distinct interfaces based on features such as betweenness, clustering, vertex degree, and Euclidean distance are assessed regarding their role in connecting utility systems and propagating failures from random and hurricane events in Harris County, Texas. The interface minimizing the Euclidean distance between electric substations and other utility nodes exhibits a slow performance decline as random failures increase, and retains the greatest functionality under hurricane events compared to alternative interfaces, although it suffers from limited efficiency and controllability during normal operation. A convenient hybrid interface using both betweenness and distance features shows adequate performance during normal operation while exhibiting tolerance to random failures and sufficient performance at increasing hurricane event levels. These findings provide utility owners and operators with new simple yet adequate strategies focused on the interface across complex systems to enhance routine operation and reduce the probability of widespread interdependent failures following disruptive events.

Hierarchical infrastructure network representation methods for risk-based decision-making

Estimating the extent of hazard-induced damage to infrastructure networks is a complex task that goes beyond computing direct costs and requires considering the effect of network connection patterns and interactions. This article presents a new model that combines a systems approach with strategies for detecting the internal structure of networks, and providing flexibility and different levels of accuracy in estimating the extent of damage. The model describes networks as hierarchical structures obtained by successive clustering. Hierarchical analysis of networks provides unique insights about how damage affects performance throughout the whole infrastructure system. The model enables using information for decision-making more efficiently by generating different levels of resolution for different problems. This is illustrated using data from hurricane Ike, Texas, USA in 2008, where the primary transportation network is studied. Estimates of population affected and loss of productivity are discussed, emphasising the importance of multiple levels for assessment, and their application on fast decision-making for emergency situations.

Synthesis of Modeling and Simulation Methods on Critical Infrastructure Interdependencies Research

National security, economic prosperity, and the quality of life of today’s societies depend on the continuous and reliable operation of interdependent infrastructures. Models to capture the performance and operation of these systems have been developed to support planning, maintenance, and retrofit decision making from multiple view points, including infrastructure owners or investors, private and public users, and government entities that ensure reliability, economic vitality and security. The study of interdependent infrastructures is challenging due to heterogeneous quality and insufficient data availability and the need to account for their spatial and temporal aspects of complex supply-demand operation. Research and implementation studies have attempted to address interdependence modeling through various techniques, such as Agent Based simulation, Input- Output Inoperability, system reliability theory, nonlinear dynamics, and graph theory. These studies are mainly targeted at understanding infrastructure behavior and response to disruptions through single modeling techniques. However, hybrid modeling techniques, multi-scale analyses, and other realizable innovative approaches are lacking, in part because few studies have characterized existing models into a single source to provide a current state of the field, elucidate connections across existing studies, and synthesize a directive for future research. This chapter introduces a conceptualization of current research that integrates the multiple ideas in the field of infrastructure interdependencies into a unified hierarchical structure that navigates through research advances from early papers in the1980’s to date. The body of knowledge is categorized according to several attributes identified in the field, such as mathematical method, modeling objective, scale of analysis, quality and quantity of input data, targeted discipline, and end user type. The hierarchical conceptualization approach synthesizes available data and is expected to ease the research and application process of interdependencies concepts by finding differences and commonalities in data collection, analyses techniques, and desired outputs. This research survey highlights that most of the existing interdependence modeling strategies are not competing but rather complementary approaches, which can provide a vehicle for immediate innovative studies on coupled infrastructures, such as stochastic interdependence, cascading failures across systems, and the establishment of risk mitigation principles. New linkages across existing research can facilitate implementation and dissemination of results, inform areas of data collection, enable benchmark models for validation predictions and model comparisons, and point to long term broader and emergent unresolved research issues in infrastructure interdependence research, possibly including smart technologies, bio-inspiration, sustainability, scalability of analysis algorithms, and dimension reduction of network abstractions.

Efficient Longitudinal Seismic Fragility Assessment of a Multispan Continuous Steel Bridge on Liquefiable Soils

The increased failure potential of aging U.S. highway bridges and their susceptibility to damage during extreme events necessitates the development of efficient reliability assessment tools to prioritize maintenance and rehabilitation interventions. Reliability communication tools become even more important when considering complex phenomena such as soil liquefaction under seismic hazards. Currently, two approaches are widely used for bridge reliability estimation under soil failure conditions via fragility curves: liquefaction multipliers and full-scale two- or three-dimensional bridge-soil-foundation models. This paper offers a computationally economical yet adequate approach that links nonlinear finite-element models of a three-dimensional bridge system with a two-dimensional soil domain and a one-dimensional set of p-y springs into a coupled bridge-soil-foundation CBSF system. A multispan continuous steel girder bridge typical of the central and eastern United States along with heterogeneous liquefiable soil profiles is used within a statistical sampling scheme to illustrate the effects of soil failure and uncertainty propagation on the fragility of CBSF system components. In general, the fragility of rocker bearings, piles, embankment soil, and the probability of unseating increases with liquefaction, while that of commonly monitored components, such as columns, depends on the type of soil overlying the liquefiable sands. This component response depen- dence on soil failure supports the use of reliability assessment frameworks that are efficient for regional applications by relying on simplified but accepted geotechnical methods to capture complex soil liquefaction effects.

Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures, II: Application

The bridge reliability in networks (BRAN) methodology introduced in the companion paper is applied to evaluate the reliability of part of the highway bridge network in South Carolina under a selected seismic scenario. The case study demonstrates Bayesian updating of deterioration parameters across bridges after spatial interpolation of data acquired from limited instrumented bridges. The updated deterioration parameters inform aging bridge seismic fragility curves through multi-dimensional integration of parameterized fragility models, which are utilized to derive bridge failure probabilities. The paper establishes the correlation structure among bridge failures from three information sources to generate realizations of bridge failures for network-level reliability assessments by Monte Carlo analysis. Positive correlations improve the reliability of the case study network, as predicted from network topology. The benefits of the BRAN methodology are highlighted in its applicability to large networks, while addressing some of the existing gaps in bridge network reliability and prioritization studies.