Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.     

A Framework to Understand Extreme Space Weather Event Probability

An extreme space weather event has the potential to disrupt or damage infrastructure systems and technologies that many societies rely on for economic and social well‐being. Space weather events occur regularly, but extreme events are less frequent, with a small number of historical examples over the last 160 years. During the past decade, published works have (1) examined the physical characteristics of the extreme historical events and (2) discussed the probability or return rate of select extreme geomagnetic disturbances, including the 1859 Carrington event. Here we present initial findings on a unified framework approach to visualize space weather event probability, using a Bayesian model average, in the context of historical extreme events. We present disturbance storm time (Dst ) probability (a proxy for geomagnetic disturbance intensity) across multiple return periods and discuss parameters of interest to policymakers and planners in the context of past extreme space weather events. We discuss the current state of these analyses, their utility to policymakers and planners, the current limitations when compared to other hazards, and several gaps that need to be filled to enhance space weather risk assessments.     

The Economic Impact of Space Weather: Where Do We Stand?

Space weather describes the way in which the Sun, and conditions in space more generally, impact human activity and technology both in space and on the ground. It is now well understood that space weather represents a significant threat to infrastructure resilience, and is a source of risk that is wide‐ranging in its impact and the pathways by which this impact may occur. Although space weather is growing rapidly as a field, work rigorously assessing the overall economic cost of space weather appears to be in its infancy. Here, we provide an initial literature review to gather and assess the quality of any published assessments of space weather impacts and socioeconomic studies. Generally speaking, there is a good volume of scientific peer‐reviewed literature detailing the likelihood and statistics of different types of space weather phenomena. These phenomena all typically exhibit “power‐law” behavior in their severity. The literature on documented impacts is not as extensive, with many case studies, but few statistical studies. The literature on the economic impacts of space weather is rather sparse and not as well developed when compared to the other sections, most probably due to the somewhat limited data that are available from end‐users. The major risk is attached to power distribution systems and there is disagreement as to the severity of the technological footprint. This strongly controls the economic impact. Consequently, urgent work is required to better quantify the risk of future space weather events.     

Critical Infrastructure Vulnerability to Spatially Localized Failures with Applications to Chinese Railway System

This article studies a general type of initiating events in critical infrastructures, called spatially localized failures (SLFs), which are defined as the failure of a set of infrastructure components distributed in a spatially localized area due to damage sustained, while other components outside the area do not directly fail. These failures can be regarded as a special type of intentional attack, such as bomb or explosive assault, or a generalized modeling of the impact of localized natural hazards on large‐scale systems. This article introduces three SLFs models: node centered SLFs, district‐based SLFs, and circle‐shaped SLFs, and proposes a SLFs‐induced vulnerability analysis method from three aspects: identification of critical locations, comparisons of infrastructure vulnerability to random failures, topologically localized failures and SLFs, and quantification of infrastructure information value. The proposed SLFs‐induced vulnerability analysis method is finally applied to the Chinese railway system and can be also easily adapted to analyze other critical infrastructures for valuable protection suggestions.     

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.     

Evaluating the Benefits of Adaptation of Critical Infrastructures to Hydrometeorological Risks

Infrastructure adaptation measures provide a practical way to reduce the risk from extreme hydrometeorological hazards, such as floods and windstorms. The benefit of adapting infrastructure assets is evaluated as the reduction in risk relative to the “do nothing” case. However, evaluating the full benefits of risk reduction is challenging because of the complexity of the systems, the scarcity of data, and the uncertainty of future climatic changes. We address this challenge by integrating methods from the study of climate adaptation, infrastructure systems, and complex networks. In doing so, we outline an infrastructure risk assessment that incorporates interdependence, user demands, and potential failure‐related economic losses. Individual infrastructure assets are intersected with probabilistic hazard maps to calculate expected annual damages. Protection measure costs are integrated to calculate risk reduction and associated discounted benefits, which are used to explore the business case for investment in adaptation. A demonstration of the methodology is provided for flood protection of major electricity substations in England and Wales. We conclude that the ongoing adaptation program for major electricity assets is highly cost beneficial.     

Integration of Critical Infrastructure and Societal Consequence Models: Impact on Swedish Power System Mitigation Decisions

Critical infrastructures provide society with services essential to its functioning, and extensive disruptions give rise to large societal consequences. Risk and vulnerability analyses of critical infrastructures generally focus narrowly on the infrastructure of interest and describe the consequences as nonsupplied commodities or the cost of unsupplied commodities; they rarely holistically consider the larger impact with respect to higher‐order consequences for the society. From a societal perspective, this narrow focus may lead to severe underestimation of the negative effects of infrastructure disruptions. To explore this theory, an integrated modeling approach, combining models of critical infrastructures and economic input–output models, is proposed and applied in a case study. In the case study, a representative model of the Swedish power transmission system and a regionalized economic input–output model are utilized. This enables exploration of how a narrow infrastructure or a more holistic societal consequence perspective affects vulnerability‐related mitigation decisions regarding critical infrastructures. Two decision contexts related to prioritization of different vulnerability‐reducing measures are considered—identifying critical components and adding system components to increase robustness. It is concluded that higher‐order societal consequences due to power supply disruptions can be up to twice as large as first‐order consequences, which in turn has a significant effect on the identification of which critical components are to be protected or strengthened and a smaller effect on the ranking of improvement measures in terms of adding system components to increase system redundancy.     

Spatially Representing Vulnerability to Extreme Rain Events Using Midwestern Farmers’ Objective and Perceived Attributes of Adaptive Capacity

Potential climate‐change‐related impacts to agriculture in the upper Midwest pose serious economic and ecological risks to the U.S. and the global economy. On a local level, farmers are at the forefront of responding to the impacts of climate change. Hence, it is important to understand how farmers and their farm operations may be more or less vulnerable to changes in the climate. A vulnerability index is a tool commonly used by researchers and practitioners to represent the geographical distribution of vulnerability in response to global change. Most vulnerability assessments measure objective adaptive capacity using secondary data collected by governmental agencies. However, other scholarship on human behavior has noted that sociocultural and cognitive factors, such as risk perceptions and perceived capacity, are consequential for modulating people's actual vulnerability. Thus, traditional assessments can potentially overlook people's subjective perceptions of changes in climate and extreme weather events and the extent to which people feel prepared to take necessary steps to cope with and respond to the negative effects of climate change. This article addresses this knowledge gap by: (1) incorporating perceived adaptive capacity into a vulnerability assessment; (2) using spatial smoothing to aggregate individual‐level vulnerabilities to the county level; and (3) evaluating the relationships among different dimensions of adaptive capacity to examine whether perceived capacity should be integrated into vulnerability assessments. The result suggests that vulnerability assessments that rely only on objective measures might miss important sociocognitive dimensions of capacity. Vulnerability indices and maps presented in this article can inform engagement strategies for improving environmental sustainability in the region.     

A Solar-Centric Approach to Improving Estimates of Exposure Processes for Coronal Mass Ejections

We present a solar-centric approach to estimating the probability of extreme coronal mass ejections (CME) using the Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph Experiment (LASCO) CME Catalog observations updated through May 2018 and an updated list of near-Earth interplanetary coronal mass ejections (ICME). We examine robust statistical approaches to the estimation of extreme events. We then assume a variety of time-independent distributions fitting, and then comparing, the different probability distributions to the relevant regions of the cumulative distributions of the observed CME speeds. Using these results, we then obtain the probability that the velocity of a CME exceeds a particular threshold by extrapolation. We conclude that about 1.72% of the CMEs recorded with SOHO LASCO arrive at the Earth over the time both data sets overlap (November 1996 to September 2017). Then, assuming that 1.72% of all CMEs pass the Earth, we can obtain a first-order estimate of the probability of an extreme space weather event on Earth. To estimate the probability over the next decade of a CME, we fit a Poisson distribution to the complementary cumulative distribution function. We inferred a decadal probability of between 0.01 and 0.09 for an event of at least the size of the large 2012 event, and a probability between 0.0002 and 0.016 for the size of the 1859 Carrington event.     

Thinking about a more sustainable business — an Indicators approach

TXU Europe is one of the leading integrated energy companies in the UK and a fast-growing player in the deregulated energy markets of continental Europe. Its services include electricity generation, energy trading, electricity distribution, and electricity and natural gas marketing. Formerly known as Eastern Group plc, TXU Europe is the European arm of global energy company TXU, which acquired Eastern in 1998 for £4.45 billion. At TXU Europe, we believe that sustainable development is about achieving economic growth — in the form of better living standards for all — while protecting and, where possible, enhancing the environment. This approach does not exclude business development and profit, but rather guides it along a route of environmental protection and social responsibility.     

Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically‐oriented models to advanced physical‐flow‐based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large‐scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.     

Rural Nevada and Climate Change: Vulnerability,Beliefs, and Risk Perception

In this article, we present the results of a study investigating the influence of vulnerability to climate change as a function of physical vulnerability, sensitivity, and adaptive capacity on climate change risk perception. In 2008/2009, we surveyed Nevada ranchers and farmers to assess their climate change‐related beliefs, and risk perceptions, political orientations, and socioeconomic characteristics. Ranchers’ and farmers’ sensitivity to climate change was measured through estimating the proportion of their household income originating from highly scarce water‐dependent agriculture to the total income. Adaptive capacity was measured as a combination of the Social Status Index and the Poverty Index. Utilizing water availability and use, and population distribution GIS databases; we assessed water resource vulnerability in Nevada by zip code as an indicator of physical vulnerability to climate change. We performed correlation tests and multiple regression analyses to examine the impact of vulnerability and its three distinct components on risk perception. We find that vulnerability is not a significant determinant of risk perception. Physical vulnerability alone also does not impact risk perception. Both sensitivity and adaptive capacity increase risk perception. While age is not a significant determinant of it, gender plays an important role in shaping risk perception. Yet, general beliefs such as political orientations and climate change‐specific beliefs such as believing in the anthropogenic causes of climate change and connecting the locally observed impacts (in this case drought) to climate change are the most prominent determinants of risk perception.     

Electricity Time‐of‐Use Tariff with Stochastic Demand

In this article, we study the electricity time‐of‐use (TOU) tariff for an electricity company with stochastic demand. The electricity company offers the flat rate (FR) and TOU tariffs to customers. Under the FR tariff, the customer pays a flat price for electricity consumption in both the peak and non‐peak periods. Under the TOU tariff, the customer pays a high price for electricity consumption in the peak period and a low price for electricity consumption in the non‐peak period. The electricity company uses two technologies, namely the base‐load and peak‐load technologies, to generate electricity. We derive the optimal capacity investment and pricing decisions for the electricity company. Furthermore, we use real data from a case study to validate the results and derive insights for implementing the TOU tariff. We show that in almost all the cases, the electricity company needs less capacity for both technologies under the TOU tariff than under the FR tariff, even though the expected demand in the non‐peak period increases. In addition, except for some extreme cases, there is essentially no signicant reduction in the total demand of the two periods, although the TOU tariff can reduce the demand in the peak period. Under the price‐cap regulation, the customer may pay a lower price on average under the TOU tariff than under the FR tariff. We conduct an extensive numerical study to assess the impacts of the model parameters on the optimal solutions and the robustness of the analytical results, and generate managerial implications of the research findings.     

An Integrated Approach for Assessing the Impact of Large-Scale Future Floods on a Highway Transport System

The negative impact of climate change continues to escalate flood risk. Floods directly and indirectly damage highway systems and disturb the socioeconomic order. In this study, we propose an integrated approach to quantitatively assess how floods impact the functioning of a highway system. The approach has three parts: (1) a multi-agent simulation model to represent traffic, heterogeneous user demand, and route choice in a highway network; (2) a flood simulator using future runoff scenarios generated from five global climate models, three representative concentration pathways (RCPs), and the CaMa-Flood model; and (3) an impact analyzer, which superimposes the simulated floods on the highway traffic simulation system, and quantifies the flood impact on a highway system based on car following model. This approach is illustrated with a case study of the Chinese highway network. The results show that (i) for different global climate models, the associated flood damage to a highway system is not linearly correlated with the forcing levels of RCPs, or with future years; (ii) floods in different years have variable impacts on regional connectivity; and (iii) extreme flood impacts can cause huge damages in highway networks; that is, in 2030, the estimated 84.5% of routes between provinces cannot be completed when the highway system is disturbed by a future major flood. These results have critical implications for transport sector policies and can be used to guide highway design and infrastructure protection. The approach can be extended to analyze other networks with spatial vulnerability, and it is an effective quantitative tool for reducing systemic disaster risk.     

Risk Analysis of Safety Service Patrol (SSP) Systems in Virginia

The transportation infrastructure is a vital backbone of any regional economy as it supports workforce mobility, tourism, and a host of socioeconomic activities. In this article, we specifically examine the incident management function of the transportation infrastructure. In many metropolitan regions, incident management is handled primarily by safety service patrols (SSPs), which monitor and resolve roadway incidents. In Virginia, SSP allocation across highway networks is based typically on average vehicle speeds and incident volumes. This article implements a probabilistic network model that partitions “business as usual” traffic flow with extreme‐event scenarios. Results of simulated network scenarios reveal that flexible SSP configurations can improve incident resolution times relative to predetermined SSP assignments.     

Modeling the Cost Effectiveness of Fire Protection Resource Allocation in the United States: Models and a 1980–2014 Case Study

The estimated cost of fire in the United States is about $329 billion a year, yet there are gaps in the literature to measure the effectiveness of investment and to allocate resources optimally in fire protection. This article fills these gaps by creating data‐driven empirical and theoretical models to study the effectiveness of nationwide fire protection investment in reducing economic and human losses. The regression between investment and loss vulnerability shows high R² values (≈0.93). This article also contributes to the literature by modeling strategic (national‐level or state‐level) resource allocation (RA) for fire protection with equity‐efficiency trade‐off considerations, while existing literature focuses on operational‐level RA. This model and its numerical analyses provide techniques and insights to aid the strategic decision‐making process. The results from this model are used to calculate fire risk scores for various geographic regions, which can be used as an indicator of fire risk. A case study of federal fire grant allocation is used to validate and show the utility of the optimal RA model. The results also identify potential underinvestment and overinvestment in fire protection in certain regions. This article presents scenarios in which the model presented outperforms the existing RA scheme, when compared in terms of the correlation of resources allocated with actual number of fire incidents. This article provides some novel insights to policymakers and analysts in fire protection and safety that would help in mitigating economic costs and saving lives.     

A Bayesian Benefit-Risk Model Applied to the South Florida Building Code

A Bayesian compound Poisson benefit-risk model is described in this paper, and used to evaluate recent revisions to the South Florida Building Code (SFBC). The model accounts for natural variability in hurricane frequency and severity, and uncertainty in the effectiveness of the revised code. Ranges of residential growth rate, code effectiveness, construction cost increase, and planning period length are assumed, to show the ranges of cost-to-performance ratio within which the code will make sense economically. The expected cost of residential hurricane damage over 50 years for ten South Florida counties assuming continuation of previous building practices was $93 billion, equivalent to the residential damage of 5.2 Andrews. Assuming a reduction in the growth of damageable housing in South Florida from 5.5% to 2% as a result of code revision, estimated damages under the new code were $45 billion. At a per-house construction cost increase of 5%, the probability of at least recovering the estimated $40 billion cost of the specified wind-resistant construction was estimated to be 47%. Expected return on investment was estimated at $7 billion over 50 years. The expected return lies between a $44 billion loss and a $47 billion gain, when growth in damageable housing is allowed to range from 1% to 4% and construction cost increases are assumed to lie between 3% and 8%. Actual monetary return for a 5% cost increase and 2% growth in damageable housing ranges from a $20 billion loss to a $100 billion gain with 95% probability, as a result of weather variability alone. Results support SFBC revisions on solely economic grounds, a conclusion strengthened considerably in light of potentially avoided deaths and hurricane traumas. The model represents one approach to evaluating economic aspects of the sustainability of new technological measures on the basis of available information.     

Sizing Up a Superstorm: Exploring the Role of Recalled Experience and Attribution of Responsibility in Judgments of Future Hurricane Risk

Research suggests that hurricane‐related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals’ distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York (n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual‐level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences.     

An Analysis of the Portfolio of Sites to Characterize for Selecting a Nuclear Repository

The U.S. Department of Energy has selected three sites, from five nominated, to characterize for a nuclear repository to permanently dispose of nuclear waste. This decision was made without the benefit of an analysis of this "portfolio" problem. This paper analyzes different portfolios of three sites for simultaneous characterization and strategies for sequential characterization. Characterization of each site, which involves significant subsurface excavation, is now estimated to cost $1 billion. Mainly because of the high characterization costs, sequential characterization strategies are identified which are the equivalent of $1.7-2.0 billion less expensive than the selected DOE simultaneous characterization of the three sites. If three sites are simultaneously characterized, one portfolio is estimated to be the equivalent of $100-400 million better than the selected DOE portfolio. Because of these potential savings and several other complicating factors that may influence the relative desirability of characterization strategies, a thorough analysis of characterization strategies that addresses the likelihood of finding disqualifying conditions during site characterization, uncertainties, and dependencies in forecast site repository costs, preclosure and postclosure health and safety impacts, potential delays of both sequential and simultaneous characterization strategies, and the environmental, socioeconomic, and health and safety impacts of characterization activities is recommended.     

Inherent Costs and Interdependent Impacts of Infrastructure Network Resilience

Recent studies in system resilience have proposed metrics to understand the ability of systems to recover from a disruptive event, often offering a qualitative treatment of resilience. This work provides a quantitative treatment of resilience and focuses specifically on measuring resilience in infrastructure networks. Inherent cost metrics are introduced: loss of service cost and total network restoration cost. Further, “costs” of network resilience are often shared across multiple infrastructures and industries that rely upon those networks, particularly when such networks become inoperable in the face of disruptive events. As such, this work integrates the quantitative resilience approach with a model describing the regional, multi‐industry impacts of a disruptive event to measure the interdependent impacts of network resilience. The approaches discussed in this article are deployed in a case study of an inland waterway transportation network, the Mississippi River Navigation System.