Risk-Management and Risk-Analysis-Based Decision Tools for Attacks on Electric Power

Incident data about disruptions to the electric power grid provide useful information that can be used as inputs into risk management policies in the energy sector for disruptions from a variety of origins, including terrorist attacks. This article uses data from the Disturbance Analysis Working Group (DAWG) database, which is maintained by the North American Electric Reliability Council (NERC), to look at incidents over time in the United States and Canada for the period 1990-2004. Negative binomial regression, logistic regression, and weighted least squares regression are used to gain a better understanding of how these disturbances varied over time and by season during this period, and to analyze how characteristics such as number of customers lost and outage duration are related to different characteristics of the outages. The results of the models can be used as inputs to construct various scenarios to estimate potential outcomes of electric power outages, encompassing the risks, consequences, and costs of such outages.     

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.     

Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System

Coupled infrastructure systems and complicated multihazards result in a high level of complexity and make it difficult to assess and improve the infrastructure system resilience. With a case study of the Greater Toronto Area energy system (including electric, gas, and oil transmission networks), an approach to analysis of multihazard resilience of an interdependent infrastructure system is presented in the article. Integrating network theory, spatial and numerical analysis methods, the new approach deals with the complicated multihazard relations and complex infrastructure interdependencies as spatiotemporal impacts on infrastructure systems in order to assess the dynamic system resilience. The results confirm that the effects of sequential hazards on resilience of infrastructure (network) are more complicated than the sum of single hazards. The resilience depends on the magnitude of the hazards, their spatiotemporal relationship and dynamic combined impacts, and infrastructure interdependencies. The article presents a comparison between physical and functional resilience of an electric transmission network, and finds functional resilience is always higher than physical resilience. The multiple hazards resilience evaluation approach is applicable to any type of infrastructure and hazard and it can contribute to the improvement of infrastructure planning, design, and maintenance decision making.     

Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk

In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind‐related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.     

Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization

In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.     

Integrating Stakeholder Mapping and Risk Scenarios to Improve Resilience of Cyber‐Physical‐Social Networks

The future of energy mobility involves networks of users, operators, organizations, vehicles, charging stations, communications, materials, transportation corridors, points of service, and so on. The integration of smart grids with plug‐in electric vehicle technologies has societal and commercial advantages that include improving grid stability, minimizing dependence on nonrenewable fuels, reducing vehicle emissions, and reducing the cost of electric vehicle ownership. However, ineffective or delayed participation of particular groups of stakeholders could disrupt industry plans and delay the desired outcomes. This article develops a framework to address enterprise resilience for two modes of disruptions—the first being the influence of scenarios on priorities and the second being the influence of multiple groups of stakeholders on priorities. The innovation of this study is to obtain the advantages of integrating two recent approaches: scenario‐based preferences modeling and stakeholder mapping. Public agencies, grid operators, plug‐in electric vehicle owners, and vehicle manufacturers are the four groups of stakeholders that are considered in this framework, along with the influence of four scenarios on priorities.     

Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models

Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.     

Toward Disaster‐Resilient Cities: Characterizing Resilience of Infrastructure Systems with Expert Judgments

Resilient infrastructure systems are essential for cities to withstand and rapidly recover from natural and human‐induced disasters, yet electric power, transportation, and other infrastructures are highly vulnerable and interdependent. New approaches for characterizing the resilience of sets of infrastructure systems are urgently needed, at community and regional scales. This article develops a practical approach for analysts to characterize a community's infrastructure vulnerability and resilience in disasters. It addresses key challenges of incomplete incentives, partial information, and few opportunities for learning. The approach is demonstrated for Metro Vancouver, Canada, in the context of earthquake and flood risk. The methodological approach is practical and focuses on potential disruptions to infrastructure services. In spirit, it resembles probability elicitation with multiple experts; however, it elicits disruption and recovery over time, rather than uncertainties regarding system function at a given point in time. It develops information on regional infrastructure risk and engages infrastructure organizations in the process. Information sharing, iteration, and learning among the participants provide the basis for more informed estimates of infrastructure system robustness and recovery that incorporate the potential for interdependent failures after an extreme event. Results demonstrate the vital importance of cross‐sectoral communication to develop shared understanding of regional infrastructure disruption in disasters. For Vancouver, specific results indicate that in a hypothetical M7.3 earthquake, virtually all infrastructures would suffer severe disruption of service in the immediate aftermath, with many experiencing moderate disruption two weeks afterward. Electric power, land transportation, and telecommunications are identified as core infrastructure sectors.     

Decision‐Making Analytics Using Plural Resilience Parameters for Adaptive Management of Complex Systems

It is critical for complex systems to effectively recover, adapt, and reorganize after system disruptions. Common approaches for evaluating system resilience typically study single measures of performance at one time, such as with a single resilience curve. However, multiple measures of performance are needed for complex systems that involve many components, functions, and noncommensurate valuations of performance. Hence, this article presents a framework for: (1) modeling resilience for complex systems with competing measures of performance, and (2) modeling decision making for investing in these systems using multiple stakeholder perspectives and multicriteria decision analysis. This resilience framework, which is described and demonstrated in this article via a real‐world case study, will be of interest to managers of complex systems, such as supply chains and large‐scale infrastructure networks.     

Operational Models of Infrastructure Resilience

We propose a definition of infrastructure resilience that is tied to the operation (or function) of an infrastructure as a system of interacting components and that can be objectively evaluated using quantitative models. Specifically, for any particular system, we use quantitative models of system operation to represent the decisions of an infrastructure operator who guides the behavior of the system as a whole, even in the presence of disruptions. Modeling infrastructure operation in this way makes it possible to systematically evaluate the consequences associated with the loss of infrastructure components, and leads to a precise notion of “operational resilience” that facilitates model verification, validation, and reproducible results. Using a simple example of a notional infrastructure, we demonstrate how to use these models for (1) assessing the operational resilience of an infrastructure system, (2) identifying critical vulnerabilities that threaten its continued function, and (3) advising policymakers on investments to improve resilience.     

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.     

基于EM填补和加权秩和比的电力系统黑启动决策方法

电力系统大停电后需要安全可靠的黑启动方案进行电力快速恢复,如何从众多黑启动方案中进行优选是一个重要课题。不同于以往完全信息情况下的研究,本文对电力系统黑启动方案评估问题的研究是基于不完全信息情况下进行的,提出一种EM填补和加权秩和比相结合的黑启动决策方法。首先,采用EM算法填补黑启动方案评价空值,得到完备的黑启动评价矩阵;然后,计算指标间的差异性,利用差异性权重法得到各个指标的权重;最后,采用加权秩和比法确定每个方案的评分值,实现黑启动方案的分级和完全排序。本文方法基于广东电网黑启动数据集进行实验验证,实验结果表明,本文方法可以对不完全信息情况下的黑启动方案进行优劣排序,且具有较高准确性。     

Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.     

Assessing Engineering Resilience for Systems with Multiple Performance Measures

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other “ilities” directly in the value hierarchy, while the second method quantifies mission resilience and uses the “ilities” in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems‐of‐systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.     

Optimization of Cascade‐Resilient Electrical Infrastructures and its Validation by Power Flow Modeling

Large‐scale outages on real‐world critical infrastructures, although infrequent, are increasingly disastrous to our society. In this article, we are primarily concerned with power transmission networks and we consider the problem of allocation of generation to distributors by rewiring links under the objectives of maximizing network resilience to cascading failure and minimizing investment costs. The combinatorial multiobjective optimization is carried out by a nondominated sorting binary differential evolution (NSBDE) algorithm. For each generators–distributors connection pattern considered in the NSBDE search, a computationally cheap, topological model of failure cascading in a complex network (named the Motter‐Lai [ML] model) is used to simulate and quantify network resilience to cascading failures initiated by targeted attacks. The results on the 400 kV French power transmission network case study show that the proposed method allows us to identify optimal patterns of generators–distributors connection that improve cascading resilience at an acceptable cost. To verify the realistic character of the results obtained by the NSBDE with the embedded ML topological model, a more realistic but also more computationally expensive model of cascading failures is adopted, based on optimal power flow (namely, the ORNL‐Pserc‐Alaska) model). The consistent results between the two models provide impetus for the use of topological, complex network theory models for analysis and optimization of large infrastructures against cascading failure with the advantages of simplicity, scalability, and low computational cost.     

Prestorm Estimation of Hurricane Damage to Electric Power Distribution Systems

Hurricanes frequently cause damage to electric power systems in the United States, leading to widespread and prolonged loss of electric service. Restoring service quickly requires the use of repair crews and materials that must be requested, at considerable cost, prior to the storm. U.S. utilities have struggled to strike a good balance between over‐ and underpreparation largely because of a lack of methods for rigorously estimating the impacts of an approaching hurricane on their systems. Previous work developed methods for estimating the risk of power outages and customer loss of power, with an outage defined as nontransitory activation of a protective device. In this article, we move beyond these previous approaches to directly estimate damage to the electric power system. Our approach is based on damage data from past storms together with regression and data mining techniques to estimate the number of utility poles that will need to be replaced. Because restoration times and resource needs are more closely tied to the number of poles and transformers that need to be replaced than to the number of outages, this pole‐based assessment provides a much stronger basis for prestorm planning by utilities. Our results show that damage to poles during hurricanes can be assessed accurately, provided that adequate past damage data are available. However, the availability of data can, and currently often is, the limiting factor in developing these types of models in practice. Opportunities for further enhancing the damage data recorded during hurricanes are also discussed.     

Critical Assessment of the Foundations of Power Transmission and Distribution Reliability Metrics and Standards

The U.S. federal government regulates the reliability of bulk power systems, while the reliability of power distribution systems is regulated at a state level. In this article, we review the history of regulating electric service reliability and study the existing reliability metrics, indices, and standards for power transmission and distribution networks. We assess the foundations of the reliability standards and metrics, discuss how they are applied to outages caused by large exogenous disturbances such as natural disasters, and investigate whether the standards adequately internalize the impacts of these events. Our reflections shed light on how existing standards conceptualize reliability, question the basis for treating large‐scale hazard‐induced outages differently from normal daily outages, and discuss whether this conceptualization maps well onto customer expectations. We show that the risk indices for transmission systems used in regulating power system reliability do not adequately capture the risks that transmission systems are prone to, particularly when it comes to low‐probability high‐impact events. We also point out several shortcomings associated with the way in which regulators require utilities to calculate and report distribution system reliability indices. We offer several recommendations for improving the conceptualization of reliability metrics and standards. We conclude that while the approaches taken in reliability standards have made considerable advances in enhancing the reliability of power systems and may be logical from a utility perspective during normal operation, existing standards do not provide a sufficient incentive structure for the utilities to adequately ensure high levels of reliability for end‐users, particularly during large‐scale events.     

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.     

配网分时电价实用预测模型研究

在电力市场环境下,进行准确的电价预测对市场中的各参与者具有极其重要的意义.针对配网分时电价,本文使用规划优化方法,采用一般时段划分,集中负荷具体到配网变电站的每条出线,建立了优化的配网分时电价预测模型,并根据经济学原理定义电价.通过数学规划的Kuhn Tucker条件,求得电价的计算公式.探讨了电价公式中各分量的计算方法,并进一步通过算例初步验证了本模型的有效及实用性.     

The disruption of the international supply chain: Firm resilience and knowledge preparedness to tackle the COVID-19 outbreak

The lockdowns of several countries due to COVID-19 outbreak resulted in severe economic consequences, among which was the immediate general disruption of the international supply chain, with few exceptions. This article aims to investigate whether some supply chains were resilient or not and why, using a knowledge-based approach and specifically focusing on the role played by supply chain innovation in building resilience to disruptions, thanks to knowledge preparedness. The study is motivated by two main rationales: the unique situation of a global pandemic and the absence of studies providing grounded evidences of supply chain resilience in a worst-case scenario.The research is based on the assumption that knowledge preparedness introduces logistics/supply chain innovations and enables companies to prevent, detect, and respond to unpredictable negative events.By using a large-scale sample of European firms' data from the Eurostat and a multivariate regression analysis, the authors cross-study the effects of supply chain knowledge preparedness – based on innovation type and expenditures – on the international trade of goods from January to June 2020. The results confirm that the most resilient supply chains were those that had previously introduced innovations, a factor that strengthens the knowledge preparedness of firms when faced with unforeseeable supply chain disruptions.