Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.     

Supply Networks as a Complex Adaptive System: Toward Simulation‐Based Theory Building on Evolutionary Decision Making

In this article, we examine how the firms embedded in supply networks engage in decision making over time. The supply networks as a complex adaptive system are simulated using cellular automata (CA) through a dynamic evolution of cooperation (i.e., “voice” decision) and defection (i.e., “exit” decision) among supply network agents (i.e., firms). Simple local rules of interaction among firms generate complex patterns of cooperation and defection decisions in the supply network. The incentive schemes underlying decision making are derived through different configurations of the payoff‐matrix based on the game theory argument. The prisoner's dilemma game allows capturing the localized decision‐making process by rational agents, and the CA model allows the self‐organizing outcome to emerge. By observing the evolution of decision making by cooperating and defecting agents, we offer testable propositions regarding relationship development and distributed nature of governance mechanisms for managing supply networks.     

Minimum power assignment in wireless ad hoc networks with spanner property

Power assignment for wireless ad hoc networks is to assign a power for each wireless node such that the induced communication graph has some required properties. Recently research efforts have focused on finding the minimum power assignment to guarantee the connectivity or fault-tolerance of the network. In this paper, we study a new problem of finding the power assignment such that the induced communication graph is a spanner for the original communication graph when all nodes have the maximum power. Here, a spanner means that the length of the shortest path in the induced communication graph is at most a constant times of the length of the shortest path in the original communication graph. Polynomial time algorithm is given to minimize the maximum assigned power with spanner property. The algorithm also works for any other property that can be tested in polynomial time and is monotone. We then give a polynomial time approximation method to minimize the total transmission radius of all nodes. Finally, we propose two heuristics and conduct extensive simulations to study their performance when we aim to minimize the total assigned power of all nodes. The author is partially supported by NSF CCR-0311174.     

Robustness of power-law networks: its assessment and optimization

Many practical complex networks, such as the Internet, WWW and social networks, are discovered to follow power-law distribution in their degree sequences, i.e., the number of nodes with degree \(i\) in these networks is proportional to \(i^{-\beta }\) for some exponential factor \(\beta > 0\). However, these networks also expose their vulnerabilities to a great number of threats such as adversarial attacks on the Internet, cyber-crimes on the WWW or malware propagations on social networks. Although power-law networks have been found robust under random attacks and vulnerable to intentional attacks via experimental observations, how to better understand their vulnerabilities from a theoretical point of view still remains open. In this paper, we study the vulnerability of power-law networks under random attacks and adversarial attacks using the in-depth probabilistic analysis on the theory of random power-law graph models. Our results indicate that power-law networks are able to tolerate random failures if their exponential factor \(\beta \) is \(<\)2.9, and they are more robust against intentional attacks if \(\beta \) is smaller. Furthermore, we reveal the best range \([1.8, 2.5]\) for the exponential factor \(\beta \) by optimizing the complex networks in terms of both their vulnerabilities and costs. When \(\beta < 1.8\), the network maintenance cost is very expensive, and when \(\beta > 2.5\) the network robustness is unpredictable since it depends on the specific attacking strategy.     

A Risk Assessment Framework for the Socioeconomic Impacts of Electricity Transmission Infrastructure Failure Due to Space Weather: An Application to the United Kingdom

Space weather phenomena have been studied in detail in the peer‐reviewed scientific literature. However, there has arguably been scant analysis of the potential socioeconomic impacts of space weather, despite a growing gray literature from different national studies, of varying degrees of methodological rigor. In this analysis, we therefore provide a general framework for assessing the potential socioeconomic impacts of critical infrastructure failure resulting from geomagnetic disturbances, applying it to the British high‐voltage electricity transmission network. Socioeconomic analysis of this threat has hitherto failed to address the general geophysical risk, asset vulnerability, and the network structure of critical infrastructure systems. We overcome this by using a three‐part method that includes (i) estimating the probability of intense magnetospheric substorms, (ii) exploring the vulnerability of electricity transmission assets to geomagnetically induced currents, and (iii) testing the socioeconomic impacts under different levels of space weather forecasting. This has required a multidisciplinary approach, providing a step toward the standardization of space weather risk assessment. We find that for a Carrington‐sized 1‐in‐100‐year event with no space weather forecasting capability, the gross domestic product loss to the United Kingdom could be as high as £15.9 billion, with this figure dropping to £2.9 billion based on current forecasting capability. However, with existing satellites nearing the end of their life, current forecasting capability will decrease in coming years. Therefore, if no further investment takes place, critical infrastructure will become more vulnerable to space weather. Additional investment could provide enhanced forecasting, reducing the economic loss for a Carrington‐sized 1‐in‐100‐year event to £0.9 billion.     

On dual power assignment optimization for biconnectivity

Topology control is an important technology of wireless ad hoc networks to achieve energy efficiency and fault tolerance. In this paper, we study the dual power assignment problem for 2-edge connectivity and 2-vertex connectivity in the symmetric graphical model which is a combinatorial optimization problem from topology control technology.The problem is arisen from the following origin. In a wireless ad hoc network where each node can switch its transmission power between high-level and low-level, how can we establish a fault-tolerantly connected network topology in the most energy-efficient way? Specifically, the objective is to minimize the number of nodes assigned with high power and yet achieve 2-edge connectivity or 2-vertex connectivity.We addressed these optimization problems (2-edge connectivity and 2-vertex connectivity version) under the general graph model in (Wang et al. in Theor. Comput. Sci., 2008). In this paper, we propose a novel approximation algorithm, called Candidate Set Filtering algorithm, to compute nearly-optimal solutions. Specifically, our algorithm can achieve 3.67-approximation ratio for both 2-edge connectivity and 2-vertex connectivity, which improves the existing 4-approximation algorithms for these two cases.     

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.     

A study of the operation efficiency and cost performance indices of power-supply companies in China based on a dynamic network slacks-based measure model

This paper applies the dynamic network slacks-based measure model with free links and fixed links (hereafter referred to as DNSBM-Free and DNSBM-Fixed respectively) to evaluate the operational performance of 31 electric power-supply companies (hereafter referred to as EPCs) in China from 2010 to 2012. This approach allows for the consideration of the group heterogeneity of electric power transmission. This approach also considers the new structural reform in the power grid of China in the near future, i.e., the separation of the transmission division (hereafter referred to as T) and the distribution division (hereafter referred to as D). We extend several performance indices to measure the cost efficiency and technology gaps in electric power supply. We estimate the efficiencies of the EPCs under different policies. The results indicate that regional economic development level has a significant impact on the performance of the EPCs. Under the DNSBM-Free and DNSBM-Fixed models, while the efficiency of D was a slightly higher than that of T before separating T and D, however, the efficiency of D was significantly lower than that of T after separation. Conversely, divisional efficiencies of some companies show a significant gap between T and D. This suggests that policy-makers should seriously balance the pros and cons of separation policy before making decisions, and the EPCs in China focus on enhancing operating efficiency of D when deciding to separate T and D in the future. Furthermore, this paper speculates that the macro-economic environment influences the policy of separation of T and D.     

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.     

The Role of Task Uncertainty in IT Project Team Advice Networks

Advice seeking is often the most critical success factor in today's IT project teams. To understand how advice seekers are motivated, we integrate the antecedents of advice seeking—as defined by network theory (Granovetter, 1983)—into a cost/benefit model based on expectancy theory (Vroom, 1964). To contribute to the research on advice network formation, we integrate the role of task uncertainty—one of the defining characteristics of IT projects—into that research (Wallace & Keil, 2004). Based on a controlled quasi‐experiment, this study demonstrates that when task uncertainty is low, individuals with attractive personalities and similar demographics will be sought out for advice more frequently, regardless of their knowledge and resources (i.e., the benefits to the advice seekers). However, when task uncertainty is high, individuals with greater knowledge and access to resources are sought out more often in an advice network. These results provide clarity to prior research that has found mixed results concerning the effectiveness of the traditional antecedents to advice seeking (e.g., knowledge, power, and transactive memory) (e.g., Xu, Kim, & Kankanhalli, 2010a). In addition, project managers may choose to alter their team structure in order to optimize the advice network based on the anticipated level of IT project risk or task uncertainty.     

Application of Graph Theory to Cost‐Effective Fire Protection of Chemical Plants During Domino Effects

In the present study, we have introduced a methodology based on graph theory and multicriteria decision analysis for cost‐effective fire protection of chemical plants subject to fire‐induced domino effects. By modeling domino effects in chemical plants as a directed graph, the graph centrality measures such as out‐closeness and betweenness scores can be used to identify the installations playing a key role in initiating and propagating potential domino effects. It is demonstrated that active fire protection of installations with the highest out‐closeness score and passive fire protection of installations with the highest betweenness score are the most effective strategies for reducing the vulnerability of chemical plants to fire‐induced domino effects. We have employed a dynamic graph analysis to investigate the impact of both the availability and the degradation of fire protection measures over time on the vulnerability of chemical plants. The results obtained from the graph analysis can further be prioritized using multicriteria decision analysis techniques such as the method of reference point to find the most cost‐effective fire protection strategy.     

Identification of Protective Actions to Reduce the Vulnerability of Safety-Critical Systems to Malevolent Intentional Acts: An Optimization-Based Decision-Making Approach

An empirical classification model based on the Majority Rule Sorting (MR-Sort) method has been previously proposed by the authors to evaluate the vulnerability of safety-critical systems (in particular, nuclear power plants [NPPs]) with respect to malevolent intentional acts. In this article, the model serves as the basis for an analysis aimed at determining a set of protective actions to be taken (e.g., increasing the number of monitoring devices, reducing the number of accesses to the safety-critical system) in order to effectively reduce the level of vulnerability of the safety-critical systems under consideration. In particular, the problem is here tackled within an optimization framework: the set of protective actions to implement is chosen as the one minimizing the overall level of vulnerability of a group of safety-critical systems. In this context, three different optimization approaches have been explored: (i) one single classification model is built to evaluate and minimize system vulnerability; (ii) an ensemble of compatible classification models, generated by the bootstrap method, is employed to perform a “robust” optimization, taking as reference the “worst-case” scenario over the group of models; (iii) finally, a distribution of classification models, still obtained by bootstrap, is considered to address vulnerability reduction in a “probabilistic” fashion (i.e., by minimizing the “expected” vulnerability of a fleet of systems). The results are presented and compared with reference to a fictitious example considering NPPs as the safety-critical systems of interest.     

Establishing symmetric connectivity in directional wireless sensor networks equipped with $$2\pi /3$$ antennas

In this paper, we study the antenna orientation problem concerning symmetric connectivity in directional wireless sensor networks. We are given a set of nodes each of which is equipped with one directional antenna with beam-width \(\theta = 2\pi /3\) and is initially assigned a transmission range 1 that yields a connected unit disk graph spanning all nodes. The objective of the problem is to compute an orientation of the antennas and to find a minimum transmission power range \(r=O(1)\) such that the induced symmetric communication graph is connected. We propose two algorithms that orient the antennas to yield symmetric connected communication graphs where the transmission power ranges are bounded by 6 and 5, which are currently the best results for this problem. We also study the performance of our algorithms through simulations.     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Can a team have too much cohesion? The dark side to network density

The goal of most work teams is high performance. Prior studies suggest that performance within work groups is influenced by that group’s social network topology. Research has generally revealed to date that group cohesion (i.e., network density) is positively related to team performance under certain conditions. However, more recent research has indicated that this is not the full story. Recent research suggests that an inverse curvilinear relationship exists between social network measures (of which group cohesion is one) and team performance. In response to the need for understanding this relationship more fully, and leveraging the promising new insights that can be garnered with the use of social network analysis (SNA), this study employs SNA as a tool to explore the structural cohesiveness of teams of travel agents. This research extends our understanding of the relationship between intragroup social network relations and team performance by confirming an inverse curvilinear relationship exists between group cohesion and team performance. This paper leverages email communication to determine the social networks of each team, and then examines such in light of team performance. In total, an analysis of more than 7 million emails was undertaken. This study was conducted with work teams within a service organization. Each team in the study carries out the same tasks, i.e., identical task contingency, yet represents a distinct unit of analysis. The study confirms that social network topology is a valuable predictor of team performance and confirms that, like so many other social network measures, group cohesion and team performance share an inverse ‘U’ shaped relationship, not strictly a positive one as previously posited.     

A Risk Analysis Framework for Cyber Security and Critical Infrastructure Protection of the U.S. Electric Power Grid

The purpose of this article is to introduce a risk analysis framework to enhance the cyber security of and to protect the critical infrastructure of the electric power grid of the United States. Building on the fundamental questions of risk assessment and management, this framework aims to advance the current risk analysis discussions pertaining to the electric power grid. Most of the previous risk-related studies on the electric power grid focus mainly on the recovery of the network from hurricanes and other natural disasters. In contrast, a disproportionately small number of studies explicitly investigate the vulnerability of the electric power grid to cyber-attack scenarios, and how they could be prevented or mitigated. Such a limited approach leaves the United States vulnerable to foreign and domestic threats (both state-sponsored and “lone wolf”) to infiltrate a network that lacks a comprehensive security environment or coordinated government response. By conducting a review of the literature and presenting a risk-based framework, this article underscores the need for a coordinated U.S. cyber security effort toward formulating strategies and responses conducive to protecting the nation against attacks on the electric power grid.     

Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

In this article, a classification model based on the majority rule sorting (MR‐Sort) method is employed to evaluate the vulnerability of safety‐critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited‐size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the classification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different app oaches are here considered to this aim: (i) a model–retrieval‐based approach, (ii) the bootstrap method, and (iii) the leave‐one‐out cross‐validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.     

Outsourcing to a Powerful Contract Manufacturer: The Effect of Learning‐by‐Doing

The contract manufacturing industry has grown rapidly in recent years as firms have increasingly outsourced production to reduce costs. This growth has created powerful contract manufacturers (CMs) in several industries. Achieving a competitive cost position is often a primary motive for outsourcing. Outsourcing influences both the original equipment manufacturer's (OEM) and the CM's production levels, and, therefore, through learning‐by‐doing renders future costs dependent on past outsourcing decisions. As such, outsourcing should not be viewed as a static decision that, once made, is not revisited. We address these considerations by analyzing a two‐period game between an OEM and a powerful CM wherein both firms can reduce their production costs through learning‐by‐doing. We find that partial outsourcing, wherein the OEM simultaneously outsources and produces in‐house, can be an optimal strategy. Also, we find that the OEM's outsourcing strategy may be dynamic—i.e., change from period to period. In addition, we find both that the OEM may engage in production for leverage (i.e., produce internally when at a cost disadvantage) and that the CM may engage in low balling. These and other findings in this paper demonstrate the importance of considering learning, the power of the CM, and future periods when making outsourcing decisions.     

关联供应链网络级联失效机理及鲁棒性研究

为了分析复杂关联供应链网络在遭遇干扰事件时的鲁棒性,研究关联网络层内和层间级联失效机理.通过随机规则生成供应链无向信息层网络和有向物理层网络,描述并分析由α、β、σ等参数表征的节点负荷、容量等网络结构特性.针对存在边流量约束的情况,提出相应的失效负荷分流策略;通过构建极大簇函数,判断关联供应链网络中经分流策略后仍具运作功能的有效节点,并依据供应链信息层网络和物理层网络间一对一匹配的关联关系,构建时变失效迭代状态方程,从而有效描述关联网络间的动态失效传递.最后,在不同参数控制下,对初始单个节点故障和多个节点故障两种情况下的关联供应链网络综合鲁棒性进行数值仿真分析.按节点度从大到小、从小到大、随机3种方式去除多个节点,结果表明在β=0.5, 1, 1.5, 2 4种参数下,按度从小到大方式去除多节点,其引发的关联网络级联失效规模较之其它两种方式大;同时发现关联供应链网络在多节点去除情况下,其级联失效较之单层网络具有明显的一级相变特性,即少量的节点移除就会导致整个关联供应链网络崩溃.     

The Origin and Role of Trust in Local Policy Elites’ Perceptions of High‐Voltage Power Line Installations in the State of Arkansas

The debate over an installation of high‐voltage power lines (HVPLs) has been intense, particularly in northwest Arkansas. Detractors claim that the installation will negatively affect both the natural environment and the local economy, which contains a large tourism component. By contrast, those in favor of installing HVPLs claim that the installation is necessary in order to reliably support the increasing demand for electric power. Using original data collected from a recent statewide Internet survey of 420 local policy elites in Arkansas, this article focuses on two key aspects. First, we examine how local policy elites’ perceptions of risks versus benefits of HVPL installation in their communities are influenced by their levels of trust toward information provided by various sources (e.g., energy industry, environmental groups, and government). Second, we utilize cultural theory to explain how the cultural worldviews of policy elites––specifically, egalitarianism, individualism, hierarchism, and fatalism––shape these levels of trust and HVPL benefit‐risk perceptions, while controlling for other factors claimed by previous literature, including levels of knowledge on energy‐related issues and demographic characteristics. In general, our analysis indicates that policy elites’ value‐oriented formation of HVPL benefit‐risk perceptions is partially due to the influence cultural values have on trust in information sources. We conclude this article by discussing broader implications for the origin and role of trust in policy elites’ decisions throughout the policy‐making process.