Author
Allison C. Reilly
Other affiliations: Johns Hopkins University, University of Michigan, Cornell University
Bio: Allison C. Reilly is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Medicine & Flood myth. The author has an hindex of 7, co-authored 16 publications receiving 235 citations. Previous affiliations of Allison C. Reilly include Johns Hopkins University & University of Michigan.
Papers
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TL;DR: The development of a hurricane power outage prediction model applicable along the full U.S. coastline is described, the use of the model is demonstrated for Hurricane Sandy, and what the impacts of a number of historic storms, including Typhoon Haiyan, would be on current U.s. energy infrastructure are estimated.
Abstract: Hurricanes regularly cause widespread and prolonged power outages along the U.S. coastline. These power outages have significant impacts on other infrastructure dependent on electric power and on the population living in the impacted area. Efficient and effective emergency response planning within power utilities, other utilities dependent on electric power, private companies, and local, state, and federal government agencies benefit from accurate estimates of the extent and spatial distribution of power outages in advance of an approaching hurricane. A number of models have been developed for predicting power outages in advance of a hurricane, but these have been specific to a given utility service area, limiting their use to support wider emergency response planning. In this paper, we describe the development of a hurricane power outage prediction model applicable along the full U.S. coastline using only publicly available data, we demonstrate the use of the model for Hurricane Sandy, and we use the model to estimate what the impacts of a number of historic storms, including Typhoon Haiyan, would be on current U.S. energy infrastructure.
134 citations
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TL;DR: In this paper, a three-player game of the interactions among a government agency, a carrier, and a terrorist was developed to guide in making these determinations, and an effective solution procedure for this game and illustrates the use of that procedure on a realistic case study based on the freight rail network.
Abstract: Government agencies can determine which specific facilities in a transportation network to restrict for each class of material and for which times of the day and/or week to stem the consequences of a terrorist event. To guide in making these determinations, this paper develops a three-player game of the interactions among a government agency, a carrier, and a terrorist. It also develops an effective solution procedure for this game and illustrates the use of that procedure on a realistic case study based on the freight rail network in the continental United States.
50 citations
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TL;DR: This perspectives article addresses risk in cyber defense and identifies opportunities to incorporate risk analysis principles into the cybersecurity field and proposes approaches to address these objectives.
Abstract: This perspectives article addresses risk in cyber defense and identifies opportunities to incorporate risk analysis principles into the cybersecurity field. The Science of Security (SoS) initiative at the National Security Agency seeks to further and promote interdisciplinary research in cybersecurity. SoS organizes its research into the Five Hard Problems (5HP): (1) scalability and composability; (2) policy-governed secure collaboration; (3) security-metrics-driven evaluation, design, development, and deployment; (4) resilient architectures; and (5) understanding and accounting for human behavior. However, a vast majority of the research sponsored by SoS does not consider risk and when it does so, only implicitly. Therefore, we identify opportunities for risk analysis in each hard problem and propose approaches to address these objectives. Such collaborations between risk and cybersecurity researchers will enable growth and insight in both fields, as risk analysts may apply existing methodology in a new realm, while the cybersecurity community benefits from accepted practices for describing, quantifying, working with, and mitigating risk.
27 citations
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TL;DR: This article offers four requirements for boundary objects that may enhance hazards research and concludes by examining both the value of and the challenges from using agent‐based models as the boundary object in interdisciplinary projects.
Abstract: Many of the most complicated and pressing problems in hazards research require the integration of numerous disciplines. The lack of a common knowledge base, however, often prohibits clear communication and interaction among interdisciplinary researchers, sometimes leading to unsuccessful outcomes. Drawing on experience with several projects and collective expertise that spans multiple disciplines, the authors argue that a promising way to enhance participation and enable communication is to have a common model, or boundary object, that can integrate knowledge from different disciplines. The result is that researchers from different disciplines who use different research methods and approaches can work together toward a shared goal. This article offers four requirements for boundary objects that may enhance hazards research. Based on these requirements, agent-based models have the necessary characteristics to be a boundary object. The article concludes by examining both the value of and the challenges from using agent-based models as the boundary object in interdisciplinary projects.
23 citations
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TL;DR: In this paper, the authors identify barriers to and opportunities for lowering transportation-related disaster losses and for improving infrastructure risk management including the need for better data and metrics to support resilience.
19 citations
Cited by
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09 May 2017TL;DR: It is concluded that networked microgrids in particular provide a universal solution for improving the resilience against extreme events in Smart Cities.
Abstract: This paper focuses on the role of networked microgrids as distributed systems for enhancing the power system resilience against extreme events. Resilience is an intrinsically complex property which requires deep understanding of microgrid operation in order to respond effectively in emergency conditions. The paper first introduces the definition and offers a generic framework for analyzing the power system resilience. The notion that large power systems can achieve a higher level of resilience through the deployment of networked microgrids is discussed in detail. In particular, the management of networked microgrids for riding through extreme events is analyzed. In addition, the merits of advanced information and communication technologies (ICTs) in microgrid-based distributed systems that can support the power system resilience are presented. The paper also points out the challenges for expanding the role of distributed systems and concludes that networked microgrids in particular provide a universal solution for improving the resilience against extreme events in Smart Cities.
393 citations
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31 Jan 2012
TL;DR: This article presents an agent-based computational model of civil violence, which shows that a central authority seeks to suppress decentralized rebellion and communal violence between two warring ethnic groups.
Abstract: This article presents an agent-based computational model of civil violence. Two variants of the civil violence model are presented. In the first a central authority seeks to suppress decentralized rebellion. In the second a central authority seeks to suppress communal violence between two warring ethnic groups.
277 citations
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TL;DR: In this article, a linear bilevel programming formulation was proposed for the problem of designing a hazmat transportation network with both total risk minimization and risk equity, and a commercial optimization solver was used for testing its stability and evaluating the range of its solution values.
Abstract: In this work we consider the following hazmat transportation network design problem. A given set of hazmat shipments has to be shipped over a road transportation network in order to transport a given amount of hazardous materials from specific origin points to specific destination points, and we assume there are regional and local government authorities that want to regulate the hazmat transportations by imposing restrictions on the amount of hazmat traffic over the network links. In particular, the regional authority aims to minimize the total
transport risk induced over the entire region in which the transportation network is
embedded, while local authorities want the risk over their local jurisdictions to be the lowest possible, forcing the regional authority to assure also risk equity. We provide a linear bilevel programming formulation for this hazmat transportation network design problem that takes into account both total risk minimization and risk equity. We transform the bilevel model into a single-level mixed integer linear program by replacing the second level (follower) problem by its KKT conditions and by linearizing the complementary constraints, and then we solve the MIP problem with a commercial optimization solver. The optimal solution may not be
stable, and we provide an approach for testing its stability and for evaluating the range of the its solution values when it is not stable. Moreover, since the bilevel model is difficult to be solved optimally and its optimal solution may not be stable, we provide a heuristic algorithm for the bilevel model able to always find a stable solution. The proposed bilevel model and
heuristic algorithm are experimented on real scenarios of an Italian regional network.
182 citations
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TL;DR: In this article, a proactive operation strategy to enhance system resilience during an unfolding extreme event is proposed, where the uncertain sequential transition of system states driven by the evolution of extreme events is modeled as a Markov process.
Abstract: Extreme weather events, many of which are climate change related, are occurring with increasing frequency and intensity and causing catastrophic outages, reminding the need to enhance the resilience of power systems This paper proposes a proactive operation strategy to enhance system resilience during an unfolding extreme event The uncertain sequential transition of system states driven by the evolution of extreme events is modeled as a Markov process At each decision epoch, the system topology is used to construct a Markov state Transition probabilities are evaluated according to failure rates caused by extreme events For each state, a recursive value function, including a current cost and a future cost, is established with operation constraints and intertemporal constraints An optimal strategy is established by optimizing the recursive model, which is transformed into a mixed integer linear programming by using the linear scalarization method, with the probability of each state as the weight of each objective The IEEE 30-bus system, the IEEE 118-bus system, and a realistic provincial power grid are used to validate the proposed method The results demonstrate that the proposed proactive operation strategies can reduce the loss of load due to the development of extreme events
146 citations
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TL;DR: The development of a hurricane power outage prediction model applicable along the full U.S. coastline is described, the use of the model is demonstrated for Hurricane Sandy, and what the impacts of a number of historic storms, including Typhoon Haiyan, would be on current U.s. energy infrastructure are estimated.
Abstract: Hurricanes regularly cause widespread and prolonged power outages along the U.S. coastline. These power outages have significant impacts on other infrastructure dependent on electric power and on the population living in the impacted area. Efficient and effective emergency response planning within power utilities, other utilities dependent on electric power, private companies, and local, state, and federal government agencies benefit from accurate estimates of the extent and spatial distribution of power outages in advance of an approaching hurricane. A number of models have been developed for predicting power outages in advance of a hurricane, but these have been specific to a given utility service area, limiting their use to support wider emergency response planning. In this paper, we describe the development of a hurricane power outage prediction model applicable along the full U.S. coastline using only publicly available data, we demonstrate the use of the model for Hurricane Sandy, and we use the model to estimate what the impacts of a number of historic storms, including Typhoon Haiyan, would be on current U.S. energy infrastructure.
134 citations