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1. Resiliency Assessment in Distribution Networks Using GIS Based Predictive Risk Analytics

   https://doi.org/10.1109/TPWRS.2019.2913090  

   Leite, Jonatas Boas; Mantovani, Jose Roberto; Dokic, Tatjana; Yan, Qin; Chen, Po-Chen; Kezunovic, Mladen (January 2019, IEEE Transactions on Power Systems)

   A new predictive risk-based framework is proposed to increase power distribution network resiliency by improving operator understanding of the status of the grid. This paper expresses the risk assessment as the correlation between likelihood and impact. The likelihood is derived from the combination of Naive Bayes learning and Jenks natural breaks classifier. The analytics included in a GIS platform fuse together a massive amount of data from outage recordings and weather historical databases in just one semantic parameter known as failure probability. The financial impact is determined by a time series-based formulation that supports spatiotemporal data from fault management events and customer interruption cost. Results offer prediction of hourly risk levels and monthly accumulated risk for each feeder section of a distribution network allowing for timely tracking of the operating condition. 

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2. “Optimized Asset Management in Distribution Systems Based on Predictive Risk Analysis

   T. Dokic, M. Kezunovic (January 2018, Energies)

   The paper introduces an optimal maintenance scheduler based on predictive assessment of risk of outage and equipment failure in distribution networks. The variety of severe weather conditions are observed and their impact on the network components is quantified. The equipment deterioration and failure rates are observed continuously across the space and time using heterogeneous data. The risk of weather-related outages for each component is generated in real-time, and can be extracted at multiple temporal and spatial scales depending on the application of interest. The optimal maintenance scheduling that minimizes the system risk while maintaining the economic investment limits is developed. The benefits of the framework are presented using a distribution network asset management example. 

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3. Embracing and Controlling Risk Dependency in Cyber Insurance Policy Underwriting

   Khalili, M.; Liu, M. and (June 2018, The Annual Workshop on the Economics of Information Security (WEIS))

   This paper highlights how cyber risk dependencies can be taken into consideration when underwrit- ing cyber-insurance policies. This is done within the context of a base rate insurance policy framework, which is widely used in practice. Specifically, we show that there is an opportunity for an underwriter to better control the risk dependency and the risk spill-over, ultimately resulting in lower overall cyber risks across its portfolio. To do so, we consider a Service Provider (SP) and its customers as the interdependent insurer’s customers: a data breach suffered by the SP can cause business interruption to its customers. In underwriting both the SP and its customers, we show that the insurer can increase its profit by incentivizing the SP (through a discount on its premium) to invest more in security, thereby decreasing the chance of business interruption to the customers and increasing social welfare. For comparison, we also consider a scenario where the insurer underwrites only the SP’s customers (but not the SP), and receives compensation from the SP’s insurance carrier when losses are attributed to the SP. We show that the insurer cannot outperform the case where it underwrites both the SP and its customers. We use an actual cyber-insurance policy and claims data to calibrate and substantiate our analytical findings. 

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4. Fast parameter estimation of generalized extreme value distribution using neural networks

   https://doi.org/10.1002/env.2845  

   Rai, Sweta; Hoffman, Alexis; Lahiri, Soumendra; Nychka, Douglas W.; Sain, Stephan R.; Bandyopadhyay, Soutir (March 2024, Environmetrics)

   Abstract The heavy‐tailed behavior of the generalized extreme‐value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires and so forth. However, estimating the distribution's parameters using conventional maximum likelihood methods can be computationally intensive, even for moderate‐sized datasets. To overcome this limitation, we propose a computationally efficient, likelihood‐free estimation method utilizing a neural network. Through an extensive simulation study, we demonstrate that the proposed neural network‐based method provides generalized extreme value distribution parameter estimates with comparable accuracy to the conventional maximum likelihood method but with a significant computational speedup. To account for estimation uncertainty, we utilize parametric bootstrapping, which is inherent in the trained network. Finally, we apply this method to 1000‐year annual maximum temperature data from the Community Climate System Model version 3 across North America for three atmospheric concentrations: 289 ppm (pre‐industrial), 700 ppm (future conditions), and 1400 ppm , and compare the results with those obtained using the maximum likelihood approach. 

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

   https://doi.org/10.1111/risa.13372  

   Garcia Tapia, Andrea; Suarez, Mildred; Ramirez‐Marquez, Jose E.; Barker, Kash (August 2019, Risk Analysis)

   Abstract 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. 

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