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

Clicks: 246
ID: 90511
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #19 of 27 articles by views in Risk analysis : an official publication of the Society for Risk Analysis

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
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.
Reference Key
garcia-tapia2019evaluatingrisk Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Garcia Tapia, Andrea;Suarez, Mildred;Ramirez-Marquez, Jose E;Barker, Kash;
Journal Risk analysis : an official publication of the Society for Risk Analysis
Year 2019
DOI
10.1111/risa.13372
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.