TY - JOUR
T1 - Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption
AU - Garcia Tapia, Andrea
AU - Suarez, Mildred
AU - Ramirez-Marquez, Jose E.
AU - Barker, Kash
N1 - Publisher Copyright:
© 2019 Society for Risk Analysis
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
KW - Cyber–physical–social networks
KW - economic impact visualization
KW - indirect analytical method
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U2 - 10.1111/risa.13372
DO - 10.1111/risa.13372
M3 - Article
C2 - 31441958
AN - SCOPUS:85071016708
SN - 0272-4332
VL - 39
SP - 2032
EP - 2053
JO - Risk Analysis
JF - Risk Analysis
IS - 9
ER -