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A data-driven approach to quantify social vulnerability to power outages: California case study

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  • Loni, Abdolah
  • Asadi, Somayeh

Abstract

The evaluation of communities' vulnerability to prolonged power outages offers valuable insights for prioritizing improvements in infrastructure resilience, thereby alleviating societal consequences. This study proposes a data-driven approach aiming at developing a Social Vulnerability Index (SoVI) to prolonged power outages leveraging three county-level datasets in California including (1) demographic features, (2) power outage factors, and (3) backup power factors. Furthermore, the study conducts a sensitivity analysis on three distinct datasets under two scenarios (Scenario 1: the current SoVI in 2022, Scenario 2: the prediction of SoVI in the year 2030). The results of Scenario 1 indicate that the counties with more affected customers, the number of power outages, and less education attainment tend to be more vulnerable to power outages in 2022. Scenario 1 reveals that the number of affected customers and power outages are the primary features influencing around 29% and 18% of counties, while educational attainment, public Electric Vehicles (EVs) chargers, and homes with rooftop photovoltaic (PV) substantially impact approximately 32%, 11%, and 8% of counties, respectively. However, in Scenario 2, crucial factors affecting the anticipated SoVI in 2030 include public EV chargers, houses with rooftop PV, power outages, and adults living alone. In contrast to Scenario 1, the prevalence of adults living alone has emerged as a notable factor impacting SoVI in 2030, while both scenarios underscore the pivotal role of EV chargers in influencing SoVI concerning power outages. The proposed SoVI facilitates informed policy decisions and infrastructure improvements in energy resilience, resource allocation, and disaster preparedness, contributing valuable insights for targeted interventions in these domains.

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  • Loni, Abdolah & Asadi, Somayeh, 2024. "A data-driven approach to quantify social vulnerability to power outages: California case study," Applied Energy, Elsevier, vol. 359(C).
    • Handle: RePEc:eee:appene:v:359:y:2024:i:c:s0306261924001223
    DOI: 10.1016/j.apenergy.2024.122739
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    References listed on IDEAS

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    1. Li, Kuanhong & Wang, Linping & Wang, Lianhui, 2024. "Consumption as the catalyst: Analyzing rural power infrastructure and agricultural growth through panel threshold regression and data-driven prediction," Applied Energy, Elsevier, vol. 365(C).

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