Using analytics to support a utility’s initial response to the COVID-19 pandemic amid an uncertain evidence base

Jeff Schlegelmilch, Aleksi Paaso, Jackie Ratner, Gunjan Saxena, Zackery White, Susanna Aguilar, Daniel Kushner, Norayr Matevosyan, Jaime Ortega and Shay Bahramirad
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Jeff Schlegelmilch: Director of National Center for Disaster Preparedness, Columbia University, USA
Aleksi Paaso: Director of Distribution Planning in Smart Grid & Innovation, Commonwealth Edison, USA
Jackie Ratner: Senior Project Manager of National Center for Disaster Preparedness, Columbia University, USA
Gunjan Saxena: Research Staff Assistant at National Center for Disaster Preparedness, Columbia University, USA
Zackery White: Research Staff Assistant at National Center for Disaster Preparedness, Columbia University, USA
Susanna Aguilar: Senior Analyst in Smart Grid Programs, Commonwealth Edison, USA
Daniel Kushner: Manager of Smart Grid Programs, Commonwealth Edison, USA
Norayr Matevosyan: Manager of Grid Data Science, Commonwealth Edison, USA
Jaime Ortega: Director of Grid Analytics, Commonwealth Edison, USA
Shay Bahramirad: Vice President of Engineering and Smart Grid, Commonwealth Edison, USA

Journal of Business Continuity & Emergency Planning, 2021, vol. 14, issue 3, 226-238

Abstract: Energy utilities play a critical role in fostering disaster resilience. Much of the world is increasingly dependent on the availability and reliability of safe and efficient energy. In addition to its importance for industrial, commercial and household functionality, energy provision is increasingly significant in determining health and equity outcomes during a disaster. Amid the COVID-19 pandemic, issues of workforce protection and absenteeism are critical for public safety as well as for the continuity of operations for utilities and the businesses that rely upon them. However, COVID-19, and pandemics generally, have rapidly evolving and imperfect evidence available to support rapid and real-time decision making. This article reflects the initial setup and operations of frameworks utilising analytics to support decision making from March through July 2020 for a major US electric utility. These initial strategies have enhanced decision making and provided a foundation for additional integration of the evidence base and use of analytics for anticipated decision support in the coming phases of the COVID-19 pandemic, as well as for future pandemics of unknown aetiology.

Keywords: COVID-19; pandemic; decision support; utilities; electricity; absenteeism; modelling (search for similar items in EconPapers)
JEL-codes: M1 M10 M12 (search for similar items in EconPapers)
Date: 2021
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