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Financial Hazard Assessment for Electricity Suppliers Due to Power Outages: The Revenue Loss Perspective

Author

Listed:
  • Ikramullah Khosa

    (Lahore Campus, COMSATS University, Islamabad 54000, Pakistan)

  • Naveed Taimoor

    (Lahore Campus, COMSATS University, Islamabad 54000, Pakistan)

  • Jahanzeb Akhtar

    (Lahore Campus, COMSATS University, Islamabad 54000, Pakistan)

  • Khurram Ali

    (Lahore Campus, COMSATS University, Islamabad 54000, Pakistan)

  • Ateeq Ur Rehman

    (Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan)

  • Mohit Bajaj

    (Department of Electrical and Electronics Engineering, National Institute of Technology, Delhi 110040, India)

  • Mohamed Elgbaily

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Mokhtar Shouran

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Salah Kamel

    (Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

Abstract

The electrical power infrastructure of the modern world is advanced, efficient, and robust, yet power outages still occur. In addition to affecting millions of people around the world, these outage events cost billions of dollars to the global economy. In this paper, the revenue loss borne by electricity-supplying companies in the United States due to power outage events is estimated and predicted. Various factors responsible for power outages are considered in order to present an exploratory data analysis at the U.S. level, followed by the top ten affected states, which bear over 85% of the total revenue loss. The loss is computed using historic observational data of electricity usage patterns and the tariff offered by the energy suppliers. The study is supplemented with reliable and publicly available records, including electricity usage patterns, the consumer category distribution, climatological annotations, population density, socio-economic indicators and land area. Machine learning techniques are used to predict the revenue loss for future outage events, as well as to characterize the key parameters for efficient prediction and their partial dependence. The results show that the revenue loss is a function of several parameters, including residential sales, percentage of industrial customer, time-period of the year, and economic indicators. This study may help energy suppliers make risk-informed decisions, while developing revenue generation strategies as well as identifying safer investment avenues for long-term returns.

Suggested Citation

  • Ikramullah Khosa & Naveed Taimoor & Jahanzeb Akhtar & Khurram Ali & Ateeq Ur Rehman & Mohit Bajaj & Mohamed Elgbaily & Mokhtar Shouran & Salah Kamel, 2022. "Financial Hazard Assessment for Electricity Suppliers Due to Power Outages: The Revenue Loss Perspective," Energies, MDPI, vol. 15(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4327-:d:837892
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    References listed on IDEAS

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    1. Rafal Ali & Ikramullah Khosa & Ammar Armghan & Jehangir Arshad & Sajjad Rabbani & Naif Alsharabi & Habib Hamam, 2022. "Financial Hazard Prediction Due to Power Outages Associated with Severe Weather-Related Natural Disaster Categories," Energies, MDPI, vol. 15(24), pages 1-25, December.

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