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

Ikramullah Khosa, Naveed Taimoor, Jahanzeb Akhtar, Khurram Ali, Ateeq Ur Rehman, Mohit Bajaj, Mohamed Elgbaily, Mokhtar Shouran and Salah Kamel
Additional contact information
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

Energies, 2022, vol. 15, issue 12, 1-24

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.

Keywords: revenue loss; power outages; prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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