Analysis of the monthly loadshedding and unplanned power outages in South Africa: mean and quantile regression count time series models

Abstract

Various nations within sub-Saharan Africa are currently facing different stages of loadshedding, with South Africa being of no exception to this trend. Loadshedding has been implemented as a strategy to manage electricity consumption during peak demand periods while allowing for increased usage during off-peak times. This study examines the monthly trends of loadshedding and unplanned power outages in South Africa, utilizing mean and quantile regression count time series models. Unplanned outages can arise from multiple factors, including maintenance activities on power lines, equipment malfunctions, adverse weather events, cable theft or emergencies such as accidents. Recurrent outages impede business activities, leading to a decrease in productivity and an increase in operational expenditures. Given the profound impact of power interruptions on economic stability and social welfare, this research aims to quantify and analyze the temporal trends and seasonal patterns of outages. By leveraging a comprehensive dataset, we first apply Poisson and negative binomial regression models to assess the average frequency and duration of outages, revealing significant trends and seasonal fluctuations. Following this, we employ quantile regression techniques to explore the distributional impacts of various factors, including socioeconomic variables and weather conditions, on the occurrence of outages. The analysis considers five quantiles—10th, 25th, 50th, 75th, and 90th. While negative binomial regression adequately captures average loadshedding dynamics, quantile regression proves superior in modelling extreme outage conditions that are most relevant for electricity system risk management and policy planning. The data was diagnosed to be highly correlated. Therefore penalised models were also employed. Our findings indicate that an increase in contracted demand, along with both planned and unplanned outages, correlates with a rise in the frequency of loadshedding. This suggests that loadshedding is influenced not only by heightened demand but also by failures in generation and distribution infrastructure. The thorough methodology adopted in this research deepens our understanding of the challenges surrounding power supply in South Africa, offering critical insights for policymakers and stakeholders to formulate targeted strategies aimed at mitigating the effects of loadshedding and enhancing energy resilience. Tackling these challenges necessitates substantial investment in infrastructure, a diversification of energy sources, and enhanced management of the electricity supply chain.