https://ir.unisa.ac.za/handle/10500/30863 Fri, 19 Jun 2026 18:58:37 GMT 2026-06-19T18:58:37Z https://ir.unisa.ac.za/handle/10500/32632 A study of synchronisation in the classical phase-oscillator model of an electrical power grid Olivier, Christiaan In this work, we study synchronisation in power grids using a classical phase oscillator model that can be thought of as a variant of the famous Kuramoto model for coupled phase oscillators. In the recent literature, the connection between a Kuramoto-like model and power grids has been made by Filatrella, Nielsen and Pedersen. Here, we will show that this connection goes much further back, to the so-called Classical Model of power grids that was introduced in 1951 by the work of Boast and Rector. We also observe that in 2018, Arinushkin and Anishchenko developed a Kuramotolike model for power grids in which, for the first time, there appear non-negligible phase-lag parameters as a result of the Kron reduced approximation. Although a single phase-lag (or frustration) parameter had been introduced much earlier in the so-called Kuramoto-Sakaguchi model (from 1986), Arinushkin and Anishchenko were the first to introduce multiple phase-lag parameters into a Kuromoto-like model for power grids. Unfortunately, our attempts to replicate their results soon revealed that they used a too-large, fixed time step for the numerical time integration of their equations, and that this led them to make several erroneous conclusions about the grid which they modelled. Therefore, in Chapter 3, we give a detailed critique of the 2018 paper by Arinushkin and Anishchenko. Then, in a follow-up work by Arinushkin and Vadivasova, from 2021, we observe that use was made of nonlinear damping to control the synchronicity of the Kron reduced grid. In this case, we were able to reproduce all the results of Arinushkin and Vadivasova. We were able to develop a more efficient proportional control scheme, based on the global order parameter. Our proposed control scheme and its results were presented at the 2023 International Conference on Electrical, Computer, and Energy Technologies (ICECET). The resulting conference proceeding is included here, in slightly revised form, as Chapter 4. Finally, in Chapter 5, we provide a brief summary of our main findings and some suggestions for future work. Sun, 01 Feb 2026 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/32632 2026-02-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32625 Determinants of biogas adopton and its impact on livehoods: evidence from Domboshava, Zimbabwe Chawarika, Admire This study investigates the socio-economic determinants of biogas technology adoption among livestock farmers in Domboshava, Zimbabwe and examines its impact on rural livelihoods. Despite Zimbabwe's potential for renewable energy technologies, biogas adoption remains limited, particularly in rural areas facing persistent energy poverty. This research addresses key knowledge gaps regarding technology uptake barriers and livelihood outcomes in developing country contexts. The study employed a mixed-methods targeting randomly selected 368 respondents, however 370 livestock farming households were interviewed and formed the basis for the analysis utilizing descriptive statistics, binary logistic regression, multinomial logistic regression and multiple linear regression for analysis. Qualitative methods, comprising institutional mapping and stakeholder analysis, were employed to complement the quantitative findings, drawing on data from 25 key informant interviews. Socio-economic variables included gender, age, education, remittances, asset ownership, non-farm income, land size, livestock units, energy costs, access to credit and extension services. A composite livelihood index was developed to measure multidimensional welfare outcomes. Analysis using binary and multinomial logistic regression revealed that off-farm income, land size, access to credit, gender, livestock units, energy costs, and extension services significantly influenced biogas adoption, while energy costs and livestock ownership were relatively weak predictors. Similarly, farmers’ plans to adopt biogas were strongly affected by income, land size, credit access and the availability of information. Multiple linear regression further indicated that biogas adoption significantly enhances household livelihoods, particularly in terms of energy security, income diversification and overall welfare. Based on these findings, the study proposes a tailored institutional framework that emphasizes coordinated roles for government, NGOs, financial institutions and local communities to promote biogas adoption and its contribution to sustainable livelihoods. Policy recommendations include targeted financial mechanisms, improved extension services, gender-sensitive strategies and cross-sectoral coordination. These insights offer valuable guidance for scaling renewable energy solutions and advancing sustainable rural development in Zimbabwe and Africa. Fri, 01 Aug 2025 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/32625 2025-08-01T00:00:00Z https://ir.unisa.ac.za/handle/10500/32292 Analysis of the monthly loadshedding and unplanned power outages in South Africa: mean and quantile regression count time series models Tshuma, Sikhulile 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. Wed, 31 Dec 2025 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/32292 2025-12-31T00:00:00Z https://ir.unisa.ac.za/handle/10500/32250 Industrialization of plug-in electric vehicles (PEV) in South Africa Magazi, Lazola Sipelele The study intended to investigate the Industrialization of Plug-In Electric Vehicles (PEV) in South Africa. The study aimed at determining whether PEVs in South Africa (SA) could be industrialized using local resources. The study also intended to identify the possibility of industrialization, the socio-economic factors and localization. South Africa previously had a Joule Electric Vehicle that had failed to industrialize to due lack of government support, the study had learned lessons from the Joule Electric Vehicle. SA automotive industry has an important role in SA’s economy, it contributes about 4,9% to the GDP, it produces close to 1% of the global car manufacturing and exports 60% of its manufactured cars to the European market. SA is currently facing a problem of losing its major markets that is Europe Union (EU) and United Kingdom (UK). The EU and UK took a political decision that by 2030 they will only accept cars that will reduce green-house gas emissions by 55% and by the year 2035 they will only accept 100% electric vehicles produced in SA. In order for SA to retain its major market it has to shift from the production of Internal Combustion Engines to Electric Vehicles. The study aimed at determining the capacity of PEVs to drive Industrialization, to identify the socio-economic factors and to establish the impact of localization. The study was conducted through qualitative research method where the data was collected using face-face interviews and had a population size of 41 participants and a sample size of 21 participants. The study interviewed participants from Department of Transport, Automobile Industry and Department of Trade, Industry and Competition. In addition, the interviewees were Chief Executive Officers, Executive Managers, Senior Managers and Middle Managers. The study recommended future research areas that included implementation of policy, localization of PEV components and emissions caused by the manufacturing of PEVs. Mon, 08 Jan 2024 00:00:00 GMT https://ir.unisa.ac.za/handle/10500/32250 2024-01-08T00:00:00Z