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In a world, driven by technology, South Africa's battle with power supply rationing, commonly known as load shedding, is sending shockwaves through the education landscape. This study investigates the intricate relationship between load shedding and e-learning, focusing on a rural university in South Africa. It explores how the disruption of the power supply affects the quest for digital education and sheds light on the hidden challenges, faced by both students and lecturers. The energy crisis loomed large, posing a unique threat to the country's academic advancement. This study was carried out at Walter Sisulu University, meticulously dissects the impacts of load shedding on e-learning, providing valuable insights into how these unforeseen challenges are altering the educational landscape. The research blends quantitative data analysis with a qualitative perspective, capturing the real-life experiences of educators and learners navigating a world where power supply can change the course of a digital classroom. The research elucidated the dominance of e-learning during lockdown regulations to curb the spread of coronavirus and a hybrid learning pedagogy that combines both e-learning and physical classes in the post-covid era. The research revealed that e-learning is significantly affected by Loadshedding. [ABSTRACT FROM AUTHOR]

The increased electricity demand amidst inadequate electricity generation in South Africa has plunged the country into frequent power outages and load shedding. However, the country still has the lowest electricity transmission and distribution losses in sub-Saharan Africa. Despite low losses, there is still an opportunity to reduce losses further and reduce power outages and load shedding. This study examines the determinants of electricity transmission and distribution losses in South Africa. The results will inform policymakers on avoiding higher electricity transmission losses to alleviate the current electricity shortfall. Using the time-series data from 1971–2020 and the autoregressive distributed-lag (ARDL) bounds testing approach to cointegration, the study confirmed a long-run relationship between electricity transmission, distribution losses, and income, price, investment, political regime, and economic integration. Regression analysis from the ARDL methods revealed that investments, political administration, and economic integration positively influence electricity transmission and distribution losses. At the same time, income reduces electricity transmission and distribution losses in the long run. However, income, price, and economic integration minimize electricity transmission losses in the short run while the remaining variables maintained their positive effects. The implication is that without proper checks in place, an expansion in South Africa's economic integration, investment, and democracy may negatively affect the electricity sector of the country through an increase in electric power losses, while higher income will help the industry via lower electric power losses. The paper, among other things, recommends building a robust economy to ensure lower levels of electricity transmission and distribution losses. [ABSTRACT FROM AUTHOR]

The strategic intent of the African Union is to develop a "Smart Integrated African Electric Power Super Grid" driven by modern tools and advances in high-voltage direct current (HVDC) engineering and flexible alternating current technology systems (FACTS), which is central in supporting Africa's sustained economic growth and development. The southern African region, including South Africa, is beset by the critical challenges of perennial load-shedding, which impedes economic growth and aggravates unemployment. This has led to the insecurity of electricity supplies and degraded the quality of life. The parallel operation of high-voltage direct current (HVDC) and flexible AC technology systems (FACTS) controllers is gaining traction as system conditions become more complex, such as weak power networks which requires increased stability requirements, resulting in load-shedding and power outages. These adversely affect business productivity and adversely affect GDP and economic growth. Thus, the application of innovative technologies such as HVDC links can stabilize weak power systems. It is established that HVDC delivery systems reduce losses in long transmission lines transporting bulk power compared with high-voltage alternating current (HVAC) transmission lines for power wheeling. This paper evaluates the parallel operation of the Cahora Bassa 1414 km bipolar HVDC link and a weak parallel 400/330 kV alternating current (AC) link. It demonstrates the use of FACTS controllers to enhance the technical performance of an existing network, such as voltage control, and technical loss reduction. It combines an HVDC line commutated converter (LCC) and HVAC transmission lines, in hybrid notation to increase the voltage stability of the system by controlling the reactive power with a Static Var Compensator (SVC). These modern tools can increase the transmission power controllability and stability of the power network. In this study, HVDC–LCC was used with a setpoint of 1000 MW in conjunction with the 850 MVAr SVC. The results show that the technical losses were reduced by 0.24% from 84.32 MW to 60.32 MW as Apollo 275 kV SVC was utilized for voltage control. The network analysis was performed using DIgSILENT PowerFactory software that is manufactured by DIgSILENT GmbH at Gomaringen, Germany [ABSTRACT FROM AUTHOR]

The integration of solar and wind energy into the electrical grid has received global research attention due to their unpredictable characteristics. Because wind energy varies across all timescales of utility activity, renewable energy generation should be supplemented and enhanced, from real-time, minute-to-minute variations to annual alterations influencing long-termstrategy. Wind energy generation does not only fluctuate but is also challenging to accurately forecast the timeframes of significance to electricity decision makers; day-ahead and long-term making plans of framework sufficiency such as meeting the network peak load annually. A utility that integrates wind and solar energy into its electricity mix would understand how to adapt to uncertainty and variability in operations while sustaining grid stability. Due to hydropower's adaptability, a systemusing hydropower as one of its generating resources could be precisely adapted to absorb the variability of wind and solar energy. The objective of this research study is to create a hybrid system comprising hydro-wind and solar (Hybrid-HWS) integration for power balancing in an isolated electrical network in Klipkop village, Pretoria region, South Africa. The desirability of designing and building goaf storage tank in regard to capability, the fullness of line throughoutwater pumping, dispensing, storage tank spillage, and pressure difference throughout liquid flow within the storage tanks were preliminary assessed using geotechnical and weather forecasting data from a distinctive area of Klipkop town in Pretoria, South Africa. Different facility hours premised on daylight accessibility are scheduled to balance maximum load at early and late hours. However, in the scenario of electrical power, time shift requiring storage for extended periods of time, such as in terms of hours, Hybrid-HWS has been found to have a crucial role. The results of simulations showed a coordinated process design for Hybrid-HWS Energy Storage (ES) to determine everyday strategic planning in reducing the variability of the system resulting from wind-solar-pumped hydro ES output inadequacies and satisfy daily load demands. It could be recommended that by considering the adaptability characteristics, extremely rapidly, ramping, peaking support and maximum stabilizing aid of the system could be archived with pump-hydro into the energy mix which can provide specific guidelines for energy policymakers. [ABSTRACT FROM AUTHOR]

In South Africa, the growing power demand, challenges of having idle infrastructure, and power delivery issues have become crucial problems. Reliability enhancement necessitates a life-cycle performance analysis of the electrical power transformers. To attain reliable operation and continuous electric power supply, methodical condition monitoring of the electrical power transformer is compulsory. Abrupt breakdown of the power transformer instigates grievous economic detriment in the context of the cost of the transformer and disturbance in the electrical energy supply. On the condition that the state of the transformer is appraised in advance, it can be superseded to reduced loading conditions as an alternative to unexpected failure. Dissolved gas analysis (DGA) nowadays has become a customary method for diagnosing transformer faults. DGA provides the concentration level of various gases dissolved, and consequently, the nature of faults can be predicted subject to the concentration level of the gases. The prediction of fault class from DGA output has so far proven to be not holistically reliable when using conventional methods on account of the volatility of the DGA data in line with the rating and working conditions of the transformer. Several faults are unpredictable using the IEC gas ratio (IECGR) method, and an artificial neural network (ANN) has the hindrance of overfitting. Nonetheless, considering that transformer fault prediction is a classification problem, in this work, a unique classification algorithm is proposed. This applies a binary classification support vector machine (BCSVM). The classification precision is not reliant on the number of features of the input gases dataset. The results indicate that the proposed BCSVM furnishes improved results concerning IECGR and ANN methods traceable to its enhanced generalization capability and constructional risk-abatement principle. [ABSTRACT FROM AUTHOR]

Energy demand and consumption across the world are increasing, putting much pressure on current supply. The solar PV system has become one of the most promising renewable energy technologies. The potential of solar PV differs from one province and household to another depends on many attributes and variables. A household energy survey was conducted through questionnaires and interviews from September 2014 to March 2015 in order to determine perceptions on household energy consumption and solar PV's potential across household categories in South Africa. The data from the residential households were collected in nine provinces across South Africa using random sampling methods (stratified and systematic). The number of questionnaires (n = 13,007) were completed across South Africa nine provinces. This study found that 25% and 43% of the middle- and high-income residents respectively are interested in paying for electricity based on green sources. These two income groups consume 9.6 and 3 TWh/year, which means that there is a potential of 12.6 TWh/year that can be generated from solar PV technology in the residential sector. [ABSTRACT FROM AUTHOR]