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Renewable energy has emerged as a promising solution to address the challenges of climate change, energy security, and socio-economic development. South Africa, with its abundant renewable energy resources, has made significant strides in the utilization of renewable energy over the past decade. This paper provides a comprehensive review of the progress of renewable energy advancement in South Africa, examining the policies, initiatives, and achievements in various renewable energy sectors. This study explores the country's transition from a heavily coal-dependent energy system to a diversified and sustainable energy mix. It analyses the growth of renewable energy technologies, such as wind power and solar photovoltaic (PV), highlighting the key milestones, challenges, and opportunities. Furthermore, this paper discusses the role of government support, regulatory frameworks, and private sector investments in driving renewable energy deployment in South Africa. Finally, it identifies the prospects and potential areas for further advancement in the renewable energy sector. This review aims to contribute to the understanding of South Africa's renewable energy journey and provides valuable insights for policy-makers, researchers, and stakeholders involved in the sustainable energy transition. [ABSTRACT FROM AUTHOR]

In South Africa, there has been a rapid adoption of solar power, particularly inverter-based solar sources, in low-voltage (LV) networks due to factors such as load shedding, rising electricity costs and greenhouse gas emissions reduction. In residential LV networks, the alignment between solar supply and energy demand is less precise, necessitating larger battery storage systems to effectively utilize solar energy. Residential areas experience peak energy demand in the morning and evening when solar irradiance is limited. As a result, substantial energy storage is important to fully utilize the potential of solar energy. However, increasing inverter-based, customer-generated power creates an imbalance in the utility supply. This is because utility LV supply transformers have three phases, while individual customers have single-phase connections and no load balancing control mechanism. This supply imbalance adversely affects the overall power quality, causing energy losses, damage to devices and other issues. To address these problems, the paper proposes a smart control approach to minimize power imbalances within utility LV supply transformers. The controller uses customer battery storage in residential areas to balance the utility transformer phases. A laboratory model was built to simulate a three-phase low-voltage network with single-phase customers, both with and without a smart controller. The results show that closely monitoring and controlling individual inverters through a central controller can significantly improve the balance of the supply network. [ABSTRACT FROM AUTHOR]

The ageing of coal-fired power stations in South Africa has led to regular power outages. Therefore, the country will need to urgently increase its electricity capacity to meet further energy demand from growing urbanization and population growth. This challenge has increased interest in alternative energy sources, such as renewable energy (RE). South Africa is gradually implementing appropriate renewable energy practices, reducing carbon emissions, cleansing the air, and assuring a more sustainable future. This paper summarizes the availability, current state, and future potential of renewable energy choices in South Africa. This paper also evaluates specific policy measures and government actions aimed at eliminating barriers and increasing renewable energy deployment in the future. It also considers the South African network's specialized techno-economic analysis. The findings show that South Africa is still heavily reliant on coal, with 78% of the current installed capacity coming from coal power in 2022, compared to 9.3% for solar and wind energy. [ABSTRACT FROM AUTHOR]

Conventional power systems have been heavily dependent on fossil fuel to meet the increasing energy demand due to exponential population growth and diverse technological advancements. This paper presents an optimal energy model and power management of an off-grid distributed energy system (DES) capable of providing sustainable and economic power supply to electrical loads. The paper models and co-optimizes multi-energy generations as a central objective for reliable and economic power supply to electrical loads while simultaneously satisfying a set of system and operational parameters. In addition, mixed integer nonlinear programing (MINLP) optimization technique is exploited to maximize power system generation between interconnected energy sources and dynamic electrical load with highest reliability and minimum operational and emission costs. Due to frequent battery cycling operation in the DES, rainflow algorithm is applied to the optimization result to estimate the depth of discharge (DOD) and subsequently count the number of cycles. The validity and performance of the power management strategy is evaluated with an aggregate load demand scenario of sixty households as a benchmark in a MATLAB program. The simulation results indicate the capability and effectiveness of optimal DES model through an enhanced MINLP optimization program in terms of significant operational costs and emission reduction of the diesel generator (DG). Specifically, the deployment of DES minimizes the daily operational cost by 71.53%. The results further indicate a drastic reduction in CO2 emissions, with 22.76% reduction for the residential community load scenario in contrast to the exclusive DG system. This study provides a framework on the economic feasibility and effective planning of green energy systems (GESs) with efficient optimization techniques with capability for further development. [ABSTRACT FROM AUTHOR]

Climate change is progressing faster than previously envisioned. Efforts to mitigate the challenges of greenhouse gas emissions by countries through the establishment of the Intergovernmental Panel on Climate Change has resulted in continuous environmental improvements in the energy efficiency and carbon emission signatures of products. In this paper, an energy–carbon emissions nexus causal model was applied using the Leontief Input–Output mathematical model for low-carbon products in future transport-manufacturing industries., The relationship between energy savings, energy efficiency, and the carbon intensity of products for the carbon emissions signature of the future transport manufacturing in South Africa was established. The interrelationship between the variables resulted in a 29% improvement in the total energy intensity of the vehicle body part products, 7.22% in the cumulative energy savings, and 16.25% in the energy efficiency. The scope that has been examined in this paper will be interesting to agencies of government, researchers, policymakers, business owners, and practicing engineers in future transport manufacturing and could serve as a fundamental guideline for future studies in these areas. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic created large disruptions in the energy industry as governments all around the world responded by enforcing lockdowns on 54% of the global population to slow down the spread of the virus. This impacts the energy consumption of the residential sector as policies prevented residents from leaving their homes and accelerated the transition towards remote work environments. The impact of lockdown on South Africa's energy sector is unique to other countries due to its extreme inequalities across the affluence divide and ongoing blackouts that affect the entire country. A case study is performed in George, a region in South Africa with a distribution of wealth and energy inequality typical for South Africa. In this paper, the following questions are answered: (i) How has the lockdown period influenced the total energy consumption of a household? and (ii) To what extent has the lockdown affected energy consumption of households based on wealth and demographics? The results of this study showed that implementing the highest stage of lockdown resulted in wealthy communities increasing their overall energy consumption by 5 percentage points and poor communities by 2.5 percentage points. The findings of this study support the use of local data and insight for the unique difference in behavioural change across different communities for optimal decision making in future pandemics or natural disasters. [ABSTRACT FROM AUTHOR]

As a developing country, South Africa relies on electricity as the most critical amenity needed for development. KwaZulu-Natal (KZN) is one of the nine provinces in South Africa that faces an energy supply challenge. KZN is also the warmest province among them all due to its location. As one of the warmest provinces, solar power could be utilised to assist in dealing with the energy crisis. This paper focuses on the cost uncertainties attributed to the implementation of solar power which will assist in meeting the demand of energy in the region. The unsustainability of energy has led to a near collapse of the region's economy. This study also contributes to improving the scientific implementation of solar power in the region to deal with the unsustainability criteria observed. The employment of Cost Benefit Analysis (CBA) revealed solar power as one of the most reliable renewable energies for planned implementation bases. With the development of the Economical, Methodological and Environmental (EME) strategic framework which addresses all social cohesion of solar power, the early turn around has been seen through the use of cost-benefit analysis (CBA) in the region of KZN. [ABSTRACT FROM AUTHOR]

In this paper, an optimal hybrid (wind and grid)-hydrogen energy system (H-HES) is proposed using multi-objective non-dominated sorting algorithm (NSGA-II) optimization. The H-HES consists of the main energy system; wind-energy system (W-ES), which supplies a proton exchange membrane (PEM) electrolyzer via an energy management system (EMS) and a rectifier. In addition, the grid-energy system (G-ES) is available to support the W-ES to meet the PEM electrolyzer's energy demand, and the EMS facilitates control between the W-ES and G-ES. The W-ES is modelled using wind data from Wind Atlas South Africa (WASA) for six Renewable Energy Development Zones (REDZs) in South Africa and their appropriate wind turbine models. The selection of appropriate wind turbine models is guided by the optimal wind turbine variables obtained from NSGA-II corresponding to the optimal H-HES model. The optimal H-HES model is developed using two objective functions: cost of electricity and efficiency, which are minimized and maximized respectively and evaluated using NSGA-II available in Pymoo framework. NSGA-II successfully converges to a Pareto front, and the best solution for the H-HES cost of electricity and efficiency for each wind REDZ is determined by compromise programming; a multi-criteria decision-making technique available in Pymoo. From the optimal cost of electricity and efficiency solutions, optimal variables are successfully obtained for optimal modelling of the H-HES for each wind REDZ. [ABSTRACT FROM AUTHOR]

South Africa is one of the most carbon-intensive economies in the world, but it is presently experiencing an energy crisis, as its utility company cannot meet the country's energy demands. The use of renewable energy sources and retiring of coal-fired power stations are two important ways of alleviating this problem, as well as decarbonizing the grid. Repurposing retiring coal-fired power stations for renewable energy generation (RCP-RES) while maintaining energy sustainability and reliability has rarely been researched. This paper proposes macro- and microelements for repurposing retiring coal-fired power stations for renewable energy generation in Camden with the aim of improving power generation through a low-carbon system. In this model, concentrated solar power (CSP) and solar photovoltaics (SPV), in combination with storage technologies (STs), were employed for RCP-RES, owing to their excellent levels of availability in the retiring fleet regions. The simulation results show that the power densities of CSP and SPV are significantly lower compared with retiring a coal-fired power plant (CFPP). Both are only able to generate 8.4% and 3.84% rated capacity of the retired CFPP, respectively. From an economic perspective, the levelized cost of electricity (LCOE) analysis indicates that CSP is significantly cheaper than coal technology, and even cheaper when considering SPV with a storage system. [ABSTRACT FROM AUTHOR]

Institutions of higher learning in South Africa are grappling with occasional power outages in lecture halls, leading to disruptions in academic activities and occasional loss of lecture hours. Therefore, this study presents a comprehensive evaluation of a grid-connected solar PV/Li-ion battery microgrid (μG) system aimed at maintaining a constant power supply to selected lecture halls at a university in the Western Cape, South Africa. The microgrid design, modelling, and simulations, conducted in the MATLAB/Simulink environment, include the solar PV, Li-ion battery, energy consumption of the lecture halls (load profile), boost converter, bidirectional converter, and grid. Using the detailed design, modelling, and simulation, the study evaluates the economic and environmental impacts of integrating μGs, focusing on enhancing energy reliability, reducing operational costs, and mitigating CO2 emissions. The results indicate that integrating the microgrid resulted in a significant 51% reduction in energy cost and a decrease in greenhouse gas emissions by 530 kgCO2e per hour. In Cases 2 and 4, where the battery supplied power, the annual battery degradation costs are 6.08% and 14.9% of the initial cost, respectively. The μG ensures an uninterrupted power supply and improves the overall reliability of the university's energy infrastructure. It promotes environmental sustainability goals of zero emissions and maintains continuous academic activities during grid outages. Furthermore, it fosters a conducive learning environment, supports innovation and creativity in sustainable energy technologies, and sets a standard for other higher education institutions to integrate renewable energy-powered μGs. [ABSTRACT FROM AUTHOR]

The long-term impact of high-energy consumption in the manufacturing sector results in adverse environmental effects. Energy consumption and carbon emission prediction in the production environment is an essential requirement to mitigate climate change. The aim of this paper is to evaluate, model, construct, and validate the electricity generated data errors of an automotive component manufacturing company in South Africa for prediction of future transport manufacturing energy consumption and carbon emissions. The energy consumption and carbon emission data of an automotive component manufacturing company were explored for decision making, using data from 2016 to 2018 for prediction of future transport manufacturing energy consumption. The result is an ARIMA model with regression-correlated error fittings in the generalized least squares estimation of future forecast values for five years. The result is validated with RSS, showing an improvement of 89.61% in AR and 99.1% in MA when combined and an RMSE value of 449.8932 at a confidence level of 95%. This paper proposes a model for efficient prediction of energy consumption and carbon emissions for better decision making and utilize appropriate precautions to improve eco-friendly operation. [ABSTRACT FROM AUTHOR]

Healthcare institutions consume large amounts of energy, ranking the second highest energy-intensive buildings in the commercial sector. Within developed countries, the energy consumption of healthcare institutions may account for up to 18% of the overall energy usage in commercial sectors. Within developing countries, such as South Africa, the energy consumption of healthcare institutions is observed to be a close second to the food service sector. Energy consumption of healthcare institutions per bed typically range from 43–92 kWh per day. In this paper, the largest energy consumers in South African healthcare institutions are identified and appropriate energy-efficiency (EE) initiatives are proposed, in terms of performance, operation, equipment and technology efficiency (POET). Two main thermal energy consumers are identified as heating, ventilation and air conditioning (HVAC) and water-heating systems. These systems are critical to patient health and may be classified as non-deferrable loads. Therefore, several initiatives are suggested to improve the energy efficiency and demand-side management capability of these systems. These initiatives are subdivided into different levels: the conceptual level, active level, technical and further improvement level, as defined in the POET framework. At each level, energy-efficiency initiatives are introduced based on potential energy savings and the effort required to achieve these savings. In addition, model predictive control (MPC) approaches are discussed and reviewed as part of the further improvement section. Average possible energy savings ranged from 50%–70% at the conceptual level, while energy savings of 15%–30% may be expected for energy-efficiency initiatives at the active level. EE activities at the technical level and the further improvement level may result in savings of 50%–70% and 5%–10%, respectively. [ABSTRACT FROM AUTHOR]

This article aims to assess the benefits of floor slab insulation measures using extruded polystyrene (XPS) and polyisocyanurate (also referred to as polyiso or PIR) insulation materials at various levels of insulation thicknesses for a detached residential building. An EnergyPlus simulation analysis was carried out within the seven energy zones (represented by eight locations) of South Africa in accordance with the South African national code for building energy efficiency (SANS10400-XA). The energy savings and payback periods related to the use of the insulation over a lifecycle period of 50 years were assessed. Cape Town (zone 4) behaved differently from other locations and hardly benefited from the application of floor slab insulation measures. Generally, polyiso (PIR) insulation performed better than XPS for vertical gap insulation, and lower insulation thicknesses required higher insulation depths to maximize energy savings. Similarly, lower insulation thicknesses (25 mm and 50 mm) required higher perimeter insulation widths to maximize energy savings for horizontal perimeter insulation, especially in Sutherland (zone 6) and Cape Town. The maximization of energy savings was also achieved at low insulation thickness for the full floor slab insulation method, except for Sutherland and Fraserburg (zone 7). The locations that benefitted most from the floor slab insulation methods were Pretoria (zone 5), Thohoyandou (zone 3), Sutherland (zone 6), Fraserburg (zone 7), Welkom (zone 1), Ixopo (zone 5H), Witbank (zone 2), and Cape Town (zone 4), in that order. Generally, higher net energy savings are achieved in areas with lower humidity levels and areas with greater annual sums of both cooling and heating degree days. [ABSTRACT FROM AUTHOR]

The operation of water heating uses a substantial amount of energy and is responsible for 30% of a household's overall electricity consumption. Determining methods of reducing energy demand is crucial for countries such as South Africa, where energy supply is almost exclusively electrical, 88% of it is generated by coal, and energy deficits cause frequent blackouts. Decreasing the energy consumption of tanked water heaters can be achieved by reducing the standing losses and thermal energy of the hot water used. In this paper, we evaluate various energy-saving strategies that have commonly been used and determine which strategy is best. These strategies include optimising the heating schedule, lowering the set-point temperature, reducing the volume of hot water used, and installing additional thermal insulation. The results show that the best strategy was providing optimal control of the heating element, and savings of 16.3% were achieved. This study also determined that the magnitude of energy savings is heavily dependent on a household's water usage intensity and seasonality. [ABSTRACT FROM AUTHOR]

The electricity supply in South Africa is characterized by load-shedding. This study analyzed the determinants of the multidimensional energy poverty index (MEPI) in South Africa. The data, which were taken from the 2019–2021 General Household Survey (GHS), were analyzed using Tobit regression. The results showed that between 2019 and 2021, the use of clean energy for cooking declined from 85.97% to 85.68%, respectively, whereas the use of clean energy for water heating declined from 87.24% in 2020 to 86.55% in 2021. Space heating with clean energy declined from 53.57% in 2019 to 50.35% in 2021. The average fuzzy MEPI was 0.143 and Western Cape and KwaZulu-Natal provinces had the highest average values with 0.180 and 0.176, respectively. In the combined dataset, the Tobit regression results showed that, compared to Western Cape, the fuzzy MEPI significantly decreased (p < 0.01) by −0.038, 0.028, 0.045, 0.023, 0.029, 0.038, 0.037, and 0.042 for residents in Eastern Cape, Northern Cape, Free State, Kwazulu-Natal, North West, Gauteng, Mpumalanga, and Limpopo provinces, respectively. In addition, the fuzzy MEPI for the Black, Coloured, Asian, and White respondents decreased by 0.042, 0.062, and 0.084, respectively. The fuzzy MEPI for male-headed households and the number of social grants increased, whereas the fuzzy MEPI significantly decreased (p < 0.01) for the monthly salary and age of household heads. It was concluded that energy poverty in South Africa manifests through unclean energy utilization for space heating. The promotion of clean energy utilization should focus on deprived provinces, farms, and tribal areas. [ABSTRACT FROM AUTHOR]

This paper aims to analyse: (a) how the attitude towards renewable energy-based heating systems, pro-environmental behaviour and the perceived attributes of technology influence intention to convert residential heating systems from fossil fuels to biofuels, and (b) the moderating role of culture based on Hofstede's individualism dimension. A total of 425 responses were collected from a panel of internet users from representative countries in three continents (the United States, the United Kingdom and South Africa); the data analysis was carried out using structural equation models in a multigroup analysis. The results showed that attitude towards renewable energy-based heating systems is influenced by environmental variables in the United States and the United Kingdom, and by the perceived attributes of clean residential heating systems in the United States and South Africa. Attitude, in turn, impacts on the intention to convert from fossil fuels to biofuels. In addition, individualism has a moderating effect between these variables and there are intercultural differences in the degree of importance attributed to them. The study concludes the use of these energy systems as drivers of environmentally-sustainable development. [ABSTRACT FROM AUTHOR]

One of the issues associated with the supply of electricity is its generation capacity, and this has led to prevalent power cuts and high costs of usage experienced in many developing nations, including South Africa. Historical research has shown that the annual rate of increase for electricity has grown at an alarming rate since 2008 and, in some years, has grown as much as 16%. The objectives of this study are to estimate the cost analysis of electricity usage at the twenty-nine residences of the University of Johannesburg (UJ-Res) and propose a model for our university, as well as other South African universities, to become more energy-efficient. This was achieved by analyzing the tariffs between 2015 and 2021. A forecast was made for a period of five years (2021 to 2026) using a non-linear autoregressive exogenous neural network (NARX-NN) time-series model. From the results obtained, the better NARX-NN model studied has a root mean squared error (RMSE) of 2.47 × 105 and a determination coefficient ( R 2 ) of 0.9661. The projection result also shows that the annual cost of energy consumed will increase for the projected years, with the year 2022 being the peak with an estimated annual cost of over ZAR 30 million (USD 2,076,268). [ABSTRACT FROM AUTHOR]

Energy consumption in the building sector is a major concern, particularly in this time of worldwide population and energy demand increases. To reduce energy consumption due to HVAC systems in the building sector, different models based on measured data have been developed to estimate the cooling load. The purpose of this work is to develop a linear regression model for cooling load of a research room based on the radiant time series (RTS) components of the cooling load that consider the building material and the environment. Using the forward step method, linear regression models were developed for both all-seasons and seasonal data from three years of cooling load data obtained from the RTS method for a research room at Mangosuthu University of Technology (MUT), South Africa. The male and female occupants, window cooling load, and roof cooling load were found to be the most influential predictors for the cooling load model. The obtained relative errors between the best all-seasons model and seasonal models built with the same predictors for the respective data subsets are almost zero and are given as 0.0073% (autumn), 0.0016% (spring), 0.0168% (summer), and 0.0162% (winter). This leads to the conclusion that the seasonal models can be represented by the all-seasons model. However, further study can be performed to improve the model by incorporating the occupancy behaviours and other components or parameters intervening in the calculation of cooling load using the radiant time series method. [ABSTRACT FROM AUTHOR]

South Africa's energy consumption takes up about one-third of that in the whole African continent, ranking the first place in Africa. However, there are few researches on the prediction of energy consumption in South Africa. In this study, based on the data of South Africa's energy consumption during 1998–2016, Autoregressive Integrated Moving Average (ARIMA) model, nonlinear grey model (NGM) and nonlinear grey model–autoregressive integrated moving average (NGM-ARIMA) model are adopted to predict South Africa's energy consumption during 2017–2030. After using these NGM, ARIMA and NGM-ARIMA, the mean absolute percent errors (MAPE) are 2.827%, 2.655% and 1.772%, respectively, which indicates that the predicted result has very high reliability. The prediction results show that the energy consumption in South Africa will keep increasing with the growth rate of about 7.49% in the next 14 years. This research result will provide scientific basis for the policy adjustment of energy supply and demand in South Africa and the prediction techniques used in the research will have reference function for the energy consumption study in other African countries. [ABSTRACT FROM AUTHOR]

The increasing demand to reduce the high consumption of end-use energy in office buildings framed the objective of this work, which was to design an intelligent system management that could be utilized to minimize office buildings' energy consumption from the national electricity grid. Heating, Ventilation and Air Conditioning (HVAC) and lighting are the two main consumers of electricity in office buildings. Advanced automation and control systems for buildings and their components have been developed by researchers to achieve low energy consumption in office buildings without considering integrating the load consumed and the Photovoltaic system (PV) input to the controller. This study investigated the use of PV to power the HVAC and lighting equipped with a suitable control strategy to improve energy saving within a building, especially in office buildings where there are reports of high misuse of electricity. The intelligent system was modelled using occupant activities, weather condition changes, load consumed and PV energy changes, as input to the control system of lighting and HVAC. The model was verified and tested using specialized simulation tools (Simulink®) and was subsequently used to investigate the impact of an integrated system on energy consumption, based on three scenarios. In addition, the direct impact on reduced energy cost was also analysed. The first scenario was tested in simulation of four offices building in a civil building in South Africa of a single occupant's activities, weather conditions, temperature and the simulation resulted in savings of HVAC energy and lighting energy of 13% and 29%, respectively. In the second scenario, the four offices were tested in simulation due to the loads' management plus temperature and occupancy and it resulted in a saving of 20% of HVAC energy and 29% of lighting electrical energy. The third scenario, which tested integrating PV energy (thus, the approach utilized) with the above-mentioned scenarios, resulted in, respectively, 64% and 73% of HVAC energy and lighting electrical energy saved. This saving was greater than that of the first two scenarios. The results of the system developed demonstrated that the loads' control and the PV integration combined with the occupancy, weather and temperature control, could lead to a significant saving of energy within office buildings. [ABSTRACT FROM AUTHOR]