This study applies a data-driven methodology to calibrate the coefficients of simple clear sky (CS) irradiance models for global horizontal irradiance (), diffuse horizontal irradiance () and direct normal irradiance () in South Africa. One-minute , and observations from 14 irradiance monitoring stations, representing six distinct climatological zones, were used to evaluate both established and newly calibrated CS models. CS periods were identified using CLAAS-2 SEVIRI cloud data in combination with the Reno algorithm. A total of 31 , 20 and 20 established models, together with newly optimised models, were assessed using standard statistical metrics and mean linear ranking. Results show that the optimised site-specific and climate-zone-based CS models consistently outperform existing models across the study area for , and . The proposed models provide a simpler and more effective approach for generating real-time CS irradiance data, requiring only basic geographical inputs (latitude, longitude, and altitude) and a limited number of derived parameters. These models are recommended for application in South Africa and in regions with similar climatic conditions. While direct transferability to other regions may be limited, the underlying data-driven calibration methodology is broadly applicable to any geographical location.
This paper investigates the key determinants of Chinese foreign direct investment (FDI) in South Africa’s critical energy sector, a field characterised by substantial investment opportunities and a persistent supply crisis. Employing a mixed-methods approach, the study combines a quantitative panel data analysis of Chinese FDI determinants across BRICS+ countries (2002–2021) with a qualitative examination of major investment cases in South Africa’s coal and renewable energy sectors. The econometric results confirm that Chinese FDI is driven by a complex mix of market-seeking, resource-seeking and efficiency-seeking motives. The case studies reveal a pragmatic “dual strategy” in South Africa, whereby strategic state-led investments in coal power coexist with market-oriented projects in renewable energy, aimed at exporting Chinese technology and services. The paper concludes that this dual strategy represents not a balanced approach to co-development but rather a flexible mechanism for advancing China’s national geoeconomic interests – allowing it to secure resources while simultaneously capturing new markets for its multinational enterprises.
South Africa is faced with persistent energy shortages and the need to develop sustainable, renewable resources. This research aimed to produce biomethane (biogas) via anaerobic digestion using a synthetic inoculum and the brown seaweed Ecklonia maxima. The biogas can be utilised in a simple combined heat and power process and converted to electricity and heat. Biogas production is estimated using the biomethane potential utilising the chemical composition of the seaweed. This is then compared to the actual biogas production from the anaerobic reaction of the seaweed and the inoculum at both mesophilic and thermophilic conditions. The research found that seaweed yielded 190.00 ml/g volatile solids (VS) of biogas after 28 days at a pH of 7.0 ±0.2 at mesophilic conditions, which is equivalent to 38% of the calculated biomethane potential of 499.64 ml/g VS. Ecklonia maxima can produce 255.18 kg biogas per ton of dry seaweed, with a possible associated 1417.71 kWh of electricity per ton of dry seaweed. Future work would investigate pre-treatment options for the seaweed to increase the biogas yield and increase the potential electricity output per ton of dry seaweed.
South Africa is faced with persistent energy shortages and the need to develop sustainable, renewable resources. This research aimed to produce biomethane (biogas) via anaerobic digestion using a synthetic inoculum and the brown seaweed Ecklonia maxima. The biogas can be utilised in a simple combined heat and power process and converted to electricity and heat. Biogas production is estimated using the biomethane potential utilising the chemical composition of the seaweed. This is then compared to the actual biogas production from the anaerobic reaction of the seaweed and the inoculum at both mesophilic and thermophilic conditions. The research found that seaweed yielded 190.00 ml/g volatile solids (VS) of biogas after 28 days at a pH of 7.0 ±0.2 at mesophilic conditions, which is equivalent to 38% of the calculated biomethane potential of 499.64 ml/g VS. Ecklonia maxima can produce 255.18 kg biogas per ton of dry seaweed, with a possible associated 1417.71 kWh of electricity per ton of dry seaweed. Future work would investigate pre-treatment options for the seaweed to increase the biogas yield and increase the potential electricity output per ton of dry seaweed.
Greenhouse gases (GHGs) are the main cause of global warming. Reducing emissions would contribute to environmental sustainability and could also have economic advantages. GHGs can be reduced by using the hybrid energy system (HES). In developing countries, analysing energy transitions and unmet energy needs should focus on identifying strategies for transitioning to more sustainable energy systems while ensuring access for all. This study looks at the mitigation of GHG emissions by using clean HES energy with battery optimisation. The paper presents an improved Lyapunov optimisation (LO) algorithm to optimally estimate the number of photovoltaic panels and battery banks for enhanced energy management. To increase system efficiency, the losses of power and over-production and under-utilised energy have been considered. The stability analysis is evaluated using the LO algorithm, specifically focusing on the changes predicted by the energy index of the self-reliance report, optimising the system’s performance under varying environmental conditions.
The paper reports on an investigation into the impact of energy poverty and social capital on well-being, comparing individuals from below- and above-poverty line households. A multidimensional energy poverty index (MEPI) was constructed using a generalised structural equation modelling approach to measure the effect of energy poverty on well-being. We found that that higher levels of energy poverty, measured objectively as an MEPI, or subjectively as energy satisfaction, affected well-being negatively, while higher levels of social capital increased well-being. However, the effects varied by household income: MEPI only had a negative effect on well-being for low-income household respondents, and subjective energy poverty only had a negative effect on low-income and middle-income household respondents. Similarly, social capital’s impact varied by household income: for both groups, the strongest source of higher well-being was a sense of belonging, whereas the impact of political trust was stronger for higher-income groups. The findings suggest that policies which decrease energy poverty would improve well-being, but should take into account specific household and community characteristics. Further, fostering neighbourhood social capital is essential, especially for the poorest of the poor.
Ground-based solar resource measurements are known to be preferred to synthetic or simulated data for a given location, but outliers present in this data can significantly impact the accuracy of predictions used in viability assessments. For solar energy installations to be self-sustaining and viable, accurate ground-based solar resource data for the location of these installations are essential for decision-making and planning. Conventional outlier detection techniques used for solar resources, including graphical plots to complex numerical approaches, often have difficulty identifying these outliers to a satisfactory degree. This study proposes the use of simulated outliers added to synthetic data to train and compare the effectiveness of traditional outlier detection methods and several statistical learning methods, including NN, naïve Bayes, support vector machines and advanced tree-based models for the purpose of outlier detection in this field. The results indicate that the advanced tree-based models provide accurate identification of outliers in the simulation step and are demonstrated to be effective on a ground-based real world data set collected in Gqeberha, South Africa. The use of the proposed approach can aid in reducing the uncertainty in measured solar resource data and, as a result, help to promote the use of solar energy solutions in areas with unreliable solar resource data.
The hotel sector is a vital component of the tourism industry, as it plays a critical role in the determination of tourism destination competitiveness. However, lack of reliable, affordable, and sustainable energy supply remains a major bottleneck to the development of the hotel sector in Malawi. The adoption of clean and renewable energy sources around the globe is driven by different factors, such as the need to address environmental challenges and reduce energy costs. Usually, less affordable energy supplies increase the cost of service delivery, thus making these hotels less competitive on the global market. The article investigates the opportunities and barriers to the hotel industry's adoption of renewable energy technologies and includes a literature review. The paper shows that renewable energy technologies have multiple applications in a typical Malawian hotel, and these present numerous economic and environmental opportunities. However, the hotel sector is failing to capitalise on the energy policy and regulation improvements taking place in the country, mainly due to a lack of technological awareness by hotel managers. To reverse this, the government needs to adopt policies and regulations that target the hotel sector to increase awareness and adoption of clean and renewable energy technologies.
Distributed energy resources (DERs), including solar panels, wind turbines, and battery storage, are becoming more prevalent in power grids. This increased penetration necessitates a closer look at how they impact the grid's operation. Power grid operators face challenges in ensuring the secure operation of the network in the presence of DERs. This includes managing voltage fluctuations, integrating diverse energy sources, and preventing grid overloads. This paper reviews the impacts of DERs on power grid operation and discusses strategies for enhancing the integration of DERs in South Africa’s grid. The strategies involve technology solutions, grid management techniques, and policy changes that facilitate the seamless integration of renewable energy sources. Smart inverters are highlighted as an essential component of the solution. Their advanced capabilities play a central role in managing voltage, frequency, and other aspects of power quality, which is critical when integrating DERs. The paper offers a comprehensive overview of the challenges of integrating inverter-based DERs into the power grid, highlighting lacks and deficiencies in existing South Africa power grid codes and standards and discussing solutions – thus paving a way for future investigations and developments. Eight international regulations are examined and compared, exposing the lack of a worldwide harmonisation and a consistent communication protocol. The paper calls for the evolution of power grid codes to adapt to the changing energy landscape and to harness the benefits of DERs and advanced smart inverter capabilities. This involves updating regulations and standards to ensure grid stability and reliability while accommodating renewable energy sources. The review can aid power utilities and regulators in making informed decisions in enhancing grid-connected DERs and ensuring safe and secure grid operation.
Dimensionality poses a challenge in developing quality predictive models. Often when modelling solar irradiance (SI), many covariates are considered. Training such data has several disadvantages. This study sought to identify the best variable embedded selection method for different location and time horizon combinations from Southern Africa solar irradiance data. It introduced new variable selection methods into solar irradiation studies, namely penalised quantile regression (PQR), regularised random forests (RRF), and quantile regression forest (QRF). Stability analysis, performance and accuracy metric evaluations were used to compare them with the common lasso, elastic and ridge regression methods. The QRF model performed best in all locations followed by the shrinkage methods on hourly data. However, it was found that QRF is not sensitive to associations through correlations, thereby ignoring the relevance of variables while focusing on importance. Among the shrinkage methods, the lasso performed best in only one location. On the 24-hour horizon, elastic net dominated the performances among the shrinkage methods, but QRF was best in three locations of the six considered. Results confirmed that variable selection methods performed differently on different situational data sets. Depending on the strengths of the methods, results were combined to identify the most paramount variables. Day, total rainfall, and wind direction were superfluous features in all situations. The study concluded that shrinkage methods are best in cases of extreme multicollinearity, while QRF is best on data sets with outliers or/and heavy tails.
Electricity usage has risen tremendously over the years, as has its price. This resulted in an increase in the quest for less expensive, viable, and ecologically acceptable means of producing energy for electricity. Currently, the primary source of power in South Africa is sourced from fossil fuels, which have negative environmental consequences. The use of biogas as an alternative can mitigate the impacts of using fossil fuels to generate power. This study has examined the availability and accessibility of waste that may be utilized to generate biogas using common South African livestock excrement. A typical South African home uses 31 kWh of power daily, which equates to 111.6 MJ of energy. According to calculations, about 30 m3 of biogas is needed to produce enough energy to power a household. For the generation of mono-digestion biogas, 12 beef cows, 8 dairy cows, 3898 chickens, 156 pigs, 281 sheep and 300 goats would be needed to meet this need. Moreover, the livestock dung required to meet the daily requirement of 31 kWh is 713 kg for beef and dairy cows, 390 kg for chickens, 468 kg for pigs, 506 kg for sheep and 466 kg for goats. Co-digestion of various wastes is nonetheless a viable and advised method for enhancing the amount and quality of biogas.
The shift towards renewable energy is resulting in increased investment in energy infrastructure, affecting communities of all sizes worldwide. A study on Bugala Island in Lake Victoria, Uganda, explored how socioeconomic factors influence households' decision to adopt hybrid solar electricity. The study utilised a binary logistic regression analysis of cross-sectional research design to understand the significant socioeconomic factors influencing the adoption. The sex of the household head, education level, monthly income, tenure status, and wall and floor materials were the most significant factors for the adoption. However, results suggest that age, household size, marital status, and main occupation were not statistically significant factors in adopting hybrid solar electricity. Insights from these variables can enable policymakers to formulate more efficient and equitable policies geared towards fostering the widespread integration of clean energy solutions. It should be noted that the socioeconomic factors vary in context and location; solar energy systems should be tailored to the needs of each community rather than being implemented using a standardised approach.
Abstract There is a global need for clean and renewable energy sources. This study investigated thermoelectric generators (TEGs) as a possible method for harvesting solar power. The TEG prototype tested here consisted of two equally sized pieces of roof sheeting, with one side exposed to a light source and the other side shaded. Experiments were carried out with the necessary testing components to investigate the effects that two variables have on the amount of power generated: first, the colour of metal inverted box rib (IBR) sheeting and, second, the ideal electrical arrangement for scalability of Peltier tiles for maximum power output. Black-coated sheets generated maximum power (Pmax) output of the TEGs. The TEGs in series configuration generated the highest Pmax when located closest to the light source. The conclusion from the experiment is that TEGs are a potential method of harvesting solar energy on IBR sheeting, specifically in a vertical position. However, applications of different orientations and geographical locations require further investigation, including into the use of TEGs on IBR sheeting for harvesting solar energy on a larger scale.
South Africa has been experiencing electricity load shedding for over sixteen years and this has affected the performance of small and medium enterprises (SMEs) across the country, with devastating effects recorded in the post-Covid-19 era. SMEs are key drivers of the economy and their performance has a great influence on it, as they contribute 36% of gross domestic product (GDP). This case study of the problem used the qualitative research method, with data collected through interviews. This study established that, in the area studied, there is a decline in the performance of the entrepreneurs and working hours have been drastically reduced due to load-shedding. Proposals are made of measures that the SME implementers and the government of South Africa can take in order to prevent the situation from further deteriorating.
The aim of this paper is to determine if a Generalised Linear Model (GLM) is a better model over the traditional simple linear regression when fitted to nitrogen dioxide (N02) emitted into the atmosphere during the production ol electricity from 13 Eskoms coal fuelled power stations. GLMs have flexibilities of allowing the variance to vary as a function of the mean (non-constant variance), and have the advantage of keeping the data in its original scale. Unlike regression, the models do not assume a linear relationship between the response variable and the explanatory variables, and instead the link function is used. The data also need not be Normally distributed. Group-lasso interaction network (glintemet) was used in variable selection for the GLM models. A similar model using regression analysis was fitted foi comparison. The results show that a GLM can be used to predict and explain NO2 emissions from coal fired electricity stations in South Africa. The Lognormal model was found to be the better model by diagnostic measures including plots that showed improved variance behavior in the residuals. Various variables such as amount of electricity sent oui (in GWhs), age of power station (in years), power station used, and interaction terms such as electricity and station, Age and station can be used in describing and predicting NO2 emissions (in tons) from Eskoms coal fuelled powei stations. Keywords: Eskom; generalised linear modelfs) (GLM); linear regression; lognormal distribution; nitrogen dioxide (NO2) emissions.
The capacity of power generation note needs to be increased globally, owing to population growth and industrial revolution. The conventional power plant across the world is inadequate to satisfy growing power demand. By optimally sizing and designing the clusters of renewable energy sources such as wind, microgrid operators can economically and environmentally sustainably provide a clean power solution that can increase the supply of electricity. Wind power (WP) generation can be utilised to reduce the stress on the power plants by minimising the peak demands in constrained distribution networks. Benefits of WP include increased energy revenue, increased system reliability, investment deferment, power loss reduction, and environmental pollution reduction. These will strengthen the performance of the power system and bring economic value to society. Moreover, many challenges are considered when integrating WP into the distribution system. These include protection device miscoordination, fundamental changes in the network topology, transmission congestion, bidirectional power flow, and harmonic current injections. In this paper, the economic cost and benefit analysis of optimal integration of WP into the distribution networks is investigated through a multi-objective analytical method. The aim is to see whether investment in the WP project is economically profitable and technically viable in the distribution system. The results obtained from the study can be utilised by power system operators, planners and designers as criteria to use WP for stimulating economic development and industrial revolution and can allow independent power producers to make appropriate investment decisions.
This work proposes an approach for the optimal sizing of a cylindrical heaving wave energy converter (WEC). The approach is based on maximising the absorbed power density (APD) of the buoy, with the diameter being the decision variable. Furthermore, two types of buoy shapes were compared to get the best option. The two buoy shapes are the cone cylinder buoy (CCB) and the hemisphere cylinder buoy (HCB). The aim was therefore to determine the best shape and as well as the optimal size of the cylindrical point absorber. To validate the approach, the simulation was performed under Durban (South Africa) sea characteristics of 3.6 m wave significant height and 8.5 s peak period, using the openWEC simulator. The buoy diameter range considered was from 0.5 m to 10 m for both shapes. Simulation results revealed that a diameter of 1 m was the optimal solution for both buoy shapes. Furthermore, the APD method revealed that the HCB was more efficient than the CCB. The power density of the HCB was 1070 W/m2, which was almost double the power density of the CCB, while the two shapes present almost the same absorbed power.
The Namibian energy sector and other energy sectors across the globe are currently in a rapid transformation era that must respond to climate change, which directly affects energy infrastructure’s resilience to the effects of resource scarcities or extreme weather conditions. The energy sector must implement adaptation to guarantee the resilience of vital infrastructure to fulfil its regulatory commitments, which cover the elements of resilience and safety. Through investigating climate change adaptation and mitigation implementation in Namibia, this study validates the existence of these co-benefits where integration is fully observed. It employed a meta-analysis and content analysis to link the observed variables to the most recognised co-benefits. The findings suggest that integration is an efficient way to generate co-benefits that contribute positively to the climate change project. Effective leadership support is one way of realising such integration, either via public-private partnership or energy policy. Namibian energy policy, it is suggested, through voluntary tools and incentives, should create key public-private partnerships and promote management. These recommendations have application beyond the Namibian energy sector, and the lessons learned here could be implemented in scenarios outside of it.
Along with the load-shedding problem that Eskom is having with the current generation system, the operator is forced to use its peaking plants at Ankerlig and Gourikwa in the Western Cape much more than planned. The two plants are set up for dual fuel operations, able to be fuelled with diesel as well as gas. As Eskom does not have access to natural gas, both plants have been fuelled with diesel. For the last three years, 2019 through 2021, Eskom has expended an average of over R4 billion per year on diesel fuel for its peaking plants, with the majority of this at Ankerlig and Gourikwa. For 2022, in their request for a rate increase, Eskom noted that their anticipated diesel fuel expenditures will increase to over R6.5 billion. This could be reduced by more than half if the plants were fuelled with natural gas. The problem Eskom faces is sourcing natural gas to fuel these plants. There has been consideration of liquefied natural gas importation into the Western Cape that could be utilised to fuel the Ankerlig plant. However, the high capital cost for this option has led to delay in the commencement of this project. There is another alternative that can be implemented in a short time-frame, using currently available gas, in the form of liquefied petroleum gas. With this fuel, the Ankerlig peaking plant could be switched to gas fuel and Eskom would have a significant reduction in the cost of fuel. In this study the economic benefit of this fuel change option is analysed.
Most rural Tanzanians have had no access to electricity. But efforts have been made to remedy this, including an extension of the national grid and the establishment of independent power plants in rural areas. The result is a recordable increase of people with access to electricity; however, the realization of reliable power for both consumers and suppliers has remained a puzzle. This paper out to examine the reliability of rural electricity systems based on consumer measures; to find out determinants for system reliability; and examine how outage incidences exacerbate households’ expenditure on backup fuels. Reliability was assessed through a stepwise approach, where a general system reliability index and trend analysis were used. It was found that system reliability was enhanced because consumers only spent 6–15 days per year without electricity due to outages. These are tolerable outages, given the volatility of the rural system. Further, weather, fire outbreaks in bushes, and lightning, significantly determined system reliability. Nonetheless, despite the reasonable reliability, some outage incidences had dragged consumers into unplanned expenditure on backup fuel. It is recommended that there should be a continuous inspection of the system, and the use of supervisory control and data acquisition device on the distribution line for accurate monitoring is imperative.
