Scielo RSS <![CDATA[Journal of Energy in Southern Africa]]> vol. 24 num. 1 lang. es <![CDATA[SciELO Logo]]> <![CDATA[<b>Energy cost versus production as a performance benchmark for analysis of companies</b>]]> In this paper, the efficiency of automobile and auto-parts producing companies is evaluated, using data envelopment analysis. The data envelopment analysis (DEA) is based on the linear programming model. This model needs a series of information by which the effectiveness and ineffectiveness of decision-making units are determined. In the present research, the inputs and outputs of the DEA model are determined by using the basic criteria, and the energy efficiency of automobile and auto-parts producing companies is specified. For this purpose, to evaluate the energy efficiency of the automotive industry and auto parts producing companies, deep2 software has been used. Finally, based on the results of the study, the efficient and inefficient companies have been identified and classified. <![CDATA[<b>Measurement and verification of load shifting interventions for a fridge plant system in South Africa</b>]]> In this paper, the author presents the measurement and verification methodology used to quantify the impacts of load shifting measures that are implemented on large industrial fridge plant systems in South Africa. A summary on the operation of fridge plant systems and the data typically available for baseline development is provided. The author discusses issues surrounding baseline development and service level adjustments for the following two scenarios: 1) the electrical data is available for both the baseline and post-implementation periods; and 2) only the thermal data is available during the baseline period, but both the thermal and electrical data is available during the post-implementation period. Typical results are provided with advantages and disadvantages of both methodologies. <![CDATA[<b>Technologies for recovery of energy from wastewaters: Applicability and potential in South Africa</b>]]> This study explored technologies for recovering energy from wastewater through production of bio-mass, combustion and gasification, generation of biogas, production of bioethanol, heat recovery and microbial fuel cells. A first order desktop analysis of the potential for applying these solutions to waste-waters in South Africa revealed that 3 200 to 9 000 MWth of energy has potential for recovery, equating to at most 7% of South Africa's current electrical power supply. Formal and informal animal husbandry, fruit and beverage industries and domestic blackwater were identified as wastewaters with the greatest potential for energy recovery. Of the reviewed technologies, anaerobic digestion shows applicability to the widest range of feedstocks. Net energy generated, reduction in pollution, and water reclamation are identified as the main benefits, but additional benefits such as certified emission reductions, fertiliser production and the production of secondary products may dictate the economic feasibility. <![CDATA[<b>The significance of relevance trees in the identification of artificial neural networks input vectors</b>]]> In the 1980s a renewed interest in artificial neural networks (ANN) has led to a wide range of applications which included demand forecasting. ANN demand forecasting algorithms were found to be preferable over parametric or also referred to as statistical based techniques. For an ANN demand forecasting algorithm, the demand may be stochastic or deterministic, linear or nonlinear. Comparative studies conducted on the two broad streams of demand forecasting methodologies, namely artificial intelligence methods and statistical methods has revealed that AI methods tend to hide the complexities of correlation analysis. In parametric methods, correlation is found by means of sometimes difficult and rigorous mathematics. Most statistical methods extract and correlate various demand elements which are usually broadly classed into weather and non-weather variables. Several models account for noise and random factors and suggest optimization techniques specific to certain model parameters. However, for an ANN algorithm, the identification of input and output vectors is critical. Predicting the future demand is conducted by observing previous demand values and how underlying factors influence the overall demand. Trend analyses are conducted on these influential variables and a medium and long term forecast model is derived. In order to perform an accurate forecast, the changes in the demand have to be defined in terms of how these input vectors correlate to the final demand. The elements of the input vectors have to be identifiable and quantifiable. This paper proposes a method known as relevance trees to identify critical elements of the input vector. The case study is of a rapid railway operator, namely the Gautrain. <![CDATA[<b>Load-shifting opportunities for typical cement plants</b>]]> Investigations into demand side reductions have been encouraged by the South African electricity utility, Eskom, in sectors with high electricity consumption, such as the cement industry. The South African cement industry is responsible for 5% of the electrical consumption for the mining and industrial sector. It has also been estimated that by 2020 this sector will be ranked fifth for energy savings potential. This paper investigates the potential of a load-shifting (altering energy use method) scheme to reduce evening peak loads and save electrical costs on a raw mill at a South African cement plant. A spread sheet-simulation was performed, which showed that six hours of load-shifting could be achieved, without adversely affecting production. This was corroborated by a pilot study where the load was successfully shifted for six hours over a week-long period. The specific raw mill would achieve a reduction in yearly electrical costs of 2% when employing this load-shifting strategy. The results, however, showed that cost-saving opportunities are highly dependent on the reliability of the mills and on the change in production demand. Therefore, load-shifting schemes have to be flexible on a daily basis to shift load whenever possible. <![CDATA[<b>Optical design of low concentrator photovoltaic modules</b>]]> This paper addresses the necessary procedures that need to be considered when designing an optical sub-system of low concentrator photovoltaic (LCPV) module. CPV systems make use of optical elements and solar tracking to concentrate solar flux onto a photovoltaic (PV) receiver. The performance of a concentrator module is highly dependent on the configuration and alignment of the optical elements in the system. In this study, various design considerations were taken into account to construct a LCPV module that was characterised with respect to optical design and electrical performance. <![CDATA[<b>Thermal modelling of low concentrator photovoltaic systems</b>]]> Efficient thermal management of low concentrator photovoltaic (LCPV) systems will allow maximizing of the power output and may also substantially prolong operating lifetime. For this reason, it is necessary to develop a thorough understanding of the thermal transfer and dissipation mechanisms associated with an LCPV system. The LCPV system under consideration uses a 7-facet reflector optical design, providing a geometric concentration ratio of approximately 4.85. The LCPV system succeeded in increasing the short circuit current from 1A to 5.6A, demonstrating an effective concentration ratio of approximately 4.75. LCPV system temperatures in excess of 80°C were recorded without a thermal management system. A basic thermal model was developed and assessed under various environmental conditions. The effectiveness of a heat-sink, which reduced the temperature difference between the LCPV receiver temperature and the ambient temperature by 37.5%, was also evaluated. The results discussed in this paper will assist the future development of techniques aimed at reducing the high temperatures associated with LCPV systems. <![CDATA[<b>Evaluation of feed-in tariff-schemes in African countries</b>]]> Almost all African countries are planning to increase their power supply capacities and to diversify the resource base of the electricity sector. In sharp contrast to the ambitious objectives, grid connected power plants, based on renewable energies, are very rare except large scale hydropower in African countries. The small number of renewable energy (RE)-plants in Africa shows that a quick diffusion of these technologies cannot be expected from the dynamic of market forces alone. Political support is necessary. By now, feed-in tariffs (FIT) is the most prominent economic instrument promoting renewable energy technologies in the power sector. They are applied in more than 50 countries, among them several African countries like Algeria, Kenya, Uganda, Ghana and Tanzania. The objective of the paper is to investigate the outcome and effectiveness of African FIT-schemes. It is assumed that most of the FIT-schemes in Africa are poorly working because of unfavourable institutional design, insufficient level of FIT rates or obstacles in the process of implementation. Deficiencies in the design of FIT-schemes and the implementation process can be explained by conflicting policy targets like affordable power prices and grid stability but also with an unclear allocation of property rights that can lead to time-consuming negotiations of Power Purchase Agreements. <![CDATA[<b>A concentrating solar power value proposition for South Africa</b>]]> ABSTRACT Concentrating solar power (CSP) offers the potential for a high degree of localization and an alternative strategy to meet electricity demand for South Africa in a future of uncertain conventional resources. The integrated resource plan (IRP) makes strides to introduce renewables to the electricity generation system by 2030, but we argue that the proposed energy mix is too reliant on resources that are not only unsustainable but also at risk in the short to medium term. Coal and other conventional resources may be more limited than originally anticipated, which if true, requires action to be taken soon. CSP is currently the only sustainable and dispatchable energy technology that could domestically supply a significant portion of South Africa's electricity needs. A balanced mix of PV, wind and CSP can provide the energy supply needed in South Africa, but steps are required soon to take advantage of the localization potential and excellent sustainable energy resources. <![CDATA[<b>Efficiency and costs of different concentrated solar power plant configurations for sites in Gauteng and the Northern Cape, South Africa</b>]]> Concentrated solar power (CSP) plants can play a major role in the future South African electricity mix. Today the Independent Power Producer (IPP) Procurement Programme aims to facilitate renewable energy projects to access the South African energy market. In spite of this incentive programme, the future role of CSP plants in South Africa has yet to be defined. Using hourly irradiance data, we present a new method to calculate the expected yield of different parabolic trough plant configurations at a site in each of Gauteng and the Northern Cape, South Africa. We also provide cost estimates of the main plant components and an economic assessment that can be used to demonstrate the feasibility of solar thermal power projects at different sites. We show that the technical configurations, as well as the resulting cost of electricity, are heavily dependent on the location of the plant and how the electricity so generated satisfies demand. Today, lev-elised electricity costs for a CSP plant without storage were found to be between 101 and 1.52 ZAR2010/kWh el, assuming a flexible electricity demand structure. A CSP configuration with Limited Storage produces electricity at costs between 1.39 and 1.90 ZAR2010/kWh el, whereas that with Extended Storage costs between 1.86 and 2.27 ZAR2010/kWh el. We found that until 2040 a decrease in investment costs results in generating costs between 0.73 ZAR2010/kWh el for a CSP plant without storage in Upington and 1.16 ZAR2010/ kWh el for a configuration with Extended Storage in Pretoria. These costs cannot compete, however, with the actual costs of the traditional South African electricity mix. Nevertheless, a more sustainable energy system will require dispatchable power which can be offered by CSP including storage. Our results show that the choice of plant configuration and the electricity demand structure have a significant effect on costs. These results can help policymakers and utilities to benchmark plant performance as a basis for planning. <![CDATA[<b>Potential and future of concentrating solar power in Namibia</b>]]> The Namibian electricity sector has mainly relied on electricity imports from the Southern African Power Pool (SAPP) over the last decade. However, a growth in electricity demand and scarce import options could cause energy shortages. Therefore, new power plants ought to be commissioned in the near future to avoid the forecasted energy crisis. In this context, Concentrating Solar Power (CSP) generation is regarded as an appropriate alternative to conventional energy technologies, particularly for the excellent solar regime available in Namibia. The study presents a GIS analysis that identifies suitable areas for CSP establishment. A broad range of geographical parameters such as solar radiation, topography, hydrology or land use are examined. The calculations show that the CSP ceiling generation in Namibia is equivalent to 70% of the worldwide electricity production. Moreover, the study offers a scenario analysis where concrete CSP alternatives are compared to coal-fired plant projects developed by the national power utility. Meteonorm and System Advisor Model (SAM) are used to design CSP alternatives located in the area offering the best combination between high solar irradiation and short distances to the infrastructures. Despite the affordability concern which has to be addressed with sound financial instruments, CSP represents a seminal opportunity for the energy sector in Namibia.