Scielo RSS <![CDATA[Journal of Energy in Southern Africa]]> vol. 28 num. 4 lang. pt <![CDATA[SciELO Logo]]> <![CDATA[<b>Experimental investigation of biogas production from feedlot cattle manure</b>]]> Biogas can be generated from biomass in an anaerobic digestion process and used to generate electricity and heat as an alternative energy source to fossil fuel-generated electricity. This study investigated biogas generation from cattle manure dried for periods up to 40 days. Manure samples were analysed for gas yield using the biochemical methane production test. The biogas volume produced by manure samples aged for periods up to 40 days after seeding with cattle rumen fluid was measured as a function of time until there was no further measurable gas production. The biogas was analysed for methane and carbon dioxide content using a gas chromatograph. The corresponding cumulative net biogas yield ranged from 154 to 369 Nml/g.VS respectively. The test results showed that an average of 240 Nml/g.VS of biogas can be produced from cattle manure that is less than 40 days old, with an average methane and carbon dioxide percentage of 63% and 31% respectively. Within 3 to 4 days the manure samples generated 80% of the final biogas volume. The drying process was found to occur at a constant rate per unit area, regardless of the manure thickness up to thickness of200 mm. Biogas formation closely followed the Gompertz equation. There was no significant difference in the biogas production nor biogas production rate for cattle feedlot manure that was fresh up until aging to 40 days. <![CDATA[<b>Decarbonisation and the transport sector: A socio-economic analysis of transport sector futures in South Africa</b>]]> Globally, governments are investigating transport solutions that not only reduce their national emissions but also decrease their reliance on energy imports and increase clean air in cities and towns. A transition in the transport sector is seemingly inevitable considering these priorities. This study outlines some key socio-economic implications of a transition in South Africa's transport system, building on work previously done. The focus was on a rapid decarbonisation of the South African economy and the potential impacts of implementing efficiency improvements in the transport sector, including mode-switching. The overall finding was that a more ambitious decarbonisation target would have marginal impact on the economy relative to South Africa's nationally-determined contribution. It was further found that the implementation of efficiency improvements and changes in behaviour (decreased mileage, increased occupancy, increased rail use and increased use of public transport) could significantly reduce the burden on the economy of a higher GHG emission reduction target. <![CDATA[<b>Crop residues as a potential renewable energy source for Malawi's cement industry</b>]]> Crop residues have been undervalued as a source of renewable energy to displace coal in the national energy mix for greenhouse emission reduction in Malawi. Switching to crop residues as an alternative energy source for energy-intensive industries such as cement manufacturing is hampered by uncertainties in crop residue availability, cost and quality. In this study, future demand for energy and availability of crop residues was assessed, based on data at the sub-national level. Detailed energy potentials from crop residues were computed for eight agricultural divisions. The results showed that the projected total energy demands in 2020, 2025 and 2030 were approximately 177 810 TJ, 184 210 TJ and 194 096 TJ respectively. The highest supply potentials were found to be in the central and southern regions of Malawi, coinciding with the locations of the two clinker plants. Crop residues could meet 45-57% of the national total energy demand. The demand from the cement industry is only 0.8% of the estimated biomass energy potential. At an annual production of 600 000 t of clinker and 20% biomass co-firing with coal, 18 562 t of coal consumption would be avoided and 46 1281 of carbon dioxide emission reduction achieved per year. For sustainability, holistic planning and implementation would be necessary to ensure the needs of various users of crop residues are met. Furthermore, there would be a need to address social, economic and environmental barriers of the crop residue-based biomass energy supply chain. Future research should focus on local residue-to-product ratios and their calorific values. Highlights • Crop residues are an under-exploited renewable energy source in Malawi. • A potential substitute for coal in the cement industry is crop residues. • Crop residues could meet about 50% of Malawi's energy demand. • There is need for a holistic approach before a roll-out. <![CDATA[<b>Design of a prototype generator based on piezoelectric power generation for vibration energy harvesting</b>]]> The concept of harvesting energy in the ambient environment arouses great interest because of the demand for wireless sensing devices and low-power electronics without external power supply. Harvesting energy by vibration with piezoelectric materials can be used to convert mechanical energy into electrical energy that can be stored and used to power other devices. This conversion of vibrations (mechanical energy) to electrical energy using piezoelectric materials is an exciting and rapidly developing area of research with a widening range of applications constantly materialising. In this context, the goal of this paper is to develop a comprehensive prototype generator that can harvest vibration energy and convert it to electrical energy by providing the output power for optimisation and its performance. Two setups of prototype are used: a cantilever beam with tip mass at the end, and a cantilever beam without tip mass at the end. Data from the experiment is compared and analysed using MatLab. The results show that the power output of the prototype with the tip mass is greater than the power output without the tip mass. The experimental results led to a power optimisation from that prototype by different characteristic of piezoelectric ceramic plate. <![CDATA[<b>Transient stability control by means of under-frequency load shedding and a hybrid control scheme</b>]]> An electrical network constantly faces unforeseen events such as faults on lines, loss of load and loss of generation. Under-frequency load shedding and generator tripping are traditional methods used to stabilise a network when a transient fault occurs. These methods will prevent any network instability by shedding load or tripping the most critical generator at a calculated time when required. By executing these methods, the network can be stabilised in terms of balancing the generation and the load of a power system. A hybrid control scheme is proposed where the traditional methods are combined to reduce the stress levels exerted on the network and to minimise the load to be shed. <![CDATA[<b>Forecasting medium-term electricity demand in a South African electric power supply system</b>]]> The paper discusses an application of generalised additive models (GAMs) in predicting medium-term hourly electricity demand using South African data for 2009 to 2013. Variable selection was done using least absolute shrinkage and selection operator (Lasso) via hierarchical interactions, resulting in a model called GAM-Lasso. The GAM-Lasso model was then extended by including tensor product interactions to yield a second model, called GAM-te-Lasso. Comparative analyses of these two models were done with a gradient-boosting model to act as a benchmark model and the third model. The forecasts from the three models were combined using a forecast combination algorithm where the average loss suffered by the models was based on the pinball loss function. The results showed significantly improved accuracy of forecasts, making this study a useful tool for decision-makers and system operators in power utility companies, particularly in maintenance planning including medium-term risk assessment. A major contribution of this paper is the inclusion of a nonlinear trend. Another contribution is the inclusion of temperature based on two thermal regions of South Africa. <![CDATA[<b>Estimation of extreme inter-day changes to peak electricity demand using Markov chain analysis: A comparative analysis with extreme value theory</b>]]> Uncertainty in electricity demand is caused by many factors. Large changes are usually attributed to extreme weather conditions and the general random usage of electricity by consumers. More understanding requires a detailed analysis using a stochastic process approach. This paper presents a Markov chain analysis to determine stationary distributions (steady state probabilities) of large daily changes in peak electricity demand. Such large changes pose challenges to system operators in the scheduling and dispatching of electrical energy to consumers. The analysis used on South African daily peak electricity demand data from 2000 to 2011 and on a simple two-state discrete-time Markov chain modelling framework was adopted to estimate steady-state probabilities of two states: positive inter-day changes (increases) and negative inter-day changes (decreases). This was extended to a three-state Markov chain by distinguishing small positive changes and extreme large positive changes. For the negative changes, a decrease state was defined. Empirical results showed that the steady state probability for an increase was 0.4022 for the two-state problem, giving a return period of 2.5 days. For the three state problem, the steady state probability of an extreme increase was 0.0234 with a return period of 43 days, giving approximately nine days in a year that experience extreme inter-day increases in electricity demand. Such an analysis was found to be important for planning, load shifting, load flow analysis and scheduling of electricity, particularly during peak periods. <![CDATA[<b>Predicting clear-sky global horizontal irradiance at eight locations in South Africa using four models</b>]]> Solar radiation under clear-sky conditions provides information about the maximum possible magnitude of the solar resource available at a location of interest. This information is useful for determining the limits of solar energy use in applications such as thermal and electrical energy generation. Measurements of solar irradiance to provide this information are limited by the associated cost. It is therefore of great interest and importance to develop models that generate these data in lieu of measurements. This study focused on four such models: Ineichen-Perez (I-P), European Solar Radiation Atlas model (ESRA), multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) models. These models were calibrated and tested using solar irradiance data measured at eight different locations in South Africa. The I-P model showed the best performance, recording relative root mean square errors of less than 2% across all hours, months and locations. The performances of the MLPNN and RBFNN were poor when averaged over all stations, but tended to show performance similar to that of the I-P model for some of the stations. The ESRA model showed performance that was in between that of the Artificial Neural Networks and that of the I-P model.