Scielo RSS <![CDATA[Journal of Energy in Southern Africa]]> vol. 23 num. 4 lang. es <![CDATA[SciELO Logo]]> <![CDATA[<b>Application of artificial neural networks for short term wind speed forecasting in Mardin, Turkey</b>]]> Artificial neural network models were used for short term wind speed forecasting in the Mardin area, located in the Southeast Anatolia region of Turkey. Using data that was obtained from the State Meteorological Service and that encompassed a ten year period, short term wind speed forecasting for the Mardin area was performed. A number of different ANN models were developed in this study. The model with 60 neurons is the most successful model for short term wind speed forecasting. The mean squared error and approximation values for training of this model were 0.378088 and 0.970490, respectively. The ANN models developed in the study have produced satisfactory results. The most successful among those models constitutes a model that can be used by the Mardin Electric Utility Control Centre. <![CDATA[<b>Estimating greenhouse gas emissions associated with achieving universal access to electricity for all households in South Africa</b>]]> Climate change, energy security and achieving universal electricity access for all households are all pressing issues that South Africa must address. These objectives need not be trade-offs, however, and achieving electricity access for the poor does not justify the building of large coal-fired power stations or threaten South Africa's climate change objectives. This paper estimates the electricity demand from the residential sector to 2020 resulting from universal access, and finds that electricity for low-income households would constitute only a small addition to total electricity demand and would represent only a minor portion of output from the coal-fired power station, Medupi. Furthermore, emissions from the additional electricity consumed by newly connected households would have a negligible impact on South Africa's emissions profile. <![CDATA[<b>A techno-economic model for optimising component sizing and energy dispatch strategy for PV-diesel-battery hybrid power systems</b>]]> This paper presents the development and application of a simple spreadsheet-based simulation model for sizing, energy performance evaluation and economic analysis of PV-diesel-battery power supply systems. The model is employed to generate a set of sizing curves that define the design space for hybrid systems using dimensionless generator component size variables, for a specified supply reliability and diesel energy dispatch strategy. The component size combination with the least unit cost of energy is selected among the many possible combinations satisfying a desired loss-of-load probability. Storage battery and diesel generator lifespan, as well as generator fuel efficiency, which depend on the operational loading stress of these components, are recognised as important variables in the economics of the system. The lifespan of the battery is premised to depend on the depth and rate of discharge of the operating cycles, while both the diesel generator lifespan and fuel efficiency are dependent on the degree and frequency of partial loading. The choice of diesel generator dispatch strategy was shown to be another important factor influencing the energy performance and economics of the system. The outputs of the model reveal several important sizing, operational and economic characteristics of the systems, and enables appraisal of comparative advantage of different types of designs and operational strategies. The merits of the hybrid concept are well demonstrated by the study results. <![CDATA[<b>Economic growth and electricity consumption: Auto regressive distributed lag analysis</b>]]> Knowledge of the direction of causality between electricity consumption and economic growth is of primary importance if appropriate energy policies and energy conservation measures are to be devised. This study estimates the causality relationship between electricity consumption and economic growth in per capita and aggregate levels. The study uses the price and income elasticities of total electricity demand and industrial demand by using the auto regressive distributed lag (ARDL) method for some developed and developing countries, including the US, UK, Canada, Japan, China, India, Brazil, Italy, France, Turkey and South Africa. There is evidence to support the growth hypothesis for the US, China, Canada and Brazil. There is evidence to support the conservation hypothesis for India, Turkey, South Africa, Japan, UK, France and Italy. <![CDATA[<b>Challenges for local community development in private sector-led renewable energy projects in South Africa: An evolving approach</b>]]> The Renewable Energy Independent Power Producer Procurement Programme in South Africa is intended to support the uptake of renewable energy, help address the current energy supply crisis and mitigate greenhouse gas emissions. Notably, it also requires project developers to engage with socio-economic development at the local level. The distributed nature of renewable energy generation may induce a more geographically dispersed pattern of development, and renewable energy sites can be highly suited to rural locations with otherwise poor potential to attract local inward investment. Socio-economic development and enterprise development are two of seven economic development elements in the programme. In order to prepare a bid submission, project developers have to assess local socio-economic needs around their project site and develop strategies on how to address these. This paper investigates the challenges for local community development. The research is based on case studies and presents findings from the perspective of a research team working alongside project developers. Early findings indicate that there are potential community benefits from commercial wind projects, providing an appropriate community engagement process that is aligned with the project cycle determined by the tender process and engineering requirements. The Passive Community Needs Assessment approach is introduced as a possible solution. <![CDATA[<b>Projecting the external health costs of a coal-fired power plant: The case of Kusile</b>]]> We examine an important subset of the expected health costs associated with the commissioning of Kusile, a new coal-fired electricity generation plant in South Africa. The subset of health impacts focuses on sulphur dioxides, nitrous oxides and large particulate matter (greater than 10 mm). The analysis makes use of the Impact Pathway Approach combined with the data transfer methodology. The plant, which is expected to contribute 4 800 MW of additional electricity to the South African grid is found to have modest health impacts, partly due to the limited additional pollutant emissions expected at the plant. Specifically, additional localised external health costs are found to be in the region of 0.09c/kWh to 6.08c/kWh. Limitations of the analysis are also examined. <![CDATA[<b>Climate change: The opportunity cost of Medupi and Kusile power stations</b>]]> Eskom has embarked on the construction of two coal-fired power stations (Medupi and Kusile) that use a new dry-cooling process with flue gas desulphurisation (FGD). While the introduction of these new technologies does have meaningful environmental benefits beyond the conventional coal-fired power stations, they still emit greenhouse gasses. The question at stake here is what is the opportunity cost, viewed from a climate change perspective, of these two new power stations? This question is answered by considering the carbon footprint of the two power stations and a range of unit values for CO2. From this analysis, it is evident that the most likely range of the opportunity cost is between R6.3 billion and R10.7 billion per year. This converts to a damage cost of between R0.10 and R0.17/kWh when assuming a net combined generation capacity of 8 677 MW and a load factor of 85%. <![CDATA[<b>Estimating the opportunity cost of water for the Kusile and Medupi coal-fired electricity power plants in South Africa</b>]]> In South Africa, water is considered a limited source, not only because of the country's arid nature, but also because of the relatively skew distribution of the resource and the fact that 98% of the resource is already allocated. Eskom, the South African electricity supplier, commenced with the construction of two new coal-fired power stations namely Kusile and Medupi. The question is: what is the opportunity cost of investing in these power stations from a water perspective? We do not argue here against the need for power plants and additional electricity generation capacity per se, but consider the opportunity cost of using this specific technology. We estimate the shadow price of water for different power generation technologies as an indicator of the opportunity cost of water. We apply a production function approach for a baseline case (coal-fired power generation using the Medupi and Kusile parameters), and four alternative technologies. The only alternative that performs worse than the baseline case is the traditional wet-cooling coal-fired power process. The baseline case, however, does show a high opportunity cost when compared to renewable alternatives (solar, wind and biomass) ranging from R0.66/kWh (biomass) to R0.83/kWh (solar) to R1.31/kWh (wind). <![CDATA[<b>The external costs of coal mining: The case of collieries supplying Kusile power station</b>]]> The aim of this paper was to quantify the external costs of mining and transporting coal to the Kusile coal-fired power station in eMalahleni. Monetary values were estimated for a number of impacts including its contribution to climate change, human health effects of classic air pollutants, mortality and morbidity, impacts of water pollution and water consumption. The results of the study disclosed that coal mining and transportation will inflict costs to both the environment and humans of between R6 538 million and R12 690 million per annum, or between 20.24 c/kWh and 39.3 c/kWh sent out. The external effect of water consumption (opportunity costs of water) constitutes over 90% of the total cost, followed by global warming damage costs and ecosystem services lost due to coal mining. The estimated externality cost is approximately between 50% and 100% of the current average electricity price.