Thesis (M. Dev.) -- University of Limpopo, 2002 / Refer to document

Thesis (MBA (Business Management))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: This research report discusses the development of a decision-making matrix during power crises. South Africa comes from a situation of excess supply and is currently in a situation where electricity demand exceeds supply. This report will provide the background of how the current electricity crisis came to be and what the future of the industry will look like. As electricity shortages is a worldwide issue, the study will explore international countries' experience of supply shortages and extract lessons learnt and best practices that can be applied in the South African context. The study reviews available solutions that exist to address the electricity crisis in South Africa and explores alternative energy sources that could be applied in South Africa. To establish an effective decision making tool for electricity shortage response mechanisms, the key decision making criteria are discussed and explained. A decision making matrix brings together all the available solutions and decision-making criteria based on the decision maker's relative importance placed on the considerations and the resultant impact on these considerations. The results of the decision-making matrix directs the decision maker to the least negative impact solution and provides a basis on which to make sound decisions during a time when quick and effective decisions will determine the success and sustainability of the power system. / AFRIKAANSE OPSOMMING: Hierdie navorsingsverslag bespreek die ontwikkeling van 'n besluitnemingsmodel wat aangewend kan word tydens kritiese tekorte in elekrisiteitsvoorsiening op 'n nasionale basis. Die elektrisiteits- ontwikkelingsvermoë in Suid-Afrika het beweeg vanaf 'n oorskot na 'n posisie waar daar nie in die aanvraag voorsien kan word nie. Hierdie navorsingsverslag skets die agtergrond van wat aanleiding gegee het tot die huidige elektrisiteitskrisis en wat die toekoms inhou vir elektrisiteitsvoorsiening in die algemeen. Die gebrek aan elektrisiteitsontwikkelingsvermoë is 'n wêreldwye tendens en die lesse wat ander lande geleer het tesame met hul beste praktyke word oorweeg binne die Suid-Afrikaanse konteks. Beskikbare oplossings wat op die manier geidentifiseer word, word ontleed, tesame met moontlike alternatiewe energiebronne wat in Suid-Afrika aangewend kan word. Die kern besluitnemingskriteria word bespreek en ontleed om sodoende 'n proses daar te stel waarvolgens die besluitnemingsmodel wat die mees effektiewe oplossing in tye van kritiese elektirsiteitstekorte sal voortbring te ontwikkel. Al die beskikbare en werkbare opsies tydens die periode van elektristeitstekorte word saamgevoeg binne die besluitnemingsmodel om die toepaslikheid daarvan op te weeg teenoor die relatiewe gewig wat die besluitnemer toeken aan al die onderliggende faktore. Die besluitnemingsmodel ondersteun die besluitnemer om, tydens 'n krisis in elektrisiteitsvoersiening, 'n vinnige en doeltreffende besluit te neem wat die minste negatiewe impak het op ekonomiese aktiwiteite en die sukses en stabiliteit van die elektrisiteitsvoorsieningsnetwerk waarborg.

M. Tech. Electrical Engineering. / This study analyses the technologies with potential to generate this much needed power and transmit the electricity from the point of generation to the end use location. South Africa has abundant supplies of indigenous primary energy resources such as coal, wind, and solar. The global shift from regulation of electricity and other energy markets, community awareness of environmental impact caused by large conventional power plants, and advances in renewable and high efficiency technologies have led to greater interest in DG technologies based on Renewable Energy Sources (RES). In this study, DG based on renewable energy sources (wind, solar and energy from the waste) is considered. Their potential contribution within Tshwane is assessed.This research project presents a technical analysis of the Tshwane Electricity Network incorporating distributed renewable energy sources such as biomass, wind, solar and small-hydro. Their sizing and placement within the distribution systems is analysed in order to minimise the electrical network losses and to guarantee acceptable voltage profile. The optimisation process is a load flow based algorithm.

Thesis (MTech (Quality))--Cape Peninsula University of Technology, 2009. / There is a South African Government initiative to use State-owned Enterprises (SOE's) to roll out a programme for the development and stimulation of local small businesses in South Africa. The state has requested SOE's to set targets on a voluntary basis to increase trade with small businesses, with the purpose of developing small enterprises to eventually enhance skills transfer, training and employment. However, when large customers such as Eskom Nuclear Generation require ISO certification as a prerequisite for a supplier to provide goods and/or services to them, most small businesses are unable to comply. The requirement of IS09000 compliance inhibits the ability of most small businesses to compete with their larger counterparts. Small businesses constitute as much as 90% of most world economies. They have many advantages to offer customers, such as a high level of flexibility, innovation and responsiveness to customer needs. These attributes can introduce healthy competition to the supply chain. Small businesses, by their very nature experience more risks, such as a higher vulnerability to volatile market forces and skills loss. In addition, they are generally less specialised.

Thesis (MTech (Quality)--Cape Peninsula University of Technology, 2009 / There is a South African Government initiative to use State-owned Enterprises (SOE’s) to roll out a programme for the development and stimulation of local small businesses in South Africa. The state has requested SOE’s to set targets on a voluntary basis to increase trade with small businesses, with the purpose of developing small enterprises to eventually enhance skills transfer, training and employment. However, when large customers such as Eskom Nuclear Generation require ISO certification as a prerequisite for a supplier to provide goods and/or services to them, most small businesses are unable to comply. The requirement of ISO9000 compliance inhibits the ability of most small businesses to compete with their larger counterparts. Small businesses constitute as much as 90% of most world economies. They have many advantages to offer customers, such as a high level of flexibility, innovation and responsiveness to customer needs. These attributes can introduce healthy competition to the supply chain. Small businesses, by their very nature experience more risks, such as a higher vulnerability to volatile market forces and skills loss. In addition, they are generally less specialised. They are under continuous competitive pressure, and are generally not able to provide assurance of a sustainable product over a longer period. Although there is an imperative to develop and use small suppliers, they introduce higher risk to the supply chain. The primary research objective of this dissertation is to develop a robust model to identify risks inherent to small businesses, and to propose measures to mitigate such risks. A classification of problems with small suppliers that have occurred at Koeberg Nuclear Power Station over a period of 3 years (from June 2005 to May 2008), will form the basis of the research methodology. The anticipated findings of the research include the following. _ Several common critical issues of failure will be identified in the internal processes of small suppliers, with variations between types of suppliers, which will indicate which elements within the context of ISO9000 can be applied to address shortcoming in the suppliers’ processes. _ A matrix will be compiled from this by which the customer can identify the type of supplier, the types of risks inherent to that supplier, and which elements of ISO9000 the customer should insist upon to be adopted into an elementary quality management system of that small supplier. This should be executed as part of a larger supplier development programme.

MSc (Statistics) / Department of Statistics / Generally, short term load forecasting is essential for any power generating utility. In this dissertation the main objective was to develop short term load forecasting models for the peak demand periods (i.e. from 18:00 to 20:00 hours) in South Africa using. Quantile semi-parametric additive models were proposed and used to forecast electricity demand during peak hours. In addition to this, forecasts obtained were then used to nd an optimal number of generating units to commit (switch on or o ) daily in order to produce the required electricity demand at minimal costs. A mixed integer linear programming technique was used to nd an optimal number of units to commit. Driving factors such as calendar e ects, temperature, etc. were used as predictors in building these models. Variable selection was done using the least absolute shrinkage and selection operator (Lasso). A feasible solution to the unit commitment problem will help utilities meet the demand at minimal costs. This information will be helpful to South Africa's national power utility, Eskom. / NRF

This study estimates the price and income elasticity coefficients of electricity demand in the mining sector of South Africa for the period ranging from April 2006 to March 2019. A time varying parameter (TVP) model with the Kalman filter is applied to monitor the evolution of the elasticity estimates. The TVP model can provide a robust estimation of elasticities and can detect any outliers and structural breaks. The results indicate that income and price elasticity coefficients of electricity demand are lower than unit. The income elasticity of demand has a positive sign and it is statistically significant. This indicates that mining production – used as a proxy for mining income – is a significant determinant of electricity consumption in the mining sector. In its final state income elasticity is estimated at 0.15 per cent. On the contrary, price does not play a significant role in explaining electricity demand. In fact, the price elasticity coefficient was found to be positive which is contrary to normal economic convention. This lack of response is attributed mainly to the mining sector’s inability to respond, rather than an unwillingness to do so. A fixed coefficient model in a form of Ordinary Least Squares (OLS) is used as a benchmark model to estimate average price and income elasticity coefficients for the period. The results of the OLS regression model confirm that price does not play a significant role in explaining electricity consumption in the mining sector. An average price elasticity coefficient of -0.007 has been estimated. Income elasticity was estimated at 0.11 for the period under review. The CUSUM of squares test indicate that parameters of the model are unstable. The Chow test confirms 2009 as a breakpoint in the data series. This means that elasticity coefficients of electricity demand in the mining sector are time variant. Thus the OLS results cannot be relied upon for inference purposes. The Kalman filter results are superior. This study cautions policy makers not to interpret the seeming lack of response to price changes as an indication that further prices increases could be implemented without hampering electricity consumption in the sector. Furthermore, it recommends that the electricity pricing policy should take into account both the negative impacts of rapid price increases and the need to invest in long-term electricity infrastructure in order to improve the security of energy supply. A long term electricity price path should be introduced in order to provide certainty and predictability in the price trajectory. This would allow all sectors of the economy sufficient time and space to make investment and operational decisions that would have the least adverse effects on economic growth and job creation. / Economics / M. Com. (Economics)

MSc (Statistics) / Department of Statistics / Daily peak electricity data from ESKOM, South African power utility company for the period, January 1997 to December 2013 consisting of 6209 observations were used in this dissertation. Since 1994, the increased electricity demand has led to sustainability issues in South Africa. In addition, the electricity demand continues to rise everyday due to a variety of driving factors. Considering this, if the electricity generating capacity in South Africa does not show potential signs of meeting the country’s demands in the subsequent years, this may have a significant impact on the national grid causing it to operate in a risky and vulnerable state, leading to disturbances, such as load shedding as experienced during the past few years. In particular, it is of greater interest to have sufficient information about the extreme value of the stochastic load process in time for proper planning, designing the generation and distribution system, and the storage devices as these would ensure efficiency in the electrical energy in order to maintain discipline in the grid systems. More importantly, electricity is an important commodity used mainly as a source of energy in industrial, residential and commercial sectors. Effective monitoring of electricity demand is of great importance because demand that exceeds maximum power generated will lead to power outage and load shedding. It is in the light of this that the study seeks to assess the frequency of occurrence of extreme peak electricity demand in order to come up with a full electricity demand distribution capable of managing uncertainties in the grid system. In order to achieve stationarity in the daily peak electricity demand (DPED), we apply a penalized regression cubic smoothing spline to ensure the data is non-linearly detrended. The R package “evmix” is used to estimate the thresholds using the bounded corrected kernel density plot. The non-linear detrended datasets were divided into summer, spring, winter and autumn according to the calender dates in the Southern Hemisphere for frequency analysis. The data is declustered using Ferro and Segers automatic declustering method. The cluster maxima is extracted using the R package “evd”. We fit Poisson GPD and stationary point process to the cluster maxima and the intensity function of the point process which measures the frequency of occurrence of the daily peak electricity demand per year is calculated for each dataset. The formal goodness-of-fit test based on Cramer-Von Mises statistics and Anderson-Darling statistics supported the null hypothesis that each dataset follow Poisson GPD (σ, ξ) at 5 percent level of significance. The modelling framework, which is easily extensible to other peak load parameters, is based on the assumption that peak power follows a Poisson process. The parameters of the developed i models were estimated using the Maximum Likelihood. The usual asymptotic properties underlying the Poisson GPD were satisfied by the model. / NRF