Abstract
D.Ing
It is every person’s social responsibility to ensure that electrical energy is used as efficiently as possible. This is as a result of the considerable fossil fuels that are currently required to generate electricity. These fuels are available in limited supply on Earth and result in air pollution when consumed in the electrical energy generation process. Moreover, as scarcity increases, not just in fuel reserves, but also in electricity infrastructure such as servitudes, generation capacity etc, the costs of electricity also rises. This then brings about an opportunity to reduce input costs if the electrical energy is utilized as efficiently as possible. This can however only be done by the application of a suitable strategy.
This thesis develops an electrical load management (ELM) strategy which may be effective in reducing input costs, by reducing electrical energy costs. This strategy has it’s foundation in tried-and-tested ELM strategies (albeit called by other names such as demand-side management (DSM) and Energy Management (EM)) developed by the world’s foremost utility research organization called EPRI over a number of decades, thereby ensuring, to some extent, the success of the proposed strategy. The strategy has been tested, in its constituent parts, in a real world environment in the South African mining industry. The examples of the sub-elements that have been tested in the industry are the artificial neural network (ANN) for short-term forecasting; the statistical regression technique for short-term load forecasting; the analysis of the external factors affecting the electricity supply industry and also the comparison of electricity tariffs in the mining industry.
The validity of the strategy is further enhanced by the involvement of Technology Managers within the mining industry which have been involved with ELM in the mining industry for a number of years. Their input was solicited via an in-depth survey which was conducted in this industry. This survey represents the ELM strategy currently in existence of: - 62 shafts or open pit operations, 44 process plants and 5 smelter operations. The largest mining groups in South Africa were involved in this survey so that this survey represents: amongst others, 40% of the gold mining industry, 62% of the platinum mining industry and 95% of the diamond mining industry. The collective experience represented by the survey is equivalent to 67 man-years in ELM in the mining industry.
Electricity tariffs are the means by which benefits for electrical load management are obtained. It thus warranted an analysis of all the factors affecting the electricity tariffs and in particular the factors affecting the price of electricity. To this end the Electricity Supply Industry (ESI) was analyzed in-depth and proactively to identify the external factors which may affect the price of electricity.
Production intrusions may not be tolerated in the mining industry and as these intrusions have been the major cause for abandoning such ELM strategies previously, an electrical load model with production correlation was developed in this research which affords production a very high priority in the ELM strategy. Moreover, this load model, which is a key element of the ELM strategy in this thesis, forecasts the electrical efficiency of a mine in the near future. The effect of this efficiency forecast is to give management a real-time and proactive tool by which to make decisions. This approach avoids potentially large inefficiencies on the overall mine load such as when the electrical efficiency was only checked at the end of each month.
This model may be used either in real-time control mode or in simulation mode to test various ELM initiatives before they are implemented. The model has either a statistical-regression based load-forecasting algorithm or an Artificial Neural Network (ANN) load-forecasting algorithm at its core. The choice of which forecasting methodology is used is determined by the value of the Pearson’s rank correlation coefficient for a set of test data.
The latest prevailing ELM technologies have also been incorporated into a matrix for easy identification. The matrix should assist with the implementation of this ELM strategy. Not all of the technologies found in the matrix result in control of the mining load for ELM initiatives such as: “peak-clipping”, “load-shifting” or “valley-filling”. Some of these technologies result in “conservation” of the electrical energy by the application of newer and more efficient techniques to perform the necessary activities found on a typical mine (drilling, ventilation, cooling etc.).
A complete strategy for ELM in the South African mining industry is thus developed in this thesis which overcomes two of the most serious pitfalls associated with previous strategies. These pitfalls being, the inadequate focus on production in those strategies and also the lack of real-time, efficiency-forecasting of the overall mine load. The strategy also focuses the potential Electrical Load Manager on the key steps of this process, by means of an intuitive, step-by-step approach. It is grounded in the demand-side management (DSM) experiences of the past, enhanced by actual case studies of the sub-elements in the mining industry and has been ratified by the involvement of very experienced Technology Managers active in ELM in South African mining industry.