Carbon tax, increased reactive power charges, tariff increases and the Energy Conservation Scheme (ECS) are some of the worrying electricity cost risks faced by large South African industries. Some of these proposed cost risks are not enforced as yet, but once approved could threaten company financial viability and thousands of jobs. Managing multiple cost risks associated with electricity consumption at several mines can be laborious and complex. This is largely due to circumstantial rules related to each potential electricity cost risk and unique mine characteristic. To limit the electricity cost risks for a mining company, clear strategies and focus areas need to be identified. No literature was found that provides a simplified integrated electricity cost risk and mitigation strategy for the South African gold mining industry. Previous studies only focused on a single mine or mining subsystem. Literature pertaining to potential risks is available, however the exact impact and mitigation on the gold mining industry has yet to be determined. The aim of this study is to accurately predict the impact of electricity cost risks and identify strategies that could alleviate their cost implications. Electricity consumption and installed capacities were used to benchmark mines and categorise them according to investigated risks. The benchmarked results provided an accurate starting point to identify best practices and develop electricity cost saving strategies. This study will highlight the additional benefits that can be obtained by managing electricity usage for a group of mines or mining company. Newly developed models are used to quantify savings on pumping, compressed air and cooling systems. To manage and report on the potential risks and mitigation, an ISO 50001 based energy management system was developed and implemented. The applied and developed models can also be adjusted to review and manage the potential cost risks on other types of mines. Derived risk and mitigation models were further used to quantify the impact on one of the largest gold mining companies in South Africa. These models indicate a potential annual price increase of 12%, while mitigation strategies could reduce the electricity consumption by more than 7%. Mitigation savings resulted from proposed projects as well as behavioural change-induced savings due to improved management. Over a five-year period the projects identified could result in electricity costs savings of between R675-million and R819-million. / PhD (Electrical Engineering), North-West University, Potchefstroom Campus, 2014

Carbon tax, increased reactive power charges, tariff increases and the Energy Conservation Scheme (ECS) are some of the worrying electricity cost risks faced by large South African industries. Some of these proposed cost risks are not enforced as yet, but once approved could threaten company financial viability and thousands of jobs. Managing multiple cost risks associated with electricity consumption at several mines can be laborious and complex. This is largely due to circumstantial rules related to each potential electricity cost risk and unique mine characteristic. To limit the electricity cost risks for a mining company, clear strategies and focus areas need to be identified. No literature was found that provides a simplified integrated electricity cost risk and mitigation strategy for the South African gold mining industry. Previous studies only focused on a single mine or mining subsystem. Literature pertaining to potential risks is available, however the exact impact and mitigation on the gold mining industry has yet to be determined. The aim of this study is to accurately predict the impact of electricity cost risks and identify strategies that could alleviate their cost implications. Electricity consumption and installed capacities were used to benchmark mines and categorise them according to investigated risks. The benchmarked results provided an accurate starting point to identify best practices and develop electricity cost saving strategies. This study will highlight the additional benefits that can be obtained by managing electricity usage for a group of mines or mining company. Newly developed models are used to quantify savings on pumping, compressed air and cooling systems. To manage and report on the potential risks and mitigation, an ISO 50001 based energy management system was developed and implemented. The applied and developed models can also be adjusted to review and manage the potential cost risks on other types of mines. Derived risk and mitigation models were further used to quantify the impact on one of the largest gold mining companies in South Africa. These models indicate a potential annual price increase of 12%, while mitigation strategies could reduce the electricity consumption by more than 7%. Mitigation savings resulted from proposed projects as well as behavioural change-induced savings due to improved management. Over a five-year period the projects identified could result in electricity costs savings of between R675-million and R819-million. / PhD (Electrical Engineering), North-West University, Potchefstroom Campus, 2014

Costs saving measures are important for South African gold producers due to increasing energy costs and decreasing production volumes. Demand Side Management (DSM) is an effective strategy to reduce electricity consumption and costs. DSM projects have been implemented widely on South African mining systems such as pumping, refrigeration, rock transport and compressed air. Implementations have, however, been limited on gold processing plants despite the significant amounts of energy that this section consumes. The main objective of gold processing plants is production orientated and energy management is not a primary focus. This rationale is re-evaluated owing to high electricity price inflation and availability of DSM incentives. This study investigated the cost saving potential of DSM interventions on gold plants. Electrical load management was identified as a key opportunity that can deliver substantial cost savings. These savings were shown to be feasible in respect of the required capital expenditure, effort of implementation and maintenance of operational targets. Investigation procedures were compiled to identify feasible load management opportunities. The most potential for electricity cost savings was identified on comminution equipment. Consequently, a methodology was developed to implement electrical load management on the identified sections. The methodology proposed simulation techniques that enabled load management and subsequent electricity cost optimisation through production planning. Two electrical load management case studies were successfully implemented on comminution equipment at two gold processing plants. Peak period load shift of 3.6 MW and 0.6 MW, respectively, was achieved on average for a period of three months. The annual cost savings of these applications could amount to R1.4-million and R 660 000. This results in specific electricity cost reductions of 3% and 7% for the two respective case studies. Results from the two case studies are an indication of potential for electrical load management on South African gold processing plants. If an average electricity cost saving of 5% is extrapolated across the South African gold processing industry, the potential cost savings amount to R 25-million per annum. Although the costs saving opportunities are feasible, it is influenced by the reliability of the equipment and the dynamics of ore supply. This insight plays a decisive role in determining the feasibility of DSM on gold processing plants. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015

Costs saving measures are important for South African gold producers due to increasing energy costs and decreasing production volumes. Demand Side Management (DSM) is an effective strategy to reduce electricity consumption and costs. DSM projects have been implemented widely on South African mining systems such as pumping, refrigeration, rock transport and compressed air. Implementations have, however, been limited on gold processing plants despite the significant amounts of energy that this section consumes. The main objective of gold processing plants is production orientated and energy management is not a primary focus. This rationale is re-evaluated owing to high electricity price inflation and availability of DSM incentives. This study investigated the cost saving potential of DSM interventions on gold plants. Electrical load management was identified as a key opportunity that can deliver substantial cost savings. These savings were shown to be feasible in respect of the required capital expenditure, effort of implementation and maintenance of operational targets. Investigation procedures were compiled to identify feasible load management opportunities. The most potential for electricity cost savings was identified on comminution equipment. Consequently, a methodology was developed to implement electrical load management on the identified sections. The methodology proposed simulation techniques that enabled load management and subsequent electricity cost optimisation through production planning. Two electrical load management case studies were successfully implemented on comminution equipment at two gold processing plants. Peak period load shift of 3.6 MW and 0.6 MW, respectively, was achieved on average for a period of three months. The annual cost savings of these applications could amount to R1.4-million and R 660 000. This results in specific electricity cost reductions of 3% and 7% for the two respective case studies. Results from the two case studies are an indication of potential for electrical load management on South African gold processing plants. If an average electricity cost saving of 5% is extrapolated across the South African gold processing industry, the potential cost savings amount to R 25-million per annum. Although the costs saving opportunities are feasible, it is influenced by the reliability of the equipment and the dynamics of ore supply. This insight plays a decisive role in determining the feasibility of DSM on gold processing plants. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015

Electrical energy has become a very important and integrated part of the current era. Electricity cost saving interventions, such as load shifting, form part of demand side management (DSM) interventions. DSM interventions have been successfully implemented in the past to ensure reliable supply of electricity during the Eskom peak periods. It has been established that there is a need to implement an electricity cost saving intervention on a large water distribution utility. This dissertation focuses on the integration of electricity cost saving interventions on a water distribution utility. An investigation methodology, as well as an integration strategy for implementing an electricity cost saving intervention were developed. This study expands on the importance of an integrated approach. It further discusses the shortcomings of the current control philosophies of a large water distribution utility in South Africa. A load shifting project was implemented as an electricity cost saving intervention on a large water distribution utility in South Africa. The proposed integrated strategy was simulated and an optimised approach developed. It was found that the implementation of the strategy was limited due to process constraints and increasing water demand. Utilising the large combined installed capacity of the pumps in the water distribution utility and the storage capacity, the strategy was implemented and cost savings obtained. It was concluded that load shifting was possible on individual pumping stations in the water distribution utility subsystems, and could, therefore, be quantified to an integrated approach. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015

Electrical energy has become a very important and integrated part of the current era. Electricity cost saving interventions, such as load shifting, form part of demand side management (DSM) interventions. DSM interventions have been successfully implemented in the past to ensure reliable supply of electricity during the Eskom peak periods. It has been established that there is a need to implement an electricity cost saving intervention on a large water distribution utility. This dissertation focuses on the integration of electricity cost saving interventions on a water distribution utility. An investigation methodology, as well as an integration strategy for implementing an electricity cost saving intervention were developed. This study expands on the importance of an integrated approach. It further discusses the shortcomings of the current control philosophies of a large water distribution utility in South Africa. A load shifting project was implemented as an electricity cost saving intervention on a large water distribution utility in South Africa. The proposed integrated strategy was simulated and an optimised approach developed. It was found that the implementation of the strategy was limited due to process constraints and increasing water demand. Utilising the large combined installed capacity of the pumps in the water distribution utility and the storage capacity, the strategy was implemented and cost savings obtained. It was concluded that load shifting was possible on individual pumping stations in the water distribution utility subsystems, and could, therefore, be quantified to an integrated approach. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015

This thesis examines the private and external costs of electricity generated in Ontario by natural gas, wind, refurbished nuclear and new nuclear power. The purpose of the assessment is to determine a capacity expansion plan that meets the forecasted electricity supply gap in Ontario at the lowest social costs (i. e. the lowest aggregated private and external costs). A levelized unit electricity cost (LUEC) analysis is employed to evaluate private costs under both public and merchant perspectives. Computable external costs are monetized by adapting estimates from the literature that were previously developed using a primarily bottom-up damage cost method. <br /><br /> The findings reveal that social cost estimates for nuclear refurbishment are the lowest of the generation alternatives studied regardless of the evaluation perspective. Therefore, if the capacity expansion decision were based solely on these estimates, nuclear refurbishment should be utilized until its capacity constraints are reached. The generation alternative with the second lowest social costs depends on the perspective from which private costs are evaluated: from a public perspective, the remainder of the supply gap should be filled by new nuclear generation and from a merchant perspective, which is assumed to be more reflective of the current Ontario electricity market, natural gas-fired generation should be used. <br /><br /> Due to inherent uncertainty and limitations associated with the estimation of social costs, the estimates obtained in this thesis are considered to be context and data specific. A sensitivity analysis, which is employed to attempt to mitigate some of the uncertainty, shows that changes to key variables alter the capacity expansion plan. This reinforces the observation that methods and assumptions significantly affect social cost estimates. <br /><br /> Despite the limitations of this kind of evaluation, it is argued that a social cost assessment that is consistent, transparent and comprehensive can be a useful tool to assess the trade-offs of electricity generation alternatives if used along with existing evaluation criteria. Such an assessment can increase the likelihood that actual social costs are minimized, which can steer electricity generation in Ontario towards a system that is more efficient and sustainable.

This thesis examines the private and external costs of electricity generated in Ontario by natural gas, wind, refurbished nuclear and new nuclear power. The purpose of the assessment is to determine a capacity expansion plan that meets the forecasted electricity supply gap in Ontario at the lowest social costs (i. e. the lowest aggregated private and external costs). A levelized unit electricity cost (LUEC) analysis is employed to evaluate private costs under both public and merchant perspectives. Computable external costs are monetized by adapting estimates from the literature that were previously developed using a primarily bottom-up damage cost method. <br /><br /> The findings reveal that social cost estimates for nuclear refurbishment are the lowest of the generation alternatives studied regardless of the evaluation perspective. Therefore, if the capacity expansion decision were based solely on these estimates, nuclear refurbishment should be utilized until its capacity constraints are reached. The generation alternative with the second lowest social costs depends on the perspective from which private costs are evaluated: from a public perspective, the remainder of the supply gap should be filled by new nuclear generation and from a merchant perspective, which is assumed to be more reflective of the current Ontario electricity market, natural gas-fired generation should be used. <br /><br /> Due to inherent uncertainty and limitations associated with the estimation of social costs, the estimates obtained in this thesis are considered to be context and data specific. A sensitivity analysis, which is employed to attempt to mitigate some of the uncertainty, shows that changes to key variables alter the capacity expansion plan. This reinforces the observation that methods and assumptions significantly affect social cost estimates. <br /><br /> Despite the limitations of this kind of evaluation, it is argued that a social cost assessment that is consistent, transparent and comprehensive can be a useful tool to assess the trade-offs of electricity generation alternatives if used along with existing evaluation criteria. Such an assessment can increase the likelihood that actual social costs are minimized, which can steer electricity generation in Ontario towards a system that is more efficient and sustainable.

Electricity costs in South Africa have risen steeply; there are a number of factors that have contributed to this increase. The increased costs have a considerable inuence on the mines and mining sector in general. It requires considerable planning to assist mines in such management. The present study addresses the development of a way to predict both electricity consumption and costs, which general involves a large range of personnel. The majority of planning personnel can be more usefully employed in other ways. The goal is not to replace such planners but make them more task e_ective. Automation, which will reduce their workload, may have little or no e_ect on performance. In some cases, however, automation may produce better results. There is a complex system to be analysed in the prediction of electricity consumption and costs. The existing prediction methodology is investigated in this study; the investigation highlights the need for a new methodology. The new method should be automated, easier to use and more accurate. Such a model is developed. The new prediction methodology extracts data from the monthly Eskom bills and stores it in a database. The data is grouped according to a new model and then normalised. An arti_cial neural network is used to \learn" the dynamics of the data to calculate new future electricity consumption. Electricity costs are predicted by multiplying the predicted electrical consumption with a calculated factor based on cost per electricity unit of the previous year with the expected increase added. The new methodology is integrated in a commercial energy management platform named Management Toolbox, which o_ers a range of functionality. In this study the prediction of electricity consumption and costs are implemented. The implementation is executed with simplicity in mind and care is taken to present the user with the optimal amount of data. The performance of the electricity consumption prediction is sensitive to production changes and the quality of the data history. Performance of the electricity costs prediction model is an improvement over the existing prediction method. The proposed methodology has greater accuracy and uses less personnel, which can lead to using most of the personnel on more important tasks. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

Electricity costs in South Africa have risen steeply; there are a number of factors that have contributed to this increase. The increased costs have a considerable inuence on the mines and mining sector in general. It requires considerable planning to assist mines in such management. The present study addresses the development of a way to predict both electricity consumption and costs, which general involves a large range of personnel. The majority of planning personnel can be more usefully employed in other ways. The goal is not to replace such planners but make them more task e_ective. Automation, which will reduce their workload, may have little or no e_ect on performance. In some cases, however, automation may produce better results. There is a complex system to be analysed in the prediction of electricity consumption and costs. The existing prediction methodology is investigated in this study; the investigation highlights the need for a new methodology. The new method should be automated, easier to use and more accurate. Such a model is developed. The new prediction methodology extracts data from the monthly Eskom bills and stores it in a database. The data is grouped according to a new model and then normalised. An arti_cial neural network is used to \learn" the dynamics of the data to calculate new future electricity consumption. Electricity costs are predicted by multiplying the predicted electrical consumption with a calculated factor based on cost per electricity unit of the previous year with the expected increase added. The new methodology is integrated in a commercial energy management platform named Management Toolbox, which o_ers a range of functionality. In this study the prediction of electricity consumption and costs are implemented. The implementation is executed with simplicity in mind and care is taken to present the user with the optimal amount of data. The performance of the electricity consumption prediction is sensitive to production changes and the quality of the data history. Performance of the electricity costs prediction model is an improvement over the existing prediction method. The proposed methodology has greater accuracy and uses less personnel, which can lead to using most of the personnel on more important tasks. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.