Thesis (M. Tech.) - Central University of Technology, Free State, 2006 / Demand-side management (DSM) is one of the integrated energy planning concepts that has only recently been introduced in South Africa. This concept needs to be fully developed in order to suit current industrial development situations. South Africa’s coal and water reserves will not last forever because of the growing population and the accompanying demands on our energy resources {[5] of Chapter 1]}. Therefore the demand-side interventions are considered on an effective means of overcoming these problems. The traditional approach of electrical energy utilisation by Eskom and its customers has to be reviewed. Socio-economic and environmental development benefits must also be reviewed. Advanced research on demand-side management has benefited the international world tremendously and this kind of research should also be done in South Africa. The research project for this study as described from chapter 1- 8 was undertaken to show the potential implementation of demand-side management and its interventions (DSM programme) on the Theunissen-Brandfort Pumps 11kV feeder (TBP). This would result in the generating of potential energy and cost-savings that would flow from the feasible DSM programme. This would be measured and verified by billing the actual saved energy at the TBP electrical system for the future. Every potentially saved-energy means one less potential reduction in emission. The case studies were conducted on Eskom’s entire TBP network and on four large power users which were identified and which provided the relevant potential results. Methodological design protocol processes for best-practice pollution prevention and the efficiency-energy (EE) audit protocol model, with its accompanying goal and objectives were used. The project concentrated on EE and time-of-use (TOU) factors related to the selected customers and the TBP as a whole, thus: potential Replacement and Rewinding of low efficiency with higher efficiency motors and the TBP feeder potential Load-Shifting. The stages within the EE, LS and DSM project process which were used for potential implementation are the following: project identification, energy audits and assumptions and recommendations for implementation. The M&V interaction with DSM, EE or LS project processes (methodology) for future implementation purposes (actual retrofitting) is also shown. The TBP feeder collective baseline (Figure 6.2) was quantified by trapezium rule. The feasible EE and LS programmes opportunities analysis on motors and the entire TBP were performed by inference and stipulation techniques and the potential energy reduction effects using a simulation programme called International Motor Selection and Savings Analysis (IMSSA). The potential LS programme was also performed based on the Eskom’s miniflex tariff defined time of use. TBP plant-wide EE and LS assessments conducted with the methodology mentioned, identified and quantified a total of two EE savings opportunities and were divided into four categories: those for short-term, long-term, none and best solution potential implementations (Table 7.9). As far as indirect results are concerned, DSM is a very new concept in South Africa and is consequently not well known. The study was based on simplicity in order to make the DSM subject very simple and easily accessible to future research. By using a simple and userfriendly IMSSA software programme, quick, relevant results were obtained. The study played an important role in influencing and educating interested parties about the importance of potential demand-side management concepts and objectives. The study compiled valuable information on EE, DSM (LS) and M&V that was previously unknown and, which will make future research much more accessible and manageable. It is recommended that all the motors identified as inefficient be rewound and replaced by new and efficient ones in the future. It is also very important that the potential LS programme be implemented only after these potential EE opportunities are implemented so that there will be sustainability and the DSM objectives may be achieved (Table 7.10). The project led to better grasp of electric energy consumption by the customers. From a socio-economic perspective, Eskom can distribute the surplus potentially saved energy of capacity at the TBP to other communities, which would also create employment if a new network could be built. Allocation of potentially saved energy to other population groups or customers of low-income groups in the Theunissen area would mean a significant lifestyle change. With regard to environmental benefits, previous research has proven that for every kWh of electricity saved, fewer emissions (e.g. C02) would be generated at the power station. The study addressed TBP-wide power use, focusing primarily on the demand-side interventions, but implications for improvements in the supply-side emission reductions were also considered.

Thesis (MBA)--Stellenbosch University, 2012. / Over the past few years the Southern African Development Community (SADC) has been facing regional power shortages. These regional power shortages currently being experienced within the SADC region have been partly due to a lack of generation capacity and transmission capacity constraints. This situation has been further aggravated by the unanticipated economic growth within the SADC region. In order for the SADC region to meet its electricity demand, the region needs to invest in new generation and transmission projects. However, due to the inability of regional utilities to raise funds to finance these projects, the projects tend to not reach financial close resulting in the increase of the regional power deficits. Although these regional power shortages have been attributed to the lack of adequate investment in power generation projects and power transmission interconnector projects, this research report was limited to the development and financing of power transmission interconnector projects. The objective of this research report was to identify the dynamics that prohibit regional power transmission interconnectors from reaching financial close and to recommend possible solutions on how best to develop and package these projects. The research used the proposed developmental approach of the ZIZABONA transmission interconnector project as a case study to develop a generic model that could be utilised on other regional transmission interconnector projects. Based on the research presented in this research report, the study has highlighted project finance as a viable funding strategy. Regional utilities can utilise this funding strategy to package and finance joint transmission regional interconnectors to alleviate the current regional power shortages by facilitating regional electricity trade.

Thesis (MDF)--Stellenbosch University, 2014. / The recent transformation in the Ugandan energy sector has led to a significant surge in private electricity generation companies in the country. These companies have a heterogeneous capital structure and they tend to charge different tariff rates for the electricity generated. While the capital structure might have an important role to play in differential tariff setting, it is not clear to what extent it influences the tariff structure of electricity generation projects. Thus, the objective of this study was to examine the effect of capital structure on the tariff of electricity generation projects in Uganda after controlling for other factors such as operation and maintenance costs, technology used for generation, project development costs, and installed capacity of generation plants on the generation tariffs. Using cross-sectional data from 29 companies as at September 2014, a bootstrap linear regression analysis was used for estimation. The results of the study indicated that the higher the debt portion in the capital structure, the lower the generation tariff. However, the impact of debt in the capital structure was not statistically significant. What stood out is that renewable technologies have a much lower generating tariff than non-renewable technologies.

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

As a consequence of an aging workforce, electric utilities are at risk of losing their most experienced and knowledgeable electrical engineers. In this research, the problem was a lack of understanding of what electric utilities were doing to capture the tacit knowledge or know-how of these engineers. The purpose of this qualitative research study was to explore the tacit knowledge capture strategies currently used in the industry by conducting a case study of 7 U.S. electrical utilities that have demonstrated an industry commitment to improving operational standards. The research question addressed the implemented strategies to capture the tacit knowledge of retiring electrical engineers and technical personnel. The research methodology involved a qualitative embedded case study. The theories used in this study included knowledge creation theory, resource-based theory, and organizational learning theory. Data were collected through one time interviews of a senior electrical engineer or technician within each utility and a workforce planning or training professional within 2 of the 7 utilities. The analysis included the use of triangulation and content analysis strategies. Ten tacit knowledge capture strategies were identified: (a) formal and informal on-boarding mentorship and apprenticeship programs, (b) formal and informal off-boarding mentorship programs, (c) formal and informal training programs, (d) using lessons learned during training sessions, (e) communities of practice, (f) technology enabled tools, (g) storytelling, (h) exit interviews, (i) rehiring of retirees as consultants, and (j) knowledge risk assessments. This research contributes to social change by offering strategies to capture the know-how needed to ensure operational continuity in the delivery of safe, reliable, and sustainable power.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.

Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.