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The thesis is divided into two parts concerning different topics. The first is solving a multi-period portfolio decision problem, and the second, more theoretical, is a numerical comparison of uncertainty measures within evidence theory. Nowadays, portfolio problems are very common and present in several fields of study. The problem is inspired by a real-world infrastructure manage- ment case in the energy distribution sector. The problem consists of the optimal selection of a set of activities and their scheduling over time. In scheduling, various constraints and limits that the company has to meet must be considered, and the selection must be based on prioritizing the activities with a higher priority value. The problem is addressed by Port- folio Decision Analysis: the priority value of activities is assigned using the Multi-Attribute Value Theory method, which is then integrated with a multi-period optimization problem with activities durations and con- straints. Compared to other problems in the literature, in this case, the ac- tivities have different durations that must be taken into account for proper planning. The planning obtained is suitable for the user’s requirements both in terms of speed in providing results and in terms of simplicity and comprehensibility. In recent years, measures of uncertainty or entropy within evidence theory have again become a topic of interest in the literature. However, this has led to an increase in the already numerous measures of total uncertainty, that is, one that considers both conflict and nonspecificity measures. The research aims to find a unique measure, but none of those proposed so far can meet the required properties. The measures are often complex, and especially in the field of application, it is difficult to understand which is the best one to choose and to understand the numerical results obtained. Therefore, a numerical approach that compares a wide range of measures in pairs is proposed alongside comparisons based on mathematical proper- ties. Rank correlation, hierarchical clustering, and eigenvector centrality are used for comparison. The results obtained are discussed and com- mented on to gain a broader understanding of the behavior of the measures and the similarities and non-similarities between them.

With increasing demand of electric power in the context of deregulated electricity markets, a good strategic planning for the growth of the power system is critical for our tomorrow. There is a need to build new resources in the form of generation plants and transmission lines while considering the effects of these new resources on power system operations, market economics and the long-term dynamics of the economy. In deregulation, the exercise of generation planning has undergone a paradigm shift. The first stage of generation planning is now undertaken by the individual investors. These investors see investments in generation capacity as an increasing business opportunity because of the increasing market prices. Therefore, the main objective of such a planning exercise, carried out by individual investors, is typically that of long-term profit maximization. This thesis presents some modeling frameworks for generation capacity expansion planning applicable to independent investor firms in the context of power industry deregulation. These modeling frameworks include various technical and financing issues within the process of power system planning. The proposed modeling frameworks consider the long-term decision making process of investor firms, the discrete nature of generation capacity addition and incorporates transmission network modeling. Studies have been carried out to examine the impact of the optimal investment plans on transmission network loadings in the long-run by integrating the generation capacity expansion planning framework within a modified IEEE 30-bus transmission system network. The work assesses the importance of arriving at an optimal IRR at which the firm’s profit maximization objective attains an extremum value. The mathematical model is further improved to incorporate binary variables while considering discrete unit sizes, and subsequently to include the detailed transmission network representation. The proposed models are novel in the sense that the planning horizon is split into plan sub-periods so as to minimize the overall risks associated with long-term plan models, particularly in the context of deregulation.

With increasing demand of electric power in the context of deregulated electricity markets, a good strategic planning for the growth of the power system is critical for our tomorrow. There is a need to build new resources in the form of generation plants and transmission lines while considering the effects of these new resources on power system operations, market economics and the long-term dynamics of the economy. In deregulation, the exercise of generation planning has undergone a paradigm shift. The first stage of generation planning is now undertaken by the individual investors. These investors see investments in generation capacity as an increasing business opportunity because of the increasing market prices. Therefore, the main objective of such a planning exercise, carried out by individual investors, is typically that of long-term profit maximization. This thesis presents some modeling frameworks for generation capacity expansion planning applicable to independent investor firms in the context of power industry deregulation. These modeling frameworks include various technical and financing issues within the process of power system planning. The proposed modeling frameworks consider the long-term decision making process of investor firms, the discrete nature of generation capacity addition and incorporates transmission network modeling. Studies have been carried out to examine the impact of the optimal investment plans on transmission network loadings in the long-run by integrating the generation capacity expansion planning framework within a modified IEEE 30-bus transmission system network. The work assesses the importance of arriving at an optimal IRR at which the firm’s profit maximization objective attains an extremum value. The mathematical model is further improved to incorporate binary variables while considering discrete unit sizes, and subsequently to include the detailed transmission network representation. The proposed models are novel in the sense that the planning horizon is split into plan sub-periods so as to minimize the overall risks associated with long-term plan models, particularly in the context of deregulation.

Thesis (Ph.D. (Chartered Accountancy))--North-West University, Potchefstroom Campus, 2009.

Thesis (Ph.D. (Chartered Accountancy))--North-West University, Potchefstroom Campus, 2009.

This thesis investigates methodologies for valuing the flexibility of demand-side response (DSR) in its ability to respond to future uncertainties. The ability to quantify this flexibility is especially important for energy systems investments given their large and irreversible capital costs. The consideration of uncertainty in electricity markets and energy networks requires solutions that allow decision makers to quickly respond to unexpected events, such as extreme short-term electricity price variations in an operational setting, or incorrect long-term demand projections in planning. This uncertainty, coupled with the irreversibility of energy network investments, results in the need for viable 'wait-and-see' investment strategies that can help hedge electicity price risk in the short-term while hedging planning risk in the long-term, until at least some, if not all, uncertainty is resolved. In both cases, this leads to an added value in the case of temporary flexible investment options like DSR, which may otherwise be considered unattractive under a deterministic analysis setting. A number of significant contributions to power systems research are offered in this work, focusing on valuation methods for quantifying the flexibility value of DSR under both short-term and long-term uncertainty. The first outcome of this research is an extensive review of current real options (RO) methods that clarifies the assumptions and utilization of RO for decision-making in engineering applications. It suggests that many of the assumptions used contribute to a misuse of the models when applied to physical systems. A framework for investing under uncertainty is proposed, where the methodologies, steps, inputs, assumptions, limitations and advantages of different RO models are described so as to offer a practical guide to decision makers for selecting the most appropriate RO model for their valuation purposes. The second outcome is the design of a probabilistic RO framework and operational model for DSR that quantifies its benefits as an energy service for hedging different market price risks. A mathematical formulation for applying “real options thinking” is presented that provides decision makers with a means of quantifying the value of DSR when both operational and planning decisions are subject to uncertainty. In particular, DSR contracts can have tremendous value as an arbitrage or portfolio-balancing tool, helping hedge almost entirely electricity price risk in day-ahead and real-time markets, especially when prices are highly volatile. This value is quantified using a novel RO framework that frees the decision maker from the assumptions needed in financial option models. A new load forecasting and price simulation model is also developed to forecast load profiles and simulate new price series with different average values, higher volatilities and extreme price spikes to represent potential future market scenarios and to determine under which conditions DSR has the most value. The valuation of a DSR investment is then presented to show how the physical characteristics of a system, in this case the physical load recovery effect of loads after a DSR activation, can tremendously affect the profitability of an investment when uncertainty is taken into account. The third outcome of this work is the development of a complete, general and practical tool for making long-term multi-staged investment decisions in future power networks under multiple uncertainties. It is argued throughout this work that many of the current methods are either unsuitable for long-term investment valuation or are too complex for practical application and implementation at the industry level. A strategic spreadsheet-based tool for making long-term investment decisions under uncertainty is therefore created and tested in collaboration with industry for solving real network planning problems.

The subject of this dissertation “Business plan proposal of company NEOCHEM plus“ is to provide company analysis. On the basis of its evaluation to propound and design steps leading to renewal of the company which activities were very limited by produce areas burning. The goal of this dissertation is to dispose plan of technical and finance support for new building-up and renewal of polyurethane foams production.

Increasingly more people lack the basic financial knowledge that would help them plan for their future. One aspect of it is not being aware of the long-term benefit of investing in the stock market. Increasing financial literacy with better financial education is a long-term solution. In the meanwhile, there is room for technological innovation to reduce the need for financial literacy which has not been covered by previous research and is therefore the topic of this research. More specifically, this study examines how financial literacy can be reduced in financial planning for households by helping them setting up a stable financial future. A case study method was used to choose three web-based robotized products and evaluate how they scored in mitigating three identified barriers to stock market participation. The result demonstrated that choosing any of the three products significantly reduced the need of financial literacy since they all scored high. In conclusion, these types of technological products can help not only the financially illiterate but also those who want to delegate the task of planning for their financial future.