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UBC Theses and Dissertations

Planning in an uncertain environment : a case study of gas turbine technology El-Ramly, Manal


The electric industry is in a time of rapid change, as it is moving from a regulated monopolistic environment with a guaranteed rate of return to a risky competitive market with many players. This change is introducing uncertainty with respect to supply, demand, pricing, input fuel costs and technology. This thesis proposes a methodology for financial decision making in such an uncertain environment. The objective of the model is to demonstrate an approach for solving the problem, not to provide a specific answer to the problem. Therefore, the model discussed here is simple; users who use the approach are capable of developing their own, more sophisticated, models. A probabilistic cash flow model technique is employed in this thesis. The model is based on a Monte Carlo Simulation. The model determines the supply price of electricity to achieve a MARR (minimum attractive rate of return) on a gas turbine project. The model also compares two technologies, namely, F Series and G Series gas turbines, to determine which technology to implement depending on an organizations' appetite for risk. The input variables for the model are associated with a large degree of uncertainty, particularly due to the rapid changes in the electric industry. Expert judgments was used to characterize this uncertainty, through a process of subjective elicitation. Based on the results of the 3 experts, if one were to invest in a new gas turbine facility, the technology to implement today, given the assumptions of the models, would be the G Series if there were no constraint on the ability to sell the demand. As the constraint on demand increases, the F Series becomes the better alternative, as its decreased capital costs becomes more advantageous than its lower efficiency. The large variations in the experts' opinions suggests that the conclusions are themselves uncertain. A different sampling of experts could very well result in a different conclusion.

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