Decisioneering in Nuclear Energy Systems: A Real Option View

Financial- and Energy markets have one predominant characteristic in common: A large degree of uncertainty that drives the value of an investment in them. The thesis criticises the poor decision making support for investments into new generation capacity on base of pure cost comparison and NPV evaluations that is still prevalent in academic literature to judge nuclear energy systems. In order to separate investments into nuclear generation capacity from other real or financial investments, a classification into Very Large Industrial Real Investments, VLIRI, has been made. This special type of Giga investments acknowledges a high degree of irreversibility, a fuzzy economic assessment horizon and a long preparation and construction time. Additionally, evaluation of profitability is more complex as revenue streams are highly uncertain with often long lead times to achieve these risky cash flows. Regarding the nuclear fuel cycle there are numerous technical alternatives at all stages during the project lifetime, which create a high degree of optionality to react on this financial risk. The presented cross-disciplinary thesis explores Real Option Modelling as simulation tool to assess nuclear energy systems from a statistical and finance-mathematical point of view. Different numerical examples explain the idea of risk management in volatile energy markets and show why decision makers intuitively defer irreversible investments into new nuclear capacity. Heart of the analytical part of the thesis is the nuclear fuel cycle and the numerous Real Options that arise at its front- and back end. Although there are ambitious goals for the next generation of nuclear energy systems regarding sustainability, life-cycle cost advantages and the level of financial risk, there is little incentive for the generating industry to deviate from the once-through fuel cycle strategy. The thesis claims that a shift from a maximizing mean-rate of return attitude towards optimizing mean-variance behaviour might be a self triggering aspect for a more resource friendly fuel cycle. The thesis recognizes trade-offs between costs and financial risk in the nuclear fuel cycle and seeks to capture uncertain input parameters with geometric Brownian motion. The results are in coherence with current behaviour in nuclear energy markets: as costs for different fuel cycle steps fluctuate unpredictably over time, a delay of the decision on reprocessing and storage of spent nuclear fuel pays off. Diversification of the fuel cycle proves to be an efficient tool for risk management, such that a rational and risk aware investor would behave indifferently but not neglecting on behalf the use of MOX fuel and reprocessing strategies.Process and EnergyMechanical, Maritime and Materials Engineerin