Uncertainty surrounds the future evolution of key factors affecting the attractiveness of various nuclear fuel cycles, rendering the concept of a unique optimal fuel cycle transition strategy invalid. This work applies decision-making under uncertainty to fuel cycle transition analysis, demonstrating a new, systematic methodology for choosing flexible, adaptable hedging strategies that yield middle-of-the-road results until uncertainties are resolved. A case study involving transition from the current once-through light water reactor (LWR) fuel cycle to one relying on continuous recycle in fast reactors (FRs) is cast as a no-data decision problem. The transition is subject to uncertainty in the cost of spent nuclear fuel (SNF) and high-level waste (HLW) disposal in a geologic repository, slated to open some years into the future. Following the repository open date, the cost of SNF and HLW disposal is made known, and may take on one of five possible values. Strategies for the transition are enumerated and simulated using VEGAS, a systems model of the nuclear fuel cycle that solves for its material balance and applies input cost data to calculate the associated annual levelized cost of electricity (LCOE). Perfect information strategies are found using the lowest average, maximum, and integrated LCOE objective functions. The loss in savings for following a strategy other than the perfect information strategy is the “regret” which is calculated by evaluating the performance of each strategy for every end-state. Hedging strategies are then selected by either minimizing the maximum or the expected regret. Generally, the optimal hedging strategy identified using the decision methodology suggests a partial transition to a closed fuel cycle prior to the repository open date. Once the repository opens, the transition may be abandoned or accelerated depending on which disposal cost outcome is realized. The lowest average and integrated LCOE objective functions perform similarly; however, the lowest maximum LCOE objective function appears overly sensitive to aberrations in the annual LCOE that arise due to idle reprocessing capacity. The minimax regret choice criterion is shown to be more conservative than the lowest expected regret choice criterion, as it acts to hedge against the worst-case outcome. By following a hedging strategy, agents may alter their fuel cycle strategy more readily once uncertainties are resolved. This results since hedging strategies provide flexibility in the nuclear fuel cycle, preserving what options exist. To this end, the work presented here may provide guidance for agent-based, behavioral modeling in fuel cycle simulators, as well as decision-making in real world applications. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/26382 |
| Date | 09 October 2014 |
| Creators | Phathanapirom, Urairisa Birdy |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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