2 research outputs found
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Autonomous dynamic decision making in fuel cycle simulators using a game theoretic approach
A novel methodology for optimizing nuclear fuel cycle transitions that captures interactions between a policy maker and electric utility company is presented. The methodology is demonstrated using a two-person general-sum sequential game with uncertainty that is implemented using a nuclear fuel cycle simulator capable of calculating a material- and technology-constrained material balance, coupled to a multi-objective optimization solver. The solver explicitly treats uncertainties using a stochastic programming approach with chance nodes depicted as a Nature player who moves randomly. The methodology is demonstrated through a Transition Game that features tradeoffs between investments in competing reprocessing and waste disposal technologies, dynamic reactor deployment responses to resolutions in reactor capital cost uncertainty, and the influence of capital subsidies on the future nuclear technology mix. Each player in the game uses a unique set of decision criteria to identify optimal near-term hedging strategies that consider all of Nature’s possible moves as well as the other player’s available decisions. These hedging strategies balance the exchange between the risk of immediate action and delay and maintain flexibility to allow for intelligent recourse decisions once uncertainties are resolved. Results from the Transition Game indicate that early transition to high-temperature gas-cooled reactors is preferred, with the option to abandon the transition following a learning period if capital costs are unfavorable. Under these conditions, transition to used fuel recycling in sodium-cooled fast reactors may be spurred by policy incentives under some certain decision criteria weightings. Otherwise, operating with a baseline set of decision criteria weightings, transition to a closed fuel is never observed when players hedge optimally against Nature’s moves. It is only when players have perfect information regarding Nature’s future moves will transition to a closed fuel be observed.Mechanical Engineerin
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Nuclear fuel cycle transition analysis under uncertainty
textUncertainty 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.Mechanical Engineerin