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Fixing the barriers: How new policies can make U.S. nuclear exports competitive again
The United States has a strong marketplace of ideas on future civil nuclear technology. President Trump wants to see 10 large reactors under construction by 2030 and has discussed making $80 billion available for that objective. Evolutionary small modular reactors based on light water reactor technology are on the market now, and the Tennessee Valley Authority expects a construction permit for a project at its Clinch River Site later this year.
U. B. Phathanapirom, E. A. Schneider
Nuclear Science and Engineering | Volume 182 | Number 4 | April 2016 | Pages 502-522
Technical Paper | doi.org/10.13182/NSE15-25
Articles are hosted by Taylor and Francis Online.
This paper introduces a new methodology for explicitly incorporating uncertainties in key parameters into decision making regarding the transition between various nuclear fuel cycles. These key uncertainties—in demand growth rates, technology availability, and technology costs, among others—are unlikely to be resolved for several decades and invalidate the concept of planning for a unique optimal transition strategy. Past time-dependent analyses of the nuclear fuel cycle have confronted uncertainties by using a scenario-based approach where key variables are parametrically varied, which gives rise to inflexible courses of action associated with optima for each scenario. Instead, this work selects hedging strategies through a decision making under uncertainty framework. These strategies are found by applying a choice criterion to select courses of action that mitigate regrets. These regrets are calculated by evaluating the performance of all possible transition strategies for every feasible outcome of the uncertain parameter(s). The methodology is applied to a case study involving transition from the current once-through light water reactor fuel cycle to one relying on continuous recycle in fast reactors, and the effect of choice criterion is explored. Hedging strategies are found that preserve significant flexibility to allow alteration of the fuel cycle strategy once these uncertainties are resolved. This work may provide guidance for agent-based, behavioral modeling in fuel cycle simulators as well as decision making in real-world applications.