Advanced Fuel Cycle Cost Basis – 2017 Edition

Dixon, Brent; Ganda, Francesco; Williams, K.A.; Hoffman, E. A.; Hanson, J. K.

doi:10.2172/1423891

Cited by 27 publications

(71 citation statements)

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“…However, in the international project of the Generation IV International Forum (GIF), one economic target has been identified so far; that is, the capital costs of GEN‐IV reactors are to be comparable with GEN‐III reactors . Accordingly, we adopted the unit overnight capital cost range for an SFR, ie, 2400, 4100, and 7600 USD/kWe from the INL (2017) . On the one hand, we consider that the relatively wide range between the low and high cost bounds may cover more possibilities with varying degree of uncertainties.…”

Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

“…Nowadays, the new separative work unit (SWU) capacity of enrichment is replacing the gaseous diffusion technology with more economical centrifuges and other advanced technologies . By recognizing that enrichment spot prices have recently fallen to historical lows (less than 50 USD/SWU), we conservatively estimate the unit costs for enrichment services as a lower boundary of 50 USD/SWU, but an upper boundary of 154 USD/SWU was referred to in the INL (2017) …”

Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

“…As a result of the 2011 Fukushima accident, the costs of the on‐site storage of spent fuel may have sharply increased due to the foreseeable increase in regulatory cost and operation enhancement. Although the on‐site wet storage costs for spent fuels cannot be neglected, herein, we followed the INL (2017)'s assumption to contain the on‐site storage costs in reactor's capital costs for the sake of simplicity, which were not considered separately . The interim storage of spent fuel before the final disposal process was assumed as the dry storage away from the reactor with a 30 years of time lag.…”

Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

“…Here, we adopted the cost assumptions for the interim storage of PHWR spent fuels in accordance with the OECD (2006), ie, 70, 100, and 150 USD/kgHM being the lower, mode, and upper boundary values . The unit cost assumptions for the interim dry storage of PWR UO 2 spent fuels assumptions were utilized from the INL (2017), (ie, 223, 501, 644 USD/kgHM). For the interim storage costs of PWR MOX spent fuels without sufficient data, we conservatively assumed them to be as 1.5 times higher than that of PWR UO 2 spent fuels, ie, 752 USD/kgHM of the mode cost, 335 USD/kgHM and 966 USD/kgHM for the lower and upper boundary costs.…”

Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

“…Specifically, they are separated from the spent fuels by the pyro‐processing treatment for decay storage, in which the short‐term heat load of nuclear wastes can be greatly reduced . In this study, the assumptions of the transport and decay storage costs were set as Cs‐Sr/kg term referred to in the INL (2017), ie, 11 400, 25 650, and 39 900 USD/kg Cs/Sr as the lower, mode, and upper values, respectively …”

Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

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The economic competitiveness of promising nuclear energy system: A closer look at the input uncertainties in LCOE analysis

et al. 2019

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Summary In this study, we aimed to provide important information about the potential economic benefits and risks of nuclear electricity generation associated with existing and prevailing nuclear technologies and to examine the economic effects of nuclear fuel cycle strategies in Korea. An economic analysis model that evaluates the overall life‐cycle costs of nuclear energy systems coupled with multiple fuel cycle options was specially developed by using the levelized cost of electricity (LCOE) as the fundamental methodology. This model is capable of identifying a range of techno‐economic uncertainties underlying each individual nuclear energy system taking into account the state of the art in fuel cycle technologies. It can also quantify and incorporate the resulting impacts into a system‐wide LCOE distribution for each fuel cycle option based on Monte Carlo probabilistic simulation. We analyzed and discussed examples of the economic performance of 13 promising candidates for nuclear energy systems integrated with extensive fuel cycle technologies (including one direct disposal and 12 specific reprocessing and recycling fuel cycle options). We also conducted a sensitivity analysis to investigate the major sensitivity factors of the system component cost in each fuel cycle option and their impacts on individual economic performances. Furthermore, a closer look at the techno‐economic uncertainties of advanced fuel cycle technologies in a break‐even analysis offers evidence of the potential economic feasibility and cost‐reduction opportunities in the reprocessing and recycling options relative to the direct disposal of spent nuclear fuel.

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Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

Section: Uncertain Variables and Input Assumptionsmentioning

confidence: 99%

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The economic competitiveness of promising nuclear energy system: A closer look at the input uncertainties in LCOE analysis

et al. 2019

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Optimization under uncertainty for robust fuel cycle analyses

Skarbeli

Bécares

2020

Intl J of Energy Research

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Optimization of nuclear fuel cycles is essential for experts and policy makers for studying and analyzing the future of the nuclear energy. In the case of advanced electronuclear transition scenarios, multiple parameters with a complex dependence have to be fine-tuned in order to achieve a set of predefined objectives. However, in the presence of uncertainties the solutions obtained in this way may not be stable since small perturbations could break the delicate balance between different parts of the scenario. In this work, the optimization of an uncertainty European-based sustainable transition scenario has been studied. This scenario, which has been analyzed with the TR_EVOL nuclear fuel cycle simulator system, is aimed at reducing the transuranic inventory masses while keeping the fuel cycle costs. To that end, an extension of the DEMO evolutionary multiobjective algorithm has been implemented within TR_EVOL for allowing the inclusion of constraints and uncertainties with a methodology that can be used by any fuel cycle simulator. Results show the importance of coupling optimization and uncertainty analyses due to the suboptimal and unstable solutions that can be obtained if not considered jointly. In addition, the uncertainties shrink the decision space. It was found that in their presence the transuranic mass can be reduced and stabilized by a factor ranging between 65% and 71% with an increase of the cost of 16% and 18.5% after 300 years of operation by using advanced systems when compared with an open fuel cycle strategy.

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