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Nuclear and renewables in deregulated markets. Nuclear fuel cycle cost estimates with cross-disciplinary modelling

Author

Listed:
  • Rodica Loisel

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - IEMN-IAE Nantes - Institut d'Économie et de Management de Nantes - Institut d'Administration des Entreprises - Nantes - UN - Université de Nantes)

  • Lionel Lemiale

    (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - IEMN-IAE Nantes - Institut d'Économie et de Management de Nantes - Institut d'Administration des Entreprises - Nantes - UN - Université de Nantes)

  • Nicolas Thiollière

    (SUBATECH - Laboratoire de physique subatomique et des technologies associées - UN UFR ST - Université de Nantes - UFR des Sciences et des Techniques - UN - Université de Nantes - IN2P3 - Institut National de Physique Nucléaire et de Physique des Particules du CNRS - CNRS - Centre National de la Recherche Scientifique - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris])

  • Xavier Doligez

    (IPNO - Institut de Physique Nucléaire d'Orsay - UP11 - Université Paris-Sud - Paris 11 - IN2P3 - Institut National de Physique Nucléaire et de Physique des Particules du CNRS - CNRS - Centre National de la Recherche Scientifique)

  • Silvana Mima

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - INRA - Institut National de la Recherche Agronomique - CNRS - Centre National de la Recherche Scientifique - UGA [2016-2019] - Université Grenoble Alpes [2016-2019])

  • Adrien Bidaud

    (LPSC - Laboratoire de Physique Subatomique et de Cosmologie - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - IN2P3 - Institut National de Physique Nucléaire et de Physique des Particules du CNRS - CNRS - Centre National de la Recherche Scientifique - UGA [2016-2019] - Université Grenoble Alpes [2016-2019])

Abstract

We first formalize the problem and identify the market factors affecting the nuclear fuel cycle such as the capacity factor, the technical lifetime and the license transient budget, the operational cost and the frequency of safety measures due to excessive cycling. A cost calculation methodology is developed based on physics, economics and regulation with the aim of assisting policy makers and industry. The contribution to the literature is the integration of the nuclear load-following operating mode into the overall nuclear cost calculation, with a deep understanding of the operation of a nuclear flexible reactor and its upstream material flow inventory. First, a physical tool (CLASS, Core Library for Advanced Scenario Simulation) describes the dynamics of a complete fuel cycle of a representative PWR kind reactor and gives insights into the material flow under the constraint of the capacity factor, i.e. the share of the fuel burned to match the nuclear power market demand. ln general, the lower the nuclear power output, the lower the share of the uranium burned, the longer the cycle and the lower the fuel cost. Secondly, a technical-economic model (POLES, Prospective Outlook on Longterm Energy Systems) integrates the cost of the uranium cycle calculated with physics criteria and simulates the French power system in 2030 and 2050 under carbon emissions constraint. Thirdly, Poles' outputs, such as renewables installed capacities and flexibility provided by nuclear power plants, are integrated into a sectoral economic model (EcoNUK, Economic dispatching of NUClear reactors). It is assessed on an hourly basis the operation of the nuclear power fleet over the complete fuel cycle. Model results allow assessing the number of cycles performed with nuclear power reactors, the market demand for nuclear power and a new value of the capacity factor, which can be different from timely aggregated models. The hourly loop shows that punctually nuclear reactors are cycling excessively triggering down the load factor, while eventually they can substitute flexible gas-fired units instead of operating base-load improving therefore the capacity factor. Iterations among disciplines ailow finding stable results in terms of uranium cycle cost and reactors' capacity factors, to ultimately compute the nuclear power generation cost, e.g. the LCOE indicator. Preliminary results show that a limited number of iterations is needed due to robust results from the model Poles in terms of nuclear plants' capacity factors. That is, for a large range of the uranium cycle costs, the nuclear power keeps stable its position in the merit order curve that is deregulated market-specific. Two indicators are instead subject to cost cycle variation, namely the system cost to operate the nuclear power plants and the nuclear generation cost.

Suggested Citation

  • Rodica Loisel & Lionel Lemiale & Nicolas Thiollière & Xavier Doligez & Silvana Mima & Adrien Bidaud, 2018. "Nuclear and renewables in deregulated markets. Nuclear fuel cycle cost estimates with cross-disciplinary modelling," Post-Print hal-01838422, HAL.
  • Handle: RePEc:hal:journl:hal-01838422
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