Development of a MOX equivalence Python code package for ANICCA

The basis of the MOX (Mixed OXide) energy equivalence principle is keeping the in-core fuel management characteristics (cycle length, feed size, etc.) of a nuclear reactor unchanged when replacing UOX (Uranium OXide) fuel assemblies by MOX. If the effect of the loading pattern is neglected, such an equivalence is obtained by tuning the Pu content in the MOX fuel, while considering the specific Pu isotopic vector at the time of the core reload to obtain a crossing of the reactivity curves of UOX and MOX at the end-of-cycle core average burnup. It is proposed in this work to extend the fuel cycle analysis tool ANICCA (Advanced Nuclear Inventory Cycle Code) with a MOX equivalence Python code package, which automatically governs the supply and demand of Pu vector isotopes required to obtain MOX equivalence. This code package can determine the reactivity evolution for any given Pu vector by means of a multidimensional interpolation on a directive grid of pre-calculated data tables generated by WIMS10, covering the physically accessible Pu vector space. A fuel cycle scenario will be assessed for a representative evolution of the Pu vector inventory available in spent UOX fuel as a demonstration case, defining the interim fuel storage building dimensional requirements for different reprocessing strategies

Good-deal investment valuation in stochastic generation capacity expansion problems

Generation capacity expansion models have a long tradition in the power industry. Designed as optimization problems for the regulated monopoly industry, they can be interpreted as equilibrium models in a competitive environment. While often written as deterministic problems, they can be adapted to accommodate the wide range of uncertainties that currently assail the industry. We consider a stochastic optimization version of the capacity expansion model where risk is assessed through risk functions. In order to combine both the criteria of coherence and time consistency while at the same time sacrificing nothing in terms of computational tractability and economic interpretation, we formulate the model using the so-called ”good deal” risk function. We show that the resulting model takes the form of a conic optimization program that can be interpreted as a multistage hedging optimization problem in an incomplete market

Impact of fresh fuel loading management in fuel cycle simulators: A functionality isolation test

International audienceFuel cycle simulator development started many years ago by several research and engineering institutions or consulting firms for a wide range of applications. To improve confidence in the results, institutions may be tempted to increase the complexity of their software even if this complexity might not be necessary. On the other hand, some simulators may be used outside their range of validity when used in very specific applications. The FIT (Functionality Isolation Test) project is an international effort devoted to improve the confidence in the data produced by fuel cycle simulation tools. The scientific goal is to determine the optimum level of detail a fuel cycle simulator needs according to the type of study and the required confidence level. The project relies on a wide variety of fuel cycle simulators with a large range of complexity levels. The FIT project consists of isolating the impact of one targeted functionality on fuel cycle simulations. The impact of the functionality is assessed using a set of simple basic exercises specifically designed for this purpose, called ”functionality isolation.” The present work focuses on the impact on simulation results of using a fuel loading model (a relation that links the stock isotopic composition with the fresh fuel fabrication according to the reactor requirements) or a fixed fraction approach (the fresh fuel fissile fraction is fixed and does not depend on the stock isotopic composition). The paper first presents the FIT project. The exercise design is described and results show that using a fuel loading model approach has an important impact on fuel cycle outputs under certain conditions that are described. This result is reinforced by the fact that all fuel cycle simulators used in this exercise provide similar conclusions

NUCLEAR FUEL CYCLE OBJECTIVES

The nuclear fuel cycle objectives are drawn from many sources, including the conclusions of major international conferences on different stages and aspects of nuclear fuel cycles, many of which are held in cooperation with the IAEA. Experts from various Member States provided advice to the IAEA through a number of consultants meetings and Technical Working Groups (TWGs), such as the TWGs on Nuclear Fuel Performance and Technology, on Nuclear Fuel Cycle Options and Spent Fuel Management, and on Research Reactors, and through the OECD/NEA–IAEA Uranium Group. The IAEA’s International Project on Innovative Nuclear Reactors and Fuel Cycles (INPRO) is another important source of guidance.JRC.E-Institute for Transuranium Elements (Karlsruhe