Development of advanced optimization methods for nuclear power scenarios

The study of possible nuclear fleet evolution is done through scenario calculations. A scenario models precisely all material flows within the fuel cycle, starting with raw material extraction, following with fuel fabrication, fuel irradiation inside the reactor, spent fuel cooling, fuel reprocessing and waste disposal. The scenario is a great tool that enables discussions among the different nuclear actors in order to identify the most promising strategies for the fleet. However, a scenario is really dependant on the set of hypotheses considered. In addition, the hypotheses that change the most are the more impredictable ones as they depend on political context. For example, the nuclear production in France was limited to 50%, until this limit was removes this year. This change affects directly the number of reactors that is to be deployed, and thus the dimensionning of all facilities associated. The current way to perform scenario calculation is not well suited to manage such hypotheses changes.

A new field of research has emerged to deal with these deep uncertainties : the study of scenario robustness and resilience. In this context, the objective is no longer to quantify the performances of a precise scenario, but its ability to be modified to answer to the objective or constraint change. To do so, it is necessary to launch several thousands of calculations, among which a large part are not useful (i.e. they lead to non viable scenarios). The goal of this thesis work is to investigate the optimization methods used in logistics in order to build efficient methods to quickly build scenario inputs. The generated inputs should lead to optimal scenarios for a set of given objectives. Then, it would be possible to identify the scenarios that are able to answer to several objectives and assess whether they can be adjusted to answer to new constraints.