Minimizing the Standard Deviation of the Thermal Load in the Spent Nuclear Fuel Cask Loading Problem

The paper assumes that, at the end of the operational period of a Spanish nuclear power plant, an Independent Spent Fuel Storage Installation will be used for long-term storage. Spent fuel assemblies are selected and transferred to casks for dry storage, with a series of imposed restrictions (e.g.,...

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Bibliographic Details
Published in:Energies (Basel) 2020-09, Vol.13 (18), p.4869
Main Authors: Bautista-Valhondo, Joaquín, Batet, Lluís, Mateo, Manuel
Format: Article
Language:English
Subjects:
Algorithms
Assemblies
Barrels
Coefficient of variation
Fuel storage
Independent Spent Fuel Storage Installation (ISFSI)
Integer programming
mixed integer linear programming (MILP)
multi-start metaheuristics
Nuclear fuels
nuclear power plant operations
Nuclear power plants
Radioactive wastes
Search algorithms
spent nuclear fuel
Spent nuclear fuels
Standard deviation
Thermal analysis
Uranium
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Summary:The paper assumes that, at the end of the operational period of a Spanish nuclear power plant, an Independent Spent Fuel Storage Installation will be used for long-term storage. Spent fuel assemblies are selected and transferred to casks for dry storage, with a series of imposed restrictions (e.g., limiting the thermal load). In this context, we present a variant of the problem of spent nuclear fuel cask loading in one stage (i.e., the fuel is completely transferred from the spent fuel pool to the casks at once), offering a multi-start metaheuristic of three phases. (1) A mixed integer linear programming (MILP-1) model is used to minimize the cost of the casks required. (2) A deterministic algorithm (A1) assigns the spent fuel assemblies to a specific region of a specific cask based on an MILP-1 solution. (3) Starting from the A1 solutions, a local search algorithm (A2) minimizes the standard deviation of the thermal load among casks. Instances with 1200 fuel assemblies (and six intervals for the decay heat) are optimally solved by MILP-1 plus A1 in less than one second. Additionally, A2 gets a Pearson’s coefficient of variation lower than 0.75% in less than 260s CPU (1000 iterations).
ISSN:1996-1073
1996-1073
DOI:10.3390/en13184869