Abstract
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).
Highlights
Optimization problems are found at different levels in electric power systems, from both technological and operations management standpoints
Can be located in the region j ∈ J at time T, and 0 in any other case; i.e., ai,j (T ) = 1 ⇔ qi (T ) ∈ q−j, q+. It follows from it that the relationship between the fuel assembly i ∈ I and the class of cask k ∈ K is given by matrix C(T ) resulting from the boolean product of matrices A(T ) and B; that is, C(T ) = A(T ) ⊗ B, fulfilling the following property: (ai,j (T ) = 1 f b j,k = 1) ⇒ ci,k (T ) = 1. Under such conditions [28], the spent nuclear fuel cask loading problem consists of transferring the maximum number of the fuel assemblies, which are located in the pool of a nuclear power plant on a date T and expressed as time after the end of operation, into a set of regionalized casks of different classes, meeting the restrictions and minimizing costs
In addition to considering the solution from mixed integer linear programming (MILP)-1 with the triplet γ 0 = (1, 1, 1), which corresponds to minimizing the total number of casks, and in order to assess the impact of the adversity coefficients γk (∀k) on the solutions offered by MILP-1, 24 other optimal solutions are analyzed, fixing the list of arbitrary costs γ = (γ1, γ2, γ3 ) to the following values:
Summary
Optimization problems are found at different levels in electric power systems, from both technological and operations management standpoints. The optimization here ranges from determining the SNF removal allocation strategy for a fleet of plants [22], to the design of the containers (e.g., what capacity, how much cooling time needed, etc.) [23], to the strategies to be followed in the loading of the casks. As for the latter, Zerovnik et al [24] propose combinatorial methods to optimize the cask loading, aiming at final geological repository.
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