Adaptable Long-Term Optimization of Dry Cask Storage Loading Patterns
Abstract
To address the evolving needs of dry storage, this research developed an optimization
methodology to identify loading configurations to minimize the number of
casks, their heat load, and the time when they meet transportation requirements.
The motivation was to investigate strategies that balance and reduce risk over the
lifetime of a site's reactor(s).
The dry cask loading problem was formulated as an adaptable dynamic bin
packing problem, accommodating different site and cask limits in broadly-defined
constraints. A new method was developed to address its complexities, named the
GRASP-enabled adaptive multiobjective memetic algorithm with partial clustering
(GAMMA-PC). This method embeds greedy randomized adaptive search procedures
in a multiobjective evolutionary algorithm with local search techniques and partial
decomposition of the objective space during crossover.
GAMMA-PC was demonstrated through integration with the unified database
from the Used Fuel Systems group at Oak Ridge National Laboratory to optimize
simulated loading configurations for Vermont Yankee, Comanche Peak, and Zion
Nuclear Power Stations. Its performance was evaluated through comparisons to test
solutions and to the real Zion loading configuration. GAMMA-PC produced diverse
solutions that dominated the testing sets. The improvement was concentrated in
the average heat load, and the third objective function was shown to be sensitive to
individual assembly characteristics.
The results suggested the usefulness of GAMMA-PC for utilities considering long-term
goals. They showed that more diverse cask loadings and strategic placements
of empty positions can be used to reduce initial heat loads. Moving to a higher capacity cask increases loading flexibility but can result in transportation delays.
Long-term planning enables a more thorough consideration of the trade-offs involved
in any decision.
This research contributes one of the first in-depth studies of the dry cask loading
problem. It expands the current treatment of assembly selection over longer timeframes
and meets user-defined requirements. It is also one of the first tri-objective
dynamic bin packing problems, and the first to pack items with time-dependent
characteristics. Future work should focus on refining the objectives and incorporating
uncertainty. With its adaptable structure, GAMMA-PC is a promising new
metaheuristic for this task and for dynamic bin packing problems in general.
Subject
nuclear waste managementused nuclear fuel
dry cask storage
decay heat
multiobjective optimization
combinatorial optimization
bin packing problems
memetic algorithms
GRASP
GAMMA-PC
Citation
Spencer, Kristina Yancey (2017). Adaptable Long-Term Optimization of Dry Cask Storage Loading Patterns. Doctoral dissertation, Texas A & M University. Available electronically from https : / /hdl .handle .net /1969 .1 /173097.