Abstract
A heuristic-based simulated annealing has been developed to optimize the operation of a multiple reservoir system. Simulated annealing is a probabilistic search algorithm, motivated by an analogy of physical annealing in solids. It has been increasingly applied to a broad spectrum of fields for which little prior knowledge is required. The developed model is illustrated by a test application to a benchmark problem of a 10-reservoir problem that had been solved previously in the literature. For this problem, simulated annealing shows its ability to provide better result than those obtained from other techniques. The model is then applied to the real multiple reservoir system in Thailand to derive optimal operating policies. The objective function is formulated to minimize the irrigation deficits during 3 years. The performance of the simulated annealing is compared with that of the genetic algorithm developed for the same problem. The results show that the simulated annealing is more efficient than the genetic algorithm. Simulated annealing produces higher quality solutions while spending lesser computation time compared with the genetic algorithm. The results obtained from these applications have proved that the simulated annealing is capable of addressing a large and complex problem. Simulated annealing is quite a new promising search algorithm for reservoir optimization problems. It is worthy of further investigation for other applications in this area of research.
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The authors would like to extend their appreciation and gratitude to the Electricity Generating Authority of Thailand for providing data used in this study.
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Tospornsampan, J., Kita, I., Ishii, M. et al. Optimization of a multiple reservoir system using a simulated annealing--A case study in the Mae Klong system, Thailand. Paddy Water Environ 3, 137–147 (2005). https://doi.org/10.1007/s10333-005-0010-x
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DOI: https://doi.org/10.1007/s10333-005-0010-x