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Optimizing Operational Policies of a Korean Multireservoir System Using Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction

Cited 61 time in Web of Science Cited 71 time in Scopus
Authors

Kim, Young-Oh; Eum, Hyung-Il; Lee, Eun-Goo; Ko, Ick Hwan

Issue Date
2007
Publisher
American Society of Civil Engineers
Citation
Journal of Water Resources Planning and Management, 133(1), 4-14
Keywords
OptimizationForecastingReservoir operationKoreaStreamflowPredictions
Abstract
This study presents state-of-the-art optimization techniques for enhancing reservoir operations which use sampling stochastic
dynamic programming SSDP with ensemble streamflow prediction ESP . SSDP used with historical inflow scenarios SSDP/Hist
derives an off-line optimal operating policy through a backward-moving solution procedure. In contrast, SSDP used with monthly
forecasts of ESP SSSDP/ESP reoptimizes the off-line policy. These stochastic models are used to derive a monthly joint operating policy
during the drawdown period of the Geum River multireservoir system in Korea. A cross-validation test of 1,900 simulation runs
demonstrates that: 1 proposed stochastic models that explicitly include inflow uncertainty are superior to those that do not; 2 updating
policy with ESP forecasts is appropriate in this reservoir system; 3 the lower dam of the Geum River multireservoir system should
maintain elevation of 66.5 m during the beginning of the drawdown period to avoid significant increase in the downstream water
shortages; and 4 forecasting accuracy may result in considerable effects on joint reservoir operations.
ISSN
0733-9496
Language
English
URI
https://hdl.handle.net/10371/67725
DOI
https://doi.org/10.1061/(ASCE)0733-9496(2007)133:1(4)
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