Abstract
This technical note introduces a reservoir operation model based on implicit stochastic optimization (ISO) in which the release policy is guided by the forecast of the mean inflow for a given future horizon rather than by the prediction of the current-month inflow, such as in typical ISO models. The model also does not require the forecast of all inflows for the future horizon and shows to be more efficient in finding less vulnerable release policies when compared to several other explicit and implicit stochastic procedures.
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Notes
Note that the use of mean inflow will not increase the accuracy of the forecast but its value will be arguably easier to estimate. It should be less problematic to estimate a single value than a good sequence of inflow values.
This was already expected since the SOP maximizes the reliability (percentage of non-failure periods) at the expense of providing more vulnerability (magnitude of failures) (Hashimoto et al. 1982).
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Acknowledgements
The first author greatly acknowledges the Alexander von Humboldt Foundation and its Georg Forster Research Fellowship program for the financial support received in order to carry out this research in Germany. The authors thank the anonymous reviewers for their insightful comments and suggestions to improve the presentation of the paper.
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Celeste, A.B., Billib, M. Improving Implicit Stochastic Reservoir Optimization Models with Long-Term Mean Inflow Forecast. Water Resour Manage 26, 2443–2451 (2012). https://doi.org/10.1007/s11269-012-0025-1
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DOI: https://doi.org/10.1007/s11269-012-0025-1