Abstract
The sequential nature of reservoir operating decisions and the variability of streamflow makes Stochastic Dynamic Programming an attractive optimization procedure for reservoir system operations. This paper examines the use of Sampling Stochastic Dynamic Programming (SSDP). SSDP models employing the National Weather Service's (NWS) Ensemble Streamflow Prediction (ESP) forecasts are compared to SSDP models based on historical streamflows and snowmelt volume forecasts. The SSDP optimization algorithm, which is driven by individual streamflow scenarios rather than a Markov description of streamflow probabilities, allows the ESP forecast traces to be employed intact, thus taking full advantage of their rich description of streamflow variability and the temporal and spatial inter-relationships captured within the traces.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
