Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts

The National Weather Service (NWS) produces ensemble streamflow prediction (ESP) forecasts. These forecasts are used as the basis of a Sampling Stocha stic Dynamic Programming (SSDP) model to optimize reservoir operations. The SSDP optimization algorithm, which is driven by individual streamflow scen arios rather than a Markov description of streamflow probabilities, allows the ESP forecast traces to be employed directly, taking full advantage of t he description of streamflow variability, and temporal and spatial correlat ions captured within the traces. Frequently-updated ESP forecasts in a real -time SSDP reservoir system optimization model (and a simpler two-stage dec ision model) provide more efficient operating decisions than a sophisticate d SSDP model employing historical time series coupled with snowmelt-season volume forecasts. Both models were driven by an appropriately weighted and representative subset of the original forecast and streamflow samples. (C) 2001 Elsevier Science B.V. All rights reserved.