VALUE OF SEASONAL FLOW FORECASTS IN BAYESIAN STOCHASTIC-PROGRAMMING

This paper presents a Bayesian Stochastic Dynamic Programming (BSDP) m odel to investigate the value of seasonal flow forecasts in hydropower generation. The proposed BSDP framework generates monthly operating p olicies for the Skagit Hydropower System (SHS), which supplies energy to the Seattle metropolitan area. The objective function maximizes the total benefits resulting from energy produced by the SHS and its inte rchange with the Bonneville Power Administration. The BSDP-derived ope rating policies for the SHS are simulated using historical monthly inf lows, as well as seasonal flow forecasts during 60 years from January 1929 through December 1988. Performance of the BSDP model is compared with alternative stochastic dynamic programming models. To illustrate the potential advantage of using the seasonal how forecasts and other hydrologic information, the sensitivity of SHS operation is evaluated by varying (1) the reservoir capacity; (2) the energy demand; and (3) the energy price. The simulation results demonstrate that including th e seasonal forecasts is beneficial to SHS operation.