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Performance of a sampling stochastic dynamic programming algorithm with various inflow scenario generation methods Schaffer, Jennifer Lynn

Abstract

We present the implementation of a Sampling Stochastic Dynamic Programming (SSDP) algorithm to maximize water value, while meeting consumer demand for the BC Hydro hydroelectric system in British Columbia, Canada. The implementation includes power generation facilities on the Columbia and Peace River systems. Variability of natural streamflow into a reservoir is a major source of uncertainty when developing reservoir operation policies and determining the value of water within a system. This study investigates SSDP model performance with various hydrologic inputs. Sixty years of historical data are used to generate hydrologic scenarios comprised of inflow and forecast sequences as input to the SSDP model. Scenario types studied include historical record data, inflows and forecasts generated from an autoregressive lag-1 model, and BC Hydro ensemble streamflow prediction forecasts. We present results of our implementation of the SSDP algorithm including a discussion on improved reservoir operation policy and the future value of water with various hydrologic inputs. We also present our investigation of the marginal value of water with the evolution of forecasts. Results indicate that forecasts are most valuable in determining the value of water during the early freshet, and the value added from updating future forecasts diminishes as the time in which the forecast is made progresses through the melting period.

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Attribution-NonCommercial-NoDerivs 2.5 Canada

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