A reservoir operation method that accounts for different inflow forecast uncertainties in different hydrological periods

Abstract

Inflow forecast is an important input for reservoir operation. Due to limited forecast techniques, the inflow forecast always has uncertainties (usually in the form of forecast errors). Handling inflow forecast uncertainties effectively is important for optimizing reservoir operation. The magnitude of this uncertainty depends on both the forecast lead times and the hydrological periods (i.e., wet season vs. dry season). Two-stage Bayesian stochastic dynamic programming (TBSDP) is an effective way to address the forecast uncertainties. However, current TBSDP methods pay more attention to the uncertainty differences caused by forecast lead times, neglecting the uncertainty differences in wet and dry seasons. Starting with a TBSDP model, we proposed a new two-stage and two-period version of the model (TTBSDP), which can not only consider the forecast uncertainty differences caused by forecast lead times, but also separately accounts for forecast uncertainty in wet and dry seasons. Because dealing separately with the uncertainties in the two periods increases the computational complexity, we further explored the conditions under which degree of forecast uncertainties this separation is necessary. We compared our new model’s results (in terms of cumulative annual power generation) with the previous Bayesian stochastic dynamic programming models. With increasing forecast uncertainty in the first stage of the overall forecast horizon (in the wet season), the TTBSDP model produced superior results at higher uncertainty, with the most stable performance (the smallest variation of cumulative annual power generation when the forecast uncertainty coefficient increases). The proposed new model is benefit for hydropower operations when the magnitude of forecast uncertainty in the wet seasons is large.