Quantifying the Risks that Propagate from the Inflow Forecast Uncertainty to the Reservoir Operations with Coupled Flood and Electricity Curtailment Risks

Abstract

Substantial uncertainty is inherent in reservoir inflow forecasting, which exerts a potential negative impact on reservoir risk. However, the risk propagation from the inflow forecast uncertainty (IFU) to reservoir operations remains elusive. Thus, a new integrated assessment framework was developed in this study to characterize the risk coupling with flood and electricity curtailment risks that propagate from the IFU to the reservoir operations. First, to incorporate the IFU, an improved Gaussian mixture distribution (IGMD) and Markov chain Monte Carlo (MCMC) algorithm were constructed to model the measured forecast errors and generate ensemble inflow forecasts, respectively. Next, to assess the reservoir risk, the flood risk induced by the IFU overestimation and the electricity curtailment risk related to the IFU underestimation were identified according to the reservoir operation rules. The sub-daily inflow forecast at the Jinping First Stage Hydropower Plant Reservoir of Yalong River, China (Jinping I Reservoir) was selected. The results indicated that the IGMD-based MCMC was capable of deriving robust ensemble forecasts. Furthermore, there was no flood risk (risk rate was zero) induced by the IFU when taking designed reservoir floods with a ≥10-year return period as the benchmark. In contrast, the electricity curtailment risk rate significantly increased up to 41% when considering the IFU. These findings suggested that compared with the flood prevention pressure, the IFU would more likely result in severe electricity curtailment risk at the Jinping I Reservoir.

Keywords

• risk assessment
• reservoir inflow forecast
• inflow forecast error modeling
• Gaussian mixture distribution
• uncertainty