Adaptive Reservoir Operation Model Incorporating Nonstationary Inflow Prediction

Abstract

Long-term changes in reservoir inflow due to climate change and human interferences have caused doubts on the assumption of hydrologic stationarity in reservoir design and operation. Incorporating uncertain predictions that consider nonstationarity into an adaptive reservoir operation is a promising strategy for handling the challenges that result from nonstationarity. This study proposes rules for multistage optimal hedging operations that incorporate uncertain inflow predictions for large reservoirs with multiyear flow regulation capacities. Three specific rules for determining the optimal numerical solution are derived. A solution algorithm is then developed based on the optimality conditions and the three rules. The optimal hedging rules and the solution algorithm are applied to the Miyun Reservoir in China, which exhibited a statistically significant decline in reservoir inflow trend from 1957 to 2009, to determine an annual operating schedule from 1996 to 2009. Nonstationary inflows are predicted by using an autoregressive integrated moving average (ARIMA) model on a period-by-period basis. The actual operation (AO) of the reservoir is compared with different operational policy scenarios, including a standard operating policy (SOP; matching the current demand as much as possible), a hedging rule (i.e., leaving a certain amount of water for the future to avoid the risk of a large water deficit) with a prediction from ARIMA (HR-1), and a hedging rule with a perfect prediction (HR-0). With a predefined benefit function, the utility of the reservoir operation under HR-1 is 3.7% lower than that under HR-2, but the utility under HR-1 is 3.1% higher than that of AO and 13.7% higher than that of SOP.