An Analytical Framework for Investigating Trade-Offs between Reservoir Power Generation and Flood Risk

Abstract

Converting floodwater into power without increasing flood risk is critical for energy-stressed regions. Over the past decades, numerous methods have been proposed to solve this problem. However, few studies have investigated the theoretical explanation of the trade-offs between power generation and flood risk. This study establishes an analytical framework to derive optimal hedging rules (OHR) and explains the economic insights into flood risk reduction and power generation improvement. A two-stage model based on the concept of dynamic control of carryover storage (DCCS) was developed as part of the framework, considering forecast uncertainty and risk tolerance. The results illustrated that hedging and trade-offs between power generation and flood risk during DCCS only occurs when the forecasted inflow and forecast uncertainty are within certain ranges, beyond which there is no hedging and trade-offs analysis; either power generation or flood risk becomes the dominant objective. The OHR was divided into three cases under different levels of forecast uncertainty and risk tolerance. Compared to forecast uncertainty, downstream risk tolerance plays a more important role in determining which case of the OHR is adopted in real-world operations. The analysis revealed what and how intense trade-offs are between power generation and flood risk under different scenarios of forecasted inflow, forecast uncertainty, and risk tolerance. The framework serves as a guideline for less abundant water resources or energy-stressed areas of operational policy. Nierji Reservoir (located in northeast China) was taken as a case study to illustrate the analysis, and the application results showed that OHR increases the average annual power generation by 4.09% without extra flood risk compared to current operation rules.