A novel impoundment framework for a mega reservoir system in the upper Yangtze River basin

Abstract

The joint and optimal impoundment operation of cascade reservoirs can dramatically boost the efficiency of water resource utilization. However, most existing techniques fail to conquer the curse of dimensionality in mega multi-objective reservoir system. To overcome this obstacle, this study proposes a novel framework that integrates aggregation-decomposition (AGDP), parameterization simulation optimization (PSO), and the parallel progressive optimization algorithm (PPOA). In detail, it involves three main steps: (1) reservoir grouping and application of AGDP in the same group; (2) derivation of the initial impoundment solution by using the non-dominated sorting genetic algorithm-II to solve the PSO model; and (3) further improvement of the impoundment policy via PPOA. The proposed framework is tested on a mega reservoir system in the upper Yangtze River basin. Results demonstrate that our hybrid method can generate a series of impoundment policies to adapt to different flood event scenarios. Compared to the conventional operating rule, the optimal policy can increase impoundment efficiency from 89.50% to 94.21%, increase hydropower generation by 6.63 billion kWh/year (3.26%) and reduce CO2 emissions by 5.21 billion kg/year while maintaining the flood control risk at a low level. These findings verify the applicability and effectiveness of the novel framework in high-dimensional multi-objective impoundment, and also highlight the substantial potential benefits of sustainable water resources.