Optimizing complementary operation of mega cascade reservoirs for boosting hydropower sustainability

Hydropower generation and flood prevention of mega cascade reservoirs have far-reaching influences on the synergies of hydropower output, water utilization, and carbon dioxide (CO2) emission reduction. However, synergetic optimization is challenging, especially true under dynamic flood forecast conditions. This study proposed a dynamic optimization framework of complementary operation of cascade reservoirs driven by hydropower generation and flood prevention for boosting synergies. A multi-objective optimization model integrating dynamic non-dominated sorting genetic algorithm-III with support vector machine was developed to simultaneously maximize reservoir hydropower generation and minimize the peak flow for a flood control station. Seven mega cascade reservoirs of the upper Yangtze River basin constituted the case study, and the standard operation policy formed the benchmark. The results suggested that the proposed method could efficiently promote synergistic benefits with improvement rates of 8.5%, 6.5%, and 8.4% in hydropower output, floodwater utilization efficiency, and CO2 emission reduction, respectively. This study not only offers science technical support for the complementary operation of mega cascade reservoirs to promote synergies between hydropower generation and flood prevention but also suggests policymakers with favorable strategies delineating both potential risks and benefits regarding the synergetic optimization of complementary operation in the interest of sustainable hydropower development.