Boosting hydropower generation of mixed reservoirs for reducing carbon emissions by using a simulation–optimization framework

Abstract

Abstract The optimization operation of reservoir seasonal Flood-Limited Water Levels (FLWLs) can counterbalance the hydropower generation and flood prevention in the flood season. This study proposes a multi-objective optimization operation model to optimize the reservoir seasonal FLWLs for enhancing synergies of hydropower generation and flood prevention. The integration of the Non-dominated Sorting Genetic Algorithm-II and a simulation-optimization framework is applied for optimizing the joint operation of reservoirs meanwhile achieving the Pareto solutions to reduce computation complexity and time. And then, the Technique for Order of Preference by Similarity to Ideal Solution is utilized to identify the best seasonal FLWL scheme grounded on multi-criteria decision-making analysis. The mixed reservoirs located in the upstream Yangtze River of China constitute the case study. The results showed that: compared with the annual FLWL scheme, the proposed seasonal FLWL schemes without increasing flood prevention risk could facilitate the joint operation of the mixed reservoirs to achieve 868 million kW·h (5.1% improvement) in average hydroelectricity production during the flood season, meanwhile reducing 681 million kg in carbon emissions accordingly. The results support that the proposed methods can boost hydropower production to benefit China's national tactics in accomplishing peak carbon dioxide emissions before 2030.