Multi-objective optimization of cascade reservoirs using NSGA-II: A case study of the Three Gorges-Gezhouba cascade reservoirs in the middle Yangtze River, China

Abstract

ABSTRACTA multi-objective optimization model of cascade reservoirs was developed to maximize the power generation and minimizie the appropriate ecological flow shortage index (AEFSI) downstream from the reservoir. Additionally, the non-dominated sorting genetic algorithm (NSGA-II) was used to search for multi-objective Pareto optimal solutions. The paper took the Three Gorges-Gezhouba cascade reservoirs as a case study. After validating the model, data from three typical years were used in the optimization. The results indicated that maximizing power generation by adjusting the optimal rules increased power generation by 1.07%, 0.91%, and 1.03% in normal, wet, and dry hydrological years, respectively, while increasing the AEFSI by 22.12%, 11.78%, and 14.67% (compared to real operations). The AEFSI was improved (decreased) by 21.90%, 10.27%, and 18.52% when the optimal rules favored the downstream ecology, but power generation decreased by 1.61%, 1.06%, and 2.29%, respectively, in the different hydrological years. Moreover, the results provide a set of well-distributed optimal solutions along the Pareto front that allow decision-makers to easily determine the best compromised solutions based on the trade-offs between the economic and ecological benefits. The results of this study provide guidance for decision-makers to improve the comprehensive benefits of the Three Gorges-Gezhouba cascade reservoirs.