Multiobjective Cascade Reservoir Operation Rules and Uncertainty Analysis Based on PA-DDS Algorithm

Abstract

Pareto archived dynamically dimensioned search (PA-DDS) is one of the meta-heuristic methods available to solve multiobjective reservoir operation problems. This study uses this method to optimize reservoir operation rules with the objectives of maximizing the power generation and water supply. The performance of PA-DDS is compared with the nondominated sorting genetic algorithm–II (NSGA-II) in terms of a hypervolume indicator and the distribution of optimized nondominated solutions (NDSs) in a case study of Hanjiang cascade reservoirs in China. The results indicate that PA-DDS can increase the amount of power generation and water supply, respectively. Moreover, the uncertainty in reservoir operation optimized by both methods is analyzed in terms of the NDS distribution and the trade-off relationship between water supply and power generation. The results demonstrate that PA-DDS outperforms NSGA-II not only in the Pareto front approximation (NDS), but also with an increase in water supply by about 300 million m3/year for Hanjiang cascade reservoir operation.