Application Research of CFD-MOEA/D Optimization Algorithm in Large-Scale Reservoir Flood Control Scheduling

Abstract

Reservoir flood control has an important impact on flood protection and plays an important role in reducing the loss of people’s lives and property. In order to play an important role in flood control operation of large-scale reservoirs, a control flood dispatching multi-objective evolutionary algorithm based on decomposition (CFD-MOEA/D) is proposed. The same type of multi-objective optimization algorithm (non-dominated sorting genetic algorithm II (NSGA-II)) is introduced, and CFD-MOEA/D, NSGA-II, and traditional MOEA/D algorithms are compared. The research results show that the CFD-MOEA/D algorithm can obtain the non-dominated solution of the higher water level in the upstream, and the solution obtained by the CFD-MOEA/D algorithm is more sufficient than the NSGA-II algorithm and the MOEA/D algorithm. When analyzing the HV value curve, the uniformity and convergence of the optimal solution obtained by the CFD-MOEA/D algorithm are better than those of the other two algorithms. The optimal dispatching scheme of the CFD-MOEA/D algorithm is compared with the actual dispatching scheme of the reservoir, and it is found that the maximum upstream water level and the final water level obtained by the CFD-MOEA/D algorithm are both kept at approximately 325 m, which is consistent with the actual dispatching scheme. The new feature of the algorithm is that it uses a decomposition method from coarse to fine and improves the hourly scheduling scheme to obtain higher scheduling efficiency.