Research on the application of reservoir flood optimal operation based on improved genetic algorithm

Aiming to address the issues of non-dynamicity, insufficient flexibility, and poor adaptability to complex environmental changes in conventional reservoir scheduling methods, this paper considers the multi-objective requirements of reservoir flood scheduling tasks and the limitations of traditional genetic algorithms under complex constraints. It proposes a multi-objective flood optimization scheduling method for reservoirs based on an improved genetic algorithm. The optimization objective functions are to maximize peak cutting and minimize the highest reservoir water level. The algorithm introduces dynamic crossover and mutation strategies, and incorporates heuristic information from conventional reservoir operating rules and general reservoir discharge principles into the traditional genetic algorithm for model solving. Taking the Yangzhuang Reservoir on the northern rivers of the Haihe River Basin as the research object, the paper constructs reservoir flood scheduling models based on the improved genetic algorithm, scheduling rules, and traditional genetic algorithm, and conducts comparative analysis. The results show that the improved genetic algorithm can significantly improve the scheduling efficiency and flood control capability of the reservoir, providing reliable technology for reservoir management during the flood season.