水库多目标智能调度模型构建与优化OA

Aiming at the dynamic coordination problem of multi-objective reservoir scheduling under water scarcity in Shanxi Province,this paper proposed a method for constructing and optimizing a multi-objective intelligent scheduling model for reservoirs within the context of smart water conservancy by integrating model predictive control(MPC)and multilayer perceptron(MLP)neural networks.A multi-objective optimization model was developed for the A and B reservoir system in the Sushui River Basin,focusing on minimizing the sum of squared irrigation shortages and ensuring municipal water-supply reliability(≥95.7%).The Levenberg-Marquardt algorithm was employed to train the MLP neural network to generate dynamic scheduling rules.The model applied the ε-constraint method to address multi-objective conflicts and combined the MPC framework to achieve rolling optimization and real-time feedback,while using the entropy-weight TOPSIS method to select optimal scheduling schemes.Using hydrological data of reservoirs A and B from 1979 to 2018 for validation,the results showed that the proposed approach increased municipal water-supply reliability to 98.0%across dry,normal,and wet years,reduced the squared irrigation-shortage metric by 12.3%,and improved computational efficiency by a factor of 60 compared with traditional models.Under extreme drought conditions,municipal water-supply reliability remained at 96.3%.The study offers an innovative,data-driven and model-integrated pathway for short-term optimization of multi-constrained reservoir systems and provides practical guidance for refined water resource management in water-scarce northern regions.