Multi-objective water optimal dispatching of the Yangtze-to-Huaihe River Water Diversion Project（Henan section）
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Abstract:
The multi-objective water optimal dispatching problem is the current research hotspot in the field of water resources. The Henan section of the Yangtze-to-Huaihe River Water Diversion Project involves a variety of water conveyance structures such as river channels, pipelines, gate pumps, and storage reservoirs, making the water supply scheduling system complex. Water scheduling for water diversion projects often needs to consider multiple scheduling objectives such as water supply assurance, energy consumption, and fairness. These objectives generally have competing relationships, making the problem relatively difficult to solve. Therefore, research on multi-objective optimization scheduling of water quantity for complex water diversion projects is particularly important.Current research adopts intelligent optimization algorithms to solve complex water scheduling models. These algorithms optimize the values of decision variables to achieve the optimal objective function under given constraints. In the process of water optimal dispatching models, handling constraints has always been a challenging problem for intelligent algorithms. Conventional intelligent algorithms perform random searches in the decision variable space within given upper and lower bounds. However, for complex scheduling systems, with numerous types of constraints and decision variables, and strong correlations among them, the feasible space is relatively small compared to the entire search space. This requires algorithms to increase iteration numbers to expand the search, resulting in longer computation time, poor convergence, and even the inability to find feasible solutions. To address this problem, a feasible search approach-based multi-objective optimization method is proposed to solve the multi-objective water optimization scheduling problem for the Henan section of the Yangtze-to-Huaihe River Water Diversion Project. The minimizing water shortage rate, total pumping volume of pump stations, and standard deviation of water shortage rate in the water destination regions are selected as objective functions, aiming to construct a multi-objective water volume optimization scheduling model from the perspectives of water supply security, energy consumption, and fairness. Based on the feasible search approach, combined with the process of reverse calculation and forward calculation, the constraints were addressed by involving the decision coefficients and maintaining the search space within the feasible domain through mapping relationships. The multi-objective non-dominated sorting genetic algorithm (NSGA-II) was utilized for the model solution, generating the Pareto optimal solution set. The entropy weight method was employed for scheme selection. A comparison was made between the scheme with the minimum water shortage rate and the scheme with the highest evaluation score based on the entropy-based weight method. Although the average water shortage rate of the optimal scheme based on the entropy-based weight method was relatively higher than that of the scheme with the minimum water shortage rate, the total pumping volume and the standard deviation of the water shortage rate were reduced. The total pumping volume decreased by approximately 2 million m3, and the standard deviation decreased by approximately 50.2%, respectively. Considering multiple objectives comprehensively, the optimal scheme with a higher balance of water shortage spatial distribution and lower energy consumption based on the entropy weight method was chosen as the optimal scheduling scheme. The results show that the NSGA-II algorithm based on the feasible search can effectively solve the multi-objective optimization problem of complex scheduling systems. The optimal schemes considering multiple objectives are more reasonable than single-objective schemes, providing decision support for the operation and management of the Henan section.
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