A segmented trial-and-error algorithm for optimized operation of water storage projects

ABSTRACT Mathematical programming methods and modern intelligent algorithms are used to solve the optimized operation models of water storage projects. Classical mathematical programming methods are prone to the curse of dimensionality when the size of the system increases. Intelligent algorithms improve the computational efficiency to a certain extent, but still suffer from defects such as unstable optimized solutions and easy to fall into local optimizers. In this article, a segmented trial-and-error algorithm is proposed to obtain the global optimizer while considering computational efficiency. First, a water balance constraint is introduced into the objective function and a Lagrangian function is constructed to derive the ideal optimizer. Constraints are then gradually introduced and the optimized model is solved by iterative trial-and-error calculations. By approximating the ideal optimizer by segments in this way, the global optimizer is finally obtained. Numerical experiments were carried out in a water supply reservoir. The results show that the segmented trial-and-error algorithm can obtain the global optimizers in different situations with objective functions that are 0.0028–0.0076 lower than those of the genetic algorithm, implying less and more uniform water shortages. The segmented trial-and-error algorithm requires less than 1% of the computational time of the dynamic programming.