Hybrid reservoir group flood control optimization scheduling scheme generation method based on energy criterion

Abstract

The invention discloses a hybrid reservoir group flood control optimization scheduling scheme generation method based on an energy criterion. The method comprises the following steps: selecting a hybrid reservoir group and a corresponding downstream protection object; performing particle swarm coding; setting constraint conditions of each reservoir in the series-parallel reservoir group; calculating the fitness value of each particle in the first generation particle swarm; carrying out iterative circulation and updating particles by adopting a particle swarm algorithm based on an attenuation cosine curve inertia weight; and calculating the fitness value of each particle in the new-generation particle swarm until an optimization condition is met, and outputting a flood control optimization scheduling scheme of the hybrid reservoir group. According to the method, the particle swarm optimization algorithm based on the attenuation cosine curve inertia weight is adopted, the defect that an existing particle swarm optimization algorithm is prone to falling into local optimum is overcome, the global and local search capacity is enhanced, the reservoir group flood control optimization scheduling scheme is finally obtained, under the condition that downstream safety is guaranteed, the flow called by a flood control reservoir group is greatly reduced, and the flood control risk is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of water conservancy engineering, and in particular relates to an energy criterion-based method for generating an optimal dispatch scheme for flood control of mixed reservoir groups. Background technique [0002] Flood disasters in my country are sudden, frequent and uncertain, and their threat to human survival and development is relatively serious, especially the disasters caused by super-standard floods are even more immeasurable. The flood control engineering system needs to formulate corresponding scheduling schemes for reservoir projects. However, in the real-time scheduling of joint flood control, it is affected by many factors, and the reservoir group involves more elements. Especially for mixed reservoir groups, the correlation is more complicated. [0003] Due to the complexity and close correlation of the system, the use of optimization theory for regular identification often results in a large op...

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Problems solved by technology

The flood control engineering system needs to formulate a corresponding scheduling plan for the reservoir project. However, in the real-time scheduling of the joint flood control, it is affected by many factors, and the reservoir group involves more elements. Especially for the mixed reservoir group, the correlation is more complicated

[0003] Due to the complexity and close correlation of the system, the use of optimization theory for regular identification often results in a large optimization space for scheduling decisions. At the same time, the problem of "dimension disaster" has always been a major problem that plagues the joint scheduling of reservoir groups. An important The reason is that the theory and physical mechanism of joint operation of reservoir groups are relatively weak

[0004] In addition, when solving the optimal scheduling model for flood control of mixed reservoir groups, it is found that when the inertia weight of the standard particle swarm optimization algorithm is set to a large value, the particle swarm optimization algorithm can enhance the search ability of the future exploration space, but the local detail search ability is weak; the standard particle swarm optimization algorithm When the inertia weight of the group algorithm is set to be small, the local search ability of the algorithm is strong, but the ability to search for new spaces is weakened

In response to this shortcoming, Berhart and Shi (1998) proposed a particle swarm optimization algorithm with a linearly decreasing inertia weight, but this linearly decreasing inertia weight is only related to the number of iterations of the algorithm, and cannot truly reflect the complexity and non-linearity of the algorithm during its operation. On the other hand, in the later stage of operation, with the decrease of the inertia weight, the particle swarm optimization algorithm lacks the ability of global search and has fallen into local optimum

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