Reservoir group scheduling method and system based on multi-population cooperative particle swarm algorithm

Abstract

The invention discloses a reservoir group scheduling method and system based on a multi-population cooperative particle swarm algorithm, and belongs to the field of reservoir scheduling. According tothe method, the global optimization capability and the local optimization capability of the particle swarm algorithm are fully explored by introducing a plurality of particle swarms of which the inertia weights are gradually reduced step by step, and meanwhile, the population is differentiated through attraction factors defined by the population optimal values obtained by multi-population searching, so that the algorithm is not easy to fall into the local optimal values, and the particle swarm optimization capability is maximized; and maximizing the optimization capability in the solution of reservoir group scheduling, wherein the solution is a global optimal value. According to the method, the optimal positions are transmitted among the populations step by step in a multi-population cooperation mode, and convergence to the global optimal value is quickly realized in the multi-population cooperation mode, so that the optimization and convergence speeds of the particle swarm algorithm are increased, and the reservoir group scheduling problem is solved in a relatively short time.

Description

technical field [0001] The invention belongs to the field of reservoir scheduling, and more specifically relates to a reservoir group scheduling method and system based on multi-group cooperative particle swarm algorithm. Background technique [0002] Reservoir group scheduling involves multiple reservoirs with close hydraulic connections. According to the comprehensive utilization tasks undertaken by each reservoir, the regulation and storage capacity of the reservoirs is used to redistribute the temporal and spatial distribution of water resources among multiple reservoirs to achieve the purpose of promoting benefits and eliminating harm. Therefore, its essence is a dynamic, high-dimensional, nonlinear function optimization problem. [0003] At present, the reservoir group scheduling optimization technology is mainly divided into mathematical programming methods and intelligent optimization algorithms. The first type of mathematical programming methods include linear prog...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a reservoir group scheduling method and system based on multi-group cooperative particle swarm optimization algorithm. Local optimum, or slow convergence speed, which makes it difficult to solve the reservoir group scheduling problem, and the solution is not optimal

Images