Reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms

A technology of difference algorithm and scheduling method, which is applied in the field of reservoirs, can solve problems such as slow convergence speed and local optimal solution, and achieve the effect of ensuring individual distribution, good diversity, and improving ergodicity

Active Publication Date: 2016-01-13
HOHAI UNIV
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a method based on multi-objective hybrid leapfrog for traditional SFLA, which is easy to fall into local optimal solution and slow convergence speed,

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  • Reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms
  • Reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms
  • Reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms

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[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0025] The present invention aims at the shortcomings of the traditional SFLA, which is easy to fall into the local optimal solution and slow convergence speed, and in order to avoid the impact of the poor initial population distribution on the optimization process, it provides a multi-objective mixed leapfrog differential algorithm (SFLA-EA ) reservoir scheduling method. This method uses chaos theory to generate initial population, adopts external archive set meth...

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Abstract

The invention discloses and proposes a reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms. The reservoir dispatching method comprises the following steps of S11, acquiring basic information data of a reservoir; S12, building a multi-target optimal dispatching mathematic model considering constraint conditions of water balance, machine set output, discharged volume and generation power, wherein the target function is built based on maximum generation power, minimum water deficit, minimum surplus water and minimum output; S13, figuring out Pareto optimal solution of the multi-target optimal dispatching mathematic model by using the multi-target shuffled frog leaping and differential algorithms; and S14, automatically determining a reservoir optimal dispatching scheme by a multi-target decision method on the basis of the Pareto optimal solution. By the reservoir dispatching method, global optimization is achieved, the calculation efficiency is improved, and the requirement of automatically selecting the multi-target optimal dispatching scheme of the reservoir is met.

Description

technical field [0001] The invention relates to the technical field of reservoirs, in particular to a reservoir scheduling method based on a multi-objective mixed leapfrog differential algorithm. Background technique [0002] Reservoir optimal scheduling is a multi-constraint and multi-stage decision-making dynamic, optimal control problem of complex nonlinear systems, especially for multi-objective comprehensive utilization of reservoirs such as flood control, power generation, irrigation, water supply, shipping, and sand discharge, it adds optimization The complexity of solving. With the development of optimization technology and the continuous deepening of reservoir scheduling research, various optimization algorithms have been introduced into the optimal scheduling of reservoirs, including some classic traditional optimization algorithms, optimal control theory methods (such as large system methods) and cluster Intelligent algorithms, etc. Traditional optimization tech...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06N3/00
Inventor 方国华付晓敏郭玉雪林杰周磊
Owner HOHAI UNIV
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