Electric system economic dispatching optimization method based on criss-cross algorithm

Abstract

The invention discloses an electric system economic dispatching optimization method based on a criss-cross algorithm. The criss-cross algorithm is a brand new swarm intelligence optimization algorithm and mainly comprises a horizontal cross operator and a longitudinal cross operator, wherein a multi-dimensional optimizing space is divided into hypercubes with half of the population size through horizontal cross, and each pair of paired parent particles searches for filial generations in its hypercube subspace and the periphery of the hypercube subspace; arithmetic cross search is executed on different dimensions in the population with a certain probability through longitudinal cross; a domination solution obtained from a moderate solution generated through two kinds of cross through a competition operator will rapidly spread into the whole population in a chain reaction mode, so that the evolution speed is greatly increased. The electric system economic dispatching optimization method based on the criss-cross algorithm has the advantages of being high in global searching ability and high in convergence rate through the criss-cross algorithm, applicable to optimizing a non-linear high-dimensional function and also applicable to achieving large-scale complex optimization in practical engineering.

Description

technical field [0001] The invention relates to an optimization method for economic dispatching of a power system, in particular to an optimization method for economic dispatching of a power system based on a crossover algorithm (CSO). Background technique [0002] Economic dispatch of power system is of great significance to the safe and economical operation of power system. As a typical optimization problem of power system, economic dispatch (ED) refers to the optimal allocation of loads to different units under the condition of meeting the demand of power dispatch and various constraints. , so that the fuel consumption of the whole system or the total cost of power generation is minimized. Economic scheduling is a non-convex, nonlinear, high-dimensional complex optimization problem. In recent years, many related studies have used swarm intelligence optimization algorithms to solve such problems, such as particle swarm optimization algorithm PSO, genetic algorithm (GA), et...

AI Technical Summary

Problems solved by technology

[0003] Although various swarm intelligence optimization algorithms have made some progress in solving nonlinear complex problems, there are still many shortcomings: for example, although PSO has a fast convergence speed, it is prone to premature problems when solving large-scale ED optimization problems, GSA, GA takes a long time to optimize the process, while other algorithms such as ABC, ACO, and BFO need to adjust multiple control parameters, showing that the problem is sensitive to parameters

In addition, some swarm intelligence optimization algorithms often have to sacrifice convergence speed or adopt more complex algorithm structures in order to maintain population diversity.

So far, no swarm intelligence optimization algorithm can show absolute advantages in solving various complex optimization problems

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