An Economic Dispatch Method of Power System Based on Wildflower Optimization Algorithm

Abstract

The invention relates to an economical scheduling method for an electric system based on a wildflower optimization algorithm. The method comprises the following steps of 1, using minimum system fuel cost as a target function, considering equality constraints and inequality constraints, and building an electric system economical scheduling model; 2, executing chaotic mechanism to initialize population in combination with the scheduling model; 3, executing normal diffusion and a convergent evolution mechanism; 4, executing a pairing propagation mechanism; 5, executing a chromosomal mutation mechanism; and 6, performing an ending condition: if the maximum number of iterations is reached, ending circulation, and outputting a result; and otherwise, turning to the step 3.

Description

technical field [0001] The invention relates to the technical field of electric power system economic dispatching, and more specifically, relates to a method for electric power system economic dispatching based on a wildflower optimization algorithm. Background technique [0002] Power system economic dispatch is of great significance to the continuous, safe and reliable operation of the power system. As a typical optimization problem in the power system, economic dispatch refers to optimizing the output of each generator under the premise of meeting the load demand at all levels and operating constraints, so that fuel can be used efficiently, thereby reducing power generation costs and grid losses. , to improve the efficiency of power generation, and then improve the security and economy of the system, is a nonlinear, multi-dimensional, multi-constraint, continuous and non-convex optimization problem. [0003] On the basis of traditional scheduling methods, in recent years...

AI Technical Summary

Problems solved by technology

[0004] Although these algorithms have their own characteristics and have made some progress to varying degrees, there are still many disadvantages for the multi-constraint, nonlinear and complex optimization problem of power system economic dispatch.

For example: Genetic Algorithm (GA) runs for a long time; Particle Swarm Optimization (PSO) is easy to fall into local optimum; Quasi-Annealing Algorithm (SA) and Wolf Pack Algorithm (WPA) have poor particle diversity and many control parameters, etc.

In addition, many algorithms have poor global convergence ability. For the complex model of economic dispatch, the global convergence ability is not strong enough, and it is easy to fall into local optimum.

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