Simulated annealing genetic algorithm based reactive power optimization method of AC/DC system

Abstract

The invention relates to a simulated annealing genetic algorithm based reactive power optimization method of an AC / DC system. The reactive power optimization method comprises the following steps of (1) building an AC / DC system model, and calculating the power flow of the AC / DC system, wherein the AC / DC system comprises an AC side and a DC side of which powers are transferred through a converter; and (2) building a reactive power optimization model according to a target function expressed by a usage cost value of network loss, and figuring out the reactive power optimization model according to a set constraint condition. The reactive power optimization method has the advantages that a simulated annealing genetic algorithm is introduced into reactive optimization and voltage control analysis of the AC / DC system; and through the combination of the characteristics of two algorithms, the respective advantage of the two algorithms is absorbed, the global optimal solution can be found with large probability, and meanwhile, the convergence rate is higher.

Description

technical field [0001] The invention belongs to the field of electric power, in particular to a reactive power optimization method of an AC / DC system based on a simulated annealing genetic algorithm. Background technique [0002] With the gradual increase of AC and DC hybrid systems, the impact of DC on the power grid has gradually become prominent. Therefore, it is necessary to optimize the reactive power of the system including AC and DC hybrid transmission, so as to maintain reactive power balance and realize voltage control. The research on reactive power optimization and voltage control of pure AC power grid is relatively mature, but the traditional method is prone to "curse of dimensionality" for reactive power optimization of AC and DC systems. [0003] Although genetic algorithm is suitable for dealing with discrete variables in reactive power optimization, it is easy to fall into local optimization; while simulated annealing method has global search ability, but it ...

AI Technical Summary

Problems solved by technology

The research on reactive power optimization and voltage control of pure AC power grid is relatively mature, but the traditional method is prone to "dimension disaster" for reactive power optimization of AC and DC systems

[0003] Although the genetic algorithm is suitable for dealing with discrete variables in reactive power optimization, it is easy to fall into local optimization; while the simulated annealing method has global search capabilities, but it takes too long

Using one of these methods alone has significant limitations

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