Power grid planning and optimal dispatch based on intelligent optimization and genetic algorithm

Abstract

Power grid planning is a prerequisite for optimal design, operation, and planning of the power grid. Based on the characteristics of the power system, this article explored a genetic algorithm-based power network optimization method. In genetic algorithms, simulated annealing algorithms were used to optimize candidate individuals, resulting in the optimal ratio of the number of selected individuals to the number of all mutated individuals. At the same time, a simulated annealing algorithm was added to the optimization process to improve the shortcomings of GA (Genetic Algorithm). When the genetic algorithm selected individuals from the parent population, a simulated annealing algorithm was used to optimize candidate individuals, making the ratio of the number of selected individuals to the number of all mutated individuals optimal. In order to overcome the shortcomings of GA, the tabu search algorithm was introduced into the optimization process, making it have better optimization ability. Through optimization experiments on 15 nodes, it was found that this method had good convergence speed. The final experiment also proved that the power planning based on the simulated annealing genetic algorithm was superior to other models (the load balance, power supply capacity matching, and coordination index scores of the substation based on the algorithm model in this article were 0.5111, 2.2693, and 120.215, respectively, which were higher than other schemes).