智能优化算法在配电网规划中的应用研究

Application of intelligent optimization algorithm in distribution network planning

配电网规划对于提升电力系统运行效率以及节约生产成本有着重要的作用。针对该类复杂优化问题,研究提出了一种基于改进群搜索优化算法(Group Search Optimization, GSO)的规划方法,其利用人工鱼群算法(Artificial Fish Swarm, AFS)对传统群搜索优化算法进行改进,从而结合了两者的优点。研究结果显示,对于小规模配电网络而言,三种算法均能取得较好的规划效果,且改进GSO算法的性能更优。而面对较大规模配电网络时,GSO算法与AFS算法易陷入局部最优。在仿真测试实验中,改进GSO算法的平均费用值、标准差、方差分别为948.17万元、5.61、34.89,均低于另外两种算法。除此之外,改进GSO算法的总有功损耗也均低于GSO算法和AFS算法。其优异的性能对于提升配电网规划效率、节约成本有着重要的意义。

Distribution network planning plays an important role in improving the operation efficiency of power system and saving production costs.Aiming at this kind of complex optimization problem,a planning method based on improved group search optimization(GSO)is proposed,which uses artificial fish swarm(AFS)to improve the traditional group search optimization algorithm,thus combining the advantages of both.The results show that for small-scale distribution networks,the three algorithms can achieve better planning results,and the improved GSO algorithm has better performance.However,in the face of large-scale distribution networks,GSO algorithm and AFS algorithm are prone to fall into local optimization.In the simulation test,the average cost,standard deviation and variance of the improved GSO algorithm are 9.4817 million yuan,5.61 and 34.89 yuan respectively,which are lower than the other two algorithms.At the same time,the total active power loss of the improved GSO algorithm is also lower than that of the GSO algorithm and the AFS algorithm.Its excellent performance is of great significance for improving the efficiency of distribution network planning and saving costs.