基于遗传禁忌混合算法的配电网无功优化

Reactive Power Optimization of Power Distribution Network Based on Genetic Tabu Hybrid Algorithm

为了有效解决由配电网分布式电源(distribution generation,DG)渗透率逐渐升高带来的电压越限、网损增加问题。建立了配电网无功优化模型,以网损最小和电压不越限为综合目标,通过采用一种遗传禁忌混合算法(genetic/tabu hybrid algorithm,GATS)研究了接入DG的配电网无功优化问题。在MATLAB软件上以IEEE-33节点配电系统为算例对所研究问题进行了分析与验证。结果表明:GATS混合算法的可行性和优越性较强,通过该算法进行无功优化可以有效改善系统电压水平、降低网损;并且适当提高DG渗透率,同样有利于网损降低、电压水平提高;但若DG渗透率过高,会导致网损增加、电压越限问题,通过GATS算法进行无功优化这些问题可得到有效解决。

In order to effectively solve the problems of voltage violation and the increase of network loss caused by the gradual increase of the distribution generation(DG) permeability in the distribution network, this paper established a reactive power optimization model of distribution network, which took the minimum network loss and the voltage non-violation as the comprehensive goal. A kind of genetic tabu hybrid algorithm(GATS) was adopted to study the problem of reactive power optimization in distribution network. In MATLAB the IEEE-33 node distribution system was taken as an example to analyze and verify the research problems.The results show that the feasibility and superiority of the GATS are stronger, the voltage profiles of the system are improved and the network loss is reduced effectively by optimization with this algorithm;and the proper increase of DG permeability can reduce the network loss and improve the voltage level, however, if the permeability of DG is too high, the problems of increasing network loss and voltage violation will be caused, but the reactive power optimization with GATS algorithm can solve these problems effectively.