基于遺傳禁忌混合算法的配電網(wǎng)無功優(yōu)化
李曉彤，李肇漢
（上海電力大學(xué) 自動化工程學(xué)院，上海 200090）
摘 要：為了有效解決由配電網(wǎng)分布式電源（distribution generation，DG）滲透率逐漸升高帶來的 電壓越限、網(wǎng)損增加問題。建立了配電網(wǎng)無功優(yōu)化模型，以網(wǎng)損最小和電壓不越限為綜合目標(biāo)，通過采用一種遺傳禁忌混合算法（genetic/tabu hybrid algorithm，GATS）研究了接入DG的配電網(wǎng)無功優(yōu)化問題。在MATLAB軟件上以IEEE-33節(jié)點配電系統(tǒng)為算例對所研究問題進(jìn)行了分析與驗證。結(jié)果表明：GATS混合算法的可行性和優(yōu)越性較強(qiáng)，通過該算法進(jìn)行無功優(yōu)化可以有效改善系統(tǒng)電壓水平、降低網(wǎng)損；并且適當(dāng)提高DG滲透率，同樣有利于網(wǎng)損降低、電壓水平提高；但若DG滲透率過高，會導(dǎo)致網(wǎng)損增加、電壓越限問題，通過GATS算法進(jìn)行無功優(yōu)化這些問題可得到有效解決。
關(guān)鍵詞：配電網(wǎng)；高滲透率分布式電源；無功優(yōu)化；遺傳算法；禁忌搜索算法；遺傳禁忌混合算法
中圖分類號：TM715 文獻(xiàn)標(biāo)識碼：A 文章編號：1007-3175(2019)04-0013-06
Reactive Power Optimization of Power Distribution Network Based on Genetic Tabu Hybrid Algorithm
LI Xiao-tong, LI Zhao-han
（College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China）
Abstract: 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.
Key words: distribution network; distributed generations of high permeability; reactive power optimization; genetic algorithm; tabu search algorithm; genetic tabu hybrid algorithm
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