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计及线损的配电网线路交流融冰优化方法研究

Research on Optimization Method for AC Melting of Distribution Network Lines Considering Line Loss
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摘要 当前配电网线路交流融冰过程中,大多只考虑成本进行融冰优化,忽略了部分属性因子,使得优化后线路最大覆冰厚度依旧较大。因此,提出计及线损的配电网线路交流融冰优化方法研究。由于线路损耗发热量会抑制覆冰增长,建立计及线损的覆冰增长模型,描述配电网线路覆冰变化情况。以最小交流融冰运行成本、最小覆冰厚度为目标,定义线路交流融冰优化目标函数,并提出电流约束、电压约束、融冰启动约束、覆冰厚度约束和融冰紧急性约束。运用禁忌搜索算法对目标函数进行不断求解,通过全局逐步寻优生成交流融冰优化决策。算法例分析结果表明:所提方法得出的优化决策实施后,配电网线路覆冰最大厚度仅为15.67mm,满足了输电网络安全运行要求。 In the current AC icing process of distribution network lines,most of them only consider cost for icing optimization,ignoring some attribute factors,resulting in the maximum icing thickness of the optimized lines still being relatively large.Therefore,a study on the optimization method of AC ice melting for distribution network lines considering line losses is proposed.Considering that line loss and heat generation will suppress the growth of icing,a icing growth model considering line loss is established to reflect the severity of icing on distribution network lines.With the goal of minimizing the operating cost and minimum icing thickness of AC icing,the objective function for optimizing AC icing on transmission lines is defined,and current constraints,voltage constraints,icing start constraints,icing thickness constraints,and icing urgency constraints are proposed.Relying on the taboo search algorithm to solve the optimization objective function,the final AC ice melting optimization decision is generated through global gradual optimization.The analysis results of algorithm examples show that after the implementation of the optimization decision obtained by the proposed method,the maximum thickness of icing on the distribution network lines is only 15.67mm,which meets the requirements for safe operation of the transmission network.
作者 梁开旺 龙斌 Liang Kaiwang;Long Bin(Yunnan Power Grid Co.,Ltd.,Zhaotong 657000,China)
出处 《现代科学仪器》 2024年第4期201-207,共7页 Modern Scientific Instruments
关键词 线损 配电网 交流融冰 禁忌搜索算法 覆冰增长模型 优化方法 Line loss Distribution network Exchange ice melting Tabu search algorithm Ice cover growth model Optimization methods
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