摘要
配电网技术线损管控受到设备规模、监测条件、通信路径等因素限制,对导致高损问题的关键因素缺乏有效研究判断,难以有针对性地量化分析损耗关键因素及实施技术降损措施后的节电量,可能导致降损策略制定的盲目性。提出一种针对双高配电网的损耗关键因素降损潜力量化方法。建立基于Pearson相关性分析的损耗关键影响因素分析模型,从数据驱动的角度分析了多维数据与线损电量的相关性,明确损耗关键因素,并结合经济运行优化、无功平衡优化、电网网架优化和设备选型优化等技术降损措施,提出针对损耗关键因素的技术降损潜力量化评估方法,为制定科学合理的降损策略提供理论支撑。
The technical line loss control of distribution network is limited by factors such as equipment scale,monitoring conditions and communication path.There is a lack of effective research and judgment on the key factors leading to high loss.It is difficult to quantitatively analyze the key factors of loss and the power saving after the implementation of technical loss reduction measures,which may lead to the blindness of loss reduction strategy strategies.A quantitative method of loss reduction potential for key loss factors of distribution network with high penetration of renewables and power electronics was proposed.The analysis model of key influencing factors of loss based on Pearson correlation analysis was established,and the correlation between multi-dimensional data and line loss power was analyzed from the perspective of data-driven,so as to clarify the key factors of loss.Combined with technical loss reduction measures such as economic operation optimization,reactive power balance optimization,power grid optimization and equipment selection optimization,a quantitative evaluation method of technical loss reduction potential for key loss factors was proposed.It provides theoretical support for formulating scientific and reasonable loss reduction strategies.
作者
于丹文
张岩
段福凯
曾祥军
YU Danwen;ZHANG Yan;DUAN Fukai;ZENG Xiangjun(State Grid Shandong Electric Power Research Institute,Jinan 250003,China;Shandong Electric Power Research Institute,Jinan 250003,China;State Grid Linyi Power Supply Company,Linyi 276004,China;School of Electrical Engineering,Shandong University,Jinan 250061,China)
出处
《山东电力技术》
2023年第2期7-13,共7页
Shandong Electric Power
基金
国家重点研发计划项目(2019YFE0118400)
山东电力研究院科技项目“双高背景下的配电网降损分析与低碳协同控制技术研究”(ZY-2021-01)。
关键词
配电网
损耗
关键因素
量化
distribution network
losses
key factors
quantification
