文章摘要
肖白,杨欣桐,田莉,等.计及元胞发展程度的空间负荷预测方法[J].电力系统自动化,2018,42(1):61-67. DOI: 10.7500/AEPS20170328001.
XIAO Bai,YANG Xintong,TIAN Li, et al.Spatial Load Forecasting Method Based on Development Degree of Cell[J].Automation of Electric Power Systems,2018,42(1):61-67. DOI: 10.7500/AEPS20170328001.
计及元胞发展程度的空间负荷预测方法
Spatial Load Forecasting Method Based on Development Degree of Cell
DOI:10.7500/AEPS20170328001
关键词: 空间负荷预测  元胞发展程度  地理信息系统  分类负荷  饱和负荷
KeyWords: spatial load forecasting  development degree of cell  geographic information system  classified load  saturated load
上网日期:2017-10-09
基金项目:国家自然科学基金资助项目(51177009)
作者单位E-mail
肖白 东北电力大学电气工程学院, 吉林省吉林市 132012 xbxiaobai@126.com 
杨欣桐 东北电力大学电气工程学院, 吉林省吉林市 132012  
田莉 国网吉林供电公司, 吉林省吉林市 132001  
綦雪松 国网吉林供电公司, 吉林省吉林市 132001  
摘要:
      针对分类负荷在不同元胞内发展程度不同导致元胞负荷分布不均衡,从而影响空间负荷预测结果精度的问题,提出一种计及元胞发展程度的空间负荷预测方法。首先建立电力地理信息系统(GIS),在电力GIS中生成元胞,并整合基础信息,其中包括用地信息、10 kV馈线的供电范围及分类负荷数据。其次求出总分类负荷密度的饱和值,再结合生长曲线揭示总分类负荷密度的发展规律。然后找到当前年各元胞内分类负荷密度在总分类负荷发展规律曲线上的位置,即为各元胞内分类负荷密度的发展程度。最后根据当前年元胞内各分类负荷密度的发展程度,结合总分类负荷密度发展规律曲线,确定目标年各元胞内分类负荷密度,再乘以元胞中每类负荷所对应的面积实现对元胞负荷值的预测。实例分析表明了该方法的正确性和有效性。
Abstract:
      For the problem of the classified loads in different cells with different development degrees that may cause the unbalanced cellular load distribution liable to lead to undesirable effects on the accuracy of spatial load forecasting results, a method of spatial load forecasting based on the development degree of cell load is proposed. Firstly, the electric power geographic information system is established, on which cells are generated. And the basic information is integrated including the land use information, 10 kV feeder power supply area and classified load data. Secondly, the saturated load value of the total classified load density, and the development law of total classified load density are determined by the growth curve. Then the position each cell classified load density is in in the current year on the total classified load density development curve is found, as the development degree of the classified load density of each cell. Finally, the current year development degree and the total classified load density development law curve are used to forecast the size of the classified load density of each cell in the target year, then multiplied by the corresponding area, and the result is the cell load of the predicted value. An example analysis shows the correctness and the effectiveness of the method.
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