摘要
气象敏感负荷的逐年增长是夏季电网负荷不断攀升的重要原因,准确估算此类负荷功率对电网运行调度、估计地区需求侧响应能力均有益处。提出了改进典型相关分析方法,建立了负荷-气象非线性关联模型,基于此可计算历史负荷数据中的气象敏感负荷功率。建立了基于堆栈自编码器(SAE)的气象敏感负荷功率估算模型,利用SAE的无监督学习提取日负荷曲线的降维特征,利用关联模型的计算结果作为有标签样本训练估算模型的全连接层,从而由日负荷曲线直接获得气象敏感负荷功率曲线。基于实际电网数据的算例结果验证了所提方法的有效性。
The annual increase of meteorological sensitive load is an important reason for the increasing load of grid in summer and correct estimation of such load power is beneficial to the operation scheduling of power grid and the estimation of regional demand side response. An improved typical correlation analysis method is proposed, and a load-meteorology nonlinear correlation model is established, based on which, the meteorological sensitive load power in the historical load data can be calculated. An estimation model of meteorological sensitive load based on SAE （ Stacked Auto-Encoder） is established. The unsupervised learning ability of SAE is utilized to extract the dimension reduction characteristics of daily load curve, and the calculative results of the correlation model is used as a label sample to train the full connection layers of the estimation model, thus the meteorological sensitive load power curve can be obtained directly by the daily load curve. Results of an example based on actual grid data verify the validity of the proposed methods.
作者
陈彦翔
秦川
鞠平
赵静波
金宇清
施佳君
CHEN Yanxiang;QIN Chuan;JU Ping;ZHAO Jingbo;JIN Yuqing;SHI Jiajun(College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;Renewable Energy Power Generation Technology Engineering Research Center of Ministry of Education, Hohai University, Nanjing 211100, China;State Grid Jiangsu Electric Power Company Research Institute, Nanjing 210008, China)
出处
《电力自动化设备》
EI
CSCD
北大核心
2018年第5期220-226,共7页
Electric Power Automation Equipment
基金
国家自然科学基金资助项目(51407060)
江苏省电力公司科技项目(J2018054)
111引智计划"新能源发电与智能电网学科创新引智基地"项目(B14022)~~
关键词
气象敏感负荷
功率估算
关联分析
负荷-气象关联模型
堆栈自编码器
meteorological sensitive load
power estimation
correlation analysis
load-meteorology correlation model
stacked auto-encoder
