摘要
由于社会经济发展不均衡、区域特性差异、统计数据质量有别等,电网设备运行效率评估缺乏同质性基础,影响评估结果的公允性。提出了电网设备运行效率评估三阶段方法,先应用规模报酬可变的数据包络分析模型获得投入松弛量,利用环境变量回归拟合松弛量后对投入变量作修正,再将修正后的投入变量代入数据包络分析模型作效率评估。实证分析表明:传统数据包络分析方法没有考虑环境因素影响导致欠发达地区电网设备运行效率被低估;环境因素影响大,其作用体现在电网设备运行效率值上;三阶段方法能更公允地评价电网设备运行效率。
Due to the unbalance of social and economic development and the differences of regional features and statistical data quality,the utilization evaluation of power grid equipm ent lacks homogeneous base,and the fairness of the evaluation results is affected.A three-stage approach for utilization evaluation of power grid equipm ent is put forward.The approach first applies the data envelopment analysis model which has variable returns to scale in order to solve the input variable slacks.Second,the environment variables are used to fit the slacks with regression for adjusting the input variables.The adjusted input variables are substituted into the data envelopment analysis model to achieve the evaluation at last.It is demonstrated that 1)the traditional data envelopment analysis does not consider the environm ental factors,which leads to the underestim ation of the power grid equipm ent utilization in underdeveloped regions;2)environmental factors have significant influences,which are reflected in the power grid equipm ent utilization;3)the three-stage method can evaluate the power grid equipm ent utilization more truly and fairly.
作者
关玉衡
汪隆君
孙川
张跃
张俊潇
GUAN Yuheng;WANG Longjun;SUN Chuan;ZHANG Yue;ZHANG Junxiao(Electric Power Dispatching Control Center of Guangdong Power Grid Co.Ltd,Guangzhou 510600,China;School of Electric Power,South China University of Technology,Guangzhou 510640,China;Electric Power Research Institute of Guangdong Power Grid Co.Ltd,Guangzhou 510080,China;Guangdong Power Grid Development Research Institute Co.Ltd,Guangzhou 510080,China)
出处
《中国电力》
CSCD
北大核心
2018年第2期61-66,共6页
Electric Power
基金
国家自然科学基金资助项目(51307063)
教育部高等学校博士学科点专项科研基金资助项目(20120172120042)~~
关键词
电网
设备运行效率
地区差异
数据包络分析
回归分析
power grid
equipm ent utilization
regional differences
data envelopment analysis
regression analysis