Technical Efficiency Evaluation Of Grid Corporations Based On The DEA Model

This paper describes the main contents of the evaluation of technical efficiency of electric power industry at home and abroad, the evaluation methods, and the development of it, and conducts an empirical analysis of the technical efficiency of the Grid in North China for the first time. This paper introduces the data envelopment analysis (DEA) to evaluate the technical efficiency of the Grid Corporations. According to the features and environment of the power grid companies, applied the index design principle which is science and system to design the suitable index system of grid companies'technical efficiency. Introducing the concept of entropy weight"degree of distinction", choose more in line with the technical efficiency evaluation index from the index system of grid companies"one strong and three excellent", it prepare for the next empirical analysis of technical efficiency evaluation below.In the empirical analysis, there introduce the concept"ideal decision making units"to make all units ranked, and have an analyzed with the results of the grid technology efficiency evaluation in detail, including the projection analysis and sensitivity analysis. Through comparative analysis of the two methods can better evaluation the technical efficiencies of grid companies, and can make a more definite improvement with oneself, and have a comprehensive monitor on the factors of technical indicators and management indicators. Aiming at the deficiencies of every DMU, it put forward some corresponding improvement measures, thus can raise the production efficiency of the grid company. Through the comparative analysis of distribution network company technical efficiency, find out the differences in, and to optimize the DMU which is non-DEA effective. Thereby better for allocation of resources, improve the production efficiency and achieve high input high output of the technical and economic benefits, and constantly adapt to the technology development of network, thus enable more effective with intelligent network development and implementation.