Some factors affect the energy loss in country distribution networks, including the load density, the load distribution, the grid structure and the operating condition of the distribution networks. As a result, the difference and the relation among these loss effect factors are considered, and the typical case library of energy loss in the distribution networks is built. On the basis of building some loss analysis scenes, the method of Grey Relational Analysis is used to evaluate the influence degree between energy loss rate and reference factors. The analysis software of the case library is developed and applied. Case studies show that there is some difference to these factors among different distribution networks and the difference can be suggested with the method, which provides a way to form the differential scheme of saving energy and reducing loss. Influence Factors Analysis According to the difference of the country distribution networks, the specific conditions of the regional distinction should be considered to study the influence factors that affect the energy loss in

Abstract:The grid structures, load levels, and running states of distribution networks in different supply regions are known as the influencing factors of energy loss. In this paper, the case library of energy loss is constructed to differentiate the crucial factors of energy loss in the different supply regions. First of all, the characteristic state values are selected as the representation of the cases based on the analysis of energy loss under various voltage classes and in different types of regions. Then, the methods of Grey Relational Analysis and the K-Nearest Neighbor are utilized to implement the critical technologies of case library construction, including case representation, processing, analysis, and retrieval. Moreover, the analysis software of the case library is designed based on the case library construction technology. Some case studies show that there are many differences and similarities concerning the factors that influence the energy loss in different types of regions. In addition, the most relevant sample case can be retrieved from the case library. Compared with the traditional techniques, constructing a case library provides a new way to find out the characteristics of energy loss in different supply regions and constitutes differentiated loss-reducing programs.