Random clustering analysis in deterministic DG-based distribution planning

Abstract

New feeders’ allocation, new lines connecting the substations to new feeders and other feeders to tie-line routes, should be defined for minimizing the costs of new facilities’ installation with some limitations as voltage loss, capacity of current, geographical restrictions of installation, and balance between loads, while feeders have been recently distributed in a system of distribution. However, finding the optimum plan for feeder distribution among different applicants, is difficult for planners. To confirm decision of planners of feeder distribution plan in this paper, the author proposes a technique to obtain the minimum cost distribution facilities by all the applicants’ searching that fulfill such distribution limitations as voltage loss, also geographical constraints of installation and power loss and etc. In this paper, optimal deterministic planning of distribution networks with (DG) will be discussed and all calculations will be done in medium voltage (MV) level. Weight clustering in distribution networks planning is a major topic that can be discussed by different methods like fuzzy algorithm. Weight clustering in reliability concepts of deterministic DG-based distribution planning such as failure rate calculations can be discussed, but its meaning in robust optimization (RO) will be very complex and profound. In this paper, the study of random clustering in both single and multi-cluster states has been investigated and concluded. The results of this research can be used in the field of robust optimization of distribution networks at the MV networks. The sample candidate of random clustering studies has been selected the Greenfield residential area. According flowcharts I and II in part of the appendix, detailed analytical results based on robust optimization of 32 candidate substations of case study has been extracted in two state consist of both single and multi-cluster states.