Determining the optimal placement and capacity of DG in intelligent distribution networks under uncertainty demands by COA

Given the cost of traditional methods such as the construction of new posts and feeders to enhance the electricity network, tendency toward the use of modern intelligent technologies has recently grown up. Hence the overall structure of the subject studied in this paper is to create distributed generation resources with optimal capacity in appropriate parts of the distribution network. Since the set is intelligent, considering uncertainty in network loads is a new idea proposed by the authors in network simulation. Monte Carlo technique has been used to implement this idea. Cuckoo optimization algorithm (COA) which has recently been embraced by researchers is the proposed method in this paper for finding the most optimal state governing the network. Evaluated units in this study are distributed generation resources of biomass and solar thermal. The effects of implementing the above methods to improve the voltage profile and reduce losses have been investigated in a separate section of this article. Finally, the economic savings achieved in these two units for implementing the proposed methods were evaluated, and the cost reduction has been expressed.