Multi-Objective Fuzzy Model for Optimal Siting and Sizing of DG Units to Reduce Losses Using Genetic Algorithm

This paper uses a probability (fuzzy) model for siting and sizing of Distributed Generation (DG) units to reduce power losses as well as cost of the plan with multi-objective function in distribution networks. In order to consider the uncertainty in the load points, the demand power at each node is modeled with the fuzzy set theory in the form of a triangular probability distribution. For analyzing the network, the load distribution, which is implemented with fuzzy set theory for modeling the production model and fuzzy load, has been used. In this quadratic problem, in the first step by minimizing the fuzzy economic cost function and the amount of specific risk (grid optimization) of the network, the Genetic Algorithm (GA) reaches non-dominant solutions, and in the second step, using max-min method, it tries to select the appropriate answer. This method can determine the types of modeling of DGs, including PQ and PV, and because of the uncertainty of the load points, makes the positioning and determination of the DG capacity in a more realistic way. This methodology has been implemented in MATLAB based on GA with technical and economic review to reduce energy losses. Results show the ability of the method to solve the problem.