In electrical power systems, unexpected outage of transmission systems, sudden increase of loads, the exit of generators from service, and equipment failure, leads to a contingency occurring on one or several transmission lines. The loads must be within the specified state and the transmission lines should not exceed the thermal limits. One of the important methods used to alleviate the contingency and reduce the congestion lines by injected a Distributed Generation (DG) within an optimal siting and optimal sizing in the distribution network that achieves improvement of the voltage profile as well as leads to reduce the losses. First, to achieve the best goals in this paper that is determined contingency lines, an index has been used called (Active Power Flow Performance Index) (PIRPF) and an equation called (Line Flow Sensitivity Index) (LFSI) is used for finding the optimum site for Distributed Generation. Second, to determine the optimum size for distributed generators, the Genetic Algorithm (GA) is used. Also, this research was distinguished by choosing new sites and sizes according to the GA to obtain the best desired results. Finally, these methodologies were applied to the IEEE-30 bus ring network using the MATPOWER Version 6.0, 16-Dec-2016 program within MATLAP R2018a environment.

This research aims to analyze the impact of Distributed Generation (DG) on congestion line mitigation, power loss reduction, and voltage profile enhancement for Iraq’s 400kV national grid system. The suggested Particle Swarm Optimization (PSO) and Exchange Market Algorithm (EMA) are implemented to specify the optimum size and location based on an analytic approach depended on voltage, active losses, and congestion lines as fitness. Then it is added to the super 400kV Iraqi grid. The results demonstrate the efficiency of the proposed approach in determining the optimum size and location, decreasing the congestion line, reducing power losses, reducing line flow, and improving the bus voltage profile. The results are obtained with proposed algorithms implementation by using programming language within MATLAB/R2018a environment.

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