Open AccessCONGESTION MITIGATION IN DISTRIBUTION NETWORK BY INTEGRATED DISTRIBUTED GENERATIONS FOR IMPROVING VOLTAGE PROFILES AND MINIMIZING THE LOSSES
Author(s) -
Ihsan Mousa Jawad,
Wafaa S. Majeed
Publication year - 2022
Publication title -
journal of engineering and sustainable development
Language(s) - English
Resource type - Journals
eISSN - 2520-0925
pISSN - 2520-0917
DOI - 10.31272/jeasd.25.2.9
Subject(s) - sizing , electric power transmission , computer science , sensitivity (control systems) , contingency , transmission (telecommunications) , distributed generation , voltage , index (typography) , genetic algorithm , reliability engineering , power (physics) , transmission line , mathematical optimization , engineering , electrical engineering , electronic engineering , telecommunications , mathematics , art , linguistics , philosophy , physics , quantum mechanics , machine learning , world wide web , visual arts
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.