Open AccessApplying a Firefly Algorithm for Optimum Allocation of Solar Photovoltaic Units in a Distribution System
Author(s) -
Ahmed S Tukkee,
Mohammed Jasim Alali,
Mahmood K Zarkani
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/671/1/012042
Subject(s) - photovoltaic system , firefly algorithm , renewable energy , voltage , genetic algorithm , computer science , automotive engineering , stability (learning theory) , electric power system , solar energy , power (physics) , reliability engineering , engineering , electrical engineering , electronic engineering , mathematical optimization , algorithm , particle swarm optimization , mathematics , physics , quantum mechanics , machine learning
Electrical power companies are currently competing to provide the best services to consumers, by providing both stability and environmental benefits. The integration of renewable energy sources into distribution systems represents the best solution for these requirements, and Solar Photovoltaic units (solar P.V.) are among the fastest growing and most powerful energy resources in the world. Solar P.V. units are installed customer side to improve the profile of buses’ voltage and to minimise losses in the system. The optimum allocation of such units provides several benefits, while choosing the wrong site could lead to over or under voltage and increase losses in the power system. This paper presents a Firefly Algorithm (FA) that can be used to locate the optimal placement for a solar P.V. in distribution systems. A Fast Voltage Stability Index (FVSI) is utilised to select candidate busses for the installation of the solar P.V. panels within the system, with the aim being to improve the voltage profile and minimise total power losses. The implemented algorithm was then examined on the IEEE 33-bus test system and the results w compared with those calculated by executing the genetic algorithm method (GA). The percentage of reduction in real power losses obtained using the FA method was 6.46 % compared with the 6 % obtained by the GA method, and an improvement in the system voltage profile was also observed.