Open AccessMulti objective functions of constraint optimal power flow based on modified ant colony system optimization technique
Author(s) -
Layth Tawfeeq Al-Bahran,
Ali Qasim Abdulrasool
Publication year - 2021
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/1105/1/012015
Subject(s) - ant colony optimization algorithms , mathematical optimization , ant colony , computer science , matlab , electric power system , constraint (computer aided design) , power flow , reduction (mathematics) , power (physics) , algorithm , mathematics , quantum mechanics , operating system , physics , geometry
This article presents the development of Ant Colony System algorithm for solving the constrained Optimal Power Flow (OPF) problem. In this modification of ant colony, a local and global update of pheromone has been used. This optimization technique is used to adjust an optimal control variables of the system while satisfying the constraint of the state variables in their limits. The proposed algorithm tested on the IEEE-30 bus with different cases of single and multiple OPF objectives. The obtained results are analysed and compared with other previous studies. This comparison illustrates the efficiency of the ACS technique for solving different OPF problems of complex objective functions where the propose algorithm of ACS has been reduce the single objective function of total fuel cost, power losses, voltage profile improvement and the total generation emission to minimum value of 800.83 $/h, 3.2723 MW, 0.1194 pu and 0.2066 ton/h respectively with reduction of these objective function of 11.216%, 43.991%, 89.660%, 13.952% respectively. The authors have been used the MATLAB software for programming the propose technique without using any simulators. The Ant Colony System has the facility of simplicity in analysis, easy in program, faster in execution, less iteration with accurate result over the other optimization techniques.