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Reseach Article

Enhancement of Optimal Scheduling and Fuel Cost Minimization using Flexible Genetic Algorithm

by Ch.srinivas
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 6
Year of Publication: 2015
Authors: Ch.srinivas
10.5120/20339-1927

Ch.srinivas . Enhancement of Optimal Scheduling and Fuel Cost Minimization using Flexible Genetic Algorithm. International Journal of Computer Applications. 116, 6 ( April 2015), 14-19. DOI=10.5120/20339-1927

@article{ 10.5120/20339-1927,
author = { Ch.srinivas },
title = { Enhancement of Optimal Scheduling and Fuel Cost Minimization using Flexible Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 6 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number6/20339-1927/ },
doi = { 10.5120/20339-1927 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:56:20.362483+05:30
%A Ch.srinivas
%T Enhancement of Optimal Scheduling and Fuel Cost Minimization using Flexible Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 6
%P 14-19
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper aims at providing a solution to optimum power flow (OPF) in considered power systems by using a flexible genetic algorithm (GA) model. The proposed approach finds the optimal setting of OPF control variables which include generator active output, generator bus voltages, transformer tap-setting and shunt devices with the objective function of minimizing the fuel cost. The proposed GA is modeled to be flexible for implementation to any power systems with the given system line, bus data, generator fuel cost parameter and forecasted load demand. The GA model has been analyzed and tested on the standard benchmark IEEE 30-bus system and two real time power systems which are an industrial park power system and a gold-copper mining power system both located in Indonesia. The results obtained outperform other approaches which are recently applied to the IEEE 30-bus system with the same control variable maximum & minimum limits and system data. Better results are also found when compared against the configurations used in the two real power systems. These superior results are achieved due to the robust and reliable algorithm of the proposed GA which utilizes the differential evaluation.

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Index Terms

Computer Science
Information Sciences

Keywords

Cross-over mutation Genetic algorithm flexible genetic algorithm Non-smooth cost functions optimal power flow (OPF).