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Highlights from
Genetic Algorithm Solution to Economic Dispatch

from Genetic Algorithm Solution to Economic Dispatch by Saloman Danaraj
This program solves the economic dispatch problam using MATLAB genetic algorithm toolbox

gatest.m
clear;
clc;
tic;
global data B Pd
% This program solves the economic dispatch with Bmn coefficients byGenetic
% Algorithm toolbox of MATLAB 7.04.For any discussion&Clarification  the
% author  can be  contacted by mail (salorajan@gmail.com)
% The data matrix should have 5 columns of fuel cost coefficients and plant  limits.
% 1.a ($/MW^2) 2. b $/MW 3. c ($) 4.lower lomit(MW) 5.Upper limit(MW)
%no of rows denote the no of plants(n)
 data=[0.007	7	240	100	500
0.0095	10	200	50	200
0.009	8.5	220	80	300
0.009	11	200	50	150
0.008	10.5	220	50	200
0.0075	12	120	50	120];
% Loss coefficients it should be squarematrix of size nXn where n is the no
% of plants
B=1e-4*[0.14	0.17	0.15	0.19	0.26	0.22
0.17	0.6	0.13	0.16	0.15	0.2
0.15	0.13	0.65	0.17	0.24	0.19
0.19	0.16	0.17	0.71	0.3	0.25
0.26	0.15	0.24	0.3	0.69	0.32
0.22	0.2	0.19	0.25	0.32	0.85
];
% Demand (MW)
Pd=700;
% setting the genetic algorithm parameters.
options = gaoptimset;
options = gaoptimset('PopulationSize', 50,'Generations', 500,'TimeLimit', 200,'StallTimeLimit', 100,'PlotFcns',  {@gaplotbestf,@gaplotbestindiv});
  [x ff]=ga(@eldga,5,options);
 [ F P1 Pl]=eldga(x)
 tic;
 %   F is the total fuel cost
 %P1 is the allocation vector
% Pl is the transmission losss

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