matlab code for economic load dispatch using genetic algorithm

ABSTRACT

Evolutionary algorithms are becoming an important aspect of artificial intelligence and are successfully applied to a variety of optimization problems. This paper presents genetic algorithm and quadratic programming concepts in solving economic load dispatch in which the total cost of generating power is minimized with a valve point loading effect while satisfying the load demand irrespective of transmission line losses. This work aims in modeling the economic load dispatch problem with transmission loss and is being applied to the test systems i.e. IEEE 14 BUS and IEEE 30 BUS using MATLAB.


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[at]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