# Economic Load Dispatch based on Genetic Algorithm-seminar report by harikumaru

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```									Economic Load Dispatch based
on Genetic Algorithm
OBJECTIVE
• Develop a Genetic Algorithm in MAT Lab for solving ELD
problem
• Develop a linear program in MAT Lab for solving ELD
problem
• Perform FDLF on IEEE 30bus system to find total power
generation.
• Solve ELD problem using GA method and LP method
• Compare the results of both method.

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Economic load dispatch (ELD)
• The economic load dispatch (ELD) is the method of
determining the most efficient low cost and reliable
operation of a power system by dispatching the available
electricity generation resources to supply the load on the
system.
• The economic load dispatching is one of the key
problems in power system operation and planning.
• The main aim of ELD problem is to minimize the total
cost of generating real power at various stations while
satisfying the loads and losses in the transmission link.

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Cost curve of a power station

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Optimization problem

n
f ( PGi)   (aiPGi  biPGi  ci)
2
Min
i 1
Sub. to

n

P
i 1
Gi    PD  PL

PGi min  PGi  PGi max
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Generator cost coefficients

Generator operating limits

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• THE GENETIC ALGORITHM PROGRAM
•
•
•   %Objective function(Fuel Cost)
•
•   function y = simple_fitness(x)
•
•   y = 0.00375*x(1)^2+2*x(1)+0.0175*x(2)^2+1.75*x(2)...
•     +.0625*x(3)^2+1*x(3)+.00834*x(4)^2+3.025*x(4)...
•     +.025*x(5)^2+3*x(5)+0.025*x(6)^2+3*x(6);
•
•   %.................................................
•
•   %constraint function
•
•   function [c, ceq] = simple_constraint(x)
•
•   c = [];
•
•   ceq = [x(1)+x(2)+x(3)+x(4)+x(5)+x(6)-200];
•
•   %...........................................
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•
•   ObjectiveFunction = @simple_fitness;
•
•   nvars = 6;% Number of variables
•
•   x0=[0 0 0 0 0 0];%starting condition
•
•   LB = [50 20 15 10 10 12]; % Lower bound
•
•   UB = [200 80 50 35 30 40]; % Upper bound
•
•   ConstraintFunction = @simple_constraint;
•
•   %options = gaoptimset('MutationFcn',@mutationadaptfeasible);
•
•   options = gaoptimset(options,'InitialPopulation',X0,'PlotFcns',...
•    {@gaplotbestf,@gaplotmaxconstr},Display','iter');
•
•   % Next we run the GA solver.
•
•   [x,fval] = ga(ObjectiveFunction,nvars,[],[],[],[],LB,UB, ...
•      ConstraintFunction,options)

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•   ceq = x(1)+x(2)+x(3)+x(4)+x(5)+x(6)-200;
•
•   x0 = [0,0,0,0,0,0]; % Make a starting guess at the solution
•
•   lb=[50,20,15,10,10,12]; %lower bound
•
•   ub=[200,80,50,35,30,40]; %upper Bound
•
•   options = optimset('LargeScale','off');
•
•   [x,fval] = fmincon(@objfun,x0,[],[],[],[],lb,ub,...
•     @confuneq,options)

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