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.




                                                       2
 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.



                                                           3
Cost curve of a power station




                                4
               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
                                          5
Generator cost coefficients




   Generator operating limits




                                6
• 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];
•
•   %...........................................
                                                           7
•
•   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)




                                                                         8
•   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)




                                                                  9
10
                                    Fast decoupled Loadflow
•   Fast-decoupled power flow converged in 26 P-iterations and 26 Q-iterations.

•   Converged in 0.42 seconds
•   ================================================================================
•   | System Summary                             |
•   ================================================================================

•   How many?                  How much?                   P (MW)             Q (MVAr)
•   --------------------- ------------------- ------------- -----------------
•   Buses              30 Total Gen Capacity 335.0                       -95.0 to 405.9
•   Generators             6 On-line Capacity              335
								
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