Hi everybody.
I willingly Need a unit commitment Code using any optimization technique, in particular using genetic algorithm. Would anybody help me please?

Best,
Rea Mat

A classical problem in scheduling and integer programming is the unit commitment problem. In this problem, our task is to turn on and off power generating plants, in order to meet a forecasted future power demand, while minimizing our costs. We have several different power plants with different characteristics and running costs, and various constraints on how they can be used. We will start with a very simple model, and then expand this model with more advanced features. To make the code easy to read, we will write it in a verbose non-vectorized format.

Note that this is a completely fictitious example created by someone (me) with very little exposure and experience from this field. The purpose is to highlight modeling tricks and YALMIP, not to give a tutorial or best-practice description on unit commitment problems.

Before running these examples, you should install a strong MILP solver (if you don't have one installed, YALMIP will use its very naive internal integer solver BNB which will fail to solve most problems here in reasonable time, if at all).

Nunits = 3;
Horizon = 48;

Pmax = [100;50;25];
Pmin = [20;40;1];

Q = diag([.04 .01 .02]);
C = [10 20 20];

Pforecast = 100 + 50*sin((1:Horizon)*2*pi/24);


onoff = binvar(Nunits,Horizon,'full');
P = sdpvar(Nunits,Horizon,'full');



Constraints = [];
for k = 1:Horizon
Constraints = [Constraints, onoff(:,k).*Pmin <= P(:,k) <= onoff(:,k).*Pmax];
end

for k = 1:Horizon
Constraints = [Constraints, sum(P(:,k)) >= Pforecast(k)];
end

Objective = 0;
for k = 1:Horizon
Objective = Objective + P(:,k)'*Q*P(:,k) + C*P(:,k);
end