MATLAB: Dose who know about Optimization with Summation objective function

cost functionMATLABminlpobjective-functionoptimizationschedulingtime series

Hello, I want make objective function for 24-h Charging / Discharging scheduling.

So, I made like this

for 1:24
    for 1:5
        fun = a*P_char*C_cost1 - (1-a)P_dischar*C_cost2 %% a=charging signal, P = charging discharging power, C = charging discharging cost
        
    end
end

but this function didn't consider other times cost, they just think current time step only.

How can I fix my function? Plz help me. thank you 🙂

Best Answer

Maybe you should update fun to remember the entire sum, not just the final one. And maybe you should name your indices.

fun = optimexpr;
for i = 1:24
    for j = 1:5
        fun = fun + a(i)*P_char(i)*C_cost1(j) - (1-a(i))*P_dischar(i)*C_cost2(j) %% a=charging signal, P = charging discharging power, C = charging discharging cost
        
    end
end

I may have messed up the i and j indices, but I hope that you get the idea. You could probably create the expression without any loops, too, and it would be more efficient to do so. See Expressions for Objective Functions.

Alan Weiss

MATLAB mathematical toolbox documentation

Related Solutions

MATLAB: Problems with fmincon, how to solve

Your problem is convex, so you can use the CVX package if you want. Also, the solution should be the global minimum; not a local minimum. To use fmincon, do the following:

Equality/nonEquality constraint:

function [c,ceq]=mycon(x)
c(1) = x(1)^2 + x(2)^2 - 4*x(1) - 3*x(2) + 5;
c(2) = -x(1)^2 - x(2)^2 + 4*x(1) + 3*x(2) - 6; % notice the fixed typo
ceq = 2*x(1)^2 + 2*x(2)^2 - 3*x(1) - 3*x(2) - 2; 
end

Construct the problem and call fmincon:

clc
clear all
Aeq=[]; 
beq=[]; 
A=[]; 
b=[]; 
% if you are not sure that vector x is within lb and ub do not restrict it
lb = []; %lb=[-3,-3]; 
ub = []; %ub=[3,3]; % 
x0=[4,4];
myObj = @(x) x(1)^2 + x(2)^2 - 5*x(1) - 6*x(2) + 15;
nonlcon = @mycon;
options = optimoptions('fmincon','Algorithm','sqp','Display','iter-detailed','MaxFunctionEvaluations',100000, 'MaxIterations',2000, 'OptimalityTolerance' ,1e-10);
[x,cost] = fmincon(myObj,x0,A,b,Aeq,beq,lb,ub,nonlcon)

Output (global minimum):


Local minimum found that satisfies the constraints.
Optimization completed because the objective function is non-decreasing in 
feasible directions, to within the value of the optimality tolerance,
and constraints are satisfied to within the value of the constraint tolerance.
<stopping criteria details>
x =
    1.6450    1.9007
cost =
    1.6896

MATLAB: Fmincon works but GA won’t work

In general, ga is not as reliable a solver as fmincon. So if you have smooth objective and constraints, you should use fmincon. See Table for Choosing a Solver.