fmincon - penalty function
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Hi,
I have the following problem:
I have a data set with half-hourly data for gas production and electricity demand. These are now to be brought together via a CHP (with a certain capacity). The produced gas is to be converted into electricity to cover the electricity demand as well as possible. So I am looking for a power P at each point in time.
To do this, I have calculated the deviation between demand and power in an objective function e and then summed it up.
q = fmincon(@(x) obj_func(x,P,demand,z,P_max,V_level,V_initial,V_size,V_proz,production,BHKW_mode,d),x0,[],[],[],[],lb,ub);
function e = obj_func(x,P,demand,z,P_max,V_level,V_initial,V_size,V_proz,production,BHKW_mode,d)
e = (demand - P).^2;
e = sum(e) + sum(d);
This objective function is then to be minimized with fmincon. This is done and at any time I get : P = demand (why should it not be so).
But now my constraint comes into play.
The gas comes first into a storage and if gas is converted into electricity, the storage content becomes smaller by this quantity.
V_level(1) = V_initial + production(1) - P(1);
for i = 2:z
V_level(i) = V_level(i-1) + production(i) - P(i);
end
V_proz = 100*V_level/V_size;
And this storage may never be fuller than 100 % and never emptier than 0 %. I tried to realize this with a penalty function, which is added to e.
for i = 1:z
if V_proz(i) > 100
d(i) = 100000;
elseif V_proz(i) < 0
d(i) = 100000;
else
d(i) = 0;
end
end
But no matter how big I choose the penalty for non-compliance with this condition: Nothing changes in the result, it is not considered by the optimizer at all....
Does anyone have any idea where my error lies or how I can solve it differently?
Thank you for your help, Matthias
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采纳的回答
Torsten
2022-3-17
编辑:Torsten
2022-3-17
As far as I can see, you could formulate your problem as
min: sum_i (abs(demand(i)-P(i)))
s.c.
Pmin <= P(i) <= Pmax
0 <= V_level(i) <= V_size
V_level(i) = V_level(i-1) + production(i) - P(i)
If the difference to your former objective
min: sum_i (demand(i)-P(i))^2
is acceptable, this can be formulated as a linear optimization problem in the unknown P and V_level.
Use linprog to solve.
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更多回答(1 个)
Matt J
2022-3-17
Problems
(1) The objective function code you've shown appears to depend on everything except the unknown variables x
(2) Assuming V_proz was supposed to be one of the unknowns, your penalty d is a discontinuous function of it.
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