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V2H Optimization: No feasible solution found

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Tim Sanders
Tim Sanders am 21 Mai 2024
Kommentiert: Torsten am 24 Mai 2024
I have some troubles to code an optimization Problem in Matlab. Since this is my first optimization i am a bit lost. This code is not running because "Linprog stopped because no point satisfies the constraints.". But I fail to see which constraint is prohibiting the code from running.
T = length(PV); % Anzahl der Zeitschritte
Unrecognized function or variable 'PV'.
% Problem
prob = optimproblem;
% battery storage system parameter
BSS_Pmax = 11; % max power
BSS_Emax = 100; % max energy
% battery variables
BSS_ch = optimvar('BSS_ch', T, 'LowerBound', 0, 'UpperBound', BSS_Pmax);
BSS_disch = optimvar('BSS_disch', T, 'LowerBound', 0, 'UpperBound', BSS_Pmax);
BSS_SOC = optimvar('BSS_SOC', T, 'LowerBound', 0, 'UpperBound', BSS_Emax);
% other variables
Grid_Import = optimvar('Grid_Import', T, 'LowerBound', 0);
% battery constraints
prob.Constraints.energyStorage = optimconstr(T);
prob.Constraints.energyStorage = BSS_SOC(1) == 0;
prob.Constraints.energyStorage = BSS_SOC(2:T) == BSS_SOC(1:T-1) - BSS_disch(2:T) + BSS_ch(2:T);
% energy flow
prob.Constraints.EnergyBalance = Grid_Import == Bedarf - PV - BSS_disch + BSS_ch;
% cost funtion
cost = Grid_Import .* Price;
prob.ObjectiveSense = 'minimize';
prob.Objective = sum(cost);
% solve
[x, fval] = solve(prob);
% optional display
%disp(x.BSS_ch);
%disp(x.BSS_disch);

Akzeptierte Antwort

Torsten
Torsten am 21 Mai 2024
Bearbeitet: Torsten am 21 Mai 2024
Use
energyStorage = optimconstr(T);
energyStorage(1) = BSS_SOC(1) == 0;
energyStorage(2:T) = BSS_SOC(2:T) == BSS_SOC(1:T-1) - BSS_disch(2:T) + BSS_ch(2:T);
prob.Constraints.energyStorage = energyStorage;
instead of
% battery constraints
prob.Constraints.energyStorage = optimconstr(T);
prob.Constraints.energyStorage = BSS_SOC(1) == 0;
prob.Constraints.energyStorage = BSS_SOC(2:T) == BSS_SOC(1:T-1) - BSS_disch(2:T) + BSS_ch(2:T);
I can't check your constraints since I cannot execute your code. So I don't know if the above modification solves your problem.
  6 Kommentare
Tim Sanders
Tim Sanders am 24 Mai 2024
Hi thanks, this does work.
But it created a new problem. I was unware of the fact that in the real dataset the price can get to a negative value. If you replace the random data generation of the price parameter with "Price = 0.006 .* randi([-100, 100], n, 1); % Beispielwerte". The Problem will become unbounded. Because it will generate an inf. Gridimport in those hours to reduce the cost.
Torsten
Torsten am 24 Mai 2024
Most probably, you need to set an upper bound for Gridimport.

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