V2H Optimization: No feasible solution found

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Tim Sanders 2024-5-21

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2121091-v2h-optimization-no-feasible-solution-found

评论： Torsten 2024-5-24

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);

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Answer 1

Torsten 2024-5-21

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2121091-v2h-optimization-no-feasible-solution-found#answer_1461156

编辑：Torsten 2024-5-21

在 MATLAB Online 中打开

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.

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Tim Sanders 2024-5-22

编辑：Tim Sanders 2024-5-22

Please see the updated code below:

% Set the size of the array

n = 8760;

% Generate random values between 0 and 500

PV = randi([0, 500], n, 1); % Beispielwerte

Household = randi([200, 1000], n, 1); % Beispielwerte

Price = 0.006 .* randi([1, 100], n, 1); % Beispielwerte

T = length(PV); % Anzahl der Zeitschritte

% 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

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;

% energy flow

prob.Constraints.EnergyBalance = Grid_Import == Household - PV - BSS_disch + BSS_ch;

% cost funtion

cost = Grid_Import .* Price;

prob.ObjectiveSense = 'minimize';

prob.Objective = sum(cost);

% solve

[x, fval] = solve(prob);

Tim Sanders 2024-5-24

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.