function definition in a script must appear at the end of the file.

% Load the Matpower case file containing the network data
mpc = loadcase('tarkwa_case');

% Define the candidate buses for FACTS device placement
candidateBuses = [2, 5, 8, 11];

% Set up the optimization parameters
populationSize = 50;
maxIterations = 100;
crossoverProb = 0.9;
scalingFactor = 0.8;

% Define the fitness function
function fitness = evaluateFitness(placement)
% Modify the network model based on the FACTS device placement
modifiedMpc = modifyNetworkModel(placement);

% Run power flow analysis using Matpower
results = runpf(modifiedMpc);

% Calculate objective based on your criteria (e.g., voltage stability, power loss reduction)
% Replace this with your specific objective calculation
fitness = calculateObjective(results);
end

% Modify network model based on FACTS device placement
function modifiedMpc = modifyNetworkModel(placement)
modifiedMpc = mpc;
% Implement modifications to the network model based on placement
% For example, change generator parameters, bus voltage setpoints, etc.
% based on the FACTS device placement
for i = 1:length(placement)
    busIndex = placement(i);
    % Modify the bus parameters as needed
    modifiedMpc.bus(busIndex, VMIN) = newVoltageSetpoint;
    modifiedMpc.bus(busIndex, VMAX) = newVoltageSetpoint;
    % Modify generator parameters if necessary
    % ...
end
end

% Run the optimization loop
population = zeros(populationSize, numel(candidateBuses));
for i = 1:populationSize
    population(i, :) = randperm(numel(candidateBuses));
end

for iteration = 1:maxIterations
    fitnessValues = zeros(populationSize, 1);
    for i = 1:populationSize
        fitnessValues(i) = evaluateFitness(candidateBuses(population(i, :)));
    end
    
    % Perform selection, crossover, and mutation
    % Implement these steps based on the DE or other optimization algorithms
    
    % Termination criterion check
    if terminationCriterionMet()
        break;
    end
end

% Get the optimal placement solution from the final population
optimalPlacement = candidateBuses(population(1, :));

% Display the optimal placement
disp('Optimal FACTS Device Placement:');
disp(optimalPlacement);

% Define the termination criterion
function shouldTerminate = terminationCriterionMet()
% Implement your termination criterion here
% Return true if the termination criterion is met, false otherwise
% For example, based on the number of iterations or convergence criteria
shouldTerminate = false; % Modify this based on your criterion
end

[Adam Danz]

This section of code is between two function definition blocks. It either needs to be moved upward as @Dyuman Joshi suggested or perhaps it is supposed to be within one of the functions. I haven't read the code.

% Run the optimization loop
population = zeros(populationSize, numel(candidateBuses));
for i = 1:populationSize
    population(i, :) = randperm(numel(candidateBuses));
end
for iteration = 1:maxIterations
    fitnessValues = zeros(populationSize, 1);
    for i = 1:populationSize
        fitnessValues(i) = evaluateFitness(candidateBuses(population(i, :)));
    end
    
    % Perform selection, crossover, and mutation
    % Implement these steps based on the DE or other optimization algorithms
    
    % Termination criterion check
    if terminationCriterionMet()
        break;
    end
end
% Get the optimal placement solution from the final population
optimalPlacement = candidateBuses(population(1, :));
% Display the optimal placement
disp('Optimal FACTS Device Placement:');
disp(optimalPlacement);

Answer 1

[Image Analyst]

Try this:

%==============================================================================
% SCRIPT STARTS HERE
% Load the Matpower case file containing the network data
mpc = loadcase('tarkwa_case');
% Define the candidate buses for FACTS device placement
candidateBuses = [2, 5, 8, 11];
% Set up the optimization parameters
populationSize = 50;
maxIterations = 100;
crossoverProb = 0.9;
scalingFactor = 0.8;
% Run the optimization loop
population = zeros(populationSize, numel(candidateBuses));
for i = 1:populationSize
    population(i, :) = randperm(numel(candidateBuses));
end
for iteration = 1:maxIterations
    fitnessValues = zeros(populationSize, 1);
    for i = 1:populationSize
        fitnessValues(i) = evaluateFitness(candidateBuses(population(i, :)));
    end
    
    % Perform selection, crossover, and mutation
    % Implement these steps based on the DE or other optimization algorithms
    
    % Termination criterion check
    if terminationCriterionMet()
        break;
    end
end
% Get the optimal placement solution from the final population
optimalPlacement = candidateBuses(population(1, :));
% Display the optimal placement
disp('Optimal FACTS Device Placement:');
disp(optimalPlacement);
%==============================================================================
% SCRIPT IS DONE NOW.  NOW PUT ALL FUNCTION DEFINITIONS BELOW THE SCRIPT.

%==============================================================================
% Define the fitness function
function fitness = evaluateFitness(placement)
% Modify the network model based on the FACTS device placement
modifiedMpc = modifyNetworkModel(placement);
% Run power flow analysis using Matpower
results = runpf(modifiedMpc);
% Calculate objective based on your criteria (e.g., voltage stability, power loss reduction)
% Replace this with your specific objective calculation
fitness = calculateObjective(results);
end

%==============================================================================
% Modify network model based on FACTS device placement
function modifiedMpc = modifyNetworkModel(placement)
modifiedMpc = mpc;
% Implement modifications to the network model based on placement
% For example, change generator parameters, bus voltage setpoints, etc.
% based on the FACTS device placement
for i = 1:length(placement)
    busIndex = placement(i);
    % Modify the bus parameters as needed
    modifiedMpc.bus(busIndex, VMIN) = newVoltageSetpoint;
    modifiedMpc.bus(busIndex, VMAX) = newVoltageSetpoint;
    % Modify generator parameters if necessary
    % ...
end
end

%==============================================================================
% Define the termination criterion
function shouldTerminate = terminationCriterionMet()
% Implement your termination criterion here
% Return true if the termination criterion is met, false otherwise
% For example, based on the number of iterations or convergence criteria
shouldTerminate = false; % Modify this based on your criterion
end

[Timothy Ampah Whyte]

THANKS MAN, really appreciate

[Image Analyst]

OK, but did it work or not?

If this Answer solves your original question, then could you please click the "Accept this answer" link to award the answerer with "reputation points" for their efforts in helping you? They'd appreciate it. Thanks in advance. 🙂 Note: you can only accept one answer (so pick the best one) but you can click the "Vote" icon for as many Answers as you want. Voting for an answer will also award reputation points.