% Economic Dispatch using Genetic Algorithm

% Define parameters

num_units = 3; % Number of generating units

P_min = [100, 150, 200]; % Minimum power output for each unit

P_max = [300, 400, 500]; % Maximum power output for each unit

a = [0.01, 0.015, 0.02]; % Cost coefficients

b = [5, 7.5, 10]; % Cost coefficients

c = [50, 75, 100]; % Cost coefficients

load_demand = 700; % Total load demand

% Define genetic algorithm options

options = optimoptions('ga', 'Display', 'iter', 'MaxGenerations', 100, 'PopulationSize', 50);

% Define fitness function

fitness_function = @(x) total_cost(x, P_min, P_max, a, b, c, load_demand);

% Solve Economic Dispatch problem using genetic algorithm

[x, fval] = ga(fitness_function, num_units, [], [], [], [], P_min, P_max, [], options);

% Display results

fprintf('Optimal power output of generating units:\n');

disp(x);

fprintf('Total cost: %.2f\n', fval);

% Define fitness function

function total_cost = total_cost(P, P_min, P_max, a, b, c, load_demand)

total_cost = sum(a .* P.^2 + b .* P + c); % Calculate total generation cost

penalty = max(0, P - P_max) + max(0, P_min - P); % Calculate penalty for violating power constraints

total_cost = total_cost + sum(penalty.^2); % Add penalty to the total cost

imbalance_penalty = abs(sum(P) - load_demand); % Penalty for load-demand imbalance

total_cost = total_cost + imbalance_penalty; % Add imbalance penalty to the total cost

end