How to get the Multiobjective GA code for the optimal placecment of DGs in Radial distribution System?

I want the coding of MOGA for my project which is entitled as OPTIMAL SITING AND SIZING OF DG IN RDS.

Answer 1

[Aditya]

Hi Bala,

Here’s an introductory MATLAB code template for Multi-Objective Genetic Algorithm (MOGA) applied to Optimal Siting and Sizing of Distributed Generation (DG) in Radial Distribution Systems (RDS).

Following is an example code for the same:

% MOGA for Optimal Siting and Sizing of DG in RDS
% ----------------------------------------------------------
% Requirements: Global Optimization Toolbox (for gamultiobj)

clc; clear; close all;

%% --- Parameters ---
nBuses = 33;              % Example: IEEE 33-bus system
DG_min = 0.1;             % Minimum DG size (pu or MW)
DG_max = 1.0;             % Maximum DG size (pu or MW)
nDG = 1;                  % Number of DG units to place

% Decision variables: [DG1_location, DG1_size, DG2_location, DG2_size, ...]

nVars = nDG * 2;          % Each DG: [location, size]

lb = [ones(1,nDG), DG_min*ones(1,nDG)];           % Lower bounds
ub = [nBuses*ones(1,nDG), DG_max*ones(1,nDG)];    % Upper bounds

IntCon = 1:2:nVars;        % Location variables are integers

%% --- MOGA Options ---
options = optimoptions('gamultiobj', ...
    'PopulationSize', 50, ...
    'MaxGenerations', 50, ...
    'Display', 'iter', ...
    'PlotFcn', {@gaplotpareto});

%% --- Run MOGA ---
[obj, fval, exitflag, output, population, scores] = ...
    gamultiobj(@(x)objectives(x, nBuses), nVars, [], [], [], [], lb, ub, [], IntCon, options);

%% --- Show Results ---
disp('Pareto optimal solutions:');
disp(fval);

%% --- Objective Function ---
function f = objectives(x, nBuses)
    % x: [DG1_location, DG1_size, DG2_location, DG2_size, ...]
    % 1. Round locations to nearest bus
    locs = round(x(1:2:end));
    sizes = x(2:2:end);
    % 2. Power flow analysis with DGs at locs with sizes
    %    [You must implement your own power flow and loss calculation]
    [Ploss, Vdev] = evaluate_RDS(locs, sizes, nBuses);
    % 3. Objectives: [minimize Ploss, minimize voltage deviation]
    f = [Ploss, Vdev];
end

%% --- Power Flow and Loss Calculation (Stub) ---
function [Ploss, Vdev] = evaluate_RDS(locs, sizes, nBuses)
    % This is a placeholder!
    % Replace this with your own load flow code for the RDS,
    % and compute total real power loss (Ploss)
    % and total voltage deviation (Vdev)
    %
    % For demonstration, we use random values.
    Ploss = rand() + sum(sizes)*0.01;      % Replace with actual loss
    Vdev = rand() + abs(mean(locs)-nBuses/2)*0.01;  % Replace with actual voltage deviation
end