All classes Distributed Generation Optimization PSO-GWO Syllabus · Episode 3 of 6

Class Optimasi Distributed Generation PSO-GWO #3: Objective Function & Constraints: Power Loss, Voltage, and DG Capacity

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Tutorial on the objective function of distributed generation placement optimization using Python: power loss, voltage constraints and capacity, penalty method, sensitivity of location and size of DG. Episode 3.

Software: Python (NumPy, Metaheuristik)
Level: Intermediate
Duration: ± 11 min
Series total: 1 h · 6 videos
Published: October 25, 2026

Episode 3 of 6 · view syllabus

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Introduction

Here is the final result, the impact of location and size of 1 DG on power loss. The best location depends on its size, and a DG that is too large actually increases power loss again. We also formulated the objective function that will be optimized by PSO and Gray Wolf. Let's start. Hello, welcome to episode three of the Optimasi Distributed Generation series. Before using an optimization algorithm, we must clearly formulate the problem. What is being sought, what is to be minimized, and what constraints must be satisfied. Today we write the objective function for 3 DG and see directly why this problem cannot be solved by trial and error.

Step 1: Solution vector & objective function

Create file objective.py We are looking for 3 DG, so the solution factors have 6 numbers, 3 bus numbers and 3 power values in megawatts. The constraints are, buses 2 to 33, each DG power 0 to 2 megawatts, total DG should not exceed total load, and voltage must be between 0.95 and 1.05 per unit. The function rounds the bus number because the algorithm works with continuous numbers. The objective function is power loss divided by base case loss, plus penalty if voltage is out of bounds, there are duplicate buses, or total DG is excessive.

Step 2: Sensitivity of location & size of DG

Create file sensitivity.py First we place 1 DG 1 megawatt at each bus, then repeat with the best size at each bus. Run. With 1 megawatt, the best location is at bus 30. But if the size is also optimized, the best location moves to bus 6 with 2.6 megawatts, and loss drops to about 104 kilowatts. This means location and size are interdependent and must be optimized together. And a 5 megawatt DG at bus 6 actually increases loss again to 180 kilowatts, because power flows back to the substation.

Step 3: Testing the objective function

Now we test the objective function on 3 solutions. A random solution gets a value of 4.1 because its voltage violates the limits. A manual solution at buses 14, 24, and 30 gets 0.35, meaning loss is reduced to 35 percent. A solution with duplicate buses gets a penalty of 1. The left graph compares power loss for each location, and the right graph shows a U-shaped curve between DG size and power loss. For 3 DGs, the search space consists of tens of thousands of bus combinations multiplied by continuous size. Therefore, we need a metaheuristic algorithm.

Done!

Done. We have formulated the solution factors for 3 DGs, the objective function with penalty constraints, and observed that the location and size of DGs are interdependent. In the next episode, we will write the Particle Swarm Optimization and Gray Wolf Optimizer from scratch. Currently working on a thesis, dissertation, or research in the field of power systems and renewable energy? Cerdas Inovasi Digital helps implement power system optimization, from backward forward sweep power flow, placement and capacity of distributed generation, PSO, Gray Wolf Optimizer, up to hybrid algorithms, and also opens Python programming courses for electrical engineering. Want to master the entire series, from IEEE 33 bus data to algorithm comparison analysis? All 6 episodes, complete with PDF step-by-step guides and source code, are available to premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or pay once Rp999.000 for lifetime access. Want just one class? Buy a single class with lifetime access. Subscribe now on the premium page at cerdasinovasidigital.com. Contact us via WhatsApp, or visit cerdasinovasidigital.com. Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

0:48Step 1: Solution vector & objective function

3:41Step 2: Sensitivity of location & size of DG

6:21Step 3: Testing the objective function

9:15Done!

Ask the class material

My notes

Questions & answers

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Recommended classes for you

Need more than a video?

Transcript

0:03Introduction

0:48Step 1: Solution vector & objective function

3:41Step 2: Sensitivity of location & size of DG

6:21Step 3: Testing the objective function

9:15Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Solution vector & objective function

Step 2: Sensitivity of location & size of DG

Step 3: Testing the objective function

Done!

Introduction

Step 1: Solution vector & objective function

Step 2: Sensitivity of location & size of DG

Step 3: Testing the objective function

Done!