Optimal Location and Sizing of Distributed Generation

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Distributed Generation Definition: 

Power generation systems that distribute power are called distributed power generation systems (DPGSs). Several types of power plants are used worldwide to generate electrical power. They do not require a centralized electricity grid to transmit power over long distances.

However, they are located at the point of use, so energy is used close to where it is produced. They can be used in various applications, such as generating clean, renewable energy, providing backup power, and reducing carbon dioxide emissions. 

Background: What is Distributed Generation?

For nearly two decades, utility companies have continuously struggled to capture what is referred to as “distributed generation” as an alternative to generating electric power for their consumers. Distributed generator means smaller and additional local sources of electric power, such as distributed generation solar and wind turbines.

Furthermore, types of distributed generation are photovoltaic panels, fuel cells, wind turbines, etc. Energy generation, if there is enough of it, would be closer to the people who need the energy and not just close in terms of distance but also actually see the people making the decisions about power production and distribution. Rather than utility companies making those decisions, someone like you would.

Introduction: Distributed Generation Solar, Wind:

We must reduce the use of fossil fuels and switch to renewable sources such as wind, solar, and geothermal energy. Many renewable energy technologies include solar photovoltaic, wind turbines, biogas, and bio thermal systems, which use sunlight, wind energy, etc. These renewable energy sources generate electricity and transform it into an appropriate form of energy.

Wind turbines are a common form of renewable energy technology that is used to harness wind power and convert it to electricity for use in homes and industries. With many electric car owners, it is easy to conclude that electric cars have become increasingly popular.

Take a look at the article, Solar and Wind Distributed Generation

Description: Benefits of Distributed Generation:

Distributed applications pose exceptional benefits, produce electricity for on-site injection, and intersect with low-voltage transformers. Incorporation of DG can shrink transmission line losses, improves grid resilience, avoid extra generation expenses, and decrease the necessity to spend on new utility generation capacity. Through proper calibration, electric utility systems aiming for energy development by distributed PV allocation have multiple benefits and provide backup in case of disturbance.

What is the MATLAB code?

MATLAB is designed for engineers and scientists to express mathematical matrices and data arrays directly. It is possible to use MATLAB to do any programming, from simple interfaces to extensive application development.

Proposed Methodology: Power System Analysis MATLAB

The scheme compromises a MATLAB simulation through the genetic algorithm in a power system for the optimal location and sizing of distributed generation in a distribution grid. The objective function comprises both power losses (real and reactive) and voltage profiles, and the techniques are verified on the IEEE 14 bus system data.

Visit the link for further learning Matlab script, Load Flow Analysis using Matlab Code

IEEE 14 bus line data
Bus load data IEEE 14

The planned scheme encloses a set of procedures that regulate each bus’s DG placement suitability index in the distribution network. The optimum allocation of the DG unit is achieved using mathematical formulation. The study comprises an assessment between the planned approaches and displays the significance of installing the perfect proportion of DG in the most suitable place.

Click the link for the particle swarm optimization example code for the 33 bus radial distribution system

Simulation executed as mentioned below and genetic algorithm in power system provides efficient performance. 

·         Without DG

·         Optimal Location and Sizing of DG Using Genetic Algorithm (1, 2, 3, 4) DGs combination – 14 Bus System IEEE – Genetic Algorithm

·         14 IEEE Bus System with 1, 2, 3, 4 DGs (Optimal Location and Sizing) using Genetic Algorithm

 Without DG Results

without distributed generation results

A Deep Dive into Distributed Generation MATLAB Code

This article can help you understand the fundamentals of practicing MATLAB code for power flow analysis and renewable power systems. It is a tutorial to clarify how power system analysis is performed in order to help you understand MATLAB for renewable power systems and smart grid systems. The subjects discussed will include network modeling, optimal placement meaning, and newton Raphson method MATLAB code. 

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Why Newton-Raphson method is better?

Newton Raphson method is considered the most sophisticated numerical approach to solving non-linear equations and power flow analysis.

Gauss-Seidel is a simple iterative method where partial derivatives are not required to solve n number of equations. Newton Raphson’s method relies on Taylor’s series and partial derivatives.

The recent Newton Raphson method MATLAB code examples require less number of iterations to reach convergence and take less computer time; hence, the computation cost is less, and convergence is inevitable. The N R method is more precise and is not responsive to elements like regulating transformers, slack bus selection, etc.

load flow analysis by using newton raphson method

The drawbacks of this method are the difficult solution technique, more calculations involved in each iteration resulting in large computer time per iteration, and the large requirement of computer memory, but the drawback has been overwhelmed through a compact storage scheme.

Newton Raphson load flow analysis using MATLAB code is performed as presented below.

matlab jacobian matrix

Newton Raphson Method MATLAB Code Example

What is the purpose of the load flow study?

Load flow analysis is used most commonly to evaluate the system’s operation, while dynamic analysis is used for detecting unavailable elements during a period. It checks whether a system operates safely and if the equipment has been overloaded.

 What is a Genetic Algorithm and its advantages?

Genetic Algorithm is a method used to generate solutions to some complex issues based on the survival of the fittest theory of Charles Darwin. In short, all of us will have heard of Darwin’s famous theory of Evolution by Natural Selection. We will learn about how it applies to genetic algorithms.

This algorithm reflects the process of natural selection. The fittest individuals are selected for reproduction, and this results in the production of offspring of the next generation. Evolutionary algorithms are a type of Genetic Algorithm typically used to generate high-quality solutions to optimization and search problems.

Genetic Algorithm Load Flow is considered during the evaluation of this article.

 Looking for particle swarm optimization code, click on the below link

 Voltage Profile Improvement & Power Loss Reduction

 A power distribution system (PDS) is a complex system comprising numerous interconnected loads and power sources, which must be carefully managed to avoid a cascading effect of load losses and voltage profile deterioration. This work proposes an optimal method for optimizing NR problems in a PDS for power loss reduction and VP improvement.

The current study presents the use of an algorithm based on GA for optimizing the network parameters in the NR process to reduce the power loss of the IEEE 14 mesh distribution system and improve the VP performance of the network. This model has been developed using MATLAB R2017a software.

voltage profile improvement

Why is voltage profile important?

As voltage profile decreases in the network, re-powering demands increase. Unless reactive power demand is met, bus voltage decrease can cause further cascading of neighboring regions. Therefore, maintaining voltage within the allowed limit is essential.

Using Convergence graph in MATLAB to Review Results

 A convergence plot can decide whether a performed analysis is converged acceptably. In a graph representing good convergence of a measured quantity, the curve based on results becomes asymptotic as it processes iterations. Here in the attached plot, as load flow analysis using MATLAB code progress along with iteration, the results satisfied the performance of a considered approach.

convergence graph in matlab


Optimal location and sizing of distributed generation MATLAB code are proficient in enhancing the voltage profile of the network and shrinking power loss, eventually reducing the network congestion as well. 

Interested in further learning, here is a playlist of complete simulation tutorials related to this topic.

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Load Flow Analysis via Matlab Script & Simulink Model

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