my name is yimam alemu from ethiopia .now i am doing my masters thesis on optimal placement of dg in distribution system.i need your help for this.i need the full matlab code for this title

Optimal Distributed Generation Placement in Power Distribution Networks: Models, Methods, and Future Research

ABSTRACT

The integration of distributed generation (DG) units in power distribution networks has become increasingly important in recent years. The aim of the optimal DG placement (ODGP) is to provide the best locations and sizes of DGs to optimize electrical distribution network operation and planning taking into account DG capacity constraints. Several models and methods have been suggested for the solution of the ODGP problem. This paper presents an overview of the state of the art models and methods applied to the ODGP problem, analyzing and classifying current and future research trends in this field.

Optimal DG Placement in Distribution Networks Using Intelligent Systems

ABSTRACT

Distributed Generation (DG) unlike centralized electrical generation aims to generate electrical energy on small scale as
near as possible to the load centers, interchanging electric power with the network. Moreover, DGs influence distribution
system parameters such as reliability, loss reduction and efficiency while they are highly dependent on their situation
in the distribution network. This paper focuses on optimal placement and estimation of DG capacity for installation
and takes more number of significant parameters into account compare to the previous studies which consider just a few
parameters for their optimization algorithms. Using a proposed optimal Genetic Algorithm, a destination function that
includes the cost parameters (such as loss reduction, fuel price, etc.) has been optimized. This method is also capable of
changing the weights of each cost parameter in the destination function of the Genetic Algorithm and the matrix of coefficients
in the DIGSILENT environment. It has been applied and simulated on a sample IEEE 13-bus network. The
obtained results show that any change in the weight of each parameter in the destination function of the Genetic Algorithm
and in the matrix of coefficients leads to a meaningful change in the location and capacity of the prospective DG
in the distribution network.

Introduction

Distributed generation (DG) is defined as small generation
units installed in distribution systems. It is predicted
that DG would have a share of about 20% of new generating
units being on lined [1]. DG applications are growing
due to environmental and economic issues, technological
improvements, and privatization of power systems.
DG application, however, has positive and negative
side effects for public industries and consumers [2].
Generally, DG effects in distribution network depend
on several factors such as the DG place, technology issues,
capacity and the way it operates in the network. DG
can significantly increase reliability, reduce losses and
save energy while is cost effective, though it suffers from
some disadvantages because of the isolated power quality
functioning, and voltage control problems. Generally, planners
assess DG functioning in two respects: costs and benefits.
Cost is one of the most important factors that should
be considered regarding DG application [3-4].

Statement of the Problem

Since the use of distributed sources is highly dependent
on climatic and regional conditions, various DG technologies
have been developed. These technologies are divided
into three general categories [11-12]:
 Technologies working on fossil fuels such as combustion
engines, micro turbines, and fuel batteries.
 Technologies working on new sources of energy such
as wind turbines, solar cells, wave energy, geothermal
and biomass.
 Technologies working on saving energy such as batteries,
fly wheels, Superconducting Magnetic Energy
Storage (SCMES), capacitors, Condensed Air Energy
Storage (CAES) and Hydro Pumps.

Simulation Network

In the proposed work, in order to observe and compare
the results with those of the specified destination function,
an IEEE 13-bus distribution network has been selected
as a sample. It should be noted that the specified
destination function can be generalized to be used for all
distribution networks with any number of buses. Moreover,
the optimization algorithm of the destination function
is a Genetic Algorithm whose chromosomes are as
analogous to the variables of location and capacity of DG.

Simulation

This study aims to optimize the placement of DG and
assess DG capacity using weight coefficients for various
parameters independently taking cost into account. The
coefficients of the first case shown in Table 3 include
loss-reduction parameters like voltage profiles, environmental
factors, fuel price and load prediction in the destination
function of the Genetic Algorithm shown by
( K1 - K5 ) in the destination function. However, other
coefficients shown in Table 4 are related to the weight of
parameters for the effects of environmental factors, fuel
price, load prediction which are defined in an input matrix
for the simulation software. In this case, since parameters
related to loss reduction and voltage profile are
calculated automatically, the coefficients of these parameters
are not considered in the input matrix for the
software. Thus, generally, parameters for any network have
two conditions of weight coefficients with any number of
buses. This has been achieved using genetic algorithm
optimization in DIGSILENT environment. The parameter
changes are illustrated because they are variable in
each bus. Optimization is carried out with Genetic Algorithm
using a cost function. For this purpose, changes in
the coefficients of the parameters are specified due to
their variability in each bus. Optimization of the destination
function has been carried out using a Genetic Algorithm.