Placement and sizing of distribution generation in distribution system

1. PLACEMENT AND SIZING OF DISTRIBUTION GENERATION IN
DISTRIBUTION SYSTEM USING PSO AND GA ALGORITHM
BUSHRA WAHAB
(2K21-MELE-09)
SUPERVISOR: DR. SAFDAR
DEPARTMENT OF ELECTRICAL ENGINEERING
NFC INSTITUTE OF ENGINEERING AND TECHNOLOGY, MULTAN (2022)

2. 2
1. Research Title:
Placement and sizing of distribution generation in distribution system using PSO and GA
2. Research Area:
Distribution generation
3. Abstract:
Electrical Power Systems are exceedingly studied by scholars/scientists these days and became the
main research topic in this era. Advances in power systems motivated by the increasing demand of
generation sources connected to the power distribution system have brought several benefits, such
as losses reduction and improved voltage profile. However, further research must be conducted to
avoid the disadvantages and to improve the benefits. For this purpose applies differential evolution
meta-heuristic to perform sizing and placement of Distributed Generation units in radial distribution
networks in order to reduce active power losses. Besides, the proposed algorithm allows an
evaluation of the active power losses behavior when compared to the number of generating units
placed and dimensioned by the algorithm. Simulations have been performed using the IEEE 14-bus
and IEEE 30-bus for evaluating the proposed methodology. The simulations of the proposed system
will be performed in MATLAB/Simulink environment. With the growing use of DGs in distribution
systems, several methods have been used to achieve various objectives in power system
optimization problems. For this purpose, Particle swarm optimization (PSO) and Genetic algorithm
(GA) are used to determine the suitable placement and optimal sizing of DG in a distribution
system. The results obtained demonstrate the ability of the algorithm for placing and sizing
Distributed Generation units in addition to evaluate the optimal number of generating units to
minimize the active power losses in the networks. Overall the power line losses of the system will
be observed.
4. Introduction:
Generation of electric power is due to non-renewable sources of energy throughout the world.
Because of excessive use and finite sources the non-renewable/conventional sources will come to an
end with the passage of time. Need of electricity and power sources is increasing with the increasing
population and industrialization, therefore excessive depletion of conventional sources is matter of
common need. Coal, petroleum, natural gas, etc. as common conventional sources are used
plentifully at the current time because the power demand is increasing on each successive day.
Because of large use of these non-renewable reservoirs these all the conventional sources are
depleting continuously and if its continues to do so there will be continuous exhaustion of these
fossil fuels and conventional sources will occur soon [1]. Besides, the excessive and continuous use

3. 3
of these conventional sources i.e. coal, petroleum, natural gas and all fossil fuels environmental
pollution is increasing and also global warming as well. These both are very extremely harmful to
the population of both animals and plants along with human being. [2].
On the other side if we look at the wind energy/Wind turbines (WT), Tidal energy, solar-PV and
geothermal energy renewable energy sources, all are resources of flow and no chance of depletion
of these sources with time [3]. Renewable power generation is environment friendly. There are a lot
of benefits of the renewable sources and due to these benefits many countries including Pakistan has
increased their dependence on all renewable sources and started mass power generation on local
grid or prosumer levels. Its highly cost effective than nonrenewable energy. All the research
scientists are proactively working on developing technology to make all the renewable energy most
cost effective and efficient to overcome all the fossil fuels. That’s why renewable energy is
considered as future energy [4]. Environmental protection and the increasing demand for high
power quality have attracted wider attention with the growing consumption of energy, renewable
energy power generation technology such as wind power and PV system, has been more widely
applied [5].
Distributed generation is electrical generation and storage performed by a variety of small, grid-
connected or distribution system-connected devices referred to as distribution energy resources
(DER). DER systems are decentralized and more flexible technologies systems can comprise
multiple generation and storage components. The presence of DGs in the distribution system may
lead to several advantages such as voltage support, improved power quality loss reduction and
improved utility system reliability. Nowadays, Researchers are working on the model of DGs
system throughout the world. It is the best way and technique for power generation and serving
local load simultaneously [6]. The proposed layout diagram is shown in Figure 1.

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Figure 1: The proposed Layout Diagram
Coal, petroleum, natural gas, and other fossil fuels have traditionally been the leading sources of
electric power generation. Interest in finding new sources as well as intermixing of electric power
generation has grown in recent years for many reasons, including environmental impacts, waste
generation, increasing energy cost, legislation, and other political influence, etc [7]. Due to these
reasons, current movements are in support of stable and reliable renewable energy resources that
will push electric utility companies, customers, and stack holders to develop renewable energy
resources based electricity generation and maintain a specific percentage of renewable energy in
their electric generation portfolio [8].Electrical Power is important part in the modern era of
advanced technology and industrialization. Increase in the energy demand due to increasing
development in different technologies, industrialization and increasing population leads to
development in the energy sector. Electrical energy is the most important type amongst all types of
energies, and is explored by many researchers of the world. Environmental issues, and
decentralization and deregulation of electrical systems have increased the penetration of distributed
electrical generation systems known as Distributed Energy Resources (DERs). However, this
readily penetration of DERs also have raised so many problems.
As the population increasing day by day the requirement for energy is also increasing. The
traditional grid is not efficient to fulfill the requirement of the world that needs a small, domestic
and smart type of grid. The main objective of the proposed work is to minimize the function cost,

5. 5
enhance reliability, customer privacy and reduce power losses and improve voltage profile.
Increased use of Distributed Energy Resources (DERs) would have significant impact on electrical
systems in the future. These distributed resources are comprised of wind turbines, PV cells, fuel
cells, Micro turbines and diesel generators, etc. [6]. Furthermore the flywheels and batteries BESS
etc. are the resources that could be included in the Distributed energy resources. Energy storing in
the DERs is the major problem in this system and can be released to local grid to control the cost of
this system. There is need of a specific interface system for interfacing and making this system
effective. Operation cost of these all the devices and systems will be a burden on the total cost of
the system. In addition, many constraints should be considered in the operation process to satisfy
the technical aspects of the system.
Worldwide society development, technology, and science evolution, industry, and hence the growth
in living standards, are accompanied by a rise in energy consumption [7] resulting in a growing
need for resources, especially non-renewable energy resources. Particularly, depletable fossil fuels
were increasingly consumed in recent decades with all the resulting negative consequences [8].
Distributed energy resources are becoming increasingly important because of its cumulative
capacity is globally growing every year. After a comprehensive literature review, the proposed
system will be tested using the Simulink Environment, and results will show the collective working
of four power sources i.e. DGs, WT, the solar PV system, and local grid covering all the issues of
system stability [9].
The search for the solution occurs through the selection of the fit individuals evaluated by the
objective function. The differential evolution in particular has three stages are Mutation, Crossover
and Selection [10].
In the literature, various approaches were used to increase DEGs usage in the existing system via
different useful strategies, which have proven their ability to decrease the losses of the distribution
networks of different sizes [11]. The problem of optimal DG location and sizing is divided into two
sub problems, where the optimal location for DG placement is the one and how to select the most
suitable size is the second. Many researches proposed different methods such as analytic procedures
as well as deterministic and heuristic methods to solve the problem Furthermore, it is proposed an
algorithm to evaluate the behavior of the active losses in power systems with increasing amount of
DG units inserted [12]. The proposed algorithm enables a further analysis of the advantages of
inserting DG units on radial distribution networks at same time it highlights a limit to their insertion
[13].

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5. Comparative Analysis:

7. 7
Table for Base papers Analysis
S.
No.
Title of
research
Year of
publication
Proposed
Research
gap
Objective Limitation
Bus
System Tool and Technique Validation
1.
A combination of
genetic algorithm
and particle
swarm
optimization for
optimal DG
locationandsizing
in distribution
system
2020
The optimal
locationforDG
placement is the
one andhowto
select the most
suitable size is
the second.
The main objectiveis
to minimizethe total
cost of thesystem by
reducinglosses
Improve voltage
profile
Customer privacy
Enhance Reliability
Used a
different
methods
Used different
parameters
33
69
Technique:
 PSO technique
 GA algorithm
Tool:
The simulations of the
proposedsystemwill be
performedin
MATLAB/Simulink
environment.
Location and
sizingfor the DG
were identified
using the method.
Enhancements in
power losses,
voltage regulation
and voltage
stability were
achieved
2.
Placement and
Sizing of
Distributed
Generation in
Distribution
System
2019 Appropriate
Placement
andsizing.
Reduction Active
power losses
Improve Voltage
profile
Used
different
methods
Used
different
parameters
33
69
Technique:
 SGA
 PSO
 KHA
 SKHA
Tool:
MATLAB
Reduction of
active power
losses achieved
3.
Optimal Sizing
and Placement of
Distributed
Generation (DG)
Using Particle
Swarm
Optimization
2021
Appropriate size
andlocation
with the general
cost andtaking
into account
environment
factors is a
very
important
process to
ensure the
stability and
reliability of
the system
Minimize losses
Improve voltage
stability andprofile
voltage
Used
different
algorithm
Used
different
parameters
14 bus
Technique :
 Particle Swarm
optimization(PS
O)
Tool :
Used MATLAB tool to
findthe optimal size and
locationof DGsystem
Improve the
voltage profile
and minimize
losses and
improve
voltage stability
is clearly seen
4 Optimal Sizing
and Placement of
Distributed
Generation in
Distribution
System
Considering
Losses and THD
using
Gravitational
Search Algorithm
2019
Appropriate
location
DG sizing
Minimize the total
cost of thesystem by
reducinglosses and
THD.
Improve voltage
profile
Various different
algorithm
Used different
parameters
69 bus
Technique :
 Gravitational
Source
Algorithm(GS
A)
 Sensitive
analysis
 Particle
Swarm
optimization(P
SO)
Tool :
MATLAB
Reduction of
losses and
THDv, is
clearly seen
after optimal
DG placement
and sizing.
5.
Optimal
placement,
sizingand
power factor
of
distributed
generation
2020 To identify the
DG placement
DG sizing
And power
factor
Minimize the total
cost of thesystem by
reducinglosses
Improve power
factor
Used different
methods
The overall
loss reduction
achieved was
96.04%
Real time
power
grid
Technique:
 Differential
Evolution-based
algorithm
Tool :
MATLAB
Minimize the
power losses
and improve
power factor is
clearly seen
6.
Optimal
placement and
sizing of the
virtual power
plant constrained
to flexible-
renewable energy
proving in the
2022.
The bi-level
problem is
converted into
a single-level
formulation
using Karush-
Kuhn-Tucker
approach to
obtain the
To findtheoptimal
locationandsize of
IBDG aimingto
minimize total
active power losses
Due to the
limitation of
the number of
components in
the distribution
network, it is
estimated that
the favorable
conditions
IEEE
119-bus
Technique:
 GA
 ASO
 GOA
 PSO
 COA
 BURO
Tool:
Optimal
placement and
sizing
Smart distribution
network achieved

8. 8
smart distribution
network
optimum
solution
through
classical
mathematical
solvers
Non-linear
problem is
complex.
depend on the
sizing and
sitting of
flexibility
and renewable
resources in the
form of VPPs.
Used MATLAB tool to
findthe optimal size and
locationof DGsystem
7.
Optimal
Placement and
Sizing of
Inverter-Based
Distributed
GenerationUnits
and Shunt
Capacitors in
Distorted
Distribution
Systems Using a
Hybrid Phasor
Particle Swarm
Optimization
and
Gravitational
Search
Algorithm
2020
To identify the
optimal sitting
and sizing
problem of DG
units andshunt
capacitors
Reduction of active
power losses
considering
constraints of the
fundamental
frequency active
andreactive power
balance, RMS
voltage, andtotal
harmonicdistortion
of voltage (THDV)
Compare
other
algorithm.
Used
different
parameters
IEEE-33
IEEE-69
Technique:
 Phasor Particle
Swarm
Optimization
and
Gravitational
Search
Algorithm
(PPSOGSA)
Tool
Used MATLAB tool to
findthe optimal size and
locationof DGsystem
Inverter Based
Distribution
generation and
shunt capacitor
achieved and
minimize the
power losses
using these
algorithm
8. A novel Crow Search
Algorithm Auto-Drive
PSO for Optimal
Allocation and Sizing of
Renewable Distributed
Generation
2020
To identify the
Optimal
allocation,
sizing, and
number of
RDGs
Power losses
minimization
objectives.
Used
different
algorithm.
19
30
 CSA-PSO
algorithmin
solvingthe
optimal power
flowproblem
with RDGs
comparedtothe
state-of-the-art
metaheuristic
techniques.
 TLBO
 PSO
Power
losses
achieved
using this
CSA-PSO
algorithm
9.
Distributedenergy
resource
placement
considering
hostingcapacity
by combining
teaching–learning-
based andhoney-
bee-mating
optimization
algorithms
2021
To identify the
optimal
locations for
newDERs
objective function
includes cost,
losses, and voltage
deviation.
Used different
algorithm
33 bus
in radial
system
Teaching–learning-
based optimization(TLBO)
andhoney-bee-mating
optimization(HBMO)
algorithm.
Fuzzy
clustering to
the multi
objective
process
improves the
DER
placement
problem
%9 and %22
faster than
TLBO and
HBMO
10.
A hybrid
algorithmbased
optimal placement
of DG units for
loss reduction in
the distribution
system
2020 To optimize
the position
and size of DG
u nits
To reduce losses in
the distribution
sy stem
Used different
algorithm 33
69
 Hybrid
technique
 The proposed
hybrid
technique is
executedin
MATLAB/Si
mulink
working
platform is
tested
Achieved
minimum
power losses
Usingthis
hybrid
algorithm

9. 9
This proposed algorithm a new method for determining suitable placement and optimal sizing
of DG units It has been observed that the proposed method performs better compared to the
other methods such as ASO, GSA, TLBO, HBMO in minimizing the total cost of the system
by reducing the losses ,improve voltage profile, voltage stability .
6. Problem Statement/Research gap
The problem of DG placement and sizing is of Importance. The installation of DG units at non-
optimal Places with non-optimal sizing can cause higher power Losses, power quality problems,
instability of the system, and escalating operational costs
The problem of DG placement and sizing is of importance. The installation of DG units at non-
optimal places with non-optimal sizing can cause higher power losses, power quality problems,
instability of the system, and escalating operational costs. There are several methods to allocate
and size the DG in the distribution power system. The optimal placement and sizing of DG
using particle swarm optimization (PSO) and Genetic algorithm (GA) is also studied in.
7. Objective of the study:
The main objective of the proposed work is to minimize the total cost of the system by
reducing power losses, enhance reliability, customer privacy and improve voltage profile.
8. Research Methodology:
PSO is an optimization technique based on the movement and intelligence of swarms. PSO
applies the concept of social interaction to problem solving. The structure of basic creature
which make a group to have some purpose such as food searching. The group of creatures has
this relative behavior, for example, bee swarm, fish school and bird flock. . The particle swarm
model will be used by fitness value consideration. Moreover the important direction of particle
movement. PSO consists of a group (swarm) of individuals (particles) moving in the search
space looking for the best solution. Each particle is represented by a vector s of length n
indicating the position and has a velocity vector v used to update the current position which
adjusts its flying according to its own flying experience as well as the flying experience of
other particles. This is a searching technique developed for optimal placement and sizing of
DG. The problem consists of two parts. The first is the optimal location of DG and the second

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is the optimal sizing. GA technique has been chosen to play this role because of its attractive
quality. PSO has the fast convergence ability which is a great attractive property for a large
iterative and time consuming problem.
The flowchart of PSO algorithm as depicted in Figure 2:
Figure 2: Flow Chart of PSO algorithm
The flowchart of GA algorithm as depicted in Figure 3:

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Figure 3: Flow chart of Genetic algorithm ( GA)
9. Conclusion :
This proposed algorithm a new method for determining suitable placement and optimal sizing
of DG units. The suitable placement and optimal sizing of DG was obtained through Genetic
algorithm (GA) and PSO technique. The multi-objective function was to minimize the total
power loss and improved voltage profile. The results indicated that the proposed algorithm is
effective in finding optimum sizes of DGs in distribution power systems. Also, the reduction of
losses is clearly seen after optimal DG placement and sizing. The proposed method performs
better compared to the other methods such as GSA and EP in minimizing the losses and
improved the voltage profile.

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Refrence List:
[1] A. Zoungrana and M. Çakmakci, "From non‐ renewable energy to renewable by
harvesting salinity gradient power by reverse electrodialysis: A review," International
Journal of Energy Research, vol. 45, no. 3, pp. 3495-3522, 2021.
[2] K. S. Hayibo and J. M. Pearce, "A review of the value of solar methodology with a case
study of the US VOS," Renewable and Sustainable Energy Reviews, vol. 137, p.
110599, 2021.
[3] S. Jabeen, S. Malik, S. Khan, N. Khan, M. I. Qureshi, and M. S. M. Saad, "A
comparative systematic literature review and bibliometric analysis on sustainability of
renewable energy sources," International Journal of Energy Economics and Policy, vol.
11, no. 1, p. 270, 2021.
[4] P. Sharma, A. Jamwal, N. Sharma, and R. Agrawal, "Opportunities and Issues with
Clean Renewable Energy Development in India: A Review," Advances in Fluid and
Thermal Engineering, pp. 527-537, 2021.
[5] H. Stančin, H. Mikulčić, X. Wang, and N. Duić, "A review on alternative fuels in future
energy system," Renewable and Sustainable Energy Reviews, vol. 128, p. 109927,
2020.
[6] P.-H. Trinh and I.-Y. Chung, "Optimal Control Strategy for Distributed Energy
Resources in a DC Microgrid for Energy Cost Reduction and Voltage Regulation,"
Energies, vol. 14, no. 4, p. 992, 2021.
[7] E. Efficiency and E. E. Indicators, "Capturing the Multiple Benefits of Energy
Efficiency A Guide to Quantifying the Value Added International Energy Agency The
traditional focus on energy savings as the main goal of energy efficiency policy has, at
times, led to an underestimation of the full value of energy efficiency in both national
and global economies. Energy efficiency can bring multiple benefits, such as enhancing
the sustainability of the energy system, supporting strategic objectives for economic and
social development, promoting environmental goals and increasing prosperity."
[8] L. Yu, J. Lv, Q. Peng, L. Zhao, and G. Li, "Research on Influencing Factors of Power
Grid Security Risk Based on “Cognitive-Constraint” Model," in Journal of Physics:
Conference Series, 2021, vol. 1771, no. 1, p. 012005: IOP Publishing.
[9] A. Levenda, I. Behrsin, and F. Disano, "Renewable energy for whom? A global
systematic review of the environmental justice implications of renewable energy
technologies," Energy Research & Social Science, vol. 71, p. 101837, 2021.

13. 13
[10] Optimal sizing and allocation of renewable based distribution generation with gravity
energy storage considering stochastic nature using particle swarm optimization in radial
distribution network - ScienceDirect
[11] Optimal Sizing and Placement of Distributed Generation in Distribution System
Considering Losses and THDv using Gravitational Search Algorithm (researchgate.net)
[12] Optimal Placement and Sizing of Inverter-Based Distributed Generation Units and Shunt
Capacitors in Distorted Distribution Systems Using a Hybrid Phasor Particle Swarm
Optimization and Gravitational Search Algorithm: Electric Power Components and
Systems: Vol 48, No 6-7 (tandfonline.com)
[13] A Novel Crow Search Algorithm Auto-Drive PSO for Optimal Allocation and Sizing of
Renewable Distributed Generation | IEEE Journals & Magazine | IEEE Xplore
[14] T. T. Nguyen, L. T. Duong, and V. A. Truong, "An effective method to solve the
problem of electric distribution network reconfiguration considering distributed
generations for energy loss reduction," Neural Computing and Applications, vol. 33, no.
5, pp. 1625-1641, 2021.
[15] I. I. PICIOROAGA, A. M. TUDOSE, D. O. SIDEA, C. BULAC, and T. Lucian,
"Application of Soft Open Points for Increasing the Supply Restoration in Active
Distribution Networks," in 2021 12th International Symposium on Advanced Topics in
Electrical Engineering (ATEE), 2021, pp. 1-6: IEEE.
[16] J. Park, J. Chun, S. H. Kim, Y. Kim, and J. Park, "Learning to schedule job-shop
problems: representation and policy learning using graph neural network and
reinforcement learning," International Journal of Production Research, pp. 1-18, 2021.
[17] J. K. Motwani and L. Piegari, "Harmonic distortion reduction in modular multilevel
converters by novel mixed harmonic injection," International Transactions on
Electrical Energy Systems, p. e12871, 2021.
[18] B. S. Jebaraj et al., "Power Quality Enhancement in Electric Arc Furnace Using Matrix
Converter and Static VAR Compensator," Electronics, vol. 10, no. 9, p. 1125, 2021.
[19] A. Sharma, M. Kolhe, A. Kontou, D. Lagos, and P. Kotsampopoulos, "Solar
photovoltaic-based microgrid hosting capacity evaluation in electrical energy
distribution network with voltage quality analysis," SN Applied Sciences, vol. 3, no. 5,
pp. 1-22, 2021.
[20] Z. M. Ali, I. M. Diaaeldin, A. El-Rafei, H. M. Hasanien, S. H. A. Aleem, and A. Y.
Abdelaziz, "A novel distributed generation planning algorithm via graphically-based
network reconfiguration and soft open points placement using Archimedes optimization
algorithm," Ain Shams Engineering Journal, 2021.

14. 14
[21] C. D. Dumitru and A. Gligor, "Modeling and simulation of renewable hybrid power
system using matlab/simulink environment," Scientific Bulletin of the" Petru Maior"
University of Targu Mures, vol. 7, no. 2, p. 5, 2010.