Electrical & Electronic Systems

Bahrami and Imari, J Electr Electron Syst 2014, 3:2

htp://dx.doi.org/10.4172/2332-0796.1000127

Research Article
Research
Article

OpenAccess
Access
Open

Optimal Placement of Distributed Generation Units for Constructing

Virtual Power Plant Using Binary Particle Swarm Optimization Algorithm
Bahrami S* and Imari A
Department of Electrical Engineering, University of Isfahan, Isfahan, Iran

Abstract
The idea of applying distributed generation resources in distribution systems has become increasingly important
due to changes in the distribution systems. Optimal sizing, location, type and installation time of DGs for constructing
virtual power plant is one of the important subjects in applying distributed generation in the power system. In this paper,
a new method is presented for optimal placement and type of distributed generation units and long term system planning
aim to constructing virtual power plant. Minimizing the long term total cost of the system is considered as the objective
function. The impacts of applying demand response on expansion planning is also investigated. The Binary particle
Swarm Optimization (BPSO) method is used for solving this problem. In order to evaluate the efficiency of the proposed
method, the method is tested on the IEEE 33 bus distribution system.

Keywords: Distributed Generation (DG); Binary particle Swarm

Optimization (BPSO); Commercial Virtual Power Plant (CVPP)
Introduction
In recent years, the penetration of Distributed Generation
(DG) units increased in power systems. Installation of DG units in
distribution systems has several benefits such as reducing system losses,
enhancing voltage profile, shaving peak demand, relieving overloaded
distribution lines, reducing environmental impacts, increasing overall
energy efficiency, and deferring investments to upgrade existing power
systems [1]. In order to receive to mentioned advantages and better
system planning, optimal placement and sizing, selecting appropriate
type and the installation time of DGs are necessary.

Literature review
Many studied have been implemented with the aim of power
loss reduction. Genetic Algorithm (GA) [2], Tabu search [3], particle
swarm optimization [4,5] and combination of PSO and GA [6] are
some of methods which their objective function is loss reduction. In
[7] an improved reinitialized social structures PSO algorithm has been
developed for optimal placement of multiple DGs in a micro grid to
minimize the real power loss within voltage and power generation
limits. The analytical approach has been demonstrated in [8,9] to find
the optimal size and location of DG to minimize the real power losses
and enhancement in voltage profile.
In [10], the optimal sizing of a small isolated power system that
contains renewable and/or conventional energy technologies was
determined to minimize the systems energy cost. In [11], DG units
were placed at the most sensitive buses to improve the voltage stability.
In [12], to determine the optimal locations of DG units in distribution
system, a new multi-objective problem based on minimized power
losses, enhanced reliability and improved voltage profile has been
presented. Several papers have focused on the use of EAs (evolutionary
algorithms), analytical methods or load flow methods to optimize the
DG placement and its sizing [13,14].
The integration of DERs is another subject which affects the
operation of a power system network. In this situation, distributed
generation and controllable demand may have the opportunity to
participate in the operation of transmission and distribution networks.
However, the aggregation of many small-capacity generators into one
large power generation project could improve the economics of DERs.
J Electr Electron Syst
ISSN: 2332-0796 JEES an open access journal

If several DER units are linked together and are operated as one unit, the
concept is often called a Virtual Power Plant (VPP). There are two types
of a VPP. Commercial Virtual Power Plant (CVPP) is one type of VPP
operation [15]. The CVPP is a competitive market actor that manages
the DER portfolio(s) to make optimal decisions on participation
in electricity markets. From the commercial point of view, VPP as a
market agent seeks to obtain the maximum benefit from the generation
and the demand portfolio without considering the network constraints.
Technical Virtual Power Plant (TVPP) is another type of VPP operation
[16]. The TVPP takes into consideration also the operation of the grid.
The TVPP aggregates and models the response characteristics of a
system containing DERs, controllable loads and networks within a
single electric-geographical (grid) area. On the other hand, a TVPP
consists of some DERs from the same geographic location. In this case,
the impact of operation on the distribution network is also considered
[17].
Demand Response (DR) is one of the important parts of a VPP.
Demand response is established to motivate changes in electricity
consumption by end users. The main benefit of DR is the improvement
of power system efficiency, since a closer alignment between customers
electricity prices and the value they place on electricity is established
[18].

Necessity of the virtual power plant

Determining the optimal place, size, capacity, type and installing
time of DGs is one of the most important subjects. If the distributed
generation units are not properly installed, it may lead to disadvantages
in power quality and increasing the costs of system. Virtual power plant
concept is a solution for the mentioned problems.
Virtual power plant (VPP) is a flexible representation of a portfolio

*Corresponding author: Bahrami S, Department of Electrical Engineering, University

of Isfahan, Isfahan, Iran, Tel: +989372154095; E-mail: s_bahrami@eng.ui.ac.ir
ReceivedFebruary 24, 2014; Accepted April 24, 2014; Published May 03, 2014
Citation: Bahrami S, Imari A (2014) Optimal Placement of Distributed Generation
Units for Constructing Virtual Power Plant Using Binary Particle Swarm Optimization
Algorithm. J Electr Electron Syst 3: 127. doi:10.4172/2332-0796.1000127
Copyright: 2014 Bahrami S, et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.

Volume 3 Issue 2 1000127

Citation: Bahrami S, Imari A (2014) Optimal Placement of Distributed Generation Units for Constructing Virtual Power Plant Using Binary Particle
Swarm Optimization Algorithm. J Electr Electron Syst 3: 127. doi:10.4172/2332-0796.1000127

Page 2 of 6
of DER that can be used to make contracts in the wholesale market and
to offer services to the system operator. The VPP integrates the capacity
of distributed energy resources and represents an operation strategy with
the combination of operation parameters of each DER. Also, VPP can
consider the effect of network in the output powers of integrated DGs.

customers for participation in demand response program is presented

in the third part of the objective function. The fourth statement of the
objective function represented the cost of system expansion in the long
term. The required cost for installing smart metering devices on the
customers is presented in the last statement of the objective function.

Distributed generation units are the main part of virtual power

plant. So, the optimal placement, type of generator, capacity and
installing time of each DG unit in the system is the first stage for
constructing virtual power plant. Demand response is the second
element of VPP. Demand response is such a capacity which depends on
the consumption patterns of consumers and means reducing the loads
of consumers in response to high power market prices or emergency
situations of system. Demand response programs have many advantages
such as improving the reliability of the system, reducing the cost,
improving the environmental problems, reducing market strength and
giving better services to the consumers.

Where (CDG,I,year (PDG,I,year(t)) is the cost of ith generator in tth hour of

yth year in respect of generated active power (PDG,I,year(t),iof mentioned
generator. The active power received from main bus and the market
price in tth hour of yth year are Ps(t) and MPyear(t), respectively. The
number of lines in the network which should bundled in the yth year
and the required cost for bounding one line are Bnum(Year) and CB(year),
respectively. DRT is the total number of customers which installed smart
meters and the cost of installing smart meters on a customer shows
by Cinst. The production cost of each DG have both fixed and variable
components. The fixed costs consist of capital and installation costs (C
& I). It is including the cost to purchase and install a DG technology at
a specified location [19]. The variable cost includes the operation and
maintenance (O & M) and the fuel cost of DG technologies (F). The
capital and investment cost of generator is defined as following:

The goals of the research are including a method for determining

the optimal placement, capacity and the type of DGs and the long term
and short term system planning. In order to aim to the mentioned
goals, the long term cost of the system must be minimized.
In this paper, a new method for optimal placement of distributed
generation units for constructing virtual power plant is presented. Also,
the long term planning of distribution system is performed. In order
to receive to the mentioned goals, the long term total cost of system
is considered as objective function and the problem is converted to
an optimization problem. The Binary Particle Swarm Optimization
(BPSO) method is used to solve in the proposed method.
The rest of the paper is organized as follows: Section 2 presents the
problem formulation. The proposed method is described in Section 3.
Section 4 describes the test system used in this paper. A brief summary
of the simulation used to obtain the results, numerical results along with
some observations and discussions are also included in this section.
Finally, the contributions and conclusions of the paper are summarized
in Section 5.

Problem Formulation

d (1 + d) n
(1 + d) n 1
CF 8760

TIC ( per KW)

C&I =

(2)

Where d, n, CF and TIC are interest rate, planning period, capacity

factor and total investment cost, respectively. Also, CF is defined as
following [19].
CF =

(working hour per day) 365 days per hour

8760

(3)

So, the cost of energy (COE) produced by a DG can be considered

as following:

COE = (C & I ) + (O & M ) + F (3)

(4)

Constraints
In the optimal placement and sizing of DG units for constructing
VPP, the following constraints should be satisfied.
i.

Unbalanced three-phase power flow equations:

Nbus

(5)
=
Pi
VV
i jYij cos( ij i + j )
Optimal placement of distributed generation units in the
i =1
distribution system is a very important subject for constructing virtual
Nbus
(6)
power plant. The problem to solve is to determine the optimal location =
Qi VV
i jYij sin( ij i + j )
i =1
and size of a given number of DG units. Since, the costs of the system
must be optimized, in this paper, the long term total cost of system
ii. Active and reactive power constraints of DG units:
including the production cost of DGs, cost due to purchasing active
The output active and reactive power of each DG units must be
power from the grid, DRs cost, upgrade cost of the system and the cost
between its minimum and maximum values:
of installing smart meters in the system are considered as the objective
min
max
function. The long time total cost of system is considered as following:
PDG
,i PDG ,i PDG ,i

(7)
Ymax 24 Gn

= TC
= CDG ,i , y ( PDG ,i , y (t ))
minimize : Total Cost
=y 1 =t 1 =i 1

Ymax 24
Ymax 24 N DR

+ ( Pst MPy (t )) + ( PDR , y , j (t ) C j MPy (t ))

=y 1 =t 1
=y 1 =t 1 =j 1

Ymax

y =1

+ ( Bnum ( y ) CB ( y ) ) + ( DRT Cinst )

(1)

iii.

Pn , m ( , V ) Pn , m , max

iv.

(8)

Active power constraints of distribution system lines:

Pm , n ( , V ) Pm , n , max

The objective function has five statements. The first statement

represents the production cost of GN distributed generations in all of
hours for a long term period (YN). Second statement describes the cost
due to purchasing energy from grid in ling term. The amount paid to
J Electr Electron Syst
ISSN: 2332-0796 JEES an open access journal

QG ,min QG QG ,max

(9)
(10)

Voltage constraints

If the voltages along the feeder are not satisfied, optimal sizing and
placement of DG are changed to the nearest values to take the feeder
voltages to the voltage limits. The voltage must be kept within standard
limits at each bus [20]:

Volume 3 Issue 2 1000127

Citation: Bahrami S, Imari A (2014) Optimal Placement of Distributed Generation Units for Constructing Virtual Power Plant Using Binary Particle
Swarm Optimization Algorithm. J Electr Electron Syst 3: 127. doi:10.4172/2332-0796.1000127

Page 3 of 6

Vmin V Vmax

(11)

Proposed Method
In this section, the proposed algorithm for optimal placement of
DG units for constructing virtual power plant in distribution system
is presented. The goals of the proposed method are determining the
optimal site, capacity, and type and installation year of each DG in
distribution system and long term expansion planning of distribution
system. In order to aim to the mentioned goals, the proposed algorithm
which shown in Figure 1 is presented. The proposed algorithm has 5
stages which are explained in following:
Stage 1: determining initial data
At the first stage of the proposed algorithm the initial data including
the configuration of understudy distribution system, power market
price, the operation cost of DG units and the peneteration value of
demand response is determined.

Stage 2: initializing BPSO

In this stage, the number of particles (N), maximum iterations of
algorithm (Nmax) and the population of PSO method are determined.
Stage 3: each particle of population is including parameters
required for installing DGs in system. In this stage, DG units are placed
in distribution system based on the data of each particle. Also, the
production cost of each DG is determined based on installing year and
using (3).
Stage 4: Optimal Power Flow (OPF)
In this stage, optimal power flow is applied to determine the optimal
output actvie power of DGs, power flow of lines, voltage of buses and
total cost of system. The objective function of OPF is maximization of
social benefit presented in [21].
Stage 5: evaluating congestion in distribution system
In this stage, the condition of distribution system lines is evaluated.
if there is congestion in lines, it should upgrade in expansion planning
of the system.
Stage 6: determining long term system costs
Since, the long term system cost is considered as objective function,
the best planning of distribution system has minimum cost in long
term. So, the long term system cost is considered as fitness in BPSO
method.
Stage 7: determining the best particle
Stages 1 to 6 are performed for N particle in (Nmax) iteration of BPSO
method. The minimum fitness is obtained in each iteration. The best
planning of the system including the site, size type and installing time
of each generator has minimum cost in long term which is determined
in the end of procedure shown in Figure 1.

Binary Particle Swarm Optimization (BPSO)

Conventional particle swarm optimization (PSO): The PSO
algorithm models the behavior of a group of particles that randomly
select the initial values. These particles search the problem space to
find new solutions. The position and the velocity of every particle at
Vi
the iteration k in the search space are described by X ki
and k ,
respectively. Each particle records its best local position
. Then, the
i
velocity of particle I in the iteration k+1 Plbest is obtained from the
following equation:

i
Vki +1 = .Vki + C1.R1 Plbest
X ki + C2 .R2 Pgilobal X ki

(12)

Where R1 and R2 are the random functions that generate a random

number between 0 and 1. Also, is the inertia weight factor and C1,C2
are the training coefficients. It should be noted that is decreased from
0.9 to 0.4 linearly. Also can be obtained as following:

=
max ((max min ) / kmax ) k

(13)

Where kmax is the number of the maximum iteration. At the end

of each iteration, a new position for each particle is obtained by the
summation of its old position and new velocity:
X ki =
+1

X ki + Vki +1

(14)

Binary PSO
Figure 1: Algorithm flow chart.

J Electr Electron Syst

ISSN: 2332-0796 JEES an open access journal

Binary particle swarm optimization is presented in [22]. BPSO

is used to solve discrete problems. The particle swarm formula (12)
remained unchanged, except that now the position and speed of

Volume 3 Issue 2 1000127

Citation: Bahrami S, Imari A (2014) Optimal Placement of Distributed Generation Units for Constructing Virtual Power Plant Using Binary Particle
Swarm Optimization Algorithm. J Electr Electron Syst 3: 127. doi:10.4172/2332-0796.1000127

Page 4 of 6
particles are integers in {0,1} and Vki +1 ,since it is a probability, must
be constrained to the interval [0,1]. A logistic transformation S (Vki ) is
used to accomplish this modification.
1
S (Vki+1 ) = sig mod e(Vki+1 ) =

(15)
1 + exp(Vki+1 )
The resulting change in position then is defined by the following
rule:
if rand S (Vki +1 ) then : X ki +1 = 1 ;
else : X ki +1 = 0 ;

(16)

Where the function S (Vki ) is a sigmoid limiting transformation and

rand is a quasi-random number selected from a uniform distribution
in [0, 1].
Figure 3: IEEE 33 bus distribution system.

Application of BPSO for solving optimization problem

In the proposed method four major parameters of each DG are
determined using BPSO method. These major parameters are: site, size,
type and installing year of each DG. In this paper, the goal is placement
of GN distributed generation in distribution system for constructing
virtual power plant. Figure 2 represents the dimensions of each
particle in PSO method. The number of dimension of each particle for
placement GN DG unit is 4* GN. Equations (17) to (20) describe the
limits of the dimensions of particles.

1 Bi Bmax
0 Pi Pi , max

(17)

(18)

Ti = {1, 2,..., T f }

(19)

0 Yi Ymax

(20)

Where Bmax is the maximum bus of system, Pi,max represents the

maximum allowable active power of ith bus of system. It should be noted
that Tf is general index for the types of DGs and ymax is the last year of
the planning of the system.

Simulation Results
In this paper, in order to evaluate the performance of the proposed
method, the method is applied on IEEE 33 bus distribution system [23].
For this purpose, four DG is placed in distribution system by using the
proposed method and an expansion planning for distribution system is
performed. The results of proposed method are compared to case which
no DG are placed in the system. Then, in order to evaluate the impact
of demand response in distribution system, two penetration value of
demand response (10% and 13%) is considered in the system and the
system planning is performed.

Network information
The understudy system (IEEE 33 bus distribution system) is
presented in Figure 3. The market price is in [24]. It should be noted

Figure 2: The dimensions of each particle in the PSO Method.

J Electr Electron Syst

ISSN: 2332-0796 JEES an open access journal

that the rate of annual load growth is considered as a fixed rate and
equal to 5%. Also, it is supposed that all of DRs have a contract with
the owner of VPP. During the contract, the amount of fines paid by
customers is considered 10 percent more than the market price of
electricity. All of the lines of distribution systems are considered as
candidates for upgrading.

Network planning without presence of DG in the system

If in the studied system, there is no DG, all the required energy
will be received from the main grid. Accordingly, long term total
cost of system in 10 year period is 17.66036 million dollars. Also, the
lines between buses 1-2 and 2-3 are expanded at 3rd and 10th years,
respectively.

Network planning by presence of DG and absence of DR in

the system
In order to evaluate the performance of the proposed method, the
algorithm which is presented in Figure 1 is used to optimal placing of
several DG units in the system. This case is under the conditions that
there is no demand response in the system. Four DG units are placed in
the 33 bus distribution system. Micro turbine and combustion turbine
are considered as the types of DGs. Results of the placing are presented
in Table 1. In this table, the placing, capacity, type and installing year of
each DG is presented. As it is demonstrated in the Table 1, installation
of one combustion turbine unit at 3rd year prohibits expansion of
distribution line between 1st and 2nd buses. Therefore, the long term
total cost of the system for optimal placing of four DGs in the system
without presence of DR is 16.68435 million dollars.

Placement of 4 DG and network planning with 10%

penetration of DR in system
For estimating the effect of demand response on 10 year planning
of the system, some parts of the system loads are equipped with smart
meters. Hence, in order to using DR for constructing VPP, the customers

DG number

site
(Bus number)

Capacity
(KW)

Types of DG

Installation time
(year)

30

500

Micro-Turbine

18

500

Micro-Turbine

14

500

CombustionTurbine

25

500

Micro-Turbine

Table 1: Results of 10 years planning of the system for 4 DG placement without

presence of DR.

Volume 3 Issue 2 1000127

Citation: Bahrami S, Imari A (2014) Optimal Placement of Distributed Generation Units for Constructing Virtual Power Plant Using Binary Particle
Swarm Optimization Algorithm. J Electr Electron Syst 3: 127. doi:10.4172/2332-0796.1000127

Page 5 of 6
of 6th to 23rd buses (DRT=18) are equipped with telecommunicating and
measuring instruments. The cost of installation of instruments on every
bus (Cinst) is considered $3000 [22]. Penetration rate of DR in the every
mentioned bus is 10%. Therefore, while the congestion occurs in the
lines of the system, the owner of the VPP can cancel utmost to 10% of
the loads of every buses which equipped with DR. The result of 10 year
planning of system for 4 DG placement with 10% penetration of DR
in the system is presented in Table 2. In this case, no transferring line
needs to be expanded. Also, the long term total cost of the system is
16.18549 million dollars.

Planning of the network by presence of DG and 13%

penetration of DR in the system
The proposed method is applied for optimal placement of four
DG 13% penetration rate of demand response in the system. The long
term planning of the system and the results of the four DG placement
is presented in Table 3. In this case, the total cost is 16.01971 million
dollars and no distribution line needs to be expanded. It should be
noted that the number of particles and iterations in BPSO method are
considered 200 and 40, respectively. Figure 4 represents the convergence
diagram of BPSO for the three investigated cases.
site
DG number (Bus number)

Capacity
(KW)

Types of DG

Installation time
(year)

Micro-Turbine

30

500

18

500

Micro-Turbine

14

500

CombustionTurbine

25

500

Micro-Turbine

Table 2: Results of 10 years planning of the system for 4 DG placement with 10%
penetration of DR in the system.
site
Capacity
DG number (Bus number)
(KW)

Installation time
(year)

Types of DG

30

500

Micro-Turbine

18

500

Micro-Turbine

14

500

CombustionTurbine

25

500

Micro-Turbine

Table 3: Results of 10 years planning of the system for 4 DG placement with 13%
penetration of DR in the system.

1.72

Min of Fitness ($)

1.7

With DG, No DR

1.68

The cases which are studied in this paper evaluated the impact
of DG and DR presence in VPP. While there is no DG in the 33 bus
distribution system, the lines between 1-2and 2-3buses needs to be
bundled at 3rd and 10th years, respectively. But in the case that four DG
units is placed in the system, the need for expanding the lines of VPP is
completely canceled. This matter shows that installing DG units in the
system can lead to delay in expansion of the system. In the case presented
in section 4.5, the long term total cost of the VPP is decreased at 5.53%
in respect to case which no DG installed in the system. If penetration
rate of DR at a part of system loads is 10%, the expansion planning
of lines is delayed to 5th year. The delay is resulted by decreasing the
load DRs at 1-4 years. Also, it will be completely canceled by installing
one DG unit at 4th year. Also, the total cost of system in case presented
in 4.5 reduced in respect to the cases NO DG-NO DR and 4 DG-NO
DR 8.35% and 2/99%, respectively. In condition that rate of penetration
of DR in system increases to 13%, expansion of line between 1st and
2nd buses will be delayed by load peak shaving to 5th year of planning.
Finally, it will be completely canceled by installing one micro-turbine
unit in 5th year at 14th bus of VPP. The total cost of the system During
10 year planning in case DG-13%DR in respect to cases NO DG-NODR, DG-NO DR, DG-10% DR is decreased to 9.29%, 3.99% and 1.02%,
respectively.

Conclusion
In this paper a new method is presented for optimal placement of
distributed generation units for constructing virtual power plant. Also,
the impacts of presence of demand response have been investigated in
the system and on VPP planning. Minimizing the long-term cost of
VPP is considered as objective function. Hence, the production costs
of distributed generation units (consist of capital and investment cost,
operation and maintenance and fuel cost), the costs of expanding the
lines and purchasing energy from the grid is considered. The Binary
Particle Swarm Optimization (BPSO) method is used to solve the
optimization problem and minimizing the objective function. The
proposed method is applied for placing 4 distributed generation units
in IEEE-33 bus distribution network. In order to evaluate the impact
of demand response on system planning, some parts of loads of the
system have been equipped with DR. Then by means of the proposed
method, 10 year planning of the system has been repeated with the
penetration rates of 10% and 13% penetration of DR in the system. The
results demonstrate that in case of correct placing of DGs in VPP, the
need for developing VPP will be delayed or removed during a specific
planning and the total cost of VPP will be reduced. The cost of VPP will
significantly decrease in simultaneous use of DG and DR in the system.
The more penetration rate of DR in the system, the more power of
owner of VPP for using of DR and as a result cost of VPP will decrease
more.
References

1.66

1. Rao RS, Ravindra K, Satish K, Narasimham SVL (2013) Power Loss

Minimization in Distribution System Using Network Reconfiguration in the
Presence of Distributed Generation . Power Systems, IEEE Transactions 28:
317-325.

With DG, DR=10%

1.64

With DG, DR=13%

2. Moradi MH, Abedini M (2010) Optimal load shedding approach in distribution

systems for improved voltage stability. 4th International Power Engineering and
Optimization Conference (PEOCO) 196-200.

1.62
1.6
0

Discussion

10

15

20

Iteration

25

30

35

40

Figure 4: The convergence diagram of BPSO for the three investigated cases.

J Electr Electron Syst

ISSN: 2332-0796 JEES an open access journal

3. Hamedi H, Gandomkar M (2011) Evaluation of reliability, losses and power

quality considering time variations of load in presence of distributed generation.
Int J Acad Res 3: 55-60.
4. Wong LY,

Abdul Rahim SR, Sulaiman MH, Aliman O (2010) Distributed

Volume 3 Issue 2 1000127

Citation: Bahrami S, Imari A (2014) Optimal Placement of Distributed Generation Units for Constructing Virtual Power Plant Using Binary Particle
Swarm Optimization Algorithm. J Electr Electron Syst 3: 127. doi:10.4172/2332-0796.1000127

Page 6 of 6
generation installation using particle swarm optimization. In; Proc Inter Power
Engineering and Optimization Conf PEOCO, Shah Alam, Selangor, Malaysia
159-163.
5. Lalitha MP, Reddy VCV, Usha V (2010) Optimal DG placement for minimum
real power loss in radial distribution systems using PSO. J Theoretical and
Applied Information Technology 107116.
6. Moradi MH, Abedinie M (2010) A combination of Genetic Algorithm and Particle
Swarm Optimization for optimal DG location and sizing in distribution systems.
IPEC, conf 858-862.
7. Prommee W, Ongsakul W (2011) Optimal multiple distributed generation
placement in microgrid system by improved reinitialized social structures
particle swarm optimization. Euro Trans Electr Power 21: 489-504.
8. Acharya N, Mahat P, Mithulananthan N (2006) An analytical approach for DG
allocation in primary distribution network. Elect Power Energy Syst 28: 669678.

DG Allocation in Primary Distribution Networks. Energy Conversion, IEEE

Transactions 25: 814-820.
15. Mashhour E, Tafreshi SMM (2011) Bidding Strategy of Virtual Power Plant
for Participating in Energy and Spinning Reserve Markets,Part I: Problem
Formulation, Power Systems, IEEE Transactions 26: 949-956.
16. Giuntoli M, Poli D (2013) Optimized Thermal and Electrical Scheduling of a
Large Scale Virtual Power Plant in the Presence of Energy Storages. Smart
Grid, IEEE Transactions 4: 942-955.
17. The FENIX vision: The virtual power plant and system integration of distributed
energy resources. Contract No: SES6 - 518272.
18. Palensky P, Dietrich D (2011) Demand Side Management: Demand Response,
Intelligent Energy Systems, and Smart Loads. Industrial Informatics, IEEE
Transactions 7: 381-388.
19. California distributed energy resources guide.

9. Gozel T, Hocaoglu MH (2009) An analytical method for the sizing and sitting of
distributed generators in radial systems. Electr Power Syst Res 79: 912-918.

20. Chakravorty M, Das D (2001) Voltage stability analysis of radial distribution

networks. Int J Elec Power 23: 29-135.

10. Katsigiannis YA, Georgilakis PS (2008) Optimal sizing of small isolated hybrid
power systems using tabu search. J Optoelectron Adv M 10: 1241-1245.

21. Xie K, Song Y, Stonham J,Yu E, Liu G (2000) Decomposition model and
interior point methods for optimal spot pricing of electricity in deregulation
environments. Power Systems, IEEE Transactions 15: 39-50.

11. Moravej Z, Akhlaghi A(2013) A novel approach based on cuckoo search for DG
allocation in distribution network. Int J Elec Power 44: 672-679.
12. Khalesi N, Rezaei N, Haghifam MR (2011) DG allocation with application of
dynamic programming for loss reduction and reliability improvement. Int J Elec
Power 33: 288-295.
13. Gzel T, Hocaoglu MH (2009) An analytical method for the sizing and siting
of distributed generators in radial systems. Electric Power Systems Research
79: 912-918.
14. Hung DQ, Mithulananthan N, Bansal RC (2010) Analytical Expressions for

22. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm
algorithm. Systems, Man, and Cybernetics, Computational Cybernetics and
Simulation, IEEE Int Conf 5: 4104-4108.
23. Kashem MA, Ganapathy V, Jasmon GB, Buhari MI (2000) A novel method
for loss minimization in distribution networks. Int Conf on Electric Utility
Deregulation and Restructuring and Power Technologies (DRPT), 251-256.
24. Li T, Shahidehpour M, Li Z (2007) Risk-Constrained Bidding Strategy with
Stochastic Unit Commitment. IEEE Trans Power Syst 22: 449-458.

Submit your next manuscript and get advantages of OMICS

Group submissions
Unique features:

User friendly/feasible website-translation of your paper to 50 worlds leading languages

Audio Version of published paper
Digital articles to share and explore

Special features:

Citation: Bahrami S, Imari A (2014) Optimal Placement of Distributed

Generation Units for Constructing Virtual Power Plant Using Binary Particle
Swarm Optimization Algorithm. J Electr Electron Syst 3: 127. doi:10.4172/23320796.1000127

J Electr Electron Syst

ISSN: 2332-0796 JEES an open access journal

300 Open Access Journals

25,000 editorial team
21 days rapid review process
Quality and quick editorial, review and publication processing
Indexing at PubMed (partial), Scopus, EBSCO, Index Copernicus and Google Scholar etc
Sharing Option: Social Networking Enabled
Authors, Reviewers and Editors rewarded with online Scientific Credits
Better discount for your subsequent articles

Submit your manuscript at: www.omicsonline.org/submission/

Volume 3 Issue 2 1000127