Distributed Generation Allocation to Improve Steady State Voltage Stability of Distribution Networks Using Imperialist Competitive Algorithm

International Journal of Applied Power Engineering (IJAPE)
Vol. 2, No. 2, August 2013, pp. 71~78
ISSN: 2252-8792  71
Journal homepage: http://iaesjournal.com/online/index.php/IJAPE
Distributed Generation Allocation to Improve Steady State
Voltage Stability of Distribution Networks using Imperialist
Competitive Algorithm
Navid Ghaffarzadeh1
, Masoud Akbari1
, Amir Khanjanzadeh2
1
Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, Iran
2
Department of Engineering, Islamic Azad University, Chalous Branch
Article Info ABSTRACT
Article history:
Received Jan 2, 2013
Revised Apr 17, 2013
Accepted May 6, 2013
In this paper, a new method is proposed to optimal distributed generation
allocation for stability enhancement in radial distribution networks. Voltage
stability is related with stable load and acceptable voltage in all buses of
system. According to the time spectrum of the incident of the phenomena the
instability is divided into steady state and transient voltage instability. The
analysis is accomplished using a steady state voltage stability index which
can be evaluated at each node of the distribution system. Different optimal
locations and capacities are used to check this effect. The location of DG is
more important in comparison with the capacities and has the main effect on
the network voltage stability. Effects of capacity and location on increasing
steady state voltage stability in radial distribution networks are evaluated
through Imperialist Competitive Algorithm (ICA) and at the end the results
are compared to particle swarm optimization and genetic algorithm on the
terms of speed, accuracy and convergence.
Keyword:
Distributed Generation
Allocation
Distribution Networks
Imperialist Competitive
Algorithm
Voltage Stability
Copyright © 2013 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Navid Ghaffarzadeh,
Assistant Professor, Faculty of Technical and Engineering,
Imam Khomeini International University,
Qazvin, Iran.
Email: ghaffarzadeh@ikiu.ac.ir
1. INTRODUCTION
Concurrent with the extension of national economy and people's life, demand of load in distribution
system are strongly increasing. Similar totransmission system, the operation conditions of distribution system
are more and more close to the voltage stability boundaries. The decrease of voltage stability level is one of
most important parameter which restricts the increase of load served by distribution companies[1].During the
planning and operation of distribution system, the problems related to voltage now have become a great
concern, since theconsiderable amount of failures which is thought that have been caused by voltage
instability. In 1997, a voltage instability problem in a distribution system, which was widespread to a
corresponding transmission system, caused a major blackout in the Brazilian system [2]. So, we think it
seems necessary to consider voltage stability constraints for planning and operation of power system.
At the sametime, the distributed generations have increased in the distribution system due to
rapidchanges intechnology, economic and environmentalissues. In recent years, the use of distributed
generations has increased as a clean renewable energy alternative generation and its advantage due to the
global warming and exhaustion fossil fuels problems [3] and also technological innovations and a changing
economic and regulatory environment have resulted in a renewed interest for distributed generation [4]. This
topic confirmed by the [5], who lists five important parameters that contribute to this evolution, i.e.
developments in distributed generation technologies, constraints on the construction of new transmission

 ISSN: 2252-8792
IJAPE Vol. 2, No. 2, August 2013 : 71 – 78
72
lines, increased in demand of customer for highly reliable electricity, the electricity market liberalization and
concerns about climate change.
A general definition was suggested in [6], which are now widely accepted as follows: ‘‘Distributed
Generation is an electric power source connected directly to the distribution network or on the customer site
of the meter’’. From distribution network planning point of view, DG is a feasible alternative for new
capacity, particularly in the competitive electricity market environment, and has immense advantages such as
short lead time and low investment risk since it is built in modules, small-capacity modules that can track
load variation more closely, small physical size that can be installed at load centers and does not need
government approval or search for utility territory and land availability, and existence of a widespread range
of DG technologies [7]. The advantages of distributed generationdepend on the location and the size of
distributed generation.
Advantages of distributed generation are [3]:
1. Reduce power flow inside the transmission system thus improve the system voltage profile
2. Reduce power losses at distribution system by supplying some load demand at the distribution
3. Reduce thermal stresses caused by loaded substations, transformers and feeders thus improve reliability
and efficiency of the power system
4. Defer upgrades for an existing infrastructure since they provide distribution and transmission capacity
release
5. Decrease related costs to transmission and distribution
6. Help in “peak load shaving” and load management programs
7. Provide local load reliability which can be used as on-site standby to supply power during emergency and
system outages
8. Supply the required spinning reserve thus maintain power system stability
9. Renewable distributed generations can eliminate or reduce emission.
The planning of the distribution network with the presence of DG requires the definition of several
parameters, such as: the best technology to be used, the number and the capacity of the DGs, the best location
of DGs, the type of network connection, etc. The impact of DG in system operating characteristics, such as
electric losses, voltage profile, stability and reliability needs to be appropriately evaluated [8]. The problem
of DG allocation and sizing is great importance. The installation of DG in non-optimal location can increase
the losses of system, increase costs and result inan adverse effectondesirable. Thus, the use of an
optimization method capable of showing the best solution for a given network can be very useful for the
system planning engineer. The selection of the best places for installation and the preferable size of the DG
units in large distribution systems is a complex combinatorial optimization problem. The optimal location
and sizing of DG on the power system has been continuously studied in order to attain various goals. The
objective can be the minimization of the active losses of the feeder [9],[10]; or the minimization of the total
network supply costs, which includes generators operation and losses compensation [11]-[14]; or even the
best utilization of the available generation capacity [15].
Voltage stability is related with stable load and acceptable voltage in all buses of system. Accurate
voltage stability contingency analysis could be accomplished by performing a PV (active load
powervoltagemagnitude) curve study [16]. Some other methods presentingvarious stability indices have also
been introducedand compared in [17].The instability is derived into steady state and transient voltage
instability as for the time spectrum of the incident of the phenomena.
In the case of instability voltage, when the disturbance occursin the power system, an uncontrollable
progressive reduction will arise .Voltage stability analysis often requires examination of system state losses
and a lot of other related scenarios [18]. According to this, the established rationale based on steady state
analysis is more feasible and it can create an overall prediction about voltage reaction problems as well.
Voltage stability phenomenon is fully known in distribution systems. In radial distribution system
resistance to reluctance ratio is high that causes a lot of power loss, hence radial distribution systems are
kinds of power systems which are threatened by voltage instability.
2. VOLTAGE STABILITY
As mentioned in the previous section, in this paper, thesteady-statevoltage stability is evaluated.
Forthis purpose anewsteady statevoltage stabilityindexin [19] is presented, which is most sensitive to voltage
collapse. In order toformulatetheindex, one method load flow for radial distribution systems was presented by
Das et al in [20].According to Equation (1) the steady state voltage stability index for each bus is:

IJAPE ISSN: 2252-8792 
Distributed Generation Allocation to Improve Steady State Voltage Stability (Navid Ghaffarzadeh)
73
2 | 1 | 4.0 2 2 4.0 2 2 | 1 | (1)
Where [19]:
SI (m2) =voltage stability index of node m2 (m2=2,3, … , NB ).
NB =the total number of nodes.
jj =branch number.
r(jj), x( jj) = resistance and reactance of branch jj.
V(m1) = voltage of node m1.
V(m2) = voltage of node m2.
P(m2) =total real power load fed through node m2.
Q(m2) = total reactive power load fed through node m2.
Steady state voltage stability index is derived for the two node equivalent system shown in Fig. (1).
Figure 1. Equivalent system of feeder
Actually,
P(m2) =sum of the real power loads of all the nodes beyond node m2 plus the real power load of node m2
itself plus the sum of the real power losses of all the branches beyond node m2.
Q(m2) =sum of the reactive power loads of all the nodes beyond node m2 plus the reactive power load of
node m2 itself plus the sum of the reactive power losses of all the branches beyond node m2. For all of the
network buses, the following Fitness function is defined:
Fitness Function=∑ SI mi , 2,3, … , (2)
3. CASE STUDY
To evaluate theproposedalgorithm, a system was selected from one part of Tehran distribution
network. Single line diagram of the network is shown in Fig. 2. That is MV feeder with 13 buses from 63/20
kV substation (Khoda-Bande-Loo substation).
Figure 2. Single Line Diagram of feeder
This network is chosen because of its practically. Table 1 and Table 2, illustrate line and bus
information. Initially, a load flow was run for the case study without installation of DG. Result of power flow
without DG shown in Table 3.

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Table1. Lines Data
From To R(ohm) X(ohm)
1 2 0.176 0.138
2 3 0.176 0.138
3 4 0.045 0.035
4 5 0.089 0.069
5 6 0.045 0.035
5 7 0.116 0.091
7 8 0.073 0.073
8 9 0.074 0.058
8 10 0.093 0.093
7 11 0.063 0.05
11 12 0.068 0.053
7 13 0.062 0.053
Table 2. Buses Data
Bus Number P(kw) Q(kvar)
1 0 0
2 890 468
3 628 470
4 1112 764
5 636 378
6 474 344
7 1342 1078
8 920 292
9 766 498
10 662 480
11 690 186
12 1292 554
13 1124 480
Table 3. Result of power flow without DG
Bus Number Stability Index
2 0.9729
3 0.9486
4 0.9429
5 0.9332
6 0.9329
7 0.9221
8 0.9199
9 0.9191
10 0.9181
11 0.9174
12 0.9198
13 0.9198
4. IMPERIALIST COMPETITIVE ALGORITHM
Imperialist Competitive Algorithm (ICA) is a new socio-politically motivated global search strategy
that has recently been introduced for dealing with different optimization tasks.
In [21] descripted ICA as follow:
This algorithm starts with an initial population. Each population in ICA is called country. Countries
are divided in two groups: imperialists and colonies. In this algorithm the more powerful imperialist, have the
more colonies. When the competition starts, imperialists attempt to achieve more colonies and the colonies
start to move toward their imperialists. So during the competition the powerful imperialists will be improved
and the weak ones will be collapsed. At the end just one imperialist will remain. In this stage the position of
imperialist and its colonies will be the same. The flowchart of this algorithm is shown in Fig. 3 [22]. More
details about this algorithm are presented in [22].

IJAPE ISSN: 2252-8792 
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Figure 3. Flowchart of the ICA.
5. RESULTS AND DISCUSSIONS
Reference [23] gave us a method synthesizing optimal power flow and Particle Swarm Optimization
(PSO) to find the best combination of sites within a distribution network for connecting DGs. Reference [24]
performed same method by genetic algorithm(GA). In [18],[25],[26]voltage stability and location of DGs
optimized by PSO , CSA and HSA were presented. result of PSO, GA and ICA presented in this section. The
results are calculated for integration of 3 DG into the distribution network. These results are obtained while
assuming that all the generators operate at a power factor of 0.9.
In this paper voltage stability index presented by three method of optimization algorithm that is
PSO, GA and ICA.
Assessment of value of steady state voltage stability index done with analysis of this system.
Newton-Raphson load flow method is performed first for load flow solution for this system. P(m2) and
Q(m2) at each node accepted by results of the load ,Finally the SI index has been app raised.
The results of optimal location and capacity of DG and impact of installing 3 DGs in the case study
network by PSO, GA and ICA are illustrated in Table 4. Comparing the results in table 3 with those of table
5, we can conclude that with installing 3 DGs, the voltage instability is improved.

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Table 4. Optimal Location and Capacity with different optimization algorithm
Solution Bus NO DG Capacity Cost Function
By PSO[19]
4 4.0573
0.08341778 4.6701
12 2.8894
By GA[19]
11 3.4455
0.08345058 3.8218
5 3.8149
By ICA
4 4.3499
0.0848013 4.5070
11 3.6147
Table 5. Result of Power Flow with DG in different optimization algorithm
Bus Number Stability
index by
PSO[18]
Stability
index by
GA[18]
Stability
index by
ICA
2 0.9986 0.9974 0.9932
3 0.9994 0.9970 0.9869
4 1.0000 0.9974 0.9854
5 0.9983 0.9996 0.9829
6 0.9980 0.9992 0.9828
7 0.9978 0.9986 0.9800
8 1.0000 1.0000 0.9794
9 0.9991 0.9991 0.9791
10 0.9981 0.9981 0.9791
11 0.9996 1.0000 0.9796
12 1.0000 0.9988 0.9793
13 0.9990 0.9979 0.9798
Fig 4 shows voltage instability of the case study network without and with 3 optimal DGs. In this paper we
compare GA, PSO and ICA methods on the terms of speed, accuracy and convergence.
Figure 4. Voltage Stability Index of the case study system without and with three optimal DGs in different
optimization algorithm
6. CONCLUSION
In this paper, a novel approach is proposed to optimal distributed generation allocation for stability
enhancement in radial distribution networks. The method is based on ICA algorithm and the result of
applying ICA algorithm for DG allocation in distribution system has been presented. The effectiveness of the
proposed algorithm in solving DG allocation problem was demonstrated through a numerical example.The
result of algorithm showed that the better solution quality of the PSO and GA in comparison with the ICA
but in the speed, ICA was better than both of them.

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ACKNOWLEDGEMENTS
The study was supported by the Imam Khomeini International University, Qazvin, Iran, under grant
no.388041-92.
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Distribution Networks”, Research Journal of Applied Sciences, Engineering and Technology, Vol/Issue: 4(15). Pp.
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BIOGRAPHIES OF AUTHORS
Navid Ghaffarzadeh is an assistant professor of electrical engineering at Imam Khomeini
Internatial University (IKIU).
His special fields of interest include power systems protection, transient in power systems and
power quality. He is the author and the coauthor of over 50 technical papers.
Email: ghaffarzadeh@ikiu.ac.ir

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