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from food sources to the nest without using visual cues. Also,
they are capable of adapting to changes in the environment.
Therefore, through a collection of cooperative agents called
ants, the near- optimal solution to the DG allocation problem
can be effectively achieved. It is designed with the best
ability of optimization in known engineering and commercial
problems. First, Dorigo and his colleagues employed ants
algorithm to solve the optimization problems. The firstproblem which solved by this algorithm is well- known
hawker problem [5, 6].
When an ant comes out of its nest, it leaves a chemical
material that is called pheromone in its way to reach the food.
The next ants are guided to the place of food by smelling and
following this marked path, while these ants themselves leave
pheromone which increases the pheromone concentration. So
the concentration of pheromone is higher in the path where
the more ants passed.
When there are different paths with different distances
between nest and food, an ant that passes on the shorter path
will leave more pheromone. By this way it emphasizes on its
optimization. Finally the shortest path is determined by theants. Fig. 1, shows the method of experiment. It is seen from
this figure that ants at first chose one of two paths ADB and
ACB. After a while, some ants are concentrated in the shorter
path ACB, finally all the ants move in the shorter path [7].
IV. DG Optimal Placement ProblemsThe target function was developed for the DG placement in
distribution network is to decrease the losses of network withlimiting of maximum DG capability usage [4].
)(11
CPPfMinimize
L
k
k
n
i
i += == (1)
The optimization of above target function is done byconsidering the following limitations:
_The maximum permissible capacity of using DG shouldbe considered.
= =
M
1l
N
1g
lgg Qn.G (2)
- The permissible voltage amplitude
VVV ni (3)- The maximum flow capacity of buses in network
max
ii
II (4)
iP : The injection power to node i in network.C: Total DG power employed in network.
Fig. 1. Experiment of ants' path
Q: Total permissible DG power to use in network.
iV : Voltage amplitude of i in network.
nV : Nominal voltage amplitude of network.
gG :thg Capacity of distribution generation power plant
lgn : 0-1 variable to determine presence or absence DG
with g capacity in place L.
kP : The load of node k in network.max
iI : Maximum acceptable flow in path i
: The penalty coefficient related to difference betweenthe amounts of power installed to the distribution generationand consumption power of network.
In fact the place of installing and the capacity of
distribution generation system in the existing network should
be determined in a manner that it minimizes the losses of
network. This practice should be done by considering the
aforementioned limitations.
V. The Stages of Using Ants Algorithm in DGPlacement Problem
For expressing a problem based on ants' algorithm, first it
should be described by graph or in the form of network to
specify the searching spaces of ants. Therefore, DG
placement problem will be in the form of sets of dots with the
paths within them as showed in fig. 2, [8].
A. Choosing the PathWhen an ant is placed in a dot of graph, it sees some paths
to move, so choosing next path means choosing the next dot
that has a probability nature. Let thK ant is placed in thi dot.In this case the probability of choosing j dot is as a target, in
the other words, choosing the replacement path ij is obtainedby (5) [9].
=
Otherwise0
s,j)k(Tabu).t(
).t(
)t)(k(Ps
isis
ijij
ij
(5)
In this equation )t(ij is the left pheromone rate in thereplacement ij path in time t. ij is the rate that shows thereplacement of propriety path ij. For placement problem DG,
the rate of ij is obtained by following equation:
ijij d
1
= (6)
In (6) ijd is the observed losses with the link between (j, i)dots and path ij. To determine the next dot from probabilities
rates, the Roulette Wheel Algorithm is used after achieving
probable choosing of path.
Algorithm roulette wheel is a method to choose one stateamong many specific states based on the occurrence probableof that state [9].
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TABLE I
DG's SPECIFACTION
Reactive Power
(K Var)
Active Power
(KW)
50100DG1100250DG2150300DG3200500DG4
Fig. 2. The space of ants' searches
B. Pheromone MatrixIt is required a common memory to perform ants algorithm.This common memory is obtained by creating a matrix with
primary pheromone ))0(( ij . Amount of primary pheromoneis considered for each graph link. By choosing the path,
amount of pheromone is added to existing pheromone.
ijkij
1kij )t()t( +=
+(7)
In this equation ij is the increasing rate of pheromone inthe path. Gradually by this way in the choosing path, the rate
of pheromone is increasing. So for simulation of pheromone
evaporation it can be supposed that in every algorithm
repetition, the primary pheromone rate in all the paths is
multiplied between 0 and 1 in a positive number. So (7) is
corrected as follow:
ijkij
1kij )t().1()t( +=
+(8)
Coefficient is a positive number between 0 and 1 in thisequation and is considered evaporation constant in all the
paths of network. This number is the same in all the paths [9].
C. Convergence ConditionRepetition of ants' algorithm will finish when the
convergence requirement satisfies. The convergence
condition for this algorithm can be considered as density of
paths pheromone or the numbers of passes on the paths.According to the mentioned explanations, total flow chart of
ants' algorithm to solve the DG placement problem is given in
fig. 3, [9].
VI. Load DistributionThe common GaussSeidel and Newton-Raphson methods
in distribution systems regarding to dimensions of branches
and nodes and also a big ratio of R/X in comparing with
transfer networks, can't provide a response with a proper
speed and accuracy.
The approach that employed to distribute load in this
project is based on two matrixes (Bus- Injection to Branch-Current) BIBC and (Bus- Current to Bus- Voltage) BCBV
and the equivalent injection flows.
A. Modeling of Distribution System
The used model for the transmission lines is inserted
spontaneously in program by forming two matrixes BIBC and
BCBV [10, 11].
AA.1. Distributed Loads
There are some loads in the distribution systems that are
distributed along the line. These distributed loads are
modeled in the form of two concentrated loads which are
added to two end buses of line. The rate of load which added
to two ends of line is obtained by following equation:
)jQP.(S
)jQ_P).(1(S
2
1
+=
+=(9)
With a good approximation, the amount of is assumed0.5. So the distributed load in the line is devoted equally to
two ends of line [12, 13].
A.2. Distributed Generation
Generator depending on its allocation and control condition
can be used in one of 3 following modes: a) parallel operation
b) operation with power and constant power factor c)
operation with constant or stable and determined terminal
voltage and power. In the first two modes, the generation bus
in the program of load distribution is displayed as PQ bus.
The generation buses should be modeled as PV in the third
mode. In this paper, the generation buses are modeled as PQ
[14].
VII. Specification of application example from middlevoltage network in distribution system
The studying distribution system in this project is IEEE 34
bus network (fig. 4.). This network has two voltage regulators
and three capacitive banks as the reactive power injector
improved the voltage profile. The complete specification of
this network is given in Reference [15]. In this network, the
DG specification used is completely presented in table 1 and
the maximum permissible capacity of using DG in network is
considered as %10 total power of system.
A. Obtained Results from Sampling Distribution SystemBy using computer program provided by programming
software (MATLAB) and also by employing the trial- error
procedure for above example the best rates of AC regulating
Parameters as showed in table ii are in 4th row included the
least losses.
Table III shows the results of improving the rate of losses
in network result by using ants' algorithm and genetic
algorithm. It must be mentioned that based on the network
specification, the losses of network before installing DG have
been 270/2kw. Fig. 5, shows the convergence manner of GA
and ACSA methods to reach the optimization answer [16].
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Start
Reading the impedances between feeders and connected loads to bus
Running distribution load for a system without DG
Evaluating the probability of choosing DG s at buses in completed tour
of ant I by calculating equation (5)
Running roulette wheel algorithm to choose one of the calculatedprobabilities in previous stage
Assessing the propriety (running the program of distributing load for
each complete tour of ants and calculation losses
The pheromone matrix is modified regarding to obtained results of
losses calculation
Did thealgorithm
converge?
Printing
the results
No
Generating next ant and running the complete tour
Yes
Fig. 3. Proposed Flow Chart
VIII. ConclusionsBy observing the tables and extracted convergence curves
result by using GA, SA and AC methods, following results
obtained:
TABLE II
The Different Rates Of Ac Regulating Parameters
regulating parametersPower
Losses (KW)
LossesReductionPercentage
6.0,1.0,1.0)1( === 194 28.2%
6.0,1.0,5.0)2( === 194.5 28%
6.0,1.0,0.1)3( === 193.9 28.22%
9.0,1.0,1.0)4( === 193.7 28.25%
6.0,5.0,1.0)5( === 195.2 27.6%
6.0,9.0,1.0)6( === 194.2 28%
1.0,1.0,1.0)7( === 195 27.5%
Table III
Rate of reduction of losses in network by using GA and AC methods
Optimizing by ACMethod
Optimizing byGA Method
Number of Installed DG s 4 4
Power Losses without DG(KW)
270.2(KW) 270.2(KW)
Power Losses with DG(KW)
193.7(KW) 213.2(KW)
Losses ReductionPercentage
28.2% 21.11 %
Converging Speed 241.5% 303.6
Fig. 4. IEEE 34 Bus Network
Fig. 5. Convergence manner of GA and ACSA methods to reach the
optimization answer
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1. AC method in DG placement problem indistribution network is efficient in reaching the
closely optimization answers
2. Decreasing the losses of network (through properoptimization) by employing DG is noticeable.
Therefore the proper placement even with
limitation maximum capacity of installing in
network decreases the losses of it.3. AC optimization method has good speed to reachthe optimization response.
4. To reach the optimization response by using ants'algorithm approach, it is necessary that the
different rates of AC regulating parameters are
tested to extract their proper rates.
REFERENCES
[1]Gandomkar, M., Vakilian M, Ehsan M, "A Genetic BasedTabu Search Algorithmfor Optimal DG Allocation in
Distribution Networks", Journal, Electrical Power andComponents System, Dec, 2005, 33, 1351-1362.
[2]Kim, J. O., Nam, S. W., Par, S.K. Singh, C., "DispersedGeneration Planning Using Improved Hereford RanchAlgorithm, "Electric Power System Research, 1997,
vol.47, pp.47-55.
[3]Niknam, T., Ranjbar, A.M, Shirani, A.R., Ostadil, A.,2005, "A New Approach Based on Ant Algorithm forVOLT/VAR Control in Distribution Network ConsideringDistributed Genreation" Iranian Journal of Science &
Technology, Transaction B, Engineeing, Vol.29, No.B4,pp.1-14.
[4]Gandomkar, M., Vakilian M, Ehsan M, "A Combinationof Genetic Algorithm and Simulated Annealing foroptimal DG allocation in Distribution Networks" CCECE,2005, IEEE, Canada, may 2005.
[5]Lachlan Kuhn , Ant Colony Optimization forContinuous Spaces Bachelor of Engineering (Software),A thesis submitted to The Department of InformationTechnology and Electrical Engineering The University of
Queensland October 2002
[6]K. KRISHNAIYER, S. HOSSEIN CHERAGHI, Antalgorithms: web-based implementation and applications tomanufacturing system problems, International Journal ofComputer Integrated Manufacturing, Vol. 19, No. 3, pp,
264 277 ,April-May 2006[7]M. Dorigo, G. DicaroAnt Algorithm for Discrete
Optimization Universite Libre de Bruxelles Vol, 5, No.3, pp. 137172, Dec. 1999.
[8]CHING-TZONG, S., CHUNG-FU, C., JI-PYNG, C.,2005, Optimal Capacitor Placement in DistributionSystems Employing Ant Colony Search Algorithm,
Electric Power Components and Systems,pp. 931946
[9]Ching- Tzong Su, Chung- Fu Changb, "DistributionNetwork Reconfiguration for Loss Reduction by Ant
colony Search Algorithm" Electric Power Systemsresearch Vol.12, No 23, pp, 190-199, 2005
[10] Teng,J.H., 2000, A Network-Topology-based Three-Phase Load Flow for Distribution Systems, Proc. Natl.
Sci. Counc. ROC (A., Vol. 24, No. 4., pp.259-264.
[11] Thakur,T., Dhiman,J., 2006, A New Approach to LoadFlow Solutions for Radial Distribution System, IEEE
PES Transmission and Distribution Conference andExposition Latin America, Venezuela, pp.1-6.
[12] E,Lakeviw,Holmes,Eg,1989,Electrical DistributionNetwork Design,IEEE Power Engineering Series9,Londo
[13] Barker,P.P., Demello, R.W., 2000, Determining theImpact Distribution Generation on Power Systems, Part
1:Radial Distribution System, IEEE Power EngineeringSociety, Summer Mettting Vol.3,pp.1645-1656.
[14] Ranjbar, A.M., Niknam, T., Torabi, A.," Introductionand Definition of Distributed Generation" Iranian Journalof Electrical Engineering, 15th year, No. 36, pp.11-19.
[15] IEEEdistributedtestfeeders;http://ewh.ieee.org/soc/pes/dsacom/testfeeders.htm.
[16] Gandomkar, M., "Programming and OptimizingDistribution Networks with the Presence of DG s by usingIntelligent Algorithms", Phd thesis, Azad University.
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