Optimal Distributed Generation Allocation in Distribution Systems Employing Ant Colony to Reduce...

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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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