A New Method for Loss Reduction and Reliability …cdn.persiangig.com/dl/L3HRC/WKA9DbRvJB/A_New_Method_for...A New Method for Loss Reduction and Reliability Improvement in Distribution

International Journal of Mechatronics, Electrical and Computer Technology

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A New Method for Loss Reduction and Reliability Improvement in

Distribution Network by Applying Optimal Placement and Sizing

of Distributed Generation Units

Mohammad Verij Kazemi, Ali Ebadi*, Seyyed Mehdi Hosseini and Sayyed Asghar

Gholamian

Department of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran

*Corresponding Author's E-mail: [email protected]

Abstract

In this paper a new method for optimal placement and sizing of DGs is presented. First

of all, DG units placement by Genetic algorithm is proposed in order to minimize the

reliability index of the system based on classification method. Then PSO combined with

Chaos algorithm is implemented for DG units allocation. In order to increase the

practicality of the proposed method, three constraints consisting of bus voltage level

limitations, the minimum and maximum generation capacity of DGs and maximum current

flow of line limitation are considered. The proposed method is simulated on bus 2 of RBTS

standard network using MATLAB software. The obtained results show significant

improvements in reliability index, loss reduction, and network bus voltage profile are

achieved using proposed method.

Keywords: PSO, Chaos algorithm , Reliability, Distributed Generations, Reliability.

1. Introduction

Installing DGs affects on many important network parameters. So before installing

a DG on the network, its impacts on important parameters like voltage profile,

network loss, reliability, stability and short circuit current should be studied.





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Programming DG-contained electrical systems needs some important factors to be

defined like best available technology, the way of connecting to the network,

quantity, capacity and location of DGs.

Among the mentioned factors, optimal location and capacity of DG units are of

great importance. In [1], different types of DGs are studied for fast recovery and load

demand reduction in CLPU (cold load pick up) situation. This approach applies

genetic algorithm for placement and capacity allocation of DGs. In [2], two levels of

programming are suggested for optimizing the price of using DGs in distribution

networks. One of the studied aspects is distribution corporations and the other one is

DG itself. In [3], the positive impacts of DG on network and some of its negative

impacts like loss increase and short circuit level are analyzed and a multi -objective

function is suggested. Then perusing the equations indicates that with appropriate

placement of DG, the loss is reduced in all circumstances. Tabu algorithm in [4],

genetic algorithm in [5] and simple genetic algorithm (SGA) in [6]-[7] and NSGA-II

in [8] are all suggested for optimal placement. In [9], the numeral method for finding

the best DG size and power is analyzed in four conditions. The appropriate DG

capacity is computed with the goal of loss reduction. Numeral analysis is done

considering all DG limitations in four conditions when only active or reactive power

is injected to the network or in addition to active power injection, reactive power is

absorbed or injected. DG and FACTS placement methods are studied in reference

[10].

With advances and developments in technology, customers‟ sensitivity to power

outage has risen day by day and power outage for a short period can cause heavy

losses and damages. Since reliability is about quality and accessibility of electrical

energy in the location where customer gets the service, reliability is one of the most

important network parameters. So this article suggests minimizing System Average





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Interruption Duration Index (SAIDI) by using genetic algorithm in order to place

DGs. The mentioned algorithm is for calculating reliability index based on Set theory

and structural characteristics of distribution network. This method of calculating the

reliability accelerates the calculations. The reason to use genetic algorithm is its

simplicity in determining if DG units should exist or not, which can be modeled by

binary coding and since genetic algorithm works with a set of numbers, so it can be

an appropriate method for determining optimal location for DG units placement.

Since algorithm‟s accuracy depends on its coding step while applying genetic

algorithm to optimally allocate DG units, improved PSO algorithm based on chaos

theory (IPSO) is suggested for allocating DG units. PSO algorithm is combined with

chaos theory to prevent PSO algorithm‟s unripe convergence and getting stuck in

local minimums while variables are increased.

2. Distribution System Reliability Analysis

In a power system, reliability standards represent how well a system has done its

main task which is securing customers‟ energy. Since 1995, more than 80% of the

corporations have used SAIDI and SAIFI indexes in their reports which shows the

importance of these indexes in these years. But since DG generators do not have any

impacts on the number of system interruptions, in this article SAIDI index is used as

a standard for optimizing system‟s reliability. In this approach sample system is

divided based on regions [11]. Applying regional classification method speeds up

calculations significantly. The sets that are used for computing certain load point

reliability by this method, are shown in Fig (1).





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L

SSL NSSL

SL

SAF NSAF

NSL

SF NSF

{S}

Figure 1: sets which are used for calculating of reliability indices

Then for more simplicity, each region‟s name is chosen based on the name of that

region‟s key or protecting device. Set L in Fig(1) contains any regions that its load

point is interrupted in case of faults occurring in that region. Name of the region that

contains the particular load point is shown with NSL or {S}. Two other sets named

SW and IS are defined hereinafter. Set SW, is a set that contains all protecting and

insulating devices like breakers and fuses, and set IS contains devices that

disconnects respective load point from power supply. Hereinafter, regions of the sets

shown in Fig (1) are determined.

ISSWNIS (1)

LNISSSL (2)

SISLSL (3)

Set SAF in Fig (1) contains regions that in case fault occurs there, the respective load point will be supplied by backup power source. Set NASF is computed from as follows:

SAFSLNSAF (4)

Set SF in Fig (1) contains regions that in case fault occurs there, the respective load point will be supplied by backup power supply, without contravening problem‟s





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limitations. Fault rate (FR) and average repair time (REP) of each region is computed from below equations:

1

n

i j

j

FR fr

(5)

1

1

n

j j

j

i n

j

j

fr rep

REP

fr

(6)

Frj and rep j respectively indicate fault rate average repair time of j-th component and n indicates the number of i-th region‟s components. Power outage time which is shown with DTs , is computed from equation (7):

, ,

,

s i i

i NSL NSAF NSF

i i

i SSL SF

DT FR REP

FR SOT

(7)

SOTi is switching required time which is assumed 1 hour a year in this article. Dead time customers index is computed from equation (8):

i i

i Circuit

DTC DT C

(8)

Ci is the number of customers who are in i-th region. In last step, the SAIDI index of the sample

system is computed:

i

i Circuit

DTCSAIDI

C

(9)





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3. Using Genetic Algorithm For Locating The Appropriate Place For

Distributed Generations To Reduce SAIDI Index

Genetic algorithm is an unclassic and direct-search optimizing method based on survival of the

fittest mechanism and genetic science. Genetic algorithm is based on population and each member of

the population indicates an answer for optimization problem by applying appropriate coding with

specific bits. In order to use genetic algorithm to find optimal location of DGs, each generator is

modeled with a bit and it is assumed that DGs can be placed in CB1 and SW1 regions. Since, in this

article, there are 14 regions for DGs to be placed, a 14 bits chromosome is chosen. Fig (2) shows the

chromosome that is applied in this article. First bit of the chromosome indicates generators condition

in CB1 region and second bit indicates generators condition in SW1 and rest of the bits indicate

generators condition in other regions. If a bit is 1, it means that DG is placed in that region, and 0

means that DG doesn‟t exist in that region.

Figure 2: the sample chromosome used for DG placement in order to reduce SAIDI

The sequence of the tasks which are done after a fault occurs in calculation of SAIDI index is listed

below:

When a fault occurs the DG should be tripped first, then fault point is located and faulty

point is separated from network with protecting devices.

If there are no DGs in damaged region, DG will be connected again.

After solving the problem, the faulty region which is repaired now, will be connected to the

main network by reclosing.

Then objective function of each chromosome is computed by equation (10).

Minimize SAIDI (10)





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In (10), SAIDI index is computed by applying set theory which is explained in part (2).

After computing the objective function of each chromosome, crossover operation is done among the

members which are chosen based on their objective function‟s value. In this article we used single

point crossover operator, and mutation operator is applied to new chromosomes in the last step. In

order to find the most appropriate location for DG placement, this procedure is repeated until

reaching the algorithm‟s ending clause.

4. Author's Capacity Allocation of DG Units By Applying IPSO

Algorithm In Order to Reduce Distribution Network Loss

PSO algorithm is based on birds‟ social behavior while searching for food and leading population

into a promising region within the searching area. The most important advantage of PSO algorithm

compared to rest of the searching algorithms, is its simplicity, so that implanting it is so easy and this

simplicity results in calculations to be done faster and also reaching the answer faster. But when

number of the variables of the optimization problem increases, the probability of PSO algorithm

unripe convergence increases likewise. In fact the algorithm doesn‟t guarantee to converge at global

minimum but says that particles converge at the best point which is found by the group yet. This

phenomenon is known as stagnation. In order to overcome this problem, combining PSO algorithm

with chaos theory is suggested. Thus, chaos generator is multiplied by inertia factor (w ) which

belongs to PSO algorithm. So the new inertia factor ( neww ) is computed by equation (12).

)1.(.11

kkk

fff

(11)

wfwnew .

(12)

In equation (11), is controlling parameter of oscillation range in chaos theory which its varying

range is [0,4]. in first point‟s chaos we have which indicates chaos‟s sensitivity to initially values

[12]:

0 {0,0.25,0.5,0.75,1}f





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Figure 3: Comparing the old PSO factor with the new one

In Fig (3), the new weighting factor is compared to the old one. As it‟s clear, in the suggested

method, inertia factor has chaotic nature. Chaotic nature of particles‟ movements prevents them to

get stuck in local minimums.

In transmission and distribution networks, a remarkable percentage of generated electrical energy,

dissipates on the lines from generation to consumption. There are losses in every level of a power

system including generation, transmission and distribution; but 75% of losses occur in distribution

networks. The reasons behind that are high current values in lines, low voltage level in distribution

networks and radial structure of these networks. So studying loss reduction of distribution networks

is of great importance.

In order to allocate DG units by applying IPSO algorithm to reduce distribution network‟s loss, the

number of columns of population matrix should be equal to the number variables (in this article, the

capacity of DGs ), and number of its lines equals to the number of population‟s particles. The value

of each population member depends on the amount of the objective function. Objective function, in

this part, is network‟s total active losses which is presented in equation (13):

n

kK

LOSSP1

(13)





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In which LOSSk is k-th line‟s loss. Computing network‟s active loss is possible using forward-

backward load distribution method.

After calculating load distribution, these clauses should be considered while calculation objective

function each time:

Network‟s voltage level being in rating range

Minimum and maximum DGs‟ generation capacity

Limitations due to maximum current flowing in lines

These statements can be shown by equations below:

0)( vf (14)

maxmin

kkkvvv

(15)

maxmin

GkGkGkppp

(16)

max

kII

(17)

( )f v = load flow equations

,k kv I = k-th bus bar voltage and current

GkP = active power generated by k-th bus bar

max

Gkp = maximum injected active power by k-th DG

max max,k kv I = maximum rating voltage and current of k-th bus bar

5. Simulation





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In order to check the efficiency of the suggested algorithm, it is applied to the bus 2 of RBTS sample

system by MATLAB software. Fig (4) shows the studied system. The sample system of distribution

network contains 22 load points with 20 MW peak load and 1908 consumers.

Figure 4: bus 2 of RBTS distribution system

Electrical resistance and reactance values of different sections are represented in addendum. In this

step it‟s assumed that four DG units can be installed. SW2, SW4, SW7 and SW10 regions which are

determined in Fig (4) are chosen as the best location for installing DGs based on mentioned

objective function.

Table (1) indicates a comparison between SAIDI index of the system in two different circumstances.

Table (2) shows power outage time of each region separately. The highlighted parts of this table

indicate the location of DGs, and it shows that power outage in these locations more optimized than

other locations. In order to compute reliability index, it‟s assumed that DG keeps the island until the

fault is completely solved. Fig(5) shows genetic algorithm convergence for optimal DG

placement with objective function of SAIDI reduction. After determining the location of

DGs, IPSO algorithm with particle structure shown in equation (18) is applied for optimal





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capacity allocation, in which PDGi is capacity of i-th DG. After generating particles and

checking clauses (15),(16) and (17), objective function (13) is finally computed.

(18)

Fig (6) indicates the convergence of IPSO algorithm with objective function of loss

reduction. The calculated capacity of mentioned regions‟ DGs, are presented in Table III.

Adding DG units with mentioned capacity of this table causes 38% of network loss

reduction. Fig (7) shows DG‟s impact on voltage profile of system‟s load points and it‟s

clear that voltage profile of all bus bars are improved after installing DG.

Table 1: a comparison between reliability index and loss before and after installing DG

Table 2: comparing reliability index of different regions before and after installing DG

Without DG With DG Item

3.4873 3.7214 SAIDI(hour/year)

391 633 PLoss(KW)

Power outage

time when DG

exists (hour per year)

Power outage

time when DG

does not exist (hour per year)

Region‟s name

0.2681 0.261 CB1

0.2441 0.5117 SW1

0.2441 0.7553 SW2

0.4390 0.9502 SW3

0.2678 0.2678 CB2

0.1951 0.4627 SW4

0.2681 0.2681 CB3

0.2603 0.5279 SW5

1 2 3 4[ , , , ]DG DG DG DGparticle P P P P





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Table 3: Optimal capacity of DGs which are found by genetic algorithm

Sw10 Sw7 Sw4 Sw2 Region‟s

name

2.204 2.021 0.418 2.358 PDG(MW)

Figure 5: genetic algorithm convergence for optimal DG placement

0.1953 0.7228 SW6

0.4390 0.9665 SW7

0.2839 0.2839 CB4

0.2441 0.5276 SW8

0.2441 0.7712 SW9

0.1953 0.9661 SW10





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Figure 6: Network power loss after executing IPSO algorithm

Figure 7: voltage profile graph of load points before and after installing DGs





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Conclusion

Since network‟s reliability, loss and voltage profile are among the most important

parameters of every power network, and on the other side, using DG units is growing for

many reasons. In this paper, a new method is presented to improve mentioned parameters

of DG contained networks. In this paper, minimizing SAIDI index of reliability by genetic

algorithm is suggested for DG units placement. The applied algorithm of this article, is

much faster than other methods in computing reliability index of extended distribution

networks. This algorithm also has the ability to compute system reliability index by using

classification method for DG units that are located in different regions, which was not

possible in old methods. Minimizing total system loss by using IPSO algorithm is

suggested for allocating these units. In fact in order to increase accuracy and speed of

calculations, and preventing stagnation phenomenon while executing PSO algorithm, this

algorithm has been combined with chaos theory.

The suggested idea was simulated on bus 2 of RBTS standard network using MATLAB

software. Outputs of this program confirm significant improvements reliability index, loss

reduction, and network bus bars voltage profile, after installing DG units in location and

with suggested method‟s resulted capacity.

References

[1] Vishal Kumar, Rohith Kumar H. C., Indra Gupta, and Hari Om Gupta, “DG Integrated Approach for

Service Restoration Under Cold Load Pickup,” IEEE Transactions On Power Delivery, Vol. 25, NO. 1,

January 2010.

[2] Jesús María López-Lezama, Antonio Padilha-Feltrin, Javier Contreras, and José Ignacio Muñoz, “Optimal

Contract Pricing of Distributed Generation in Distribution Networks,” IEEE Transactions On Power

Systems, Vol. 26, NO. 1, pp. 128-136, February 2011.

[3] Elnashar, M.M., El Shatshat, R., Salama, M.M.A.: „Optimum siting and sizing of a large distributed

generator in a mesh connected system‟, Electr. Power Syst. Res., 2010, 80, (6), pp. 690–697.





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[4] Golshan, M.E.H., Arefifar, S.A.: „Optimal allocation of distributed generation and reactive sources

considering tap positions of voltage regulators as control variables‟, Eur. Trans. Electr. Power, 2007, 17,

(3), pp. 219–239.

[5] A. A. Abou El-Ela, S. M. Allam and M. M. Shatla, “Maximal optimal benefits of distributed generation

using genetic algorithms”, Electric Power Systems Research, 2010, 80, pp. 869–877.

[6] Kumar, V., Kumar, H.C.R., Gupta, I., Gupta, H.O.: „DG integrated approach for service restoration under

cold load pickup‟,IEEE Trans. Power Deliv., 2010, 25, (1), pp. 398–406.

[7] Singh, R.K., Goswami, S.K.: „Optimum allocation of distributed generations based on nodal pricing for

profit, loss reduction, and voltage improvement including voltage rise issue‟, Int. J. Electr. Power

Energy Syst., 2010, 32, (6), pp. 637–644.

[8] Duong Quoc Hung, Nadarajah Mithulananthan, and R. C. Bansal, “Analytical Expressions for DG

Allocation in Primary Distribution Networks,” IEEE Transactions On Energy Conversion, Vol. 25, NO.

3, pp. 814-820, September 2010.

[9] Haghifam, M.R., Falaghi, H., Malik, O.P.:

„Risk-based distributed generation placement‟, IET Gener. Transm. Distrib.,2008, 2, (2), pp. 252–260

[10] Patricio Mendoza-Araya, , Jaime Muñoz Castro, Jaime Cotos Nolasco, and Rodrigo E. Palma-Behnke,

“Lab-Scale TCR-Based SVC System for Educational and DG Applications,” IEEE Transactions On

Power Systems, Vol. 26, NO. 1, pp. 3-11, February 2011.

[11] D. Zhu,"Power System Reliability Analysis with Distributed Generators", Dept. of Electrical & Computer

Eng. May 2003.Virginia Polytechnic Inst.Stat Univ.

[12] R. Caponetto, L. Fortuna, S. Fazzino, and M. G. Xibilia, “Chaotic sequences to improve the performance

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Appendix

Table 4: Electrical parameters of different parts of sample distribution network

Section R(Ω) X(Ω) Section R(Ω) X(Ω)

1 0.1668 0.263 19 0.7954 0.311

2 0.6362 0.2488 20 0.8484 0.3318

3 0.8484 0.3318 21 0.1334 0.2104

4 0.1668 0.263 22 0.7954 0.311

5 0.8484 0.3318 23 0.8484 0.3318

6 0.6362 0.2488 24 0.1667 0.263

7 0.1668 0.263 25 0.6362 0.2488

8 0.8484 0.3318 26 0.178 0.2805

9 0.7954 0.311 27 0.795 0.311

10 0.1334 0.2104 28 0.6362 0.2488

11 0.8484 0.3318 29 0.1668 0.263

12 0.1668 0.263 30 0.6362 0.2488

13 0.8484 0.3318 31 0.8484 0.3318

14 0.1334 0.2104 32 0.1668 0.263

15 0.8484 0.3318 33 0.8484 0.3318

16 0.1668 0.263 34 0.1334 0.2104

17 0.6362 0.2488 35 0.7954 0.311

18 0.178 0.2805 36 0.8484 0.3318

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A New Method for Loss Reduction and Reliability …cdn.persiangig.com/dl/L3HRC/WKA9DbRvJB/A_New_Method_for...A New Method for Loss Reduction and Reliability Improvement in Distribution