Optimal Allocation of DG and DSTATCOM in Radial Distribution System Using Cuckoo ...downloads.hindawi.com/journals/mse/2017/2857926.pdf · · 2017-03-22ResearchArticle Optimal Allocation

Research ArticleOptimal Allocation of DG and DSTATCOM inRadial Distribution System Using Cuckoo SearchOptimization Algorithm

T Yuvaraj1 K Ravi1 and K R Devabalaji2

1School of Electrical Engineering VIT University Vellore India2Department of Electrical Engineering MVJ College of Engineering Bengaluru India

Correspondence should be addressed to K R Devabalaji eeedevabalajigmailcom

Received 6 August 2016 Revised 24 September 2016 Accepted 30 January 2017 Published 19 February 2017

Academic Editor Nikos D Lagaros

Copyright copy 2017 T Yuvaraj et alThis is an open access article distributed under theCreativeCommonsAttribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

This paper proposes a new approach to determine the optimal location and sizing of Distributed Generation (DG) andDistributionSTATic COMpensator (DSTATCOM) simultaneously in the distribution networkThe objective function is formulated tominimizethe total power losses of the system subjected to equality and inequality constraints Loss sensitivity factor (LSF) and VoltageStability Index (VSI) are used to predetermine the optimal location of DG and DSTATCOM respectively Recently developednature-inspired cuckoo search algorithm (CSA) has been used to determine the optimal size of both DG and DSTATCOM In thepresent work five different cases have been considered during DG and DSTATCOM placement to access the performance of theproposed technique To check the feasibility the proposed method is tested on IEEE 12-bus 34-bus and 69-bus radial distributionsystem and the results were compared with other existing techniques

1 Introduction

Generally the majority of the distribution network loads areinductive in nature So the network power factor will belagging in nature It leads to increasing the power lossescauses poor voltage profile and creates network securityproblems in the distribution networks The distributionsystem total power losses can be divided into real and reactivepower losses Compared to the effect of reactive power lossesin the system the real power losses (1198682119877) affect the efficiencyof the power transfer and lead to poor voltage profile [1]

Studies indicated that 10ndash13 of the total power gen-eration is consumed as 1198682119877 losses (real power loss) at thedistribution system [2ndash5] Hence it is necessary to place thecompensating devices in the distribution system to reducepower losses and improve the voltages between the buses Inthis work DG and DSTATCOM units are placed simultane-ously in the distribution for compensationThere are differentbenefits of simultaneous allocation of DG and DSTATCOMin the distribution system including reducing system power

loss voltage profile enhancement power factor correctionload balancing power quality improvement on-peak oper-ating costs reduction releasing the overloading of distribu-tion lines system stability improvement pollutant emissionreduction and increased overall energy efficiency

In recent years Distributed Generation integration playsan important role in distribution system planning whichresults in major system upgrade power loss reduction andvoltage profile enhancement and finally improving overallsystem reliability DG is defined as electricity generation withlimited size generator connected to the distribution systemSeveral factors have been responsible for the appearance ofDG in radial distribution system The environment issuessuch as reducing the greenhouse effect reduction of fossilfuel and current scenario of deregulation of electricitymarket recommend the requirement formore flexible electricsystems [6]

STATCOM was initially developed for transmission sys-tems to regulate the voltage profile so as to provide reactivepower compensation and power factor control then similar

HindawiModelling and Simulation in EngineeringVolume 2017 Article ID 2857926 11 pageshttpsdoiorg10115520172857926

2 Modelling and Simulation in Engineering

concept has been started to be applied to distribution systems[7 8] DSTATCOM is used to improve the voltage profilepower factor and voltage stability of the distribution systemDSTATCOM is a shunt connected voltage source converter(VSC) that can be used to compensate power quality issues[9]TheDSTATCOM is a fast and rapid compensating devicewhich enhances voltage profile and power losses reductionthrough injection of compensating current into the system[10] It is advised to place the DG and DSTATCOM units atoptimal place with optimal size to achieve maximum benefitsof the system Improper placement of DG and DSTATCOMunits will lead to collapse and even endanger the entiresystem operation [11] The main objective of DG placementis to compensate the real power whereas the DSTATCOMplacement is to compensate reactive power in the distributionsystem

In the recent past several population basedmetaheuristictechniques such as Genetic Algorithm (GA) Ant ColonyOptimization (ACO) Immune Algorithm (IA) DifferentialEvolution Algorithm (DEA) Firefly Algorithm (FA) ParticleSwarm Optimization (PSO) Teaching Learning Based Opti-mization (TLBO) Artificial Bee Colony (ABC) HarmonySearch Algorithm (HSA) and Bat Algorithm (BA) haveshown their potential to solve optimal DSTATCOM place-ment problem [13ndash18] or optimalDGplacement problem [19ndash27]

A lot of research work has been carried out to suc-cessfully optimize the siting and sizing problems of DGand DSTATCOMdevices when allocated separatelyThoughonly a single research work has been done in simultaneousallocation of DG and DSTATCOM in the radial distributionnetworksThe authors have used particle swarmoptimizationalgorithm for the problem of simultaneous placement ofDG and DSTACOM with an objective of total power lossminimization [12]

Cuckoo Search Algorithm (CSA) [28] is one of the newnature-inspired algorithms that has been proposed recentlyto solve complex optimization problems CSA can be used toefficiently solve global optimization problems [29] as well asNP-hard problems that cannot be solved by exact solutionmethods [30] The most powerful feature of CS is its useof Levy flights to update the search space for generatingnew candidate solutions This mechanism allows the candi-date solutions to be modified by applying many smallchanges during the iteration of the algorithm This in turnmakes a compromised relationship between exploration andexploitation which enhance the search capability [31] To thisend recent studies proved that CSA is potentially far moreefficient than GA and PSO [32] In addition it is a simpleand population based stochastic optimization algorithmMoreover it requires less control parameters to be tunedAlso it is a compatible optimization tool for power systemcontroller design Such feature has motivated the use of CSAto solve different kinds of engineering problems such asmultiobjective scheduling problem [33] reliability optimiza-tion problems [34] DG allocation in distribution network[35] economic dispatch [36] network reconfiguration andDistributed Generation allocation in distribution network[37]

m

Vm

Pmm+1 + jQmm+1

Rmm+1 + jXmm+1

m + 1

Pm+1 + jQm+1

Vm+1

Figure 1 Sample distribution system

The present work is aimed at developing a fast and newtechnique to determine the optimal location and sizing of DGandDSTATCOM forminimize the power losses and enhancevoltage profile The optimal location of the DG and DSTAT-COM can be identified using LSF and VSI respectively Theoptimal size of the DG and DSTATCOM can be determinedby using cuckoo search algorithmThe novelty of this work isimplementing an integrated approach of LSF and VSI withCSA to determine the optimal location and sizing of DGand DSTATCOM for the sake of power loss minimizationand voltage profile enhancement Another advantage of thiswork is that multiple DG and DSTATCOM are placed simul-taneously in the radial distribution system The comparisonover single andmultiple DG andDSTATCOMplacement hasbeen analyzed and it gives encouraging results In additionto that the total operating cost (TOC) of the DG andDSTATCOM simultaneously has been considered for all thecases which is not considered before in the literature Toshow the effectiveness of the proposed method it has beenapplied on standard IEEE test radial distribution system andthe obtained results are compared with other techniques

2 Problem Formulation

21 Load Flow Analysis The direct approach for distributionload flow is used to find the power losses and also the voltageat each branch [38] The single line diagram of a sampledistribution system is shown in Figure 1

The voltage at node119898 + 1 is given by

119881119898+1 = 119881119898 minus 119868 (119877119898119898+1 + 119895119883119898119898+1) (1)

where 119881119898+1 is the voltage magnitude of the bus 119898 + 1 119881119898 isthe voltage magnitude of the bus 119898 119877119898119898+1 is the resistanceof the line between119898 and119898+ 1 and119883119898119898+1 is the reactanceof the line between119898 and119898 + 1

119868 = [BIBC] [119894] (2)

where 119868 is the branch current BIBC is the bus currentinjection to branch current matrix

119894119898 = (119875119898+1 + 119895119876119898+1)lowast119881119898 (3)

where 119875119898+1 is the real power load at bus 119898 + 1 119876119898+1 is thereactive power load at bus 119898 + 1 and 119894119898+1 is the currentinjected at node119898 + 1

Modelling and Simulation in Engineering 3

The real and reactive power losses of the system arecalculated by using the following equation

119875loss (119898119898 + 1) = (1198752119898119898+1 + 1198762119898119898+1100381610038161003816100381611988111989810038161003816100381610038162 )119877119898119898+1 (4)

119876loss (119898119898 + 1) = (1198752119898119898+1 + 1198762119898119898+1100381610038161003816100381611988111989810038161003816100381610038162 )119883119898119898+1 (5)

where 119875119898119898+1 is the real power flow in the line between119898 and119898+1 and119876119898119898+1 is the reactive power flow in the line between119898 and119898 + 1The total real and reactive power losses of the system can

be easily found by summing all the branch power losses andit is expressed in

119875119879 Loss = nbsum119898=1

119875Loss (119898119898 + 1) (6)

where nb is the number of the branches

22 Objective Function The objective function (119865) of theproposed work is formulated to minimize the power lossesof the system

The mathematical formulation of the objective functionis given by

Minmize (119865) = min (119875119879 Loss) (7)

where 119875119879 Loss is the total power loss of the radial distributionsystem

Constraints The optimal allocation of DG and DSTATCOMin distribution system is subjected to the following con-straints

(a) Power Balance Power generation is equal to the powerdemand and power losses

Voltage Limit

119881119898min le 10038161003816100381610038161198811198981003816100381610038161003816 le 119881119898max (8)

where 119881119898min and 119881119898max are the minimum and maximumvoltage limits at bus119898 respectively

(b) Real Power Compensation

119875min119863119898 le 119875119863119898 le 119875max

119863119898 119898 = 1 119873119861 (9)

where 119875min119863119898 and 119875max

119863119898 are the minimum and maximum realpower limits of compensated bus119898 respectively

(c) Reactive Power Compensation

119876min119888119898 le 119876119888119898 le 119876max

119888119898 119898 = 1 119873119861 (10)

where 119876min119888119898 and 119876max

119888119898 are the minimum and maximumreactive power limits of compensated bus119898 respectively

3 Optimal Location

The loss sensitivity factor is used to preidentify the optimallocation of the DG and the voltage stability index is usedto preidentify the optimal location of the DSTATCOM Theoptimal size of the DG and DSTATCOM will be obtainedusing cuckoo search algorithm Another advantage of prei-dentifying the optimal location is that it has to reduce thesearch space of the optimization process

31 Loss Sensitivity Factor The loss sensitivity factor is usedto identify the optimal location for DG placement The nodewhich has the highest value of LSF with respect to the realpower has more chance to place DG [39 40] The LSF valuesof all buses are calculated and then they are arranged indescending order The top most LSF value has more chanceto be selected as a candidate location of DG

Equation (4) is partially differentiated with respect to realpower and it is given by

120597119875loss (119898119898 + 1)120597119875119898119898+1 = 2119875119898119898+1119877119898119898+1100381610038161003816100381611988111989810038161003816100381610038162 (11)

32 Voltage Stability Index There are many indices used tocheck the power system security level In this section a newsteady state voltage stability index is used in order to identifythe node which has more chance of voltage collapse and itis expressed in (12) [2 41 42] In order to attain the stableoperation of the radial distribution system the VSI should be119898 ge 0 The voltage stability at each node is calculated fromthe power flow using (12) The node which has the low valueof VSI has more chance to install DSTATCOM

VSI (119899) = 1003816100381610038161003816119881119898+110038161003816100381610038164minus 4 [119875119898119898+1 lowast 119883119898119898+1 minus 119876119898119898+1 lowast 119877119898119898+1]2minus 4 [119875119898119898+1 lowast 119877119898119898+1 + 119876119898119898+1 lowast 119883119898119898+1]sdot 1003816100381610038161003816119881119898119898+110038161003816100381610038162

(12)

4 Cuckoo Search Algorithm

Cuckoo search algorithm is introduced by Yang and Deb [2829] CSA have twomain operators One is direct search basedon Levy flights and another one is random search based onthe probability for a host bird to discover an alien egg in itsnest The parameters used in cuckoo search algorithm are asfollows

N number of nests or different solutions (25)Pa discovery rate of alien eggssolutions (025)Nd dimension search space (1 or 3)Lb and Ub the lower and upper bounds limits

CSA consists of three steps They are as follows

(i) Every cuckoo lays one egg at a time and dumps its eggin a randomly chosen nest


(ii) The best nests with high quality of eggs will carry overto the next generation

(iii) The number of available host nests is fixed and theegg laid by a cuckoo is discovered by the host bird

Cuckoos are attractive birds they not only make beautifulsounds but also have fantastic reproduction strategy Someof the species in cuckoo like Ani and Guira lay their eggs incommon nests though they may remove otherrsquos eggs to risethe hatching probability of their own eggs The cuckoo eggsmay hatch earlier than that of their host eggs When the firstcuckoo eggs is hatched the first action is to remove the hosteggs by blindly pushing out the egg from the nestThe cuckoochick may also mimic the call of host chick to increase thefeeding opportunity

The term Levy flight was introduced by Benoit Mandel-brot who used this term for one specific definition of thedistribution of step size Naturally most of the animals searchfor (cuckoo bird will search for host nest) their food in therandommanner (the next step is always based on the currentlocation and the probability of moving to the next location)It can be modeled with a Levy distribution (a continuousprobability distribution for nonnegative random variables)know as Levy flights

The cuckoo bird will find the best nest to lay their egg(solution) tomaximize their eggs survival rate Actually everycuckoo lays only one egg at a time The high quality eggs(optimal value) which are more similar to the host birdrsquos eggshave more chance to develop (next generation) and becomea mature cuckoo Unhealthy eggs (not optimal value) areidentified by host bird with a probability Pa euro[0 1] and theseeggs are thrown away or the nest is discarded and the newnest is built at a new location A randomly distributed initialpopulation of host nest is generated and then the populationof solutions is subjected to repeated cycles of the searchprocess of the cuckoo birds The cuckoo randomly choosesthe nest position to lay egg using

119883gen+1119901119902 = 119883gen

119901119902 + 119878119901119902 lowast Levy (120582) lowast 120572Levy (120582) = 10038161003816100381610038161003816100381610038161003816

Γ (1 + 120582) lowast sin (120587 lowast 1205822)Γ (1 + 1205822) lowast 120582 lowast 119878(120582minus1)2100381610038161003816100381610038161003816100381610038161120582 (13)

where 120582 is constant (1 lt 120582 le 3) 120572 is a random numbergenerated between [minus1 1] Γ is gamma function and 119878 gt 0which is step size

The step size can be obtained using

119878119901119902 = 119883gen119901119902 minus 119883gen

119891119902 (14)

where 119901 119891 isin 1 2 119898 and 119902 isin 1 2 119863 are randomlychosen indexes and 119891 is chosen randomly but its value mustbe different from 119901

The host bird will identify the cuckoo egg and choose thehigh quality egg with probability of using

pro119902 = (09 lowast fit119902max (fit)) + 01 (15)

where fit119902 is the fitness value of the solution and 119902 is theproportional to the quality of egg in the nest position 119902

If the host bird identifies the cuckoo egg then the hostbird may throw the egg away or leave that nest and built anew nest using (16) Otherwise the egg will grow and is alivefor the next generation

nest119902 = 119883119902min + rand (0 1) lowast (119883119902max minus 119883119902min) (16)

41 Steps to Be Followed for Optimization

Step 1 Run load flow analysis

Step 2 Obtain the base power losses and voltage at each bus

Step 3 Run the LSF andVSI to find the candidate location forDG and DSTATCOM

Step 4 Set the lower and upper limits for the constraints

Step 5 Initiate random population of 119899 host nests 119883119894for amount of kW or kVAr that will be injected withinconstraints

Step 6 Obtain cuckoo randomly using Levy flights 119894Step 7 Evaluate its fitness (119865119894) according to objective func-tion

Step 8 Get a nest randomly from population 119895Step 9 If 119865119894 gt 119865119895 then go to Step 11 If not go to Step 12

Step 10 Let 119895 be the solutionStep 11 Replace 119895 as the new solution

Step 12 If a fraction of nest is replaced by new nests thencreate a new nest at new location with the help of Levy flights

Step 13 Choose the best current nests

Step 14 Allow the current best solution to the next genera-tion

Step 15 If maximum iteration is not reached then go toStep 6 otherwise it is the best nest (optimal solution)

Step 16 Display optimal solution

These are the steps involved to minimize 1198655 Test Result and Discussion

In order to analyze the performance of the proposedmethodit has been tested on IEEE 12-bus system 34-bus systemand 69-bus system The direct load flow analysis is used tofind the power losses voltage magnitude and phase angle atvarious buses For all the test systems the substation voltageis considered as 1 pu The load is assumed to be constantpower load The DG that is used in the test system is capable


Table 1 Result of 12-bus system

Cases PSO [12] Proposed method

Only DG (Case II)

DG size in MW(location) 00378 (9) 02355 (9)

Power loss (kW) 1768 1077119881min (pu) NA 09830VSI (pu) NA 09340TOC ($) NA 1220

Average computationtime (s) NA 1212

Only DSTATCOM (Case III)

DSTATCOM (size andlocation) 00321 (9) 02102 (9)



Both DG and DSTATCOM placed simultaneously(Case IV)

DG amp DSTATCOM (sizeand location)

00390 (9)00320 (9) 02324 (9)

02121 (9)Power loss (kW) 1105 317

119881min (pu) 09608 09908VSI (pu) NA 09636TOC ($) NA 22356


of delivering only real power The maximum limit of theDG unit is 60 of the total kW loading of the networkThe maximum limit of the DSTATCOM unit is 100 of thetotal kVAr loading of the network Regarding multiple DGandDSTATCOM themaximumnumber of DG andDSTAT-COM placement is limited to three since beyond this limitthere is no significant improvement in power loss reductionThe total operating cost (TOC) of DG and DSTATCOM isgiven by [11]

TOC = (1205731 lowast 119875lossDG or DSTATCOM)+ (1205732119875DGT or DSTATCOMT)

(17)

Let us assume that 1205731 and 1205732 are the cost coefficient and theirvalues are 4$kW or kVAr and 5$kW or kVAr respectively

The five different cases are considered to analyze theeffectiveness of the proposed method

Case I The system is without DG and DSTATCOM units(base case)

Case II The system is with only DG Results are presented inTables 1ndash3(a)

Case III The system is with only DSTATCOM Results arepresented in Tables 1ndash3(a)

Case IV The system is with single DG and DSTATCOMResults are presented in Tables 1ndash3(a)

Case V The system is with multiple DG and DSTATCOMResults are presented in Table 3(b)

51 12-Bus System The IEEE 12-bus radial distribution sys-tem consists of 12 buses and 11 branches The line dataand bus data of this system are taken from [43] The basevalues are 100MVA and 11 KV and the total real and reactivepower loads of this system are 0435MW and 0405MVArrespectively The loss sensitivity factor is calculated for all thenodes in order to find the optimal placement of DG for thecases II IV andV As soon as the values of LSF are calculatedthen the next step is to arrange all the values in descendingorder The top most three values which are more sensitiveare selected to install the DSTATCOM units in the systemThe VSI are calculated for all the buses and then they aresorted in ascending orderThe bus which hasmore sensitivityto voltage collapse is chosen to place the DSTATCOM unitsThese steps are to be followed for the cases III IV and VIn order to avoid incongruity in values the existing methodresults are obtained using our load flow analysis

Case I The total power loss minimum voltage and mini-mumVSI of this case are 207 kW 09431 pu and 07912 purespectively




Only DG case (II)

DG (size and location) 01996 (21) 23278 (23)Power loss (kW) 20398 9842

119881min (pu) NA 09740VSI (pu) NA 08999TOC ($) NA 120326








01371 (21)01634 (21)

23905 (23)13419 (23)

Power loss (kW) 17710 5503119881min (pu) 09483 09771VSI (pu) NA 09115TOC ($) NA 188821


Case II In this case the DG units are optimally placed at 9thbus with the optimal size of 02355MW Because of this thepower losses of this case have been reduced to 1077 kW from207 kWTheminimumvoltage andminimumVSI of this caseare found to be 09830 pu and 09340 pu respectively

Case IIIThe power losses of this case are reduced to 1258 kWfrom 207 kW after placement of DSTATCOM units of02102MW at bus 9The voltage profile and minimumVSI ofthis case have been improved as 09562 pu and 08235 purespectively

Case IV In this case the single DG and DSTATCOM areplaced simultaneously at 9th bus and the size of the DGand DSTATCOM units is 02324MW and 02121MVArrespectively The voltage profile and minimum VSI havebeen improved to 09908 pu and 09636 pu and they are09431 pu and 07912 pu before placement of DG andDSTATCOM As a result the total power losses of the systemhave been reduced to 317 kW from 207 kW

Case V Regarding this case the multiple DG and DSTAT-COM are placed simultaneously at 7th bus (0075 +119895 011MVA) 9th bus (009 + 119895 0075MVA) and 12th bus(0065 + 119895 010MVA) respectively so that the total powerlosses of this case are reduced to 134 kW from 207 kW

Figures 2(a) and 2(b) show the comparison of powerlosses and voltage profile of the system under different casesdiscussed in this paper

52 34-Bus System This is amedium scale radial distributionsystem with 34 buses and 33 branches The line data and loaddata are taken from [44] The base values are 100MVA and11 KV and the total real and reactive power loads of the systemare 3715MW and 23MVAr respectively

Case I The total power loss minimum voltage and min-imum VSI of this case are 02213MW 09420 pu and07875 pu respectively

Case II In this case the DG units are optimally placed at23rd bus with the size of 23278MW Because of this thepower losses in this case have been reduced to 9842 kW from2212860 kWTheminimumvoltage andminimumVSI of thiscase are found to be 09740 pu and 08999 pu respectively

Case III The power losses of this case are reduced to17501 kW from 2212860 kW after placement of DSTATCOMunits of 13705MW at bus 23 The voltage profile andminimum VSI of this case have been improved as 09488 puand 08105 pu respectively


Table 3(a) Result of 69-bus system


Only DG (Case II)

DG (location) 18761 (61) 18727 (61)Power loss (kW) 8322 8321









01223 (61)09045 (61)

17500 (61)115 (61)



(b) Result of multiple DG and DSTATCOM placement (Case V)

12-bus system 34-bus system 69-bus system

DG size in MW (location)0075 (7)009 (9)0065 (12)

190 (23)077 (32)010 (34)

049 (17)140 (61)025 (63)

DSTATCOM size in MVAr (location)011 (7)0075 (9)010 (12)

065 (11)085 (19)075 (25)

027 (25)098 (61)020 (63)

Power loss (kW) 134 1932 807119881min (pu) 09947 09919 09925VSI (pu) 09778 09682 09587TOC ($) 25804 26327 17982Average computational time (s) 1198 1212 1256

Case IV In this case the single DG and DSTATCOM areplaced simultaneously at 23rd bus and the optimal size of theDG and DSTATCOM units is 23905MW and 13419MVArrespectively The voltage profile and minimum VSI havebeen improved to 09771 pu and 09115 pu and they are09420 pu and 07875 pu before placement of DG andDSTATCOM The total real power losses of this case are5503 kW

Case V Regarding this case the multiple DG and DSTAT-COMareplacedsimultaneouslyat23rdbus(19+119895 065MVA)32ndbus(077+119895 085MVA)and34thbus(010+119895 075MVA)

respectively The total power losses are reduced to 1932 kWafter the placement of multiple DG and DSTATCOM


53 69-Bus System This is a large scale radial distributionsystem with 69 buses and 68 branches The line and busdata of this system are taken from [45] The base values are100MVA and 1266KV and the total real and reactive powerloads are 380MW and 269MVAr respectively


3 4 5 6 7 8 9 10 11 122Bus number

0

05

1

15

2

25

3

35

4

45Po

wer

loss

(kW

)

Case ICase IICase III

Case IVCase V

(a)


Case IVCase V

094

095

096

097

098

099

1

101

Volta

ge (p

u)

3 4 5 6 7 8 9 10 11 122Bus number

(b)

Figure 2 (a) Comparison of line losses for different cases in 12-bus system (b) Comparison of voltage profile for different cases in 12-bussystem


Case IVCase V

0

5

10

15

20

25

30

35

Pow

er lo

ss (k

W)

10 15 20 25 305Bus number

(a)

10 15 20 25 305Bus number

094

095

096

097

098

099

1

Volta

ge (p

u)


Case IVCase V

(b)


Case I The total power loss minimum voltage and min-imum VSI of the system are 0255MW 09090 pu and06822 pu respectively

Case II In this case the DG units are optimally placed at61st bus with the optimal size of 18727MW Because of thisthe power losses in this case are reduced to 8321 kW from225 kWTheminimum voltage andminimumVSI of this caseare found to be 09682 pu and 08788 pu respectively

Case III The power losses of this case are reduced to15295 kW from 225 kW after placement of DSTATCOMunits of 1200MWat bus 61The voltage profile andminimumVSI of this case have been improved as 09285 pu and07375 pu respectively

Case IV In this case the single DG and DSTATCOM areplaced simultaneously at 61st bus and the size of the DG andDSTATCOM units is 175MW and 115MVAr respectively



Case IVCase V

10 20 30 40 50 60 700Bus number

0

5

10

15

20

25

30

35

40

45

50Po

wer

loss

(kW

)

(a)

091

092

093

094

095

096

097

098

099

1

Volta

ge (p

u)

20 30 40 50 6010Bus number


Case IVCase V

(b)


The voltage profile andminimumVSI have been improved to09715 pu and 08908 pu and it is 09090 pu and 06822 pubefore placement of DG and DSTATCOM The total realpower losses of this case are reduced to 2415 kW

Case V Regarding this case the multiple DG and DSTAT-COM are placed simultaneously at 17th bus (049 +119895 027MVA) 61st bus (140 + 119895 098MVA) and 63rd bus(025 + 119895 020MVA) respectively The total power losses arereduced to 807 kW after the placement of multiple DG andDSTATCOM

Figures 4(a) and 4(b) show the comparison of voltageprofile and power losses of the system under different casesdiscussed in this paper

Overall Analysis When compared with all the cases it isvery clear that the prodigious improvement in the voltageprofile and satisfactory power losses reduction was achievedusing case V (ie simultaneous placement of multiple DGand DSTATCOM units) as presented in Table 3(b) Hence itis recommend to install simultaneous placement of multipleDG and DSTATCOM units in distribution system to achievemaximum benefits of the system The simulation results arecompared with PSO method and it was found that the resultobtained by the CSAmethod gives encouraging results Sincethe existing methodrsquos computational time is not available thecomputational efficiency in terms of CPU time of the CSAmethod could not be compared with other methods

6 Conclusion

Simultaneous allocation ofDG andDSTATCOM in the radialdistribution system is used to compensate the real and reac-tive power which leads to reducing system power loss voltage

profile enhancement power factor correction load balanc-ing power quality improvement on-peak operating costsreduction system stability improvement pollutant emissionreduction and increased overall energy efficiency It is essen-tial to place theDGs andDSTATCOMs at candidate locationswith optimal kW and kVAr to ensure the maximum benefitsof the system In this work an integrated approach has beenused to find the optimal locations of DG and DSTATCOMin the RDS The sizing of the both compensating devicescan be obtained by using cuckoo search algorithmThe mainadvantage of using CSA is that it does not need to spendmore effort in tuning the control parameters as in the case ofGA PGS MINLP DSA and other evolutionary algorithmsThe proposed method is applied to IEEE 12-bus 34-bus and69-bus radial distribution system with different cases Thesimulated results obtained using CSA are compared withthe other existing techniques and the results show that theperformance of the proposed method for minimization ofpower loss and maximization of voltage profile is found tobe better than the other existing methods From the abovediscussion it can be concluded that the proposed method canbe easily applied to any large scale and real time distributionsystem

Competing Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors gratefully acknowledge support from the man-agement at VIT University Vellore India


References

[1] J A Martın Garcıa and A J Gil Mena ldquoOptimal distributedgeneration location and size using a modified teachingndashlearning based optimization algorithmrdquo International Journal ofElectrical Power and Energy Systems vol 50 no 1 pp 65ndash752013

[2] A Mohamed Imran M Kowsalya and D P Kothari ldquoA novelintegration technique for optimal network reconfigurationand distributed generation placement in power distributionnetworksrdquo International Journal of Electrical Power and EnergySystems vol 63 pp 461ndash472 2014

[3] A A El-Fergany ldquoOptimal capacitor allocations using evolu-tionary algorithmsrdquo IET Generation Transmission and Distri-bution vol 7 no 6 pp 593ndash601 2013

[4] MHMoradi A Zeinalzadeh YMohammadi andMAbedinildquoAn efficient hybrid method for solving the optimal sitting andsizing problem of DG and shunt capacitor banks simultane-ously based on imperialist competitive algorithm and geneticalgorithmrdquo International Journal of Electrical Power and EnergySystems vol 54 pp 101ndash111 2014

[5] K R Devabalaji K Ravi and D P Kothari ldquoOptimal locationand sizing of capacitor placement in radial distribution systemusing Bacterial Foraging Optimization Algorithmrdquo Interna-tional Journal of Electrical Power and Energy Systems vol 71 pp383ndash390 2015

[6] S Gopiya Naik D K Khatod and M P Sharma ldquoOptimalallocation of combined DG and capacitor for real power lossminimization in distribution networksrdquo International Journal ofElectrical Power and Energy Systems vol 53 pp 967ndash973 2013

[7] B-S Chen and Y-Y Hsu ldquoA minimal harmonic controller fora STATCOMrdquo IEEE Transactions on Industrial Electronics vol55 no 2 pp 655ndash664 2008

[8] K R Devabalaji and K Ravi ldquoPower quality improvement inwind farm connected to grid using STATCOMrdquo in Proceedingsof the International Conference on Advances in Electrical Engi-neering (ICAEE rsquo14) pp 1ndash5 Unnao India January 2014

[9] A Valderrabano and J M Ramirez ldquoDStatCom regulation by afuzzy segmented PI controllerrdquo Electric Power Systems Researchvol 80 no 6 pp 707ndash715 2010

[10] M Hosseini H A Shayanfar andM Fotuhi-Firuzabad ldquoMod-eling of static series voltage regulator (SSVR) in distributionsystems for voltage improvement and loss reductionrdquo LeonardoElectronic Journal of Practices and Technologies vol 7 no 122008

[11] I A Mohamed and M Kowsalya ldquoOptimal size and sitingof multiple distributed generators in distribution system usingbacterial foraging optimizationrdquo Swarm and Evolutionary Com-putation vol 15 pp 58ndash65 2014

[12] S Devi and M Geethanjali ldquoOptimal location and sizingdetermination of Distributed Generation and DSTATCOMusing Particle Swarm Optimization algorithmrdquo InternationalJournal of Electrical Power and Energy Systems vol 62 pp 562ndash570 2014

[13] S Jazebi S H Hosseinian and B Vahidi ldquoDSTATCOMallocation in distribution networks considering reconfigurationusing differential evolution algorithmrdquo Energy Conversion andManagement vol 52 no 7 pp 2777ndash2783 2011

[14] S A Taher and S A Afsari ldquoOptimal location and sizing ofDSTATCOM in distribution systems by immune algorithmrdquoInternational Journal of Electrical Power andEnergy Systems vol60 pp 34ndash44 2014

[15] M Farhoodnea A Mohamed H Shareef and H Zayan-dehroodi ldquoOptimum D-STATCOM placement using fireflyalgorithm for power quality enhancementrdquo in Proceedings ofthe IEEE 7th International Power Engineering and OptimizationConference (PEOCO rsquo13) pp 98ndash102 Langkawi IslandMalaysiaJune 2013

[16] A Bagherinasab M Zadehbagheri S Abdul Khalid M Gan-domkar and N A Azli ldquoOptimal placement of D-STATCOMusing hybrid genetic and ant colony algorithm to losses reduc-tionrdquo International Journal of Applied Power Engineering vol 2no 2 pp 53ndash60 2013

[17] T Yuvaraj K Ravi and K R Devabalaji ldquoDSTATCOM alloca-tion in distribution networks considering load variations usingbat algorithmrdquo Ain Shams Engineering Journal 2015

[18] T Yuvaraj K Devabalaji and K Ravi ldquoOptimal placement andsizing ofDSTATCOMusing harmony search algorithmrdquoEnergyProcedia vol 79 pp 759ndash765 2015

[19] A Tah and D Das ldquoNovel analytical method for the place-ment and sizing of distributed generation unit on distributionnetworks with and without considering P and PQV busesrdquoInternational Journal of Electrical Power andEnergy Systems vol78 pp 401ndash413 2016

[20] S R Gampa and D Das ldquoOptimum placement and sizing ofDGs considering average hourly variations of loadrdquo Interna-tional Journal of Electrical Power and Energy Systems vol 66pp 25ndash40 2015

[21] R Kollu S R Rayapudi and V L N Sadhu ldquoA novel methodfor optimal placement of distributed generation in distributionsystems using HSDOrdquo International Transactions on ElectricalEnergy Systems vol 24 no 4 pp 547ndash561 2014

[22] S Ganguly N C Sahoo and D Das ldquoMulti-objective particleswarm optimization based on fuzzy-Pareto-dominance forpossibilistic planning of electrical distribution systems incor-porating distributed generationrdquo Fuzzy Sets and Systems AnInternational Journal in Information Science and Engineeringvol 213 pp 47ndash73 2013

[23] J A Martın Garcıa and A J Gil Mena ldquoOptimal distributedgeneration location and size using a modified teaching learningbased optimization algorithmrdquo International Journal of Electri-cal Power and Energy Systems vol 50 no 1 pp 65ndash75 2013

[24] R S Rao K Ravindra K Satish and S V L NarasimhamldquoPower loss minimization in distribution system using networkreconfiguration in the presence of distributed generationrdquo IEEETransactions on Power Systems vol 28 no 1 pp 317ndash325 2013

[25] S Kansal V Kumar and B Tyagi ldquoOptimal placement of differ-ent type of DG sources in distribution networksrdquo InternationalJournal of Electrical Power and Energy Systems vol 53 no 1 pp752ndash760 2013

[26] D K Khatod V Pant and J Sharma ldquoEvolutionary pro-gramming based optimal placement of renewable distributedgeneratorsrdquo IEEE Transactions on Power Systems vol 28 no 2pp 683ndash695 2013

[27] S Ganguly N C Sahoo and D Das ldquoA novel multi-objectivePSO for electrical distribution system planning incorporatingdistributed generationrdquo Energy Systems vol 1 no 3 pp 291ndash3372010

[28] X S Yang and S Deb ldquoCuckoo search via Levy flightsrdquo inProceedings of the World Congress on Nature amp BiologicallyInspired Computing (NaBIC rsquo09) pp 210ndash214 IEEE Publica-tions Coimbatore India December 2009


[29] X-S Yang and S Deb ldquoCuckoo search recent advances andapplicationsrdquo Neural Computing and Applications vol 24 no1 pp 169ndash174 2014

[30] S Dejam M Sadeghzadeh and S J Mirabedini ldquoCombiningcuckoo and tabu algorithms for solving quadratic assignmentproblemsrdquo Journal of Academic and Applied Studies vol 2 pp1ndash8 2012

[31] S Walton O Hassan K Morgan and M R Brown ldquoA reviewof the development and applications of the cuckoo search algo-rithmrdquo in Swarm Intelligence and Bio-Inspired Computation X-S Yang Z Cui R Xiao A H Gandomi and M KaramanogluEds pp 257ndash271 Elsevier Oxford UK 2013

[32] X-S Yang and S Deb ldquoEngineering optimisation by cuckoosearchrdquo International Journal of Mathematical Modelling andNumerical Optimisation vol 1 no 4 pp 330ndash343 2010

[33] K Chandrasekaran and S P Simon ldquoMulti-objective schedul-ing problem hybrid approach using fuzzy assisted cuckoosearch algorithmrdquo Swarm and Evolutionary Computation vol5 pp 1ndash16 2012

[34] E Valian S Tavakoli S Mohanna and A Haghi ldquoImprovedcuckoo search for reliability optimization problemsrdquoComputersand Industrial Engineering vol 64 no 1 pp 459ndash468 2013

[35] Z Moravej and A Akhlaghi ldquoA novel approach based oncuckoo search for DG allocation in distribution networkrdquoInternational Journal of Electrical Power andEnergy Systems vol44 no 1 pp 672ndash679 2013

[36] M Basu and A Chowdhury ldquoCuckoo search algorithm foreconomic dispatchrdquo Energy vol 60 no 1 pp 99ndash108 2013

[37] T T Nguyen A V Truong and T A Phung ldquoA novel methodbased on adaptive cuckoo search for optimal network recon-figuration and distributed generation allocation in distributionnetworkrdquo International Journal of Electrical Power and EnergySystems vol 78 pp 801ndash815 2016

[38] J-H Teng ldquoA direct approach for distribution system load flowsolutionsrdquo IEEE Transactions on Power Delivery vol 18 no 3pp 882ndash887 2003

[39] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society GeneralMeeting (PESrsquo07) Tampa Fla USA June 2007

[40] K R Devabalaji and K Ravi ldquoOptimal size and siting ofmultiple DG and DSTATCOM in radial distribution systemusing bacterial foraging optimization algorithmrdquo Ain ShamsEngineering Journal pp 959ndash971 2015

[41] M Chakravorty and D Das ldquoVoltage stability analysis of radialdistribution networksrdquo International Journal of Electrical Powerand Energy System vol 23 no 2 pp 129ndash135 2001

[42] K R Devabalaji T Yuvaraj and K Ravi ldquoAn efficient methodfor solving the optimal sitting and sizing problem of capacitorbanks based on cuckoo search algorithmrdquo Ain Shams Engineer-ing Journal In press

[43] D Das H S Nagi and D P Kothari ldquoNovel method for solv-ing radial distribution networksrdquo IEE Proceedings GenerationTransmission and Distribution vol 141 no 4 1994

[44] M Chis M Salama and S Jayaram ldquoCapacitor placementin distribution systems using heuristic search strategiesrdquo IEEProceedingsmdashGeneration Transmission and Distribution vol144 no 3 pp 225ndash230 1997

[45] N C Sahoo and K Prasad ldquoA fuzzy genetic approach fornetwork reconfiguration to enhance voltage stability in radialdistribution systemsrdquo Energy Conversion and Management vol47 no 18-19 pp 3288ndash3306 2006

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpswwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



concept has been started to be applied to distribution systems[7 8] DSTATCOM is used to improve the voltage profilepower factor and voltage stability of the distribution systemDSTATCOM is a shunt connected voltage source converter(VSC) that can be used to compensate power quality issues[9]TheDSTATCOM is a fast and rapid compensating devicewhich enhances voltage profile and power losses reductionthrough injection of compensating current into the system[10] It is advised to place the DG and DSTATCOM units atoptimal place with optimal size to achieve maximum benefitsof the system Improper placement of DG and DSTATCOMunits will lead to collapse and even endanger the entiresystem operation [11] The main objective of DG placementis to compensate the real power whereas the DSTATCOMplacement is to compensate reactive power in the distributionsystem

In the recent past several population basedmetaheuristictechniques such as Genetic Algorithm (GA) Ant ColonyOptimization (ACO) Immune Algorithm (IA) DifferentialEvolution Algorithm (DEA) Firefly Algorithm (FA) ParticleSwarm Optimization (PSO) Teaching Learning Based Opti-mization (TLBO) Artificial Bee Colony (ABC) HarmonySearch Algorithm (HSA) and Bat Algorithm (BA) haveshown their potential to solve optimal DSTATCOM place-ment problem [13ndash18] or optimalDGplacement problem [19ndash27]

A lot of research work has been carried out to suc-cessfully optimize the siting and sizing problems of DGand DSTATCOMdevices when allocated separatelyThoughonly a single research work has been done in simultaneousallocation of DG and DSTATCOM in the radial distributionnetworksThe authors have used particle swarmoptimizationalgorithm for the problem of simultaneous placement ofDG and DSTACOM with an objective of total power lossminimization [12]

Cuckoo Search Algorithm (CSA) [28] is one of the newnature-inspired algorithms that has been proposed recentlyto solve complex optimization problems CSA can be used toefficiently solve global optimization problems [29] as well asNP-hard problems that cannot be solved by exact solutionmethods [30] The most powerful feature of CS is its useof Levy flights to update the search space for generatingnew candidate solutions This mechanism allows the candi-date solutions to be modified by applying many smallchanges during the iteration of the algorithm This in turnmakes a compromised relationship between exploration andexploitation which enhance the search capability [31] To thisend recent studies proved that CSA is potentially far moreefficient than GA and PSO [32] In addition it is a simpleand population based stochastic optimization algorithmMoreover it requires less control parameters to be tunedAlso it is a compatible optimization tool for power systemcontroller design Such feature has motivated the use of CSAto solve different kinds of engineering problems such asmultiobjective scheduling problem [33] reliability optimiza-tion problems [34] DG allocation in distribution network[35] economic dispatch [36] network reconfiguration andDistributed Generation allocation in distribution network[37]

m

Vm

Pmm+1 + jQmm+1

Rmm+1 + jXmm+1

m + 1

Pm+1 + jQm+1

Vm+1

Figure 1 Sample distribution system

The present work is aimed at developing a fast and newtechnique to determine the optimal location and sizing of DGandDSTATCOM forminimize the power losses and enhancevoltage profile The optimal location of the DG and DSTAT-COM can be identified using LSF and VSI respectively Theoptimal size of the DG and DSTATCOM can be determinedby using cuckoo search algorithmThe novelty of this work isimplementing an integrated approach of LSF and VSI withCSA to determine the optimal location and sizing of DGand DSTATCOM for the sake of power loss minimizationand voltage profile enhancement Another advantage of thiswork is that multiple DG and DSTATCOM are placed simul-taneously in the radial distribution system The comparisonover single andmultiple DG andDSTATCOMplacement hasbeen analyzed and it gives encouraging results In additionto that the total operating cost (TOC) of the DG andDSTATCOM simultaneously has been considered for all thecases which is not considered before in the literature Toshow the effectiveness of the proposed method it has beenapplied on standard IEEE test radial distribution system andthe obtained results are compared with other techniques

2 Problem Formulation

21 Load Flow Analysis The direct approach for distributionload flow is used to find the power losses and also the voltageat each branch [38] The single line diagram of a sampledistribution system is shown in Figure 1

The voltage at node119898 + 1 is given by

119881119898+1 = 119881119898 minus 119868 (119877119898119898+1 + 119895119883119898119898+1) (1)

where 119881119898+1 is the voltage magnitude of the bus 119898 + 1 119881119898 isthe voltage magnitude of the bus 119898 119877119898119898+1 is the resistanceof the line between119898 and119898+ 1 and119883119898119898+1 is the reactanceof the line between119898 and119898 + 1

119868 = [BIBC] [119894] (2)

where 119868 is the branch current BIBC is the bus currentinjection to branch current matrix

119894119898 = (119875119898+1 + 119895119876119898+1)lowast119881119898 (3)

where 119875119898+1 is the real power load at bus 119898 + 1 119876119898+1 is thereactive power load at bus 119898 + 1 and 119894119898+1 is the currentinjected at node119898 + 1



119875loss (119898119898 + 1) = (1198752119898119898+1 + 1198762119898119898+1100381610038161003816100381611988111989810038161003816100381610038162 )119877119898119898+1 (4)

119876loss (119898119898 + 1) = (1198752119898119898+1 + 1198762119898119898+1100381610038161003816100381611988111989810038161003816100381610038162 )119883119898119898+1 (5)



119875119879 Loss = nbsum119898=1

119875Loss (119898119898 + 1) (6)




Minmize (119865) = min (119875119879 Loss) (7)




Voltage Limit

119881119898min le 10038161003816100381610038161198811198981003816100381610038161003816 le 119881119898max (8)



119875min119863119898 le 119875119863119898 le 119875max

119863119898 119898 = 1 119873119861 (9)

where 119875min119863119898 and 119875max



119876min119888119898 le 119876119888119898 le 119876max

119888119898 119898 = 1 119873119861 (10)

where 119876min119888119898 and 119876max


3 Optimal Location




120597119875loss (119898119898 + 1)120597119875119898119898+1 = 2119875119898119898+1119877119898119898+1100381610038161003816100381611988111989810038161003816100381610038162 (11)



(12)












119883gen+1119901119902 = 119883gen





119878119901119902 = 119883gen119901119902 minus 119883gen

119891119902 (14)


























Only DG (Case II)










00390 (9)00320 (9) 02324 (9)

02121 (9)Power loss (kW) 1105 317





(17)













Only DG case (II)










01371 (21)01634 (21)

23905 (23)13419 (23)















Only DG (Case II)










01223 (61)09045 (61)

17500 (61)115 (61)






190 (23)077 (32)010 (34)

049 (17)140 (61)025 (63)


065 (11)085 (19)075 (25)

027 (25)098 (61)020 (63)








3 4 5 6 7 8 9 10 11 122Bus number

0

05

1

15

2

25

3

35

4

45Po

wer

loss

(kW

)


Case IVCase V

(a)


Case IVCase V

094

095

096

097

098

099

1

101

Volta

ge (p

u)

3 4 5 6 7 8 9 10 11 122Bus number

(b)



Case IVCase V

0

5

10

15

20

25

30

35

Pow

er lo

ss (k

W)

10 15 20 25 305Bus number

(a)

10 15 20 25 305Bus number

094

095

096

097

098

099

1

Volta

ge (p

u)


Case IVCase V

(b)








Case IVCase V

10 20 30 40 50 60 700Bus number

0

5

10

15

20

25

30

35

40

45

50Po

wer

loss

(kW

)

(a)

091

092

093

094

095

096

097

098

099

1

Volta

ge (p

u)

20 30 40 50 6010Bus number


Case IVCase V

(b)






6 Conclusion



Competing Interests


Acknowledgments



References

















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











119875loss (119898119898 + 1) = (1198752119898119898+1 + 1198762119898119898+1100381610038161003816100381611988111989810038161003816100381610038162 )119877119898119898+1 (4)

119876loss (119898119898 + 1) = (1198752119898119898+1 + 1198762119898119898+1100381610038161003816100381611988111989810038161003816100381610038162 )119883119898119898+1 (5)



119875119879 Loss = nbsum119898=1

119875Loss (119898119898 + 1) (6)




Minmize (119865) = min (119875119879 Loss) (7)




Voltage Limit

119881119898min le 10038161003816100381610038161198811198981003816100381610038161003816 le 119881119898max (8)



119875min119863119898 le 119875119863119898 le 119875max

119863119898 119898 = 1 119873119861 (9)

where 119875min119863119898 and 119875max



119876min119888119898 le 119876119888119898 le 119876max

119888119898 119898 = 1 119873119861 (10)

where 119876min119888119898 and 119876max


3 Optimal Location




120597119875loss (119898119898 + 1)120597119875119898119898+1 = 2119875119898119898+1119877119898119898+1100381610038161003816100381611988111989810038161003816100381610038162 (11)



(12)












119883gen+1119901119902 = 119883gen





119878119901119902 = 119883gen119901119902 minus 119883gen

119891119902 (14)


























Only DG (Case II)










00390 (9)00320 (9) 02324 (9)

02121 (9)Power loss (kW) 1105 317





(17)













Only DG case (II)










01371 (21)01634 (21)

23905 (23)13419 (23)















Only DG (Case II)










01223 (61)09045 (61)

17500 (61)115 (61)






190 (23)077 (32)010 (34)

049 (17)140 (61)025 (63)


065 (11)085 (19)075 (25)

027 (25)098 (61)020 (63)








3 4 5 6 7 8 9 10 11 122Bus number

0

05

1

15

2

25

3

35

4

45Po

wer

loss

(kW

)


Case IVCase V

(a)


Case IVCase V

094

095

096

097

098

099

1

101

Volta

ge (p

u)

3 4 5 6 7 8 9 10 11 122Bus number

(b)



Case IVCase V

0

5

10

15

20

25

30

35

Pow

er lo

ss (k

W)

10 15 20 25 305Bus number

(a)

10 15 20 25 305Bus number

094

095

096

097

098

099

1

Volta

ge (p

u)


Case IVCase V

(b)








Case IVCase V

10 20 30 40 50 60 700Bus number

0

5

10

15

20

25

30

35

40

45

50Po

wer

loss

(kW

)

(a)

091

092

093

094

095

096

097

098

099

1

Volta

ge (p

u)

20 30 40 50 6010Bus number


Case IVCase V

(b)






6 Conclusion



Competing Interests


Acknowledgments



References

















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















119883gen+1119901119902 = 119883gen





119878119901119902 = 119883gen119901119902 minus 119883gen

119891119902 (14)


























Only DG (Case II)










00390 (9)00320 (9) 02324 (9)

02121 (9)Power loss (kW) 1105 317





(17)













Only DG case (II)










01371 (21)01634 (21)

23905 (23)13419 (23)















Only DG (Case II)










01223 (61)09045 (61)

17500 (61)115 (61)






190 (23)077 (32)010 (34)

049 (17)140 (61)025 (63)


065 (11)085 (19)075 (25)

027 (25)098 (61)020 (63)








3 4 5 6 7 8 9 10 11 122Bus number

0

05

1

15

2

25

3

35

4

45Po

wer

loss

(kW

)


Case IVCase V

(a)


Case IVCase V

094

095

096

097

098

099

1

101

Volta

ge (p

u)

3 4 5 6 7 8 9 10 11 122Bus number

(b)



Case IVCase V

0

5

10

15

20

25

30

35

Pow

er lo

ss (k

W)

10 15 20 25 305Bus number

(a)

10 15 20 25 305Bus number

094

095

096

097

098

099

1

Volta

ge (p

u)


Case IVCase V

(b)








Case IVCase V

10 20 30 40 50 60 700Bus number

0

5

10

15

20

25

30

35

40

45

50Po

wer

loss

(kW

)

(a)

091

092

093

094

095

096

097

098

099

1

Volta

ge (p

u)

20 30 40 50 6010Bus number


Case IVCase V

(b)






6 Conclusion



Competing Interests


Acknowledgments



References

















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












Only DG (Case II)










00390 (9)00320 (9) 02324 (9)

02121 (9)Power loss (kW) 1105 317





(17)













Only DG case (II)










01371 (21)01634 (21)

23905 (23)13419 (23)















Only DG (Case II)










01223 (61)09045 (61)

17500 (61)115 (61)






190 (23)077 (32)010 (34)

049 (17)140 (61)025 (63)


065 (11)085 (19)075 (25)

027 (25)098 (61)020 (63)








3 4 5 6 7 8 9 10 11 122Bus number

0

05

1

15

2

25

3

35

4

45Po

wer

loss

(kW

)


Case IVCase V

(a)


Case IVCase V

094

095

096

097

098

099

1

101

Volta

ge (p

u)

3 4 5 6 7 8 9 10 11 122Bus number

(b)



Case IVCase V

0

5

10

15

20

25

30

35

Pow

er lo

ss (k

W)

10 15 20 25 305Bus number

(a)

10 15 20 25 305Bus number

094

095

096

097

098

099

1

Volta

ge (p

u)


Case IVCase V

(b)








Case IVCase V

10 20 30 40 50 60 700Bus number

0

5

10

15

20

25

30

35

40

45

50Po

wer

loss

(kW

)

(a)

091

092

093

094

095

096

097

098

099

1

Volta

ge (p

u)

20 30 40 50 6010Bus number


Case IVCase V

(b)






6 Conclusion



Competing Interests


Acknowledgments



References

















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












Only DG case (II)










01371 (21)01634 (21)

23905 (23)13419 (23)















Only DG (Case II)










01223 (61)09045 (61)

17500 (61)115 (61)






190 (23)077 (32)010 (34)

049 (17)140 (61)025 (63)


065 (11)085 (19)075 (25)

027 (25)098 (61)020 (63)








3 4 5 6 7 8 9 10 11 122Bus number

0

05

1

15

2

25

3

35

4

45Po

wer

loss

(kW

)


Case IVCase V

(a)


Case IVCase V

094

095

096

097

098

099

1

101

Volta

ge (p

u)

3 4 5 6 7 8 9 10 11 122Bus number

(b)



Case IVCase V

0

5

10

15

20

25

30

35

Pow

er lo

ss (k

W)

10 15 20 25 305Bus number

(a)

10 15 20 25 305Bus number

094

095

096

097

098

099

1

Volta

ge (p

u)


Case IVCase V

(b)








Case IVCase V

10 20 30 40 50 60 700Bus number

0

5

10

15

20

25

30

35

40

45

50Po

wer

loss

(kW

)

(a)

091

092

093

094

095

096

097

098

099

1

Volta

ge (p

u)

20 30 40 50 6010Bus number


Case IVCase V

(b)






6 Conclusion



Competing Interests


Acknowledgments



References

















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












Only DG (Case II)










01223 (61)09045 (61)

17500 (61)115 (61)






190 (23)077 (32)010 (34)

049 (17)140 (61)025 (63)


065 (11)085 (19)075 (25)

027 (25)098 (61)020 (63)








3 4 5 6 7 8 9 10 11 122Bus number

0

05

1

15

2

25

3

35

4

45Po

wer

loss

(kW

)


Case IVCase V

(a)


Case IVCase V

094

095

096

097

098

099

1

101

Volta

ge (p

u)

3 4 5 6 7 8 9 10 11 122Bus number

(b)



Case IVCase V

0

5

10

15

20

25

30

35

Pow

er lo

ss (k

W)

10 15 20 25 305Bus number

(a)

10 15 20 25 305Bus number

094

095

096

097

098

099

1

Volta

ge (p

u)


Case IVCase V

(b)








Case IVCase V

10 20 30 40 50 60 700Bus number

0

5

10

15

20

25

30

35

40

45

50Po

wer

loss

(kW

)

(a)

091

092

093

094

095

096

097

098

099

1

Volta

ge (p

u)

20 30 40 50 6010Bus number


Case IVCase V

(b)






6 Conclusion



Competing Interests


Acknowledgments



References

















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










3 4 5 6 7 8 9 10 11 122Bus number

0

05

1

15

2

25

3

35

4

45Po

wer

loss

(kW

)


Case IVCase V

(a)


Case IVCase V

094

095

096

097

098

099

1

101

Volta

ge (p

u)

3 4 5 6 7 8 9 10 11 122Bus number

(b)



Case IVCase V

0

5

10

15

20

25

30

35

Pow

er lo

ss (k

W)

10 15 20 25 305Bus number

(a)

10 15 20 25 305Bus number

094

095

096

097

098

099

1

Volta

ge (p

u)


Case IVCase V

(b)








Case IVCase V

10 20 30 40 50 60 700Bus number

0

5

10

15

20

25

30

35

40

45

50Po

wer

loss

(kW

)

(a)

091

092

093

094

095

096

097

098

099

1

Volta

ge (p

u)

20 30 40 50 6010Bus number


Case IVCase V

(b)






6 Conclusion



Competing Interests


Acknowledgments



References

















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











Case IVCase V

10 20 30 40 50 60 700Bus number

0

5

10

15

20

25

30

35

40

45

50Po

wer

loss

(kW

)

(a)

091

092

093

094

095

096

097

098

099

1

Volta

ge (p

u)

20 30 40 50 6010Bus number


Case IVCase V

(b)






6 Conclusion



Competing Interests


Acknowledgments



References

















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










References

















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation





























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









Documents

Optimal Allocation of DG and DSTATCOM in Radial Distribution System Using Cuckoo ...downloads.hindawi.com/journals/mse/2017/2857926.pdf · · 2017-03-22ResearchArticle Optimal Allocation