Research ArticleOptimal Siting and Sizing of Multiple DG Units forthe Enhancement of Voltage Profile and Loss Minimization inTransmission Systems Using Nature Inspired Algorithms

Ambika Ramamoorthy1 and Rajeswari Ramachandran2

1Department of Electrical Engineering Anna University Chennai Tamil Nadu 600 025 India2Department of Electrical Engineering Government College of Technology Coimbatore Tamil Nadu 641 041 India

Correspondence should be addressed to Ambika Ramamoorthy rambi 2004yahoocoin

Received 29 April 2015 Revised 3 September 2015 Accepted 17 December 2015

Academic Editor Adnan Parlak

Copyright copy 2016 A Ramamoorthy and R RamachandranThis is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Power grid becomes smarter nowadays along with technological developmentThe benefits of smart grid can be enhanced throughthe integration of renewable energy sources In this paper several studies have been made to reconfigure a conventional networkinto a smart grid Amongst all the renewable sources solar power takes the prominent position due to its availability in abundanceProposed methodology presented in this paper is aimed at minimizing network power losses and at improving the voltage stabilitywithin the frame work of system operation and security constraints in a transmission system Locations and capacities of DGs havea significant impact on the system losses in a transmission system In this paper combined nature inspired algorithms are presentedfor optimal location and sizing of DGs This paper proposes a two-step optimization technique in order to integrate DG In a firststep the best size of DG is determined through PSOmetaheuristics and the results obtained through PSO is tested for reverse powerflow by negative load approach to find possible bus locations Then optimal location is found by Loss Sensitivity Factor (LSF) andweak (WK) bus methods and the results are compared In a second step optimal sizing of DGs is determined by PSO GSA andhybrid PSOGSA algorithms Apart from optimal sizing and siting of DGs different scenarios with number of DGs (3 4 and 5) and119875119876 capacities of DGs (119875 alone 119876 alone and 119875 and 119876 both) are also analyzed and the results are analyzed in this paper A detailedperformance analysis is carried out on IEEE 30-bus system to demonstrate the effectiveness of the proposed methodology

1 Introduction

Today the power grid is transforming and evolving into afaster-acting potentially more controllable grid than in thepast This so-called smart grid will incorporate new digitaland intelligent devices to replace the existing power network[1] This grants an opportunity for new innovations andmodernizations

The massive penetration of distributed generation intoelectric grid is one of the salient features of smart grid Butthe integration of DGs perturbs the power flow and voltageconditions of the network So voltage regulation is one of themajor issues to be addressed [2]

The 16 of global final energy consumption comesfrom renewable sources during 2012 with 10 coming from

traditional biomass 34 coming from hydroelectricity andthe remaining 26 coming from new renewable sources likewind solar power and so forth [3]

Solar power takes the prominent position among all othersources due to its continuous availability and cost effective-ness Solar energy is available in abundance [4] But there areseveral challenges in adding renewable energy sources intothe conventional grid [5]The size and location ofDGs are thecrucial factors in the application of DG for loss minimization[6]

One of the key requirements for reliable electric powersystem operation is the balancing of reactive power supplyand demand to maintain adequate system voltages Lackof sufficient reactive power supplies can result in voltageinstability The peripheral method of balancing the reactive

Hindawi Publishing Corporatione Scientific World JournalVolume 2016 Article ID 1086579 16 pageshttpdxdoiorg10115520161086579

2 The Scientific World Journal

power and voltage of the system is to add capacitors tapchanging transformers and FACTS devices at necessarynodes On the other hand these demands can be internallymet with the help of inverters on solar panels [7] The gridtied solar inverters act as a reactive power source to balancethe reactive power of the system

The optimal operation of a power system is requiredto precede the optimal planning of facilities like generatingplants reactive power compensation and transmission net-works In order to handle the large scale optimal power flowproblem the problem is decomposed into real power (119875)optimization problem and reactive power (119876) optimizationproblem [8] The 119875-problem is to minimize the productioncost under the assumption that system voltages are heldconstant and the 119876-problem is to minimize the transmissionloss under the assumption that real power generation is heldconstant This paper addresses the reactive power dispatchproblem for IEEE 30-bus system [8] since the objective is tominimize the transmission line losses

The network reconfiguration is done to convert a conven-tional grid into smart grid Multiple DG units with different119875119876 capacities are integrated to the traditional grid In [9]different cases are addressed with real and reactive powerpenetration of the solar plant

In the integration of solar power both the siting andsizing of solar Distributed Generators (DGs) have a signifi-cant impact on the system losses in a transmission networkThis paper presents a novel multiobjective approach forcalculating the DG optimum placement and sizing In [10]optimization for sizing and placement of DGs is done byusing PSO algorithm

Several metaheuristics algorithms were developed forsolving the multiobjective reactive power optimization prob-lem Genetic Algorithm (GA) [11] Evolutionary Program-ming (EP) [12 13] Bacterial Foraging Optimization (BFO)[14] Ant Colony Optimization (ACO) [15] Differential Evo-lution (DE) [16] and recently Gravitational SearchAlgorithm(GSA) [17] are some of the most common optimization algo-rithms The paper [18] deals with the GA for optimum sitingand sizing ofDGsDeshmukh et al [19] have formulatedVARcontrol problem thatminimizes the combined reactive powerinjection by DGs

Many literatures address the optimal siting and sizing ofmultipleDGs by any one of the algorithms or combinations ofalgorithms Some papers have found the optimal location ofDGs byweak (WK) bus placementmethod or Loss SensitivityFactor (LSF) method Some references analyze the impactof increasing the number of DGs But this research paperanalyzes all the above-said aspects simultaneously

In this paper the problem of optimal DG location andsizing is divided into two steps In the first step optimalsize of DG to be placed at each bus is found out by PSOmetaheuristics with the assumption that all 30 buses havesolar generation subjected to the inequality constraints of linelimits solar generation real power constraints and DG sizeconstraints The results obtained through PSO are checkedfor reverse power flow by negative load approach The buseswhich satisfy the negative loading conditions are the possiblelocations for DG placement Again this search of optimal

location is fine-tuned by weak (WK) bus placement methodand Loss Sensitivity Factor (LSF) method and the results areanalyzed Then in the second step optimal sizing of DGsis done by three nature inspired algorithms namely ParticleSwarm Optimization (PSO) Gravitational Search Algorithm(GSA) and hybrid PSOGSA subjected to many equalityand inequality constraints An augmented multiobjectivefunctionwith real power loss reactive power loss and voltagedeviation is used as a fitness function

Apart from optimal allocation and sizing this paperanalyzes the work in two scenarios In one aspect the effectof increasing the number of DGs is analyzed In the secondscenario different119875119876 capacities of DGs supplying real poweralone reactive power alone and both real and reactive powerare also discussed and compared

Thus the concepts of smart grid network reconfigurationand integration of renewable and reactive power optimizationwith soft computing techniques are all discussed under asingle tree And several aspects are compared and analyzedin this paper

2 Problem Formulation for DG Sizing

The optimal size of DG to be placed at each bus is foundout using PSO algorithm The connection between the DGunit and a bus is modelled as negative 119875119876 load in load flowanalysis

First it is assumed that all 30 buses of the system havesolar generation The PSO algorithm is used to find out thesize of DG that can be placed at each node Choosing anobjective function as in (1) and considering DGrsquos real powergeneration as the control variable the optimal value of DGsize is obtained

As shown in Figure 1 the flowchart explains the overallconcept of the paper The first half inside the dotted boxbelongs to this part of the section The PSO algorithm beingmore efficient gives the better results This nature inspiredswarm intelligence algorithm achieves the best size of solarDG to be placed in the system

The optimal size of DG at each node is determined byPSO For that the following multiobjective function whichuses weighted sum of single objective functions is to beminimized using PSO algorithm

21 Objective Function The objective or fitness function ofthe ORPD problem tends to minimize the real power lossesreactive power losses and voltage deviations subjected to theequality and inequality constraints

119891 = min (119908119875lowast 119875119871) + (119908

119876lowast 119876119871) + (119908

119881lowast VD) (1)

where 119875119871is the real power loss 119876

119871is the reactive power loss

and VD is the voltage deviation and weighing factor for 119908119875

is 035 for 119908119876is 01 and for 119908

119881is 055 and sum of all three

is maintained as 1 The value of weighing factor is based onthe importance of the values in the objective function Sincevoltage deviations are of greater concern it is given higher

The Scientific World Journal 3

PSO

Scenario 1 Scenario 2

Start

Generate the random initial population for DG real power generation assuming solar generation at each bus

Obtain global best solution for optimal size of distributed generation at each bus

Check for negative loading condition at all buses

Select the possible bus locations for DG placement

Create a priority list by two methods (LSF WK)

Loss Sensitivity Factor (LSF) method Weak (WK) bus voltage method

Identification of top ranked DG locations by two methods

3 DGs 4 DGs 5 DGs QP

Optimal sizing of DGs by PSO

GSA and HPSOGSA algorithms

Evaluate results

Stop

Both P and Q

Evaluate fitness of each particle using fit = (wP PL) + (wQ QL)+(wV VD)lowast lowast lowast

Figure 1 Flowchart showing the proposed method

value and then the real and reactive power losses respectivelyThe individual variables 119875

119871 119876119871 and VD are as follows

(i) Real Power Loss (119875119871) The total real power losses of the

system are given in

119875119871=

119873119897

sum

119896=1

119866119896(1198812

119894+ 1198812

119895minus 2119881119894119881119895cos (120575

119894minus 120575119895)) (2)

where 119873119897is the total number of transmission lines in the

system 119866119896is the conductance of the line 119896 119881

119894and 119881

119895are the

magnitudes of the sending end and receiving end voltages ofthe line 120575

119894and 120575119895are angles of the end voltages

(ii) Reactive Power Loss (119876119871)The total reactive power loss of

the system is given by

119876119871=

119873119897

sum

119896=1

119861119896(1198812

119894+ 1198812

119895minus 2119881119894119881119895sin (120575

119894minus 120575119895)) (3)

where 119873119897is the total number of transmission lines in the

system 119861119896is the susceptance of the line 119896 119881

119894and 119881

119895are the

magnitudes of the sending end and receiving end voltages ofthe line 120575

119894and 120575119895are angles of the end voltages

(iii) Load Bus Voltage Deviation (VD) Bus voltage magnitudeis maintained within the allowable limit to ensure quality

4 The Scientific World Journal

service As shown in (4) voltage profile is improved byminimizing the deviation of the load bus voltage from thereference value (it is taken as 10 pu)

VD =

119873119875119876

sum

119896=1

1003816100381610038161003816(119881119896 minus 119881ref)1003816100381610038161003816

(4)

where119873119875119876

is the total number of 119875119876 buses in the system

22 Constraints The minimization problem is subjected tothe equality and inequality constraints as follows

(i) Equality Constraints

Load Flow Constraints The real and reactive power flowconstraints are according to (5) and (6) respectively as givenbelow

119875119866119894minus 119875119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895cos 120579119894119895+ 119861119894119895sin 120579119894119895] = 0 (5)

119876119866119894minus 119876119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895sin 120579119894119895+ 119861119894119895cos 120579119894119895] = 0 (6)

where 119899119887is the number of buses 119875

119866and 119876

119866are the real and

reactive power generation of generator119875119863and119876

119863are the real

and reactive load of the generator 119866119894119895and 119861

119894119895are the mutual

conductance and susceptance between bus 119894 and bus 119895 and120579119894119895is the voltage angle difference between bus 119894 and bus 119895

(ii) Inequality Constraints

Line Limits Constraints The line thermal flow limits aresubjected to

119878119897le 119878

max119897

forall119897 = 1 2 3 119871 (7)

where 119878119897is the thermal limit of each line and 119871 is the number

of lines in the system

Solar Generation Real Power Constraints Consider

119875minGS119899 le 119875GS119899 le 119875

maxGS119899 forall119899 119899 = 1 2 3 119873 (8)

where 119875GS119899 is the real power supplied by DG and 119873 is thenumber of DGs

DG Size Constraint To obtain a reasonable and economicsolution the size of solar generators added at each nodeshould be so small or so high with respect to total load valueAs found in various literatures [20] only 30 of renewableenergy penetration is allowable for effective performance ofthe system And thus the solar generation at each bus is asfollows

1 119871 le 119878 le 5 119871 (9)

119871 is the total load value and 119878 is the solar capacity

Steps to find the optimal size of solar DG at each locationusing PSO are as follows

(1) Assume that all the 30 buses of the system have solarpower generation

(2) The solar DG supplying real power alone is subjectedto the inequality constraints that is 1 to 5 of totalload at each node DGunit ismodelled as negative119875119876load in load flow analysis

(3) The PSO algorithm generates random values forthe size of DGs and the algorithm runs with 50populations and up to 500 iterations

(4) The algorithm gives out the optimal size of DG tobe placed at each node That also achieves the globaloptimal fitness values

(5) These values are taken for further processing of theresults

The solution obtained is carried out to the second part of thework

3 Problem Formulation for OptimalSiting and Sizing

The results obtained from the PSO are checked for negativeload approach [10] since DG is added to load terminalsThisis to ensure that the power flow studies do not end up withreverse power flowThat is when eachDG is added to the loadterminal it supplies the immediate load of that particular busand returns the remaining power demand to the conventionalgenerators In that case the following condition should besatisfied by DG at each bus

119875119863119894minus 119875GS119894 gt 0 forall119894 = 1 2 3 119873 (10)

where 119875119863119894

is the real power demand at bus 119894 119875GS119894 is the realpower generated by solar DG at bus 119894 and 119873 is the numberof solar generators

When a particular bus satisfies the above condition thenit is suitable for DG placement If not the correspondinglocation is not suitable forDGplacementThus the number ofcandidate buses opt for DG placement is reduced providingeasier solution to find the optimal location

In [21] Celli et al have formulated a multiobjective func-tion for optimal sizing and siting with the best compromisebetween various costs The effect of ordering DGs location iswell addressed in [22] which proves that the order in whichthe DGs are placed has a significant impact on the systemlosses

And the negative load approach determines the buses thatare capable of DGs placement and the priority list determinesthe order of buses to which the DGs are placed among thepossible candidate buses

31 Finding the Priority Location of DG The two methodsused for creating the priority list are (i) Loss Sensitivity Factormethod and (ii) weak bus placement method

(i) Loss Sensitivity Factor (LSF) Method The Loss SensitivityFactor method is the best method to find out the order of

The Scientific World Journal 5

buses for DG placement [23 24] Loss sensitivity can besimply defined as ldquothe ratio of change in total loss of thesystem when subjected to a small disturbance to the value ofdisturbance that causes the changerdquo

Among the selected candidate buses the order of DGallocation is found out by LSF which reduces the searchspace for allocation problem [25 26]The following equationdefines the numerical evaluation of LSF

LSF =(losswith DG minus losswithout DG)

size of DG (11)

NR power flow is run for system with DG and systemwithout DG Then the priority list is created and buses areplaced according to the descending order of the LSF valuesThe bus with highest sensitivity is first selected for placingDG The number of DGs to be placed depends on the totalallowable penetration to the system [27 28]

(ii) Weak (WK) Bus Placement Method The weak bus place-ment is a simple but effective method of ordering buses forDG placement According to this method NR power flowis performed for base case of the system and the voltagemagnitude of each bus is noted down

Now the priority list is created in ascending order of thebus voltages and the DG is first placed at the weakest bus ofthe system [29] But the number of buses in which the DG isto be placed is a separate issue which will be discussed later

32 Mathematical Formulation of a Problem forOptimal Sizing

(1) Objective FunctionThe objective function of this problemis to find the optimal settings of reactive power controlvariables which minimize the real power losses reactivepower losses and voltage deviation Hence the objectivefunction is expressed as in

119891 = min (119908119875lowast 119875119871) + (119908

119876lowast 119876119871) + (119908

119881lowast VD) (12)

where the representation of all the variables is already dis-cussed in Section 2

(2) Constraints

(i) Equality Constraints

Load Flow Constraints The real and reactive power con-straints are according to (13) and (14) respectively as givenbelow

119875119866119894minus 119875119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895cos 120579119894119895+ 119861119894119895sin 120579119894119895] = 0 (13)

119876119866119894minus 119876119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895sin 120579119894119895+ 119861119894119895cos 120579119894119895] = 0 (14)

where the representation of all the variables is alreadydiscussed in Section 2

After addition of renewable energy source the aboveequation becomes

119875119866minus 119875119863minus 119875loss + 119875SG = 0

119876119866minus 119876119863minus 119876loss + 119876SG = 0

(15)

where119875119866and119876

119866are the conventional real and reactive power

generation 119875119863and 119876

119863are the total real and reactive power

demand 119875loss and 119876loss are the total system losses and 119875SGand 119876SG are the real and reactive power generation of solarDG

(ii) Inequality Constraints

Generator Bus Voltage (119881119866119894) Inequality Constraint Consider

119881min119866119894

le 119881119866119894le 119881

max119866119894

119894 isin 119899119892 (16)

Load Bus Voltage (119881119871 119894) Inequality Constraint Consider

119881min119871 119894

le 119881119871 119894le 119881

max119871 119894

119894 isin 119899119875119876 (17)

Switchable Reactive Power Compensation (119876119862119894) Inequality

Constraint Consider

119876min119862119894

le 119876119862119894le 119876

max119862119894

119894 isin 119899119888 (18)

Reactive Power Generation (119876119866119894) Inequality Constraint Con-

sider

119876min119866119894

le 119876119866119894le 119876

max119866119894

119894 isin 119899119892 (19)

Transformer Tap Setting (119879119894) Inequality Constraint Consider

119879min119894

le 119879119894le 119879

max119894

119894 isin 119899119905 (20)

where 119899119892 119899119875119876 119899119888 and 119899

119905are the numbers of generator

buses load buses switchable reactive power sources and tapsettings

Solar Generators Real Power Constraints Consider

119875minGS119899 le 119875GS119899 le 119875

maxGS119899 forall119899 119899 = 1 2 3 119873 (21)

where 119875GS119899 is the real power supplied by DG and 119873 is thenumber of DGs

Solar Generators Reactive Power Constraints Consider

QminGS119899 le QGS119899 le Qmax

GS119899 forall119899 119899 = 1 2 3 119873 (22)

where119876GS119899 is the reactive power supplied byDG and119873 is thenumber of DGs

6 The Scientific World Journal

4 Proposed Work

Integrating the renewable source to grid or the conversionof the traditional grid into smart grid involves severalessential steps The placement of renewable sources theirsize and amount of penetration into the system are all tobe considered The smart grid possesses five most significantcharacteristics like being adaptive predictive integratedinteractive optimized and secured [30] In this paper the gridachieves all the five perspectives by integrating solar energyplacement of solar energy and performing reactive poweroptimization to maintain voltage profile and thus havingsecured and reliable power system

Solar DG is found to be the best DG that adapts thegrid among several renewable sources And also this researchincludes the grid interactive solar inverters and their behaviorwhen operated in a grid [31] The grid tied solar inverters actas a reactive power source to balance the reactive power ofthe system [7] In the [32] optimal siting and sizing of DGsare found by combined GAPSO algorithms

The proposed work consists of two scenarios

Scenario 1 number of DG placementsScenario 2 DG supplying various 119875119876 capacities

41 Scenario 1 Number of DG Placements This scenariodiscusses the number of DGs to be placed on the system andeffect of increased number of DGs on the system [18]

The number of DGs to be placed depends on the loaddemand of the system and maximum allowable size of DGThe maximum allowable size of DG is up to 25 to 30 ofthe total load as found in various literatures [18 20] In thispaper the maximum numbers of DGs are taken as 5 So it isproposed to select 5 numbers of buses among 30-bus systemThen for each bus it is advisable to take maximum of 5 loadin order to satisfy the total load of 25 amongst 5 buses Alsominimum numbers of DGs are chosen as 3 In this aspectthis paper does not include constant solar DG penetration of25 That means total 25 of load is not divided amongst3 buses Instead in each bus solar DG size of 1 to 5 loadis maintained independent of number of DGs So this workattains the fact that minimum of 15 load is satisfied byincluding 3 numbers of DGs and maximum of 25 load ismet by including 5 numbers of DGs

Therefore the number of DGs is selected as 3 4 and 5respectively And they are placed on the top priority orderedby LSF and weak bus method Now three nature inspiredmetaheuristics algorithms PSO [33] GSA [34 35] and hybridPSOGSA [36 37] are used to optimize the solar sizing In [38]economic dispatch problem in a microgrid is addressed withcost minimization

(i) Basic Concepts of PSO [33] PSO has been developedthrough simulation of simplified social models The featuresof the method are as follows

(i) Based on swarms like fish schooling and a flock ofbirds

(ii) Simple and less time consuming

(iii) Solving nonlinear optimization problems with con-tinuous variables

The convergence is provided by the acceleration term in(23)

The modified velocity of each agent can be calculatedusing the current velocity and the distances from 119901119887119890119904119905 and119892119887119890119904119905 are as shown below

V119896+1119894

= 119908V119896119894+ 1198881rand (119901119887119890119904119905

119894minus 119904119896

119894)

+ 1198882rand (119892119887119890119904119905 minus 119904

119896

119894)

(23)

where V119896119894is the velocity of agent 119894 at 119896th iteration V119896+1

119894is

the modified velocity of agent rand represent functions thatgenerate independent random numbers which are uniformlydistributed between 0 and 1 119904119896

119894is the current position of agent

at 119896th iteration119901119887119890119904119905119894is the119901119887119890119904119905 of agent 119894119892119887119890119904119905 is the119892119887119890119904119905

of the group119908 is the inertia weight factor used to control theimpact of the previous history of velocities V119896

119894on the current

velocity V119896+1119894

and 1198881and 1198882are the acceleration factors named

cognitive and social parameters determine the influence of119901119887119890119904119905119894and 119892119887119890119904119905 in determining the new solutions

And the updated position can be calculated from thefollowing equation

119904119896+1

119894= 119904119896

119894+ V119896+1119894

119894 = 1 2 3 119873119905119898 (24)

where 119873119905119898

is the number of swarms and 119896 represents theiteration

(ii) Basic Concepts of GSA [34 35] GSA is a novel heuristicoptimization method which has been proposed by Rashediet al in 2009 [35] The basic physical theory from whichGSA is inspired is from Newtonrsquos theory The GSA couldbe considered as an isolated system of masses It is like asmall artificial world of masses obeying the Newtonian lawsof gravitation and motion

The convergence is reached indirectly by the accelerationterm in (25)The acceleration of anymass is equal to the forceacted on the system divided by mass of inertia

The velocity and position of the agents for next (119905 + 1)

iteration are calculated using the following equations

V119889119894(119905 + 1) = rand

119894V119889119894(119905) + 119886

119889

119894(119905) (25)

119909119889

119894(119905 + 1) = 119909

119889

119894(119905) + V119889

119894(119905 + 1) (26)

where V119889119894(119905 + 1) is the updated velocity of the particle at 119894th

position V119889119894(119905) is the previous velocity 119886119889

119894(119905) is the acceleration

of the particle at 119894 and 119909119889119894is the position of the particle

(iii) Basic Concepts of Hybrid PSOGSA [36 37] Two algo-rithms can be hybridized in high level or low level withrelay or coevolutionarymethod as homogeneous or heteroge-neous In this paper low level coevolutionary heterogeneoushybrid method is used The hybrid is low level because ofthe combination of the functionality of both algorithms Itis coevolutionary because this algorithm does not use both

The Scientific World Journal 7

algorithms one after another In other words they run inparallel It is heterogeneous because there are two differentalgorithms that are involved to produce final results Themain idea is to integrate the ability of exploitation in PSOwith the ability of exploration in GSA to synthesize bothalgorithmsrsquo strength

The main objective is to combine the social thinkingability of PSO (119892119887119890119904119905) with the local search capability ofGSA hence achieving a new formula for hybrid PSOGSA forvelocity updating as

V119894 (119905 + 1) = 119908V

119894 (119905) + 1198881015840

1rand ac

119894 (119905)

+ 1198881015840

2rand (119892119887119890119904119905 minus 119909

119894 (119905))

(27)

where V119894(119905) is the velocity of agent 119894 at iteration 119905 1198881015840

119895is a

weighting factor119908 is a weighting function rand is a randomnumber between 0 and 1 ac

119894(119905) is the acceleration of agent 119894

at iteration 119905 and 119892119887119890119904119905 is the best solution so far Positionupdate is done by the following formula

119909119894 (119905 + 1) = 119909

119894 (119905) + V119894 (119905 + 1) (28)

42 Scenario 2 DG Supplying Various 119875119876 Capacities Thispaper deals with solar power and grid tied solar inverters inwhich both real power and reactive power of solar energy areconsidered And this scenario deals with 4 types of cases [918 39]

The four different cases as shown below are separatelyanalyzed with respect to LSF and WK method in order tofind out the location of solar power and they are also analyzedwith respect to number of DGs using different optimizationalgorithms to find out the sizing of solar power and the resultsare discussed in the next section

(i) Type 1 system without DG (initial case)(ii) Type 2 DG supplying real power alone (119875)(iii) Type 3 DG supplying reactive power alone (119876)(iv) Type 4 DG supplying both real and reactive power

All the four types are tested with above-mentioned threealgorithms and results are evaluated

5 Results and Discussions

In this section of the paper the entire work is explainedin a precise manner The standard IEEE 30-bus system[8] as in Figure 2 [37] is used as a test system and theresults are evaluated The test system consists of 30 busesof which 6 generating buses are present including slack busand remaining 24 are load buses 41 lines 4 tap changingtransformers and 9 shunt capacitors are present in this testcase MATPOWER open source power system software isused to run NR power flow The initial real power loss of thesystem is 58316MW and initial voltage deviation is 09819

The proposed work is divided into two steps In thefirst step optimal size of DG at each node is found byPSO algorithm assuming that all the 30 buses have solar

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

simsim

sim

sim

sim

sim

Figure 2 Single line diagram of IEEE 30-bus test system

generation And then results obtained through PSO arechecked for reverse power flow by negative load approachto find the possible bus locations for DG placement Thenthe search for optimal location of DGs is fine-tuned by twomethods namely weak (WK) voltage bus placement and LossSensitivity Factor (LSF) method In order to emphasize pointon the effect of increasing the number of solar generatorsthis paper analyses and compares the result with numbers3 4 and 5 of DGs which are placed using priority locationfound fromWK and LSFmethods Further the 119875119876 capacitiesof solar DGs are also analyzed with DG supplying 119875 alone(real power alone) 119876 alone (reactive power alone) and both119875 and 119876 (both real and reactive power) Thus several aspectslike size of DGs number of DGs location of DGs order ofDG placement and 119875119876 capacities of DGs are all discussedunder one roof in this paper

51 Optimal Size of DG Using PSO The PSO algorithm isused to find out the size of DG to be placed on the systemAt each bus solar DG penetration is allowed and size ofsolar power is taken as a control variable The size is limitedbetween 2834MW and 11336MWwhich is 1 to 5 of totalload

Table 1 illustrates the optimal size of the solar DG at eachbus to be included satisfying the multiobjective function ofminimizing the real power losses reactive power losses andvoltage deviations

Then the size of the DG is tested for negative loadapproach That is NR power flow is run after placing DG ateach bus In the literature listed in [40] Kansal et alrsquos workis limited to reverse power flow and they have not includednegative load approach But this paper provides best resultsand does not have reverse power flow

The buses that withstand negative load effect are 2 4 5 78 12 15 19 21 24 and 30 Among these buses the best locationand the best combination for DG placement are selected as inTables 1 and 2

8 The Scientific World Journal

Table 1 Size of DGs from PSO

Bus number Optimal size (MW)1 49847682 43584673 5217384 64699435 54963056 82662427 10215498 71671979 750025610 678641411 377825412 756259313 105648114 704602615 600733616 742512517 109480418 691723119 461379820 755944721 103330822 512553223 396942324 629310125 64554426 416699227 563055328 745293629 548090130 7336586

52 Priority List Creation The priority list is created to orderthe DG placement (shown in Table 2) It is done by twomethods (1) Loss Sensitivity Factor (LSF) method and (2)weak (WK) bus placement method

(1) The LSF method of placement is used because ofthe sensitivity towards loss Since this paper aimsfor loss minimization with voltage enhancement LSFmethod of finding DG location is most appropriateThe buses are placed in decreasing order of losssensitivity that is the highly sensitive bus is first givenwith DG sitingThus the order of buses is obtained asin Table 2

(2) The weak (WK) bus voltage method is the simplestand easy method for bus placement The buses withweak voltage profile are provided with the DG Thisis in order to improve the voltage profile of thesystem and to obtain minimum voltage deviationsThe ordering is done as in Table 2

The bus number in which solar DG can be fit ishighlighted that is the DGs which sustain negative load

Table 2 Sequence of priority list by WK and LSF method

Bus number LSF Priority list Bus voltage (119881) Priority list1 605119890 minus 14 30 1050 302 107e minus 01 29 1040 273 235119890 minus 01 27 1028 264 244e minus 01 26 1022 255 247e minus 01 25 1010 216 263119890 minus 01 24 1017 247 283e minus 01 23 1006 208 230e minus 01 28 1010 229 449119890 minus 01 19 0976 1710 502119890 minus 01 16 0955 1411 429119890 minus 01 20 1050 2912 476e minus 01 18 0998 1913 412119890 minus 01 21 1050 2814 561119890 minus 01 13 0997 1815 575e minus 01 12 0968 1516 519119890 minus 01 17 0972 1617 526119890 minus 01 15 0954 1318 595119890 minus 01 7 0950 1219 588e minus 01 8 0943 920 576119890 minus 01 11 0945 1021 576e minus 01 10 0941 722 577119890 minus 01 9 0941 823 623119890 minus 01 4 0947 1124 614e minus 01 5 0927 625 590119890 minus 01 6 0920 426 664119890 minus 01 3 0901 227 556119890 minus 01 14 0926 528 304119890 minus 01 22 1012 2329 695119890 minus 01 2 0903 330 738e minus 01 1 0891 1

approachTherefore those buses are provided with DG in theorder of LSF or WK method Now the LSF method bringsout the priority order as 30 24 19 21 15 12 7 5 4 8 and 2whereas by WKmethod the priority order is 30 24 21 19 1512 7 5 8 4 and 2

53 Location and Sizing After finding out the order andlocation of buses the number of DGs and type of DG are tobe found The cases may be 3 DGs 4 DGs and 5 DGs basedon number of placements Here the size of DG is same andis subjected to the same limits And type of DGs conceptis provided in this paper to answer the following questionldquoWhat purpose is DG placement meant forrdquo According tothis concept four cases are dealt with ldquono DG 119875 alone 119876alone and both 119875 and119876 combinedrdquo All these cases are testedwith both LSF and WK bus ordering

531 Control Variables and Its Ranges Consider the follow-ing

(1) Real power supplied by DG 119875SG 2834 to 11336MW

The Scientific World Journal 9

Table 3 Best outcome among all cases

5 DGsrsquo LSFAlgorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

FIT 30851 4260241 4165504 2309231 4260241 2358722 1696819 4295037 1920378119875119871(MW) 3930302 5447339 5004235 2973936 5447339 3022499 2149479 5496311 2407849

119876119871(MVAR) 1706019 2353672 2141433 1268353 2353672 1300848 9444453 2370543 1057728

VD 0006318 511119864 minus 10 0495616 163119864 minus 10 511119864 minus 10 615119864 minus 11 102E minus 04 0001427 0036186119875 solar (MW) 3501449 mdash 3457517 3523621 mdash 3572001 4664924 mdash 4169984 119875 solar 1235515 mdash 1220013 1243338 mdash 126041 1646056 mdash 1471413119876 solar (MVAR) mdash 1541866 1355552 mdash 1541866 1543187 mdash 1540234 1453383 119876 solar mdash 1221764 107413 mdash 1221764 1222811 mdash 122047 1151651

(2) Reactive power supplied by DG 119876SG 1262 to631MVAR

(3) Voltage magnitude of PV buses 09 to 11 pu(4) Tap settings 09 to 11 pu(5) Shunt capacitors 0 to 10 MVAR

Allowed total solar power generation is from 5 to 25of the total load of the system For an IEEE 30-bus system thetotal real power demand is 2834MW and reactive power is1262MWTherefore eachDG is subjected to 1 to 5 of totaldemand andmaximum of 5 DGs are considered to satisfy 5to 25 of total demand

Four types of system cases are evaluated

(1) No DG In this case there is no renewable penetrationand NR load flow is run with the basic case withconventional generators Here the values of fitnessfunction are evaluated for the base case using GSAPSO and hybrid PSOGSA algorithms And the resultsare tabulated in Table 4

(2) Three DGs placed In this case totally three DGs areplaced in the system By LSF method DG locationis prioritized at bus numbers 30 24 and 19 By WKmethod it is found to be at 30 24 and 21 This bringsout reduced loss results than no DG case

(3) Four DGs placed In this case 4 DGs are placed atlocation of bus numbers 30 24 19 and 21 under LSFand 30 24 21 and 19 under WK This case exhibitsmore reduced losses than previous case

(4) Five DGs placed (as shown in Figure 3) Under LSFDG location is found to be at bus numbers 30 24 1921 and 15 and under WK it is at 30 24 21 19 and 15Five-DG case brings out the most wondering resultsas in Table 3

It is to be noted that there is only slight variation in theorder of placement by both methods but the results obtainedhave shown ridiculous performance The DG supplyingvarious 119875119876 capacities concept is discussed next

(1) 119875 aloneThis system consists of multiple DGs supply-ing real power alone located under LSF and WK busmethods The system with multiple DGs supplying 119875

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

sim

simsim

simsim

sim

Figure 3 Test system after addition of DGs at 5 locations

alone is optimized using 3 algorithms and the resultsare tabulated in Table 4

(2) 119876 aloneThis scheme consists ofmultipleDGs supply-ing reactive power alone located under LSF and WKbus method is solved under all three algorithmsThatis the DG is placed as a reactive power compensatingdevice This is a worst case with high power lossincurred compared to other cases Also it is noteconomical to include DG just for providing reactivepower alone So this case will be the worst case amongall and the results are tabulated in the Appendix

(3) Both 119875 and 119876 Under this scheme multiple DGs areplaced satisfying LSF and WK methods meant forproviding both real and reactive power This case isanalyzed with 3 4 and 5 DGs using 3 algorithmsThis provides a trustworthy way of DG installationHere the real and reactive power needs of the systemare compensated at the same time The inverters ofthe solar DG act as reactive power sources Thiscondition suits the main objective of the paper andit aims to compensate both the powers of the system

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

2 The Scientific World Journal

power and voltage of the system is to add capacitors tapchanging transformers and FACTS devices at necessarynodes On the other hand these demands can be internallymet with the help of inverters on solar panels [7] The gridtied solar inverters act as a reactive power source to balancethe reactive power of the system

The optimal operation of a power system is requiredto precede the optimal planning of facilities like generatingplants reactive power compensation and transmission net-works In order to handle the large scale optimal power flowproblem the problem is decomposed into real power (119875)optimization problem and reactive power (119876) optimizationproblem [8] The 119875-problem is to minimize the productioncost under the assumption that system voltages are heldconstant and the 119876-problem is to minimize the transmissionloss under the assumption that real power generation is heldconstant This paper addresses the reactive power dispatchproblem for IEEE 30-bus system [8] since the objective is tominimize the transmission line losses

The network reconfiguration is done to convert a conven-tional grid into smart grid Multiple DG units with different119875119876 capacities are integrated to the traditional grid In [9]different cases are addressed with real and reactive powerpenetration of the solar plant

In the integration of solar power both the siting andsizing of solar Distributed Generators (DGs) have a signifi-cant impact on the system losses in a transmission networkThis paper presents a novel multiobjective approach forcalculating the DG optimum placement and sizing In [10]optimization for sizing and placement of DGs is done byusing PSO algorithm

Several metaheuristics algorithms were developed forsolving the multiobjective reactive power optimization prob-lem Genetic Algorithm (GA) [11] Evolutionary Program-ming (EP) [12 13] Bacterial Foraging Optimization (BFO)[14] Ant Colony Optimization (ACO) [15] Differential Evo-lution (DE) [16] and recently Gravitational SearchAlgorithm(GSA) [17] are some of the most common optimization algo-rithms The paper [18] deals with the GA for optimum sitingand sizing ofDGsDeshmukh et al [19] have formulatedVARcontrol problem thatminimizes the combined reactive powerinjection by DGs

Many literatures address the optimal siting and sizing ofmultipleDGs by any one of the algorithms or combinations ofalgorithms Some papers have found the optimal location ofDGs byweak (WK) bus placementmethod or Loss SensitivityFactor (LSF) method Some references analyze the impactof increasing the number of DGs But this research paperanalyzes all the above-said aspects simultaneously

In this paper the problem of optimal DG location andsizing is divided into two steps In the first step optimalsize of DG to be placed at each bus is found out by PSOmetaheuristics with the assumption that all 30 buses havesolar generation subjected to the inequality constraints of linelimits solar generation real power constraints and DG sizeconstraints The results obtained through PSO are checkedfor reverse power flow by negative load approach The buseswhich satisfy the negative loading conditions are the possiblelocations for DG placement Again this search of optimal

location is fine-tuned by weak (WK) bus placement methodand Loss Sensitivity Factor (LSF) method and the results areanalyzed Then in the second step optimal sizing of DGsis done by three nature inspired algorithms namely ParticleSwarm Optimization (PSO) Gravitational Search Algorithm(GSA) and hybrid PSOGSA subjected to many equalityand inequality constraints An augmented multiobjectivefunctionwith real power loss reactive power loss and voltagedeviation is used as a fitness function

Apart from optimal allocation and sizing this paperanalyzes the work in two scenarios In one aspect the effectof increasing the number of DGs is analyzed In the secondscenario different119875119876 capacities of DGs supplying real poweralone reactive power alone and both real and reactive powerare also discussed and compared

Thus the concepts of smart grid network reconfigurationand integration of renewable and reactive power optimizationwith soft computing techniques are all discussed under asingle tree And several aspects are compared and analyzedin this paper

2 Problem Formulation for DG Sizing

The optimal size of DG to be placed at each bus is foundout using PSO algorithm The connection between the DGunit and a bus is modelled as negative 119875119876 load in load flowanalysis

First it is assumed that all 30 buses of the system havesolar generation The PSO algorithm is used to find out thesize of DG that can be placed at each node Choosing anobjective function as in (1) and considering DGrsquos real powergeneration as the control variable the optimal value of DGsize is obtained

As shown in Figure 1 the flowchart explains the overallconcept of the paper The first half inside the dotted boxbelongs to this part of the section The PSO algorithm beingmore efficient gives the better results This nature inspiredswarm intelligence algorithm achieves the best size of solarDG to be placed in the system

The optimal size of DG at each node is determined byPSO For that the following multiobjective function whichuses weighted sum of single objective functions is to beminimized using PSO algorithm

21 Objective Function The objective or fitness function ofthe ORPD problem tends to minimize the real power lossesreactive power losses and voltage deviations subjected to theequality and inequality constraints

119891 = min (119908119875lowast 119875119871) + (119908

119876lowast 119876119871) + (119908

119881lowast VD) (1)

where 119875119871is the real power loss 119876

119871is the reactive power loss

and VD is the voltage deviation and weighing factor for 119908119875

is 035 for 119908119876is 01 and for 119908

119881is 055 and sum of all three

is maintained as 1 The value of weighing factor is based onthe importance of the values in the objective function Sincevoltage deviations are of greater concern it is given higher

The Scientific World Journal 3

PSO

Scenario 1 Scenario 2

Start

Generate the random initial population for DG real power generation assuming solar generation at each bus

Obtain global best solution for optimal size of distributed generation at each bus

Check for negative loading condition at all buses

Select the possible bus locations for DG placement

Create a priority list by two methods (LSF WK)

Loss Sensitivity Factor (LSF) method Weak (WK) bus voltage method

Identification of top ranked DG locations by two methods

3 DGs 4 DGs 5 DGs QP

Optimal sizing of DGs by PSO

GSA and HPSOGSA algorithms

Evaluate results

Stop

Both P and Q

Evaluate fitness of each particle using fit = (wP PL) + (wQ QL)+(wV VD)lowast lowast lowast

Figure 1 Flowchart showing the proposed method

value and then the real and reactive power losses respectivelyThe individual variables 119875

119871 119876119871 and VD are as follows

(i) Real Power Loss (119875119871) The total real power losses of the

system are given in

119875119871=

119873119897

sum

119896=1

119866119896(1198812

119894+ 1198812

119895minus 2119881119894119881119895cos (120575

119894minus 120575119895)) (2)

where 119873119897is the total number of transmission lines in the

system 119866119896is the conductance of the line 119896 119881

119894and 119881

119895are the

magnitudes of the sending end and receiving end voltages ofthe line 120575

119894and 120575119895are angles of the end voltages

(ii) Reactive Power Loss (119876119871)The total reactive power loss of

the system is given by

119876119871=

119873119897

sum

119896=1

119861119896(1198812

119894+ 1198812

119895minus 2119881119894119881119895sin (120575

119894minus 120575119895)) (3)

where 119873119897is the total number of transmission lines in the

system 119861119896is the susceptance of the line 119896 119881

119894and 119881

119895are the

magnitudes of the sending end and receiving end voltages ofthe line 120575

119894and 120575119895are angles of the end voltages

(iii) Load Bus Voltage Deviation (VD) Bus voltage magnitudeis maintained within the allowable limit to ensure quality

4 The Scientific World Journal

service As shown in (4) voltage profile is improved byminimizing the deviation of the load bus voltage from thereference value (it is taken as 10 pu)

VD =

119873119875119876

sum

119896=1

1003816100381610038161003816(119881119896 minus 119881ref)1003816100381610038161003816

(4)

where119873119875119876

is the total number of 119875119876 buses in the system

22 Constraints The minimization problem is subjected tothe equality and inequality constraints as follows

(i) Equality Constraints

Load Flow Constraints The real and reactive power flowconstraints are according to (5) and (6) respectively as givenbelow

119875119866119894minus 119875119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895cos 120579119894119895+ 119861119894119895sin 120579119894119895] = 0 (5)

119876119866119894minus 119876119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895sin 120579119894119895+ 119861119894119895cos 120579119894119895] = 0 (6)

where 119899119887is the number of buses 119875

119866and 119876

119866are the real and

reactive power generation of generator119875119863and119876

119863are the real

and reactive load of the generator 119866119894119895and 119861

119894119895are the mutual

conductance and susceptance between bus 119894 and bus 119895 and120579119894119895is the voltage angle difference between bus 119894 and bus 119895

(ii) Inequality Constraints

Line Limits Constraints The line thermal flow limits aresubjected to

119878119897le 119878

max119897

forall119897 = 1 2 3 119871 (7)

where 119878119897is the thermal limit of each line and 119871 is the number

of lines in the system

Solar Generation Real Power Constraints Consider

119875minGS119899 le 119875GS119899 le 119875

maxGS119899 forall119899 119899 = 1 2 3 119873 (8)

where 119875GS119899 is the real power supplied by DG and 119873 is thenumber of DGs

DG Size Constraint To obtain a reasonable and economicsolution the size of solar generators added at each nodeshould be so small or so high with respect to total load valueAs found in various literatures [20] only 30 of renewableenergy penetration is allowable for effective performance ofthe system And thus the solar generation at each bus is asfollows

1 119871 le 119878 le 5 119871 (9)

119871 is the total load value and 119878 is the solar capacity

Steps to find the optimal size of solar DG at each locationusing PSO are as follows

(1) Assume that all the 30 buses of the system have solarpower generation

(2) The solar DG supplying real power alone is subjectedto the inequality constraints that is 1 to 5 of totalload at each node DGunit ismodelled as negative119875119876load in load flow analysis

(3) The PSO algorithm generates random values forthe size of DGs and the algorithm runs with 50populations and up to 500 iterations

(4) The algorithm gives out the optimal size of DG tobe placed at each node That also achieves the globaloptimal fitness values

(5) These values are taken for further processing of theresults

The solution obtained is carried out to the second part of thework

3 Problem Formulation for OptimalSiting and Sizing

The results obtained from the PSO are checked for negativeload approach [10] since DG is added to load terminalsThisis to ensure that the power flow studies do not end up withreverse power flowThat is when eachDG is added to the loadterminal it supplies the immediate load of that particular busand returns the remaining power demand to the conventionalgenerators In that case the following condition should besatisfied by DG at each bus

119875119863119894minus 119875GS119894 gt 0 forall119894 = 1 2 3 119873 (10)

where 119875119863119894

is the real power demand at bus 119894 119875GS119894 is the realpower generated by solar DG at bus 119894 and 119873 is the numberof solar generators

When a particular bus satisfies the above condition thenit is suitable for DG placement If not the correspondinglocation is not suitable forDGplacementThus the number ofcandidate buses opt for DG placement is reduced providingeasier solution to find the optimal location

In [21] Celli et al have formulated a multiobjective func-tion for optimal sizing and siting with the best compromisebetween various costs The effect of ordering DGs location iswell addressed in [22] which proves that the order in whichthe DGs are placed has a significant impact on the systemlosses

And the negative load approach determines the buses thatare capable of DGs placement and the priority list determinesthe order of buses to which the DGs are placed among thepossible candidate buses

31 Finding the Priority Location of DG The two methodsused for creating the priority list are (i) Loss Sensitivity Factormethod and (ii) weak bus placement method

(i) Loss Sensitivity Factor (LSF) Method The Loss SensitivityFactor method is the best method to find out the order of

The Scientific World Journal 5

buses for DG placement [23 24] Loss sensitivity can besimply defined as ldquothe ratio of change in total loss of thesystem when subjected to a small disturbance to the value ofdisturbance that causes the changerdquo

Among the selected candidate buses the order of DGallocation is found out by LSF which reduces the searchspace for allocation problem [25 26]The following equationdefines the numerical evaluation of LSF

LSF =(losswith DG minus losswithout DG)

size of DG (11)

NR power flow is run for system with DG and systemwithout DG Then the priority list is created and buses areplaced according to the descending order of the LSF valuesThe bus with highest sensitivity is first selected for placingDG The number of DGs to be placed depends on the totalallowable penetration to the system [27 28]

(ii) Weak (WK) Bus Placement Method The weak bus place-ment is a simple but effective method of ordering buses forDG placement According to this method NR power flowis performed for base case of the system and the voltagemagnitude of each bus is noted down

Now the priority list is created in ascending order of thebus voltages and the DG is first placed at the weakest bus ofthe system [29] But the number of buses in which the DG isto be placed is a separate issue which will be discussed later

32 Mathematical Formulation of a Problem forOptimal Sizing

(1) Objective FunctionThe objective function of this problemis to find the optimal settings of reactive power controlvariables which minimize the real power losses reactivepower losses and voltage deviation Hence the objectivefunction is expressed as in

119891 = min (119908119875lowast 119875119871) + (119908

119876lowast 119876119871) + (119908

119881lowast VD) (12)

where the representation of all the variables is already dis-cussed in Section 2

(2) Constraints

(i) Equality Constraints

Load Flow Constraints The real and reactive power con-straints are according to (13) and (14) respectively as givenbelow

119875119866119894minus 119875119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895cos 120579119894119895+ 119861119894119895sin 120579119894119895] = 0 (13)

119876119866119894minus 119876119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895sin 120579119894119895+ 119861119894119895cos 120579119894119895] = 0 (14)

where the representation of all the variables is alreadydiscussed in Section 2

After addition of renewable energy source the aboveequation becomes

119875119866minus 119875119863minus 119875loss + 119875SG = 0

119876119866minus 119876119863minus 119876loss + 119876SG = 0

(15)

where119875119866and119876

119866are the conventional real and reactive power

generation 119875119863and 119876

119863are the total real and reactive power

demand 119875loss and 119876loss are the total system losses and 119875SGand 119876SG are the real and reactive power generation of solarDG

(ii) Inequality Constraints

Generator Bus Voltage (119881119866119894) Inequality Constraint Consider

119881min119866119894

le 119881119866119894le 119881

max119866119894

119894 isin 119899119892 (16)

Load Bus Voltage (119881119871 119894) Inequality Constraint Consider

119881min119871 119894

le 119881119871 119894le 119881

max119871 119894

119894 isin 119899119875119876 (17)

Switchable Reactive Power Compensation (119876119862119894) Inequality

Constraint Consider

119876min119862119894

le 119876119862119894le 119876

max119862119894

119894 isin 119899119888 (18)

Reactive Power Generation (119876119866119894) Inequality Constraint Con-

sider

119876min119866119894

le 119876119866119894le 119876

max119866119894

119894 isin 119899119892 (19)

Transformer Tap Setting (119879119894) Inequality Constraint Consider

119879min119894

le 119879119894le 119879

max119894

119894 isin 119899119905 (20)

where 119899119892 119899119875119876 119899119888 and 119899

119905are the numbers of generator

buses load buses switchable reactive power sources and tapsettings

Solar Generators Real Power Constraints Consider

119875minGS119899 le 119875GS119899 le 119875

maxGS119899 forall119899 119899 = 1 2 3 119873 (21)

where 119875GS119899 is the real power supplied by DG and 119873 is thenumber of DGs

Solar Generators Reactive Power Constraints Consider

QminGS119899 le QGS119899 le Qmax

GS119899 forall119899 119899 = 1 2 3 119873 (22)

where119876GS119899 is the reactive power supplied byDG and119873 is thenumber of DGs

6 The Scientific World Journal

4 Proposed Work

Integrating the renewable source to grid or the conversionof the traditional grid into smart grid involves severalessential steps The placement of renewable sources theirsize and amount of penetration into the system are all tobe considered The smart grid possesses five most significantcharacteristics like being adaptive predictive integratedinteractive optimized and secured [30] In this paper the gridachieves all the five perspectives by integrating solar energyplacement of solar energy and performing reactive poweroptimization to maintain voltage profile and thus havingsecured and reliable power system

Solar DG is found to be the best DG that adapts thegrid among several renewable sources And also this researchincludes the grid interactive solar inverters and their behaviorwhen operated in a grid [31] The grid tied solar inverters actas a reactive power source to balance the reactive power ofthe system [7] In the [32] optimal siting and sizing of DGsare found by combined GAPSO algorithms

The proposed work consists of two scenarios

Scenario 1 number of DG placementsScenario 2 DG supplying various 119875119876 capacities

41 Scenario 1 Number of DG Placements This scenariodiscusses the number of DGs to be placed on the system andeffect of increased number of DGs on the system [18]

The number of DGs to be placed depends on the loaddemand of the system and maximum allowable size of DGThe maximum allowable size of DG is up to 25 to 30 ofthe total load as found in various literatures [18 20] In thispaper the maximum numbers of DGs are taken as 5 So it isproposed to select 5 numbers of buses among 30-bus systemThen for each bus it is advisable to take maximum of 5 loadin order to satisfy the total load of 25 amongst 5 buses Alsominimum numbers of DGs are chosen as 3 In this aspectthis paper does not include constant solar DG penetration of25 That means total 25 of load is not divided amongst3 buses Instead in each bus solar DG size of 1 to 5 loadis maintained independent of number of DGs So this workattains the fact that minimum of 15 load is satisfied byincluding 3 numbers of DGs and maximum of 25 load ismet by including 5 numbers of DGs

Therefore the number of DGs is selected as 3 4 and 5respectively And they are placed on the top priority orderedby LSF and weak bus method Now three nature inspiredmetaheuristics algorithms PSO [33] GSA [34 35] and hybridPSOGSA [36 37] are used to optimize the solar sizing In [38]economic dispatch problem in a microgrid is addressed withcost minimization

(i) Basic Concepts of PSO [33] PSO has been developedthrough simulation of simplified social models The featuresof the method are as follows

(i) Based on swarms like fish schooling and a flock ofbirds

(ii) Simple and less time consuming

(iii) Solving nonlinear optimization problems with con-tinuous variables

The convergence is provided by the acceleration term in(23)

The modified velocity of each agent can be calculatedusing the current velocity and the distances from 119901119887119890119904119905 and119892119887119890119904119905 are as shown below

V119896+1119894

= 119908V119896119894+ 1198881rand (119901119887119890119904119905

119894minus 119904119896

119894)

+ 1198882rand (119892119887119890119904119905 minus 119904

119896

119894)

(23)

where V119896119894is the velocity of agent 119894 at 119896th iteration V119896+1

119894is

the modified velocity of agent rand represent functions thatgenerate independent random numbers which are uniformlydistributed between 0 and 1 119904119896

119894is the current position of agent

at 119896th iteration119901119887119890119904119905119894is the119901119887119890119904119905 of agent 119894119892119887119890119904119905 is the119892119887119890119904119905

of the group119908 is the inertia weight factor used to control theimpact of the previous history of velocities V119896

119894on the current

velocity V119896+1119894

and 1198881and 1198882are the acceleration factors named

cognitive and social parameters determine the influence of119901119887119890119904119905119894and 119892119887119890119904119905 in determining the new solutions

And the updated position can be calculated from thefollowing equation

119904119896+1

119894= 119904119896

119894+ V119896+1119894

119894 = 1 2 3 119873119905119898 (24)

where 119873119905119898

is the number of swarms and 119896 represents theiteration

(ii) Basic Concepts of GSA [34 35] GSA is a novel heuristicoptimization method which has been proposed by Rashediet al in 2009 [35] The basic physical theory from whichGSA is inspired is from Newtonrsquos theory The GSA couldbe considered as an isolated system of masses It is like asmall artificial world of masses obeying the Newtonian lawsof gravitation and motion

The convergence is reached indirectly by the accelerationterm in (25)The acceleration of anymass is equal to the forceacted on the system divided by mass of inertia

The velocity and position of the agents for next (119905 + 1)

iteration are calculated using the following equations

V119889119894(119905 + 1) = rand

119894V119889119894(119905) + 119886

119889

119894(119905) (25)

119909119889

119894(119905 + 1) = 119909

119889

119894(119905) + V119889

119894(119905 + 1) (26)

where V119889119894(119905 + 1) is the updated velocity of the particle at 119894th

position V119889119894(119905) is the previous velocity 119886119889

119894(119905) is the acceleration

of the particle at 119894 and 119909119889119894is the position of the particle

(iii) Basic Concepts of Hybrid PSOGSA [36 37] Two algo-rithms can be hybridized in high level or low level withrelay or coevolutionarymethod as homogeneous or heteroge-neous In this paper low level coevolutionary heterogeneoushybrid method is used The hybrid is low level because ofthe combination of the functionality of both algorithms Itis coevolutionary because this algorithm does not use both

The Scientific World Journal 7

algorithms one after another In other words they run inparallel It is heterogeneous because there are two differentalgorithms that are involved to produce final results Themain idea is to integrate the ability of exploitation in PSOwith the ability of exploration in GSA to synthesize bothalgorithmsrsquo strength

The main objective is to combine the social thinkingability of PSO (119892119887119890119904119905) with the local search capability ofGSA hence achieving a new formula for hybrid PSOGSA forvelocity updating as

V119894 (119905 + 1) = 119908V

119894 (119905) + 1198881015840

1rand ac

119894 (119905)

+ 1198881015840

2rand (119892119887119890119904119905 minus 119909

119894 (119905))

(27)

where V119894(119905) is the velocity of agent 119894 at iteration 119905 1198881015840

119895is a

weighting factor119908 is a weighting function rand is a randomnumber between 0 and 1 ac

119894(119905) is the acceleration of agent 119894

at iteration 119905 and 119892119887119890119904119905 is the best solution so far Positionupdate is done by the following formula

119909119894 (119905 + 1) = 119909

119894 (119905) + V119894 (119905 + 1) (28)

42 Scenario 2 DG Supplying Various 119875119876 Capacities Thispaper deals with solar power and grid tied solar inverters inwhich both real power and reactive power of solar energy areconsidered And this scenario deals with 4 types of cases [918 39]

The four different cases as shown below are separatelyanalyzed with respect to LSF and WK method in order tofind out the location of solar power and they are also analyzedwith respect to number of DGs using different optimizationalgorithms to find out the sizing of solar power and the resultsare discussed in the next section

(i) Type 1 system without DG (initial case)(ii) Type 2 DG supplying real power alone (119875)(iii) Type 3 DG supplying reactive power alone (119876)(iv) Type 4 DG supplying both real and reactive power

All the four types are tested with above-mentioned threealgorithms and results are evaluated

5 Results and Discussions

In this section of the paper the entire work is explainedin a precise manner The standard IEEE 30-bus system[8] as in Figure 2 [37] is used as a test system and theresults are evaluated The test system consists of 30 busesof which 6 generating buses are present including slack busand remaining 24 are load buses 41 lines 4 tap changingtransformers and 9 shunt capacitors are present in this testcase MATPOWER open source power system software isused to run NR power flow The initial real power loss of thesystem is 58316MW and initial voltage deviation is 09819

The proposed work is divided into two steps In thefirst step optimal size of DG at each node is found byPSO algorithm assuming that all the 30 buses have solar

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

simsim

sim

sim

sim

sim

Figure 2 Single line diagram of IEEE 30-bus test system

generation And then results obtained through PSO arechecked for reverse power flow by negative load approachto find the possible bus locations for DG placement Thenthe search for optimal location of DGs is fine-tuned by twomethods namely weak (WK) voltage bus placement and LossSensitivity Factor (LSF) method In order to emphasize pointon the effect of increasing the number of solar generatorsthis paper analyses and compares the result with numbers3 4 and 5 of DGs which are placed using priority locationfound fromWK and LSFmethods Further the 119875119876 capacitiesof solar DGs are also analyzed with DG supplying 119875 alone(real power alone) 119876 alone (reactive power alone) and both119875 and 119876 (both real and reactive power) Thus several aspectslike size of DGs number of DGs location of DGs order ofDG placement and 119875119876 capacities of DGs are all discussedunder one roof in this paper

51 Optimal Size of DG Using PSO The PSO algorithm isused to find out the size of DG to be placed on the systemAt each bus solar DG penetration is allowed and size ofsolar power is taken as a control variable The size is limitedbetween 2834MW and 11336MWwhich is 1 to 5 of totalload

Table 1 illustrates the optimal size of the solar DG at eachbus to be included satisfying the multiobjective function ofminimizing the real power losses reactive power losses andvoltage deviations

Then the size of the DG is tested for negative loadapproach That is NR power flow is run after placing DG ateach bus In the literature listed in [40] Kansal et alrsquos workis limited to reverse power flow and they have not includednegative load approach But this paper provides best resultsand does not have reverse power flow

The buses that withstand negative load effect are 2 4 5 78 12 15 19 21 24 and 30 Among these buses the best locationand the best combination for DG placement are selected as inTables 1 and 2

8 The Scientific World Journal

Table 1 Size of DGs from PSO

Bus number Optimal size (MW)1 49847682 43584673 5217384 64699435 54963056 82662427 10215498 71671979 750025610 678641411 377825412 756259313 105648114 704602615 600733616 742512517 109480418 691723119 461379820 755944721 103330822 512553223 396942324 629310125 64554426 416699227 563055328 745293629 548090130 7336586

52 Priority List Creation The priority list is created to orderthe DG placement (shown in Table 2) It is done by twomethods (1) Loss Sensitivity Factor (LSF) method and (2)weak (WK) bus placement method

(1) The LSF method of placement is used because ofthe sensitivity towards loss Since this paper aimsfor loss minimization with voltage enhancement LSFmethod of finding DG location is most appropriateThe buses are placed in decreasing order of losssensitivity that is the highly sensitive bus is first givenwith DG sitingThus the order of buses is obtained asin Table 2

(2) The weak (WK) bus voltage method is the simplestand easy method for bus placement The buses withweak voltage profile are provided with the DG Thisis in order to improve the voltage profile of thesystem and to obtain minimum voltage deviationsThe ordering is done as in Table 2

The bus number in which solar DG can be fit ishighlighted that is the DGs which sustain negative load

Table 2 Sequence of priority list by WK and LSF method

Bus number LSF Priority list Bus voltage (119881) Priority list1 605119890 minus 14 30 1050 302 107e minus 01 29 1040 273 235119890 minus 01 27 1028 264 244e minus 01 26 1022 255 247e minus 01 25 1010 216 263119890 minus 01 24 1017 247 283e minus 01 23 1006 208 230e minus 01 28 1010 229 449119890 minus 01 19 0976 1710 502119890 minus 01 16 0955 1411 429119890 minus 01 20 1050 2912 476e minus 01 18 0998 1913 412119890 minus 01 21 1050 2814 561119890 minus 01 13 0997 1815 575e minus 01 12 0968 1516 519119890 minus 01 17 0972 1617 526119890 minus 01 15 0954 1318 595119890 minus 01 7 0950 1219 588e minus 01 8 0943 920 576119890 minus 01 11 0945 1021 576e minus 01 10 0941 722 577119890 minus 01 9 0941 823 623119890 minus 01 4 0947 1124 614e minus 01 5 0927 625 590119890 minus 01 6 0920 426 664119890 minus 01 3 0901 227 556119890 minus 01 14 0926 528 304119890 minus 01 22 1012 2329 695119890 minus 01 2 0903 330 738e minus 01 1 0891 1

approachTherefore those buses are provided with DG in theorder of LSF or WK method Now the LSF method bringsout the priority order as 30 24 19 21 15 12 7 5 4 8 and 2whereas by WKmethod the priority order is 30 24 21 19 1512 7 5 8 4 and 2

53 Location and Sizing After finding out the order andlocation of buses the number of DGs and type of DG are tobe found The cases may be 3 DGs 4 DGs and 5 DGs basedon number of placements Here the size of DG is same andis subjected to the same limits And type of DGs conceptis provided in this paper to answer the following questionldquoWhat purpose is DG placement meant forrdquo According tothis concept four cases are dealt with ldquono DG 119875 alone 119876alone and both 119875 and119876 combinedrdquo All these cases are testedwith both LSF and WK bus ordering

531 Control Variables and Its Ranges Consider the follow-ing

(1) Real power supplied by DG 119875SG 2834 to 11336MW

The Scientific World Journal 9

Table 3 Best outcome among all cases

5 DGsrsquo LSFAlgorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

FIT 30851 4260241 4165504 2309231 4260241 2358722 1696819 4295037 1920378119875119871(MW) 3930302 5447339 5004235 2973936 5447339 3022499 2149479 5496311 2407849

119876119871(MVAR) 1706019 2353672 2141433 1268353 2353672 1300848 9444453 2370543 1057728

VD 0006318 511119864 minus 10 0495616 163119864 minus 10 511119864 minus 10 615119864 minus 11 102E minus 04 0001427 0036186119875 solar (MW) 3501449 mdash 3457517 3523621 mdash 3572001 4664924 mdash 4169984 119875 solar 1235515 mdash 1220013 1243338 mdash 126041 1646056 mdash 1471413119876 solar (MVAR) mdash 1541866 1355552 mdash 1541866 1543187 mdash 1540234 1453383 119876 solar mdash 1221764 107413 mdash 1221764 1222811 mdash 122047 1151651

(2) Reactive power supplied by DG 119876SG 1262 to631MVAR

(3) Voltage magnitude of PV buses 09 to 11 pu(4) Tap settings 09 to 11 pu(5) Shunt capacitors 0 to 10 MVAR

Allowed total solar power generation is from 5 to 25of the total load of the system For an IEEE 30-bus system thetotal real power demand is 2834MW and reactive power is1262MWTherefore eachDG is subjected to 1 to 5 of totaldemand andmaximum of 5 DGs are considered to satisfy 5to 25 of total demand

Four types of system cases are evaluated

(1) No DG In this case there is no renewable penetrationand NR load flow is run with the basic case withconventional generators Here the values of fitnessfunction are evaluated for the base case using GSAPSO and hybrid PSOGSA algorithms And the resultsare tabulated in Table 4

(2) Three DGs placed In this case totally three DGs areplaced in the system By LSF method DG locationis prioritized at bus numbers 30 24 and 19 By WKmethod it is found to be at 30 24 and 21 This bringsout reduced loss results than no DG case

(3) Four DGs placed In this case 4 DGs are placed atlocation of bus numbers 30 24 19 and 21 under LSFand 30 24 21 and 19 under WK This case exhibitsmore reduced losses than previous case

(4) Five DGs placed (as shown in Figure 3) Under LSFDG location is found to be at bus numbers 30 24 1921 and 15 and under WK it is at 30 24 21 19 and 15Five-DG case brings out the most wondering resultsas in Table 3

It is to be noted that there is only slight variation in theorder of placement by both methods but the results obtainedhave shown ridiculous performance The DG supplyingvarious 119875119876 capacities concept is discussed next

(1) 119875 aloneThis system consists of multiple DGs supply-ing real power alone located under LSF and WK busmethods The system with multiple DGs supplying 119875

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

sim

simsim

simsim

sim

Figure 3 Test system after addition of DGs at 5 locations

alone is optimized using 3 algorithms and the resultsare tabulated in Table 4

(2) 119876 aloneThis scheme consists ofmultipleDGs supply-ing reactive power alone located under LSF and WKbus method is solved under all three algorithmsThatis the DG is placed as a reactive power compensatingdevice This is a worst case with high power lossincurred compared to other cases Also it is noteconomical to include DG just for providing reactivepower alone So this case will be the worst case amongall and the results are tabulated in the Appendix

(3) Both 119875 and 119876 Under this scheme multiple DGs areplaced satisfying LSF and WK methods meant forproviding both real and reactive power This case isanalyzed with 3 4 and 5 DGs using 3 algorithmsThis provides a trustworthy way of DG installationHere the real and reactive power needs of the systemare compensated at the same time The inverters ofthe solar DG act as reactive power sources Thiscondition suits the main objective of the paper andit aims to compensate both the powers of the system

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

StructuresJournal of

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World Journal 3

PSO

Scenario 1 Scenario 2

Start

Generate the random initial population for DG real power generation assuming solar generation at each bus

Obtain global best solution for optimal size of distributed generation at each bus

Check for negative loading condition at all buses

Select the possible bus locations for DG placement

Create a priority list by two methods (LSF WK)

Loss Sensitivity Factor (LSF) method Weak (WK) bus voltage method

Identification of top ranked DG locations by two methods

3 DGs 4 DGs 5 DGs QP

Optimal sizing of DGs by PSO

GSA and HPSOGSA algorithms

Evaluate results

Stop

Both P and Q

Evaluate fitness of each particle using fit = (wP PL) + (wQ QL)+(wV VD)lowast lowast lowast

Figure 1 Flowchart showing the proposed method

value and then the real and reactive power losses respectivelyThe individual variables 119875

119871 119876119871 and VD are as follows

(i) Real Power Loss (119875119871) The total real power losses of the

system are given in

119875119871=

119873119897

sum

119896=1

119866119896(1198812

119894+ 1198812

119895minus 2119881119894119881119895cos (120575

119894minus 120575119895)) (2)

where 119873119897is the total number of transmission lines in the

system 119866119896is the conductance of the line 119896 119881

119894and 119881

119895are the

magnitudes of the sending end and receiving end voltages ofthe line 120575

119894and 120575119895are angles of the end voltages

(ii) Reactive Power Loss (119876119871)The total reactive power loss of

the system is given by

119876119871=

119873119897

sum

119896=1

119861119896(1198812

119894+ 1198812

119895minus 2119881119894119881119895sin (120575

119894minus 120575119895)) (3)

where 119873119897is the total number of transmission lines in the

system 119861119896is the susceptance of the line 119896 119881

119894and 119881

119895are the

magnitudes of the sending end and receiving end voltages ofthe line 120575

119894and 120575119895are angles of the end voltages

(iii) Load Bus Voltage Deviation (VD) Bus voltage magnitudeis maintained within the allowable limit to ensure quality

4 The Scientific World Journal

service As shown in (4) voltage profile is improved byminimizing the deviation of the load bus voltage from thereference value (it is taken as 10 pu)

VD =

119873119875119876

sum

119896=1

1003816100381610038161003816(119881119896 minus 119881ref)1003816100381610038161003816

(4)

where119873119875119876

is the total number of 119875119876 buses in the system

22 Constraints The minimization problem is subjected tothe equality and inequality constraints as follows

(i) Equality Constraints

Load Flow Constraints The real and reactive power flowconstraints are according to (5) and (6) respectively as givenbelow

119875119866119894minus 119875119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895cos 120579119894119895+ 119861119894119895sin 120579119894119895] = 0 (5)

119876119866119894minus 119876119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895sin 120579119894119895+ 119861119894119895cos 120579119894119895] = 0 (6)

where 119899119887is the number of buses 119875

119866and 119876

119866are the real and

reactive power generation of generator119875119863and119876

119863are the real

and reactive load of the generator 119866119894119895and 119861

119894119895are the mutual

conductance and susceptance between bus 119894 and bus 119895 and120579119894119895is the voltage angle difference between bus 119894 and bus 119895

(ii) Inequality Constraints

Line Limits Constraints The line thermal flow limits aresubjected to

119878119897le 119878

max119897

forall119897 = 1 2 3 119871 (7)

where 119878119897is the thermal limit of each line and 119871 is the number

of lines in the system

Solar Generation Real Power Constraints Consider

119875minGS119899 le 119875GS119899 le 119875

maxGS119899 forall119899 119899 = 1 2 3 119873 (8)

where 119875GS119899 is the real power supplied by DG and 119873 is thenumber of DGs

DG Size Constraint To obtain a reasonable and economicsolution the size of solar generators added at each nodeshould be so small or so high with respect to total load valueAs found in various literatures [20] only 30 of renewableenergy penetration is allowable for effective performance ofthe system And thus the solar generation at each bus is asfollows

1 119871 le 119878 le 5 119871 (9)

119871 is the total load value and 119878 is the solar capacity

Steps to find the optimal size of solar DG at each locationusing PSO are as follows

(1) Assume that all the 30 buses of the system have solarpower generation

(2) The solar DG supplying real power alone is subjectedto the inequality constraints that is 1 to 5 of totalload at each node DGunit ismodelled as negative119875119876load in load flow analysis

(3) The PSO algorithm generates random values forthe size of DGs and the algorithm runs with 50populations and up to 500 iterations

(4) The algorithm gives out the optimal size of DG tobe placed at each node That also achieves the globaloptimal fitness values

(5) These values are taken for further processing of theresults

The solution obtained is carried out to the second part of thework

3 Problem Formulation for OptimalSiting and Sizing

The results obtained from the PSO are checked for negativeload approach [10] since DG is added to load terminalsThisis to ensure that the power flow studies do not end up withreverse power flowThat is when eachDG is added to the loadterminal it supplies the immediate load of that particular busand returns the remaining power demand to the conventionalgenerators In that case the following condition should besatisfied by DG at each bus

119875119863119894minus 119875GS119894 gt 0 forall119894 = 1 2 3 119873 (10)

where 119875119863119894

is the real power demand at bus 119894 119875GS119894 is the realpower generated by solar DG at bus 119894 and 119873 is the numberof solar generators

When a particular bus satisfies the above condition thenit is suitable for DG placement If not the correspondinglocation is not suitable forDGplacementThus the number ofcandidate buses opt for DG placement is reduced providingeasier solution to find the optimal location

In [21] Celli et al have formulated a multiobjective func-tion for optimal sizing and siting with the best compromisebetween various costs The effect of ordering DGs location iswell addressed in [22] which proves that the order in whichthe DGs are placed has a significant impact on the systemlosses

And the negative load approach determines the buses thatare capable of DGs placement and the priority list determinesthe order of buses to which the DGs are placed among thepossible candidate buses

31 Finding the Priority Location of DG The two methodsused for creating the priority list are (i) Loss Sensitivity Factormethod and (ii) weak bus placement method

(i) Loss Sensitivity Factor (LSF) Method The Loss SensitivityFactor method is the best method to find out the order of

The Scientific World Journal 5

buses for DG placement [23 24] Loss sensitivity can besimply defined as ldquothe ratio of change in total loss of thesystem when subjected to a small disturbance to the value ofdisturbance that causes the changerdquo

Among the selected candidate buses the order of DGallocation is found out by LSF which reduces the searchspace for allocation problem [25 26]The following equationdefines the numerical evaluation of LSF

LSF =(losswith DG minus losswithout DG)

size of DG (11)

NR power flow is run for system with DG and systemwithout DG Then the priority list is created and buses areplaced according to the descending order of the LSF valuesThe bus with highest sensitivity is first selected for placingDG The number of DGs to be placed depends on the totalallowable penetration to the system [27 28]

(ii) Weak (WK) Bus Placement Method The weak bus place-ment is a simple but effective method of ordering buses forDG placement According to this method NR power flowis performed for base case of the system and the voltagemagnitude of each bus is noted down

Now the priority list is created in ascending order of thebus voltages and the DG is first placed at the weakest bus ofthe system [29] But the number of buses in which the DG isto be placed is a separate issue which will be discussed later

32 Mathematical Formulation of a Problem forOptimal Sizing

(1) Objective FunctionThe objective function of this problemis to find the optimal settings of reactive power controlvariables which minimize the real power losses reactivepower losses and voltage deviation Hence the objectivefunction is expressed as in

119891 = min (119908119875lowast 119875119871) + (119908

119876lowast 119876119871) + (119908

119881lowast VD) (12)

where the representation of all the variables is already dis-cussed in Section 2

(2) Constraints

(i) Equality Constraints

Load Flow Constraints The real and reactive power con-straints are according to (13) and (14) respectively as givenbelow

119875119866119894minus 119875119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895cos 120579119894119895+ 119861119894119895sin 120579119894119895] = 0 (13)

119876119866119894minus 119876119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895sin 120579119894119895+ 119861119894119895cos 120579119894119895] = 0 (14)

where the representation of all the variables is alreadydiscussed in Section 2

After addition of renewable energy source the aboveequation becomes

119875119866minus 119875119863minus 119875loss + 119875SG = 0

119876119866minus 119876119863minus 119876loss + 119876SG = 0

(15)

where119875119866and119876

119866are the conventional real and reactive power

generation 119875119863and 119876

119863are the total real and reactive power

demand 119875loss and 119876loss are the total system losses and 119875SGand 119876SG are the real and reactive power generation of solarDG

(ii) Inequality Constraints

Generator Bus Voltage (119881119866119894) Inequality Constraint Consider

119881min119866119894

le 119881119866119894le 119881

max119866119894

119894 isin 119899119892 (16)

Load Bus Voltage (119881119871 119894) Inequality Constraint Consider

119881min119871 119894

le 119881119871 119894le 119881

max119871 119894

119894 isin 119899119875119876 (17)

Switchable Reactive Power Compensation (119876119862119894) Inequality

Constraint Consider

119876min119862119894

le 119876119862119894le 119876

max119862119894

119894 isin 119899119888 (18)

Reactive Power Generation (119876119866119894) Inequality Constraint Con-

sider

119876min119866119894

le 119876119866119894le 119876

max119866119894

119894 isin 119899119892 (19)

Transformer Tap Setting (119879119894) Inequality Constraint Consider

119879min119894

le 119879119894le 119879

max119894

119894 isin 119899119905 (20)

where 119899119892 119899119875119876 119899119888 and 119899

119905are the numbers of generator

buses load buses switchable reactive power sources and tapsettings

Solar Generators Real Power Constraints Consider

119875minGS119899 le 119875GS119899 le 119875

maxGS119899 forall119899 119899 = 1 2 3 119873 (21)

where 119875GS119899 is the real power supplied by DG and 119873 is thenumber of DGs

Solar Generators Reactive Power Constraints Consider

QminGS119899 le QGS119899 le Qmax

GS119899 forall119899 119899 = 1 2 3 119873 (22)

where119876GS119899 is the reactive power supplied byDG and119873 is thenumber of DGs

6 The Scientific World Journal

4 Proposed Work

Integrating the renewable source to grid or the conversionof the traditional grid into smart grid involves severalessential steps The placement of renewable sources theirsize and amount of penetration into the system are all tobe considered The smart grid possesses five most significantcharacteristics like being adaptive predictive integratedinteractive optimized and secured [30] In this paper the gridachieves all the five perspectives by integrating solar energyplacement of solar energy and performing reactive poweroptimization to maintain voltage profile and thus havingsecured and reliable power system

Solar DG is found to be the best DG that adapts thegrid among several renewable sources And also this researchincludes the grid interactive solar inverters and their behaviorwhen operated in a grid [31] The grid tied solar inverters actas a reactive power source to balance the reactive power ofthe system [7] In the [32] optimal siting and sizing of DGsare found by combined GAPSO algorithms

The proposed work consists of two scenarios

Scenario 1 number of DG placementsScenario 2 DG supplying various 119875119876 capacities

41 Scenario 1 Number of DG Placements This scenariodiscusses the number of DGs to be placed on the system andeffect of increased number of DGs on the system [18]

The number of DGs to be placed depends on the loaddemand of the system and maximum allowable size of DGThe maximum allowable size of DG is up to 25 to 30 ofthe total load as found in various literatures [18 20] In thispaper the maximum numbers of DGs are taken as 5 So it isproposed to select 5 numbers of buses among 30-bus systemThen for each bus it is advisable to take maximum of 5 loadin order to satisfy the total load of 25 amongst 5 buses Alsominimum numbers of DGs are chosen as 3 In this aspectthis paper does not include constant solar DG penetration of25 That means total 25 of load is not divided amongst3 buses Instead in each bus solar DG size of 1 to 5 loadis maintained independent of number of DGs So this workattains the fact that minimum of 15 load is satisfied byincluding 3 numbers of DGs and maximum of 25 load ismet by including 5 numbers of DGs

Therefore the number of DGs is selected as 3 4 and 5respectively And they are placed on the top priority orderedby LSF and weak bus method Now three nature inspiredmetaheuristics algorithms PSO [33] GSA [34 35] and hybridPSOGSA [36 37] are used to optimize the solar sizing In [38]economic dispatch problem in a microgrid is addressed withcost minimization

(i) Basic Concepts of PSO [33] PSO has been developedthrough simulation of simplified social models The featuresof the method are as follows

(i) Based on swarms like fish schooling and a flock ofbirds

(ii) Simple and less time consuming

(iii) Solving nonlinear optimization problems with con-tinuous variables

The convergence is provided by the acceleration term in(23)

The modified velocity of each agent can be calculatedusing the current velocity and the distances from 119901119887119890119904119905 and119892119887119890119904119905 are as shown below

V119896+1119894

= 119908V119896119894+ 1198881rand (119901119887119890119904119905

119894minus 119904119896

119894)

+ 1198882rand (119892119887119890119904119905 minus 119904

119896

119894)

(23)

where V119896119894is the velocity of agent 119894 at 119896th iteration V119896+1

119894is

the modified velocity of agent rand represent functions thatgenerate independent random numbers which are uniformlydistributed between 0 and 1 119904119896

119894is the current position of agent

at 119896th iteration119901119887119890119904119905119894is the119901119887119890119904119905 of agent 119894119892119887119890119904119905 is the119892119887119890119904119905

of the group119908 is the inertia weight factor used to control theimpact of the previous history of velocities V119896

119894on the current

velocity V119896+1119894

and 1198881and 1198882are the acceleration factors named

cognitive and social parameters determine the influence of119901119887119890119904119905119894and 119892119887119890119904119905 in determining the new solutions

And the updated position can be calculated from thefollowing equation

119904119896+1

119894= 119904119896

119894+ V119896+1119894

119894 = 1 2 3 119873119905119898 (24)

where 119873119905119898

is the number of swarms and 119896 represents theiteration

(ii) Basic Concepts of GSA [34 35] GSA is a novel heuristicoptimization method which has been proposed by Rashediet al in 2009 [35] The basic physical theory from whichGSA is inspired is from Newtonrsquos theory The GSA couldbe considered as an isolated system of masses It is like asmall artificial world of masses obeying the Newtonian lawsof gravitation and motion

The convergence is reached indirectly by the accelerationterm in (25)The acceleration of anymass is equal to the forceacted on the system divided by mass of inertia

The velocity and position of the agents for next (119905 + 1)

iteration are calculated using the following equations

V119889119894(119905 + 1) = rand

119894V119889119894(119905) + 119886

119889

119894(119905) (25)

119909119889

119894(119905 + 1) = 119909

119889

119894(119905) + V119889

119894(119905 + 1) (26)

where V119889119894(119905 + 1) is the updated velocity of the particle at 119894th

position V119889119894(119905) is the previous velocity 119886119889

119894(119905) is the acceleration

of the particle at 119894 and 119909119889119894is the position of the particle

(iii) Basic Concepts of Hybrid PSOGSA [36 37] Two algo-rithms can be hybridized in high level or low level withrelay or coevolutionarymethod as homogeneous or heteroge-neous In this paper low level coevolutionary heterogeneoushybrid method is used The hybrid is low level because ofthe combination of the functionality of both algorithms Itis coevolutionary because this algorithm does not use both

The Scientific World Journal 7

algorithms one after another In other words they run inparallel It is heterogeneous because there are two differentalgorithms that are involved to produce final results Themain idea is to integrate the ability of exploitation in PSOwith the ability of exploration in GSA to synthesize bothalgorithmsrsquo strength

The main objective is to combine the social thinkingability of PSO (119892119887119890119904119905) with the local search capability ofGSA hence achieving a new formula for hybrid PSOGSA forvelocity updating as

V119894 (119905 + 1) = 119908V

119894 (119905) + 1198881015840

1rand ac

119894 (119905)

+ 1198881015840

2rand (119892119887119890119904119905 minus 119909

119894 (119905))

(27)

where V119894(119905) is the velocity of agent 119894 at iteration 119905 1198881015840

119895is a

weighting factor119908 is a weighting function rand is a randomnumber between 0 and 1 ac

119894(119905) is the acceleration of agent 119894

at iteration 119905 and 119892119887119890119904119905 is the best solution so far Positionupdate is done by the following formula

119909119894 (119905 + 1) = 119909

119894 (119905) + V119894 (119905 + 1) (28)

42 Scenario 2 DG Supplying Various 119875119876 Capacities Thispaper deals with solar power and grid tied solar inverters inwhich both real power and reactive power of solar energy areconsidered And this scenario deals with 4 types of cases [918 39]

The four different cases as shown below are separatelyanalyzed with respect to LSF and WK method in order tofind out the location of solar power and they are also analyzedwith respect to number of DGs using different optimizationalgorithms to find out the sizing of solar power and the resultsare discussed in the next section

(i) Type 1 system without DG (initial case)(ii) Type 2 DG supplying real power alone (119875)(iii) Type 3 DG supplying reactive power alone (119876)(iv) Type 4 DG supplying both real and reactive power

All the four types are tested with above-mentioned threealgorithms and results are evaluated

5 Results and Discussions

In this section of the paper the entire work is explainedin a precise manner The standard IEEE 30-bus system[8] as in Figure 2 [37] is used as a test system and theresults are evaluated The test system consists of 30 busesof which 6 generating buses are present including slack busand remaining 24 are load buses 41 lines 4 tap changingtransformers and 9 shunt capacitors are present in this testcase MATPOWER open source power system software isused to run NR power flow The initial real power loss of thesystem is 58316MW and initial voltage deviation is 09819

The proposed work is divided into two steps In thefirst step optimal size of DG at each node is found byPSO algorithm assuming that all the 30 buses have solar

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

simsim

sim

sim

sim

sim

Figure 2 Single line diagram of IEEE 30-bus test system

generation And then results obtained through PSO arechecked for reverse power flow by negative load approachto find the possible bus locations for DG placement Thenthe search for optimal location of DGs is fine-tuned by twomethods namely weak (WK) voltage bus placement and LossSensitivity Factor (LSF) method In order to emphasize pointon the effect of increasing the number of solar generatorsthis paper analyses and compares the result with numbers3 4 and 5 of DGs which are placed using priority locationfound fromWK and LSFmethods Further the 119875119876 capacitiesof solar DGs are also analyzed with DG supplying 119875 alone(real power alone) 119876 alone (reactive power alone) and both119875 and 119876 (both real and reactive power) Thus several aspectslike size of DGs number of DGs location of DGs order ofDG placement and 119875119876 capacities of DGs are all discussedunder one roof in this paper

51 Optimal Size of DG Using PSO The PSO algorithm isused to find out the size of DG to be placed on the systemAt each bus solar DG penetration is allowed and size ofsolar power is taken as a control variable The size is limitedbetween 2834MW and 11336MWwhich is 1 to 5 of totalload

Table 1 illustrates the optimal size of the solar DG at eachbus to be included satisfying the multiobjective function ofminimizing the real power losses reactive power losses andvoltage deviations

Then the size of the DG is tested for negative loadapproach That is NR power flow is run after placing DG ateach bus In the literature listed in [40] Kansal et alrsquos workis limited to reverse power flow and they have not includednegative load approach But this paper provides best resultsand does not have reverse power flow

The buses that withstand negative load effect are 2 4 5 78 12 15 19 21 24 and 30 Among these buses the best locationand the best combination for DG placement are selected as inTables 1 and 2

8 The Scientific World Journal

Table 1 Size of DGs from PSO

Bus number Optimal size (MW)1 49847682 43584673 5217384 64699435 54963056 82662427 10215498 71671979 750025610 678641411 377825412 756259313 105648114 704602615 600733616 742512517 109480418 691723119 461379820 755944721 103330822 512553223 396942324 629310125 64554426 416699227 563055328 745293629 548090130 7336586

52 Priority List Creation The priority list is created to orderthe DG placement (shown in Table 2) It is done by twomethods (1) Loss Sensitivity Factor (LSF) method and (2)weak (WK) bus placement method

(1) The LSF method of placement is used because ofthe sensitivity towards loss Since this paper aimsfor loss minimization with voltage enhancement LSFmethod of finding DG location is most appropriateThe buses are placed in decreasing order of losssensitivity that is the highly sensitive bus is first givenwith DG sitingThus the order of buses is obtained asin Table 2

(2) The weak (WK) bus voltage method is the simplestand easy method for bus placement The buses withweak voltage profile are provided with the DG Thisis in order to improve the voltage profile of thesystem and to obtain minimum voltage deviationsThe ordering is done as in Table 2

The bus number in which solar DG can be fit ishighlighted that is the DGs which sustain negative load

Table 2 Sequence of priority list by WK and LSF method

Bus number LSF Priority list Bus voltage (119881) Priority list1 605119890 minus 14 30 1050 302 107e minus 01 29 1040 273 235119890 minus 01 27 1028 264 244e minus 01 26 1022 255 247e minus 01 25 1010 216 263119890 minus 01 24 1017 247 283e minus 01 23 1006 208 230e minus 01 28 1010 229 449119890 minus 01 19 0976 1710 502119890 minus 01 16 0955 1411 429119890 minus 01 20 1050 2912 476e minus 01 18 0998 1913 412119890 minus 01 21 1050 2814 561119890 minus 01 13 0997 1815 575e minus 01 12 0968 1516 519119890 minus 01 17 0972 1617 526119890 minus 01 15 0954 1318 595119890 minus 01 7 0950 1219 588e minus 01 8 0943 920 576119890 minus 01 11 0945 1021 576e minus 01 10 0941 722 577119890 minus 01 9 0941 823 623119890 minus 01 4 0947 1124 614e minus 01 5 0927 625 590119890 minus 01 6 0920 426 664119890 minus 01 3 0901 227 556119890 minus 01 14 0926 528 304119890 minus 01 22 1012 2329 695119890 minus 01 2 0903 330 738e minus 01 1 0891 1

approachTherefore those buses are provided with DG in theorder of LSF or WK method Now the LSF method bringsout the priority order as 30 24 19 21 15 12 7 5 4 8 and 2whereas by WKmethod the priority order is 30 24 21 19 1512 7 5 8 4 and 2

53 Location and Sizing After finding out the order andlocation of buses the number of DGs and type of DG are tobe found The cases may be 3 DGs 4 DGs and 5 DGs basedon number of placements Here the size of DG is same andis subjected to the same limits And type of DGs conceptis provided in this paper to answer the following questionldquoWhat purpose is DG placement meant forrdquo According tothis concept four cases are dealt with ldquono DG 119875 alone 119876alone and both 119875 and119876 combinedrdquo All these cases are testedwith both LSF and WK bus ordering

531 Control Variables and Its Ranges Consider the follow-ing

(1) Real power supplied by DG 119875SG 2834 to 11336MW

The Scientific World Journal 9

Table 3 Best outcome among all cases

5 DGsrsquo LSFAlgorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

FIT 30851 4260241 4165504 2309231 4260241 2358722 1696819 4295037 1920378119875119871(MW) 3930302 5447339 5004235 2973936 5447339 3022499 2149479 5496311 2407849

119876119871(MVAR) 1706019 2353672 2141433 1268353 2353672 1300848 9444453 2370543 1057728

VD 0006318 511119864 minus 10 0495616 163119864 minus 10 511119864 minus 10 615119864 minus 11 102E minus 04 0001427 0036186119875 solar (MW) 3501449 mdash 3457517 3523621 mdash 3572001 4664924 mdash 4169984 119875 solar 1235515 mdash 1220013 1243338 mdash 126041 1646056 mdash 1471413119876 solar (MVAR) mdash 1541866 1355552 mdash 1541866 1543187 mdash 1540234 1453383 119876 solar mdash 1221764 107413 mdash 1221764 1222811 mdash 122047 1151651

(2) Reactive power supplied by DG 119876SG 1262 to631MVAR

(3) Voltage magnitude of PV buses 09 to 11 pu(4) Tap settings 09 to 11 pu(5) Shunt capacitors 0 to 10 MVAR

Allowed total solar power generation is from 5 to 25of the total load of the system For an IEEE 30-bus system thetotal real power demand is 2834MW and reactive power is1262MWTherefore eachDG is subjected to 1 to 5 of totaldemand andmaximum of 5 DGs are considered to satisfy 5to 25 of total demand

Four types of system cases are evaluated

(1) No DG In this case there is no renewable penetrationand NR load flow is run with the basic case withconventional generators Here the values of fitnessfunction are evaluated for the base case using GSAPSO and hybrid PSOGSA algorithms And the resultsare tabulated in Table 4

(2) Three DGs placed In this case totally three DGs areplaced in the system By LSF method DG locationis prioritized at bus numbers 30 24 and 19 By WKmethod it is found to be at 30 24 and 21 This bringsout reduced loss results than no DG case

(3) Four DGs placed In this case 4 DGs are placed atlocation of bus numbers 30 24 19 and 21 under LSFand 30 24 21 and 19 under WK This case exhibitsmore reduced losses than previous case

(4) Five DGs placed (as shown in Figure 3) Under LSFDG location is found to be at bus numbers 30 24 1921 and 15 and under WK it is at 30 24 21 19 and 15Five-DG case brings out the most wondering resultsas in Table 3

It is to be noted that there is only slight variation in theorder of placement by both methods but the results obtainedhave shown ridiculous performance The DG supplyingvarious 119875119876 capacities concept is discussed next

(1) 119875 aloneThis system consists of multiple DGs supply-ing real power alone located under LSF and WK busmethods The system with multiple DGs supplying 119875

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

sim

simsim

simsim

sim

Figure 3 Test system after addition of DGs at 5 locations

alone is optimized using 3 algorithms and the resultsare tabulated in Table 4

(2) 119876 aloneThis scheme consists ofmultipleDGs supply-ing reactive power alone located under LSF and WKbus method is solved under all three algorithmsThatis the DG is placed as a reactive power compensatingdevice This is a worst case with high power lossincurred compared to other cases Also it is noteconomical to include DG just for providing reactivepower alone So this case will be the worst case amongall and the results are tabulated in the Appendix

(3) Both 119875 and 119876 Under this scheme multiple DGs areplaced satisfying LSF and WK methods meant forproviding both real and reactive power This case isanalyzed with 3 4 and 5 DGs using 3 algorithmsThis provides a trustworthy way of DG installationHere the real and reactive power needs of the systemare compensated at the same time The inverters ofthe solar DG act as reactive power sources Thiscondition suits the main objective of the paper andit aims to compensate both the powers of the system

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

4 The Scientific World Journal

service As shown in (4) voltage profile is improved byminimizing the deviation of the load bus voltage from thereference value (it is taken as 10 pu)

VD =

119873119875119876

sum

119896=1

1003816100381610038161003816(119881119896 minus 119881ref)1003816100381610038161003816

(4)

where119873119875119876

is the total number of 119875119876 buses in the system

22 Constraints The minimization problem is subjected tothe equality and inequality constraints as follows

(i) Equality Constraints

Load Flow Constraints The real and reactive power flowconstraints are according to (5) and (6) respectively as givenbelow

119875119866119894minus 119875119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895cos 120579119894119895+ 119861119894119895sin 120579119894119895] = 0 (5)

119876119866119894minus 119876119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895sin 120579119894119895+ 119861119894119895cos 120579119894119895] = 0 (6)

where 119899119887is the number of buses 119875

119866and 119876

119866are the real and

reactive power generation of generator119875119863and119876

119863are the real

and reactive load of the generator 119866119894119895and 119861

119894119895are the mutual

conductance and susceptance between bus 119894 and bus 119895 and120579119894119895is the voltage angle difference between bus 119894 and bus 119895

(ii) Inequality Constraints

Line Limits Constraints The line thermal flow limits aresubjected to

119878119897le 119878

max119897

forall119897 = 1 2 3 119871 (7)

where 119878119897is the thermal limit of each line and 119871 is the number

of lines in the system

Solar Generation Real Power Constraints Consider

119875minGS119899 le 119875GS119899 le 119875

maxGS119899 forall119899 119899 = 1 2 3 119873 (8)

where 119875GS119899 is the real power supplied by DG and 119873 is thenumber of DGs

DG Size Constraint To obtain a reasonable and economicsolution the size of solar generators added at each nodeshould be so small or so high with respect to total load valueAs found in various literatures [20] only 30 of renewableenergy penetration is allowable for effective performance ofthe system And thus the solar generation at each bus is asfollows

1 119871 le 119878 le 5 119871 (9)

119871 is the total load value and 119878 is the solar capacity

Steps to find the optimal size of solar DG at each locationusing PSO are as follows

(1) Assume that all the 30 buses of the system have solarpower generation

(2) The solar DG supplying real power alone is subjectedto the inequality constraints that is 1 to 5 of totalload at each node DGunit ismodelled as negative119875119876load in load flow analysis

(3) The PSO algorithm generates random values forthe size of DGs and the algorithm runs with 50populations and up to 500 iterations

(4) The algorithm gives out the optimal size of DG tobe placed at each node That also achieves the globaloptimal fitness values

(5) These values are taken for further processing of theresults

The solution obtained is carried out to the second part of thework

3 Problem Formulation for OptimalSiting and Sizing

The results obtained from the PSO are checked for negativeload approach [10] since DG is added to load terminalsThisis to ensure that the power flow studies do not end up withreverse power flowThat is when eachDG is added to the loadterminal it supplies the immediate load of that particular busand returns the remaining power demand to the conventionalgenerators In that case the following condition should besatisfied by DG at each bus

119875119863119894minus 119875GS119894 gt 0 forall119894 = 1 2 3 119873 (10)

where 119875119863119894

is the real power demand at bus 119894 119875GS119894 is the realpower generated by solar DG at bus 119894 and 119873 is the numberof solar generators

When a particular bus satisfies the above condition thenit is suitable for DG placement If not the correspondinglocation is not suitable forDGplacementThus the number ofcandidate buses opt for DG placement is reduced providingeasier solution to find the optimal location

In [21] Celli et al have formulated a multiobjective func-tion for optimal sizing and siting with the best compromisebetween various costs The effect of ordering DGs location iswell addressed in [22] which proves that the order in whichthe DGs are placed has a significant impact on the systemlosses

And the negative load approach determines the buses thatare capable of DGs placement and the priority list determinesthe order of buses to which the DGs are placed among thepossible candidate buses

31 Finding the Priority Location of DG The two methodsused for creating the priority list are (i) Loss Sensitivity Factormethod and (ii) weak bus placement method

(i) Loss Sensitivity Factor (LSF) Method The Loss SensitivityFactor method is the best method to find out the order of

The Scientific World Journal 5

buses for DG placement [23 24] Loss sensitivity can besimply defined as ldquothe ratio of change in total loss of thesystem when subjected to a small disturbance to the value ofdisturbance that causes the changerdquo

Among the selected candidate buses the order of DGallocation is found out by LSF which reduces the searchspace for allocation problem [25 26]The following equationdefines the numerical evaluation of LSF

LSF =(losswith DG minus losswithout DG)

size of DG (11)

NR power flow is run for system with DG and systemwithout DG Then the priority list is created and buses areplaced according to the descending order of the LSF valuesThe bus with highest sensitivity is first selected for placingDG The number of DGs to be placed depends on the totalallowable penetration to the system [27 28]

(ii) Weak (WK) Bus Placement Method The weak bus place-ment is a simple but effective method of ordering buses forDG placement According to this method NR power flowis performed for base case of the system and the voltagemagnitude of each bus is noted down

Now the priority list is created in ascending order of thebus voltages and the DG is first placed at the weakest bus ofthe system [29] But the number of buses in which the DG isto be placed is a separate issue which will be discussed later

32 Mathematical Formulation of a Problem forOptimal Sizing

(1) Objective FunctionThe objective function of this problemis to find the optimal settings of reactive power controlvariables which minimize the real power losses reactivepower losses and voltage deviation Hence the objectivefunction is expressed as in

119891 = min (119908119875lowast 119875119871) + (119908

119876lowast 119876119871) + (119908

119881lowast VD) (12)

where the representation of all the variables is already dis-cussed in Section 2

(2) Constraints

(i) Equality Constraints

Load Flow Constraints The real and reactive power con-straints are according to (13) and (14) respectively as givenbelow

119875119866119894minus 119875119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895cos 120579119894119895+ 119861119894119895sin 120579119894119895] = 0 (13)

119876119866119894minus 119876119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895sin 120579119894119895+ 119861119894119895cos 120579119894119895] = 0 (14)

where the representation of all the variables is alreadydiscussed in Section 2

After addition of renewable energy source the aboveequation becomes

119875119866minus 119875119863minus 119875loss + 119875SG = 0

119876119866minus 119876119863minus 119876loss + 119876SG = 0

(15)

where119875119866and119876

119866are the conventional real and reactive power

generation 119875119863and 119876

119863are the total real and reactive power

demand 119875loss and 119876loss are the total system losses and 119875SGand 119876SG are the real and reactive power generation of solarDG

(ii) Inequality Constraints

Generator Bus Voltage (119881119866119894) Inequality Constraint Consider

119881min119866119894

le 119881119866119894le 119881

max119866119894

119894 isin 119899119892 (16)

Load Bus Voltage (119881119871 119894) Inequality Constraint Consider

119881min119871 119894

le 119881119871 119894le 119881

max119871 119894

119894 isin 119899119875119876 (17)

Switchable Reactive Power Compensation (119876119862119894) Inequality

Constraint Consider

119876min119862119894

le 119876119862119894le 119876

max119862119894

119894 isin 119899119888 (18)

Reactive Power Generation (119876119866119894) Inequality Constraint Con-

sider

119876min119866119894

le 119876119866119894le 119876

max119866119894

119894 isin 119899119892 (19)

Transformer Tap Setting (119879119894) Inequality Constraint Consider

119879min119894

le 119879119894le 119879

max119894

119894 isin 119899119905 (20)

where 119899119892 119899119875119876 119899119888 and 119899

119905are the numbers of generator

buses load buses switchable reactive power sources and tapsettings

Solar Generators Real Power Constraints Consider

119875minGS119899 le 119875GS119899 le 119875

maxGS119899 forall119899 119899 = 1 2 3 119873 (21)

where 119875GS119899 is the real power supplied by DG and 119873 is thenumber of DGs

Solar Generators Reactive Power Constraints Consider

QminGS119899 le QGS119899 le Qmax

GS119899 forall119899 119899 = 1 2 3 119873 (22)

where119876GS119899 is the reactive power supplied byDG and119873 is thenumber of DGs

6 The Scientific World Journal

4 Proposed Work

Integrating the renewable source to grid or the conversionof the traditional grid into smart grid involves severalessential steps The placement of renewable sources theirsize and amount of penetration into the system are all tobe considered The smart grid possesses five most significantcharacteristics like being adaptive predictive integratedinteractive optimized and secured [30] In this paper the gridachieves all the five perspectives by integrating solar energyplacement of solar energy and performing reactive poweroptimization to maintain voltage profile and thus havingsecured and reliable power system

Solar DG is found to be the best DG that adapts thegrid among several renewable sources And also this researchincludes the grid interactive solar inverters and their behaviorwhen operated in a grid [31] The grid tied solar inverters actas a reactive power source to balance the reactive power ofthe system [7] In the [32] optimal siting and sizing of DGsare found by combined GAPSO algorithms

The proposed work consists of two scenarios

Scenario 1 number of DG placementsScenario 2 DG supplying various 119875119876 capacities

41 Scenario 1 Number of DG Placements This scenariodiscusses the number of DGs to be placed on the system andeffect of increased number of DGs on the system [18]

The number of DGs to be placed depends on the loaddemand of the system and maximum allowable size of DGThe maximum allowable size of DG is up to 25 to 30 ofthe total load as found in various literatures [18 20] In thispaper the maximum numbers of DGs are taken as 5 So it isproposed to select 5 numbers of buses among 30-bus systemThen for each bus it is advisable to take maximum of 5 loadin order to satisfy the total load of 25 amongst 5 buses Alsominimum numbers of DGs are chosen as 3 In this aspectthis paper does not include constant solar DG penetration of25 That means total 25 of load is not divided amongst3 buses Instead in each bus solar DG size of 1 to 5 loadis maintained independent of number of DGs So this workattains the fact that minimum of 15 load is satisfied byincluding 3 numbers of DGs and maximum of 25 load ismet by including 5 numbers of DGs

Therefore the number of DGs is selected as 3 4 and 5respectively And they are placed on the top priority orderedby LSF and weak bus method Now three nature inspiredmetaheuristics algorithms PSO [33] GSA [34 35] and hybridPSOGSA [36 37] are used to optimize the solar sizing In [38]economic dispatch problem in a microgrid is addressed withcost minimization

(i) Basic Concepts of PSO [33] PSO has been developedthrough simulation of simplified social models The featuresof the method are as follows

(i) Based on swarms like fish schooling and a flock ofbirds

(ii) Simple and less time consuming

(iii) Solving nonlinear optimization problems with con-tinuous variables

The convergence is provided by the acceleration term in(23)

The modified velocity of each agent can be calculatedusing the current velocity and the distances from 119901119887119890119904119905 and119892119887119890119904119905 are as shown below

V119896+1119894

= 119908V119896119894+ 1198881rand (119901119887119890119904119905

119894minus 119904119896

119894)

+ 1198882rand (119892119887119890119904119905 minus 119904

119896

119894)

(23)

where V119896119894is the velocity of agent 119894 at 119896th iteration V119896+1

119894is

the modified velocity of agent rand represent functions thatgenerate independent random numbers which are uniformlydistributed between 0 and 1 119904119896

119894is the current position of agent

at 119896th iteration119901119887119890119904119905119894is the119901119887119890119904119905 of agent 119894119892119887119890119904119905 is the119892119887119890119904119905

of the group119908 is the inertia weight factor used to control theimpact of the previous history of velocities V119896

119894on the current

velocity V119896+1119894

and 1198881and 1198882are the acceleration factors named

cognitive and social parameters determine the influence of119901119887119890119904119905119894and 119892119887119890119904119905 in determining the new solutions

And the updated position can be calculated from thefollowing equation

119904119896+1

119894= 119904119896

119894+ V119896+1119894

119894 = 1 2 3 119873119905119898 (24)

where 119873119905119898

is the number of swarms and 119896 represents theiteration

(ii) Basic Concepts of GSA [34 35] GSA is a novel heuristicoptimization method which has been proposed by Rashediet al in 2009 [35] The basic physical theory from whichGSA is inspired is from Newtonrsquos theory The GSA couldbe considered as an isolated system of masses It is like asmall artificial world of masses obeying the Newtonian lawsof gravitation and motion

The convergence is reached indirectly by the accelerationterm in (25)The acceleration of anymass is equal to the forceacted on the system divided by mass of inertia

The velocity and position of the agents for next (119905 + 1)

iteration are calculated using the following equations

V119889119894(119905 + 1) = rand

119894V119889119894(119905) + 119886

119889

119894(119905) (25)

119909119889

119894(119905 + 1) = 119909

119889

119894(119905) + V119889

119894(119905 + 1) (26)

where V119889119894(119905 + 1) is the updated velocity of the particle at 119894th

position V119889119894(119905) is the previous velocity 119886119889

119894(119905) is the acceleration

of the particle at 119894 and 119909119889119894is the position of the particle

(iii) Basic Concepts of Hybrid PSOGSA [36 37] Two algo-rithms can be hybridized in high level or low level withrelay or coevolutionarymethod as homogeneous or heteroge-neous In this paper low level coevolutionary heterogeneoushybrid method is used The hybrid is low level because ofthe combination of the functionality of both algorithms Itis coevolutionary because this algorithm does not use both

The Scientific World Journal 7

algorithms one after another In other words they run inparallel It is heterogeneous because there are two differentalgorithms that are involved to produce final results Themain idea is to integrate the ability of exploitation in PSOwith the ability of exploration in GSA to synthesize bothalgorithmsrsquo strength

The main objective is to combine the social thinkingability of PSO (119892119887119890119904119905) with the local search capability ofGSA hence achieving a new formula for hybrid PSOGSA forvelocity updating as

V119894 (119905 + 1) = 119908V

119894 (119905) + 1198881015840

1rand ac

119894 (119905)

+ 1198881015840

2rand (119892119887119890119904119905 minus 119909

119894 (119905))

(27)

where V119894(119905) is the velocity of agent 119894 at iteration 119905 1198881015840

119895is a

weighting factor119908 is a weighting function rand is a randomnumber between 0 and 1 ac

119894(119905) is the acceleration of agent 119894

at iteration 119905 and 119892119887119890119904119905 is the best solution so far Positionupdate is done by the following formula

119909119894 (119905 + 1) = 119909

119894 (119905) + V119894 (119905 + 1) (28)

42 Scenario 2 DG Supplying Various 119875119876 Capacities Thispaper deals with solar power and grid tied solar inverters inwhich both real power and reactive power of solar energy areconsidered And this scenario deals with 4 types of cases [918 39]

The four different cases as shown below are separatelyanalyzed with respect to LSF and WK method in order tofind out the location of solar power and they are also analyzedwith respect to number of DGs using different optimizationalgorithms to find out the sizing of solar power and the resultsare discussed in the next section

(i) Type 1 system without DG (initial case)(ii) Type 2 DG supplying real power alone (119875)(iii) Type 3 DG supplying reactive power alone (119876)(iv) Type 4 DG supplying both real and reactive power

All the four types are tested with above-mentioned threealgorithms and results are evaluated

5 Results and Discussions

In this section of the paper the entire work is explainedin a precise manner The standard IEEE 30-bus system[8] as in Figure 2 [37] is used as a test system and theresults are evaluated The test system consists of 30 busesof which 6 generating buses are present including slack busand remaining 24 are load buses 41 lines 4 tap changingtransformers and 9 shunt capacitors are present in this testcase MATPOWER open source power system software isused to run NR power flow The initial real power loss of thesystem is 58316MW and initial voltage deviation is 09819

The proposed work is divided into two steps In thefirst step optimal size of DG at each node is found byPSO algorithm assuming that all the 30 buses have solar

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

simsim

sim

sim

sim

sim

Figure 2 Single line diagram of IEEE 30-bus test system

generation And then results obtained through PSO arechecked for reverse power flow by negative load approachto find the possible bus locations for DG placement Thenthe search for optimal location of DGs is fine-tuned by twomethods namely weak (WK) voltage bus placement and LossSensitivity Factor (LSF) method In order to emphasize pointon the effect of increasing the number of solar generatorsthis paper analyses and compares the result with numbers3 4 and 5 of DGs which are placed using priority locationfound fromWK and LSFmethods Further the 119875119876 capacitiesof solar DGs are also analyzed with DG supplying 119875 alone(real power alone) 119876 alone (reactive power alone) and both119875 and 119876 (both real and reactive power) Thus several aspectslike size of DGs number of DGs location of DGs order ofDG placement and 119875119876 capacities of DGs are all discussedunder one roof in this paper

51 Optimal Size of DG Using PSO The PSO algorithm isused to find out the size of DG to be placed on the systemAt each bus solar DG penetration is allowed and size ofsolar power is taken as a control variable The size is limitedbetween 2834MW and 11336MWwhich is 1 to 5 of totalload

Table 1 illustrates the optimal size of the solar DG at eachbus to be included satisfying the multiobjective function ofminimizing the real power losses reactive power losses andvoltage deviations

Then the size of the DG is tested for negative loadapproach That is NR power flow is run after placing DG ateach bus In the literature listed in [40] Kansal et alrsquos workis limited to reverse power flow and they have not includednegative load approach But this paper provides best resultsand does not have reverse power flow

The buses that withstand negative load effect are 2 4 5 78 12 15 19 21 24 and 30 Among these buses the best locationand the best combination for DG placement are selected as inTables 1 and 2

8 The Scientific World Journal

Table 1 Size of DGs from PSO

Bus number Optimal size (MW)1 49847682 43584673 5217384 64699435 54963056 82662427 10215498 71671979 750025610 678641411 377825412 756259313 105648114 704602615 600733616 742512517 109480418 691723119 461379820 755944721 103330822 512553223 396942324 629310125 64554426 416699227 563055328 745293629 548090130 7336586

52 Priority List Creation The priority list is created to orderthe DG placement (shown in Table 2) It is done by twomethods (1) Loss Sensitivity Factor (LSF) method and (2)weak (WK) bus placement method

(1) The LSF method of placement is used because ofthe sensitivity towards loss Since this paper aimsfor loss minimization with voltage enhancement LSFmethod of finding DG location is most appropriateThe buses are placed in decreasing order of losssensitivity that is the highly sensitive bus is first givenwith DG sitingThus the order of buses is obtained asin Table 2

(2) The weak (WK) bus voltage method is the simplestand easy method for bus placement The buses withweak voltage profile are provided with the DG Thisis in order to improve the voltage profile of thesystem and to obtain minimum voltage deviationsThe ordering is done as in Table 2

The bus number in which solar DG can be fit ishighlighted that is the DGs which sustain negative load

Table 2 Sequence of priority list by WK and LSF method

Bus number LSF Priority list Bus voltage (119881) Priority list1 605119890 minus 14 30 1050 302 107e minus 01 29 1040 273 235119890 minus 01 27 1028 264 244e minus 01 26 1022 255 247e minus 01 25 1010 216 263119890 minus 01 24 1017 247 283e minus 01 23 1006 208 230e minus 01 28 1010 229 449119890 minus 01 19 0976 1710 502119890 minus 01 16 0955 1411 429119890 minus 01 20 1050 2912 476e minus 01 18 0998 1913 412119890 minus 01 21 1050 2814 561119890 minus 01 13 0997 1815 575e minus 01 12 0968 1516 519119890 minus 01 17 0972 1617 526119890 minus 01 15 0954 1318 595119890 minus 01 7 0950 1219 588e minus 01 8 0943 920 576119890 minus 01 11 0945 1021 576e minus 01 10 0941 722 577119890 minus 01 9 0941 823 623119890 minus 01 4 0947 1124 614e minus 01 5 0927 625 590119890 minus 01 6 0920 426 664119890 minus 01 3 0901 227 556119890 minus 01 14 0926 528 304119890 minus 01 22 1012 2329 695119890 minus 01 2 0903 330 738e minus 01 1 0891 1

approachTherefore those buses are provided with DG in theorder of LSF or WK method Now the LSF method bringsout the priority order as 30 24 19 21 15 12 7 5 4 8 and 2whereas by WKmethod the priority order is 30 24 21 19 1512 7 5 8 4 and 2

53 Location and Sizing After finding out the order andlocation of buses the number of DGs and type of DG are tobe found The cases may be 3 DGs 4 DGs and 5 DGs basedon number of placements Here the size of DG is same andis subjected to the same limits And type of DGs conceptis provided in this paper to answer the following questionldquoWhat purpose is DG placement meant forrdquo According tothis concept four cases are dealt with ldquono DG 119875 alone 119876alone and both 119875 and119876 combinedrdquo All these cases are testedwith both LSF and WK bus ordering

531 Control Variables and Its Ranges Consider the follow-ing

(1) Real power supplied by DG 119875SG 2834 to 11336MW

The Scientific World Journal 9

Table 3 Best outcome among all cases

5 DGsrsquo LSFAlgorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

FIT 30851 4260241 4165504 2309231 4260241 2358722 1696819 4295037 1920378119875119871(MW) 3930302 5447339 5004235 2973936 5447339 3022499 2149479 5496311 2407849

119876119871(MVAR) 1706019 2353672 2141433 1268353 2353672 1300848 9444453 2370543 1057728

VD 0006318 511119864 minus 10 0495616 163119864 minus 10 511119864 minus 10 615119864 minus 11 102E minus 04 0001427 0036186119875 solar (MW) 3501449 mdash 3457517 3523621 mdash 3572001 4664924 mdash 4169984 119875 solar 1235515 mdash 1220013 1243338 mdash 126041 1646056 mdash 1471413119876 solar (MVAR) mdash 1541866 1355552 mdash 1541866 1543187 mdash 1540234 1453383 119876 solar mdash 1221764 107413 mdash 1221764 1222811 mdash 122047 1151651

(2) Reactive power supplied by DG 119876SG 1262 to631MVAR

(3) Voltage magnitude of PV buses 09 to 11 pu(4) Tap settings 09 to 11 pu(5) Shunt capacitors 0 to 10 MVAR

Allowed total solar power generation is from 5 to 25of the total load of the system For an IEEE 30-bus system thetotal real power demand is 2834MW and reactive power is1262MWTherefore eachDG is subjected to 1 to 5 of totaldemand andmaximum of 5 DGs are considered to satisfy 5to 25 of total demand

Four types of system cases are evaluated

(1) No DG In this case there is no renewable penetrationand NR load flow is run with the basic case withconventional generators Here the values of fitnessfunction are evaluated for the base case using GSAPSO and hybrid PSOGSA algorithms And the resultsare tabulated in Table 4

(2) Three DGs placed In this case totally three DGs areplaced in the system By LSF method DG locationis prioritized at bus numbers 30 24 and 19 By WKmethod it is found to be at 30 24 and 21 This bringsout reduced loss results than no DG case

(3) Four DGs placed In this case 4 DGs are placed atlocation of bus numbers 30 24 19 and 21 under LSFand 30 24 21 and 19 under WK This case exhibitsmore reduced losses than previous case

(4) Five DGs placed (as shown in Figure 3) Under LSFDG location is found to be at bus numbers 30 24 1921 and 15 and under WK it is at 30 24 21 19 and 15Five-DG case brings out the most wondering resultsas in Table 3

It is to be noted that there is only slight variation in theorder of placement by both methods but the results obtainedhave shown ridiculous performance The DG supplyingvarious 119875119876 capacities concept is discussed next

(1) 119875 aloneThis system consists of multiple DGs supply-ing real power alone located under LSF and WK busmethods The system with multiple DGs supplying 119875

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

sim

simsim

simsim

sim

Figure 3 Test system after addition of DGs at 5 locations

alone is optimized using 3 algorithms and the resultsare tabulated in Table 4

(2) 119876 aloneThis scheme consists ofmultipleDGs supply-ing reactive power alone located under LSF and WKbus method is solved under all three algorithmsThatis the DG is placed as a reactive power compensatingdevice This is a worst case with high power lossincurred compared to other cases Also it is noteconomical to include DG just for providing reactivepower alone So this case will be the worst case amongall and the results are tabulated in the Appendix

(3) Both 119875 and 119876 Under this scheme multiple DGs areplaced satisfying LSF and WK methods meant forproviding both real and reactive power This case isanalyzed with 3 4 and 5 DGs using 3 algorithmsThis provides a trustworthy way of DG installationHere the real and reactive power needs of the systemare compensated at the same time The inverters ofthe solar DG act as reactive power sources Thiscondition suits the main objective of the paper andit aims to compensate both the powers of the system

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

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FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World Journal 5

buses for DG placement [23 24] Loss sensitivity can besimply defined as ldquothe ratio of change in total loss of thesystem when subjected to a small disturbance to the value ofdisturbance that causes the changerdquo

Among the selected candidate buses the order of DGallocation is found out by LSF which reduces the searchspace for allocation problem [25 26]The following equationdefines the numerical evaluation of LSF

LSF =(losswith DG minus losswithout DG)

size of DG (11)

NR power flow is run for system with DG and systemwithout DG Then the priority list is created and buses areplaced according to the descending order of the LSF valuesThe bus with highest sensitivity is first selected for placingDG The number of DGs to be placed depends on the totalallowable penetration to the system [27 28]

(ii) Weak (WK) Bus Placement Method The weak bus place-ment is a simple but effective method of ordering buses forDG placement According to this method NR power flowis performed for base case of the system and the voltagemagnitude of each bus is noted down

Now the priority list is created in ascending order of thebus voltages and the DG is first placed at the weakest bus ofthe system [29] But the number of buses in which the DG isto be placed is a separate issue which will be discussed later

32 Mathematical Formulation of a Problem forOptimal Sizing

(1) Objective FunctionThe objective function of this problemis to find the optimal settings of reactive power controlvariables which minimize the real power losses reactivepower losses and voltage deviation Hence the objectivefunction is expressed as in

119891 = min (119908119875lowast 119875119871) + (119908

119876lowast 119876119871) + (119908

119881lowast VD) (12)

where the representation of all the variables is already dis-cussed in Section 2

(2) Constraints

(i) Equality Constraints

Load Flow Constraints The real and reactive power con-straints are according to (13) and (14) respectively as givenbelow

119875119866119894minus 119875119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895cos 120579119894119895+ 119861119894119895sin 120579119894119895] = 0 (13)

119876119866119894minus 119876119863119894minus 119881119894

119899119887

sum

119895=1

119881119895[119866119894119895sin 120579119894119895+ 119861119894119895cos 120579119894119895] = 0 (14)

where the representation of all the variables is alreadydiscussed in Section 2

After addition of renewable energy source the aboveequation becomes

119875119866minus 119875119863minus 119875loss + 119875SG = 0

119876119866minus 119876119863minus 119876loss + 119876SG = 0

(15)

where119875119866and119876

119866are the conventional real and reactive power

generation 119875119863and 119876

119863are the total real and reactive power

demand 119875loss and 119876loss are the total system losses and 119875SGand 119876SG are the real and reactive power generation of solarDG

(ii) Inequality Constraints

Generator Bus Voltage (119881119866119894) Inequality Constraint Consider

119881min119866119894

le 119881119866119894le 119881

max119866119894

119894 isin 119899119892 (16)

Load Bus Voltage (119881119871 119894) Inequality Constraint Consider

119881min119871 119894

le 119881119871 119894le 119881

max119871 119894

119894 isin 119899119875119876 (17)

Switchable Reactive Power Compensation (119876119862119894) Inequality

Constraint Consider

119876min119862119894

le 119876119862119894le 119876

max119862119894

119894 isin 119899119888 (18)

Reactive Power Generation (119876119866119894) Inequality Constraint Con-

sider

119876min119866119894

le 119876119866119894le 119876

max119866119894

119894 isin 119899119892 (19)

Transformer Tap Setting (119879119894) Inequality Constraint Consider

119879min119894

le 119879119894le 119879

max119894

119894 isin 119899119905 (20)

where 119899119892 119899119875119876 119899119888 and 119899

119905are the numbers of generator

buses load buses switchable reactive power sources and tapsettings

Solar Generators Real Power Constraints Consider

119875minGS119899 le 119875GS119899 le 119875

maxGS119899 forall119899 119899 = 1 2 3 119873 (21)

where 119875GS119899 is the real power supplied by DG and 119873 is thenumber of DGs

Solar Generators Reactive Power Constraints Consider

QminGS119899 le QGS119899 le Qmax

GS119899 forall119899 119899 = 1 2 3 119873 (22)

where119876GS119899 is the reactive power supplied byDG and119873 is thenumber of DGs

6 The Scientific World Journal

4 Proposed Work

Integrating the renewable source to grid or the conversionof the traditional grid into smart grid involves severalessential steps The placement of renewable sources theirsize and amount of penetration into the system are all tobe considered The smart grid possesses five most significantcharacteristics like being adaptive predictive integratedinteractive optimized and secured [30] In this paper the gridachieves all the five perspectives by integrating solar energyplacement of solar energy and performing reactive poweroptimization to maintain voltage profile and thus havingsecured and reliable power system

Solar DG is found to be the best DG that adapts thegrid among several renewable sources And also this researchincludes the grid interactive solar inverters and their behaviorwhen operated in a grid [31] The grid tied solar inverters actas a reactive power source to balance the reactive power ofthe system [7] In the [32] optimal siting and sizing of DGsare found by combined GAPSO algorithms

The proposed work consists of two scenarios

Scenario 1 number of DG placementsScenario 2 DG supplying various 119875119876 capacities

41 Scenario 1 Number of DG Placements This scenariodiscusses the number of DGs to be placed on the system andeffect of increased number of DGs on the system [18]

The number of DGs to be placed depends on the loaddemand of the system and maximum allowable size of DGThe maximum allowable size of DG is up to 25 to 30 ofthe total load as found in various literatures [18 20] In thispaper the maximum numbers of DGs are taken as 5 So it isproposed to select 5 numbers of buses among 30-bus systemThen for each bus it is advisable to take maximum of 5 loadin order to satisfy the total load of 25 amongst 5 buses Alsominimum numbers of DGs are chosen as 3 In this aspectthis paper does not include constant solar DG penetration of25 That means total 25 of load is not divided amongst3 buses Instead in each bus solar DG size of 1 to 5 loadis maintained independent of number of DGs So this workattains the fact that minimum of 15 load is satisfied byincluding 3 numbers of DGs and maximum of 25 load ismet by including 5 numbers of DGs

Therefore the number of DGs is selected as 3 4 and 5respectively And they are placed on the top priority orderedby LSF and weak bus method Now three nature inspiredmetaheuristics algorithms PSO [33] GSA [34 35] and hybridPSOGSA [36 37] are used to optimize the solar sizing In [38]economic dispatch problem in a microgrid is addressed withcost minimization

(i) Basic Concepts of PSO [33] PSO has been developedthrough simulation of simplified social models The featuresof the method are as follows

(i) Based on swarms like fish schooling and a flock ofbirds

(ii) Simple and less time consuming

(iii) Solving nonlinear optimization problems with con-tinuous variables

The convergence is provided by the acceleration term in(23)

The modified velocity of each agent can be calculatedusing the current velocity and the distances from 119901119887119890119904119905 and119892119887119890119904119905 are as shown below

V119896+1119894

= 119908V119896119894+ 1198881rand (119901119887119890119904119905

119894minus 119904119896

119894)

+ 1198882rand (119892119887119890119904119905 minus 119904

119896

119894)

(23)

where V119896119894is the velocity of agent 119894 at 119896th iteration V119896+1

119894is

the modified velocity of agent rand represent functions thatgenerate independent random numbers which are uniformlydistributed between 0 and 1 119904119896

119894is the current position of agent

at 119896th iteration119901119887119890119904119905119894is the119901119887119890119904119905 of agent 119894119892119887119890119904119905 is the119892119887119890119904119905

of the group119908 is the inertia weight factor used to control theimpact of the previous history of velocities V119896

119894on the current

velocity V119896+1119894

and 1198881and 1198882are the acceleration factors named

cognitive and social parameters determine the influence of119901119887119890119904119905119894and 119892119887119890119904119905 in determining the new solutions

And the updated position can be calculated from thefollowing equation

119904119896+1

119894= 119904119896

119894+ V119896+1119894

119894 = 1 2 3 119873119905119898 (24)

where 119873119905119898

is the number of swarms and 119896 represents theiteration

(ii) Basic Concepts of GSA [34 35] GSA is a novel heuristicoptimization method which has been proposed by Rashediet al in 2009 [35] The basic physical theory from whichGSA is inspired is from Newtonrsquos theory The GSA couldbe considered as an isolated system of masses It is like asmall artificial world of masses obeying the Newtonian lawsof gravitation and motion

The convergence is reached indirectly by the accelerationterm in (25)The acceleration of anymass is equal to the forceacted on the system divided by mass of inertia

The velocity and position of the agents for next (119905 + 1)

iteration are calculated using the following equations

V119889119894(119905 + 1) = rand

119894V119889119894(119905) + 119886

119889

119894(119905) (25)

119909119889

119894(119905 + 1) = 119909

119889

119894(119905) + V119889

119894(119905 + 1) (26)

where V119889119894(119905 + 1) is the updated velocity of the particle at 119894th

position V119889119894(119905) is the previous velocity 119886119889

119894(119905) is the acceleration

of the particle at 119894 and 119909119889119894is the position of the particle

(iii) Basic Concepts of Hybrid PSOGSA [36 37] Two algo-rithms can be hybridized in high level or low level withrelay or coevolutionarymethod as homogeneous or heteroge-neous In this paper low level coevolutionary heterogeneoushybrid method is used The hybrid is low level because ofthe combination of the functionality of both algorithms Itis coevolutionary because this algorithm does not use both

The Scientific World Journal 7

algorithms one after another In other words they run inparallel It is heterogeneous because there are two differentalgorithms that are involved to produce final results Themain idea is to integrate the ability of exploitation in PSOwith the ability of exploration in GSA to synthesize bothalgorithmsrsquo strength

The main objective is to combine the social thinkingability of PSO (119892119887119890119904119905) with the local search capability ofGSA hence achieving a new formula for hybrid PSOGSA forvelocity updating as

V119894 (119905 + 1) = 119908V

119894 (119905) + 1198881015840

1rand ac

119894 (119905)

+ 1198881015840

2rand (119892119887119890119904119905 minus 119909

119894 (119905))

(27)

where V119894(119905) is the velocity of agent 119894 at iteration 119905 1198881015840

119895is a

weighting factor119908 is a weighting function rand is a randomnumber between 0 and 1 ac

119894(119905) is the acceleration of agent 119894

at iteration 119905 and 119892119887119890119904119905 is the best solution so far Positionupdate is done by the following formula

119909119894 (119905 + 1) = 119909

119894 (119905) + V119894 (119905 + 1) (28)

42 Scenario 2 DG Supplying Various 119875119876 Capacities Thispaper deals with solar power and grid tied solar inverters inwhich both real power and reactive power of solar energy areconsidered And this scenario deals with 4 types of cases [918 39]

The four different cases as shown below are separatelyanalyzed with respect to LSF and WK method in order tofind out the location of solar power and they are also analyzedwith respect to number of DGs using different optimizationalgorithms to find out the sizing of solar power and the resultsare discussed in the next section

(i) Type 1 system without DG (initial case)(ii) Type 2 DG supplying real power alone (119875)(iii) Type 3 DG supplying reactive power alone (119876)(iv) Type 4 DG supplying both real and reactive power

All the four types are tested with above-mentioned threealgorithms and results are evaluated

5 Results and Discussions

In this section of the paper the entire work is explainedin a precise manner The standard IEEE 30-bus system[8] as in Figure 2 [37] is used as a test system and theresults are evaluated The test system consists of 30 busesof which 6 generating buses are present including slack busand remaining 24 are load buses 41 lines 4 tap changingtransformers and 9 shunt capacitors are present in this testcase MATPOWER open source power system software isused to run NR power flow The initial real power loss of thesystem is 58316MW and initial voltage deviation is 09819

The proposed work is divided into two steps In thefirst step optimal size of DG at each node is found byPSO algorithm assuming that all the 30 buses have solar

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

simsim

sim

sim

sim

sim

Figure 2 Single line diagram of IEEE 30-bus test system

generation And then results obtained through PSO arechecked for reverse power flow by negative load approachto find the possible bus locations for DG placement Thenthe search for optimal location of DGs is fine-tuned by twomethods namely weak (WK) voltage bus placement and LossSensitivity Factor (LSF) method In order to emphasize pointon the effect of increasing the number of solar generatorsthis paper analyses and compares the result with numbers3 4 and 5 of DGs which are placed using priority locationfound fromWK and LSFmethods Further the 119875119876 capacitiesof solar DGs are also analyzed with DG supplying 119875 alone(real power alone) 119876 alone (reactive power alone) and both119875 and 119876 (both real and reactive power) Thus several aspectslike size of DGs number of DGs location of DGs order ofDG placement and 119875119876 capacities of DGs are all discussedunder one roof in this paper

51 Optimal Size of DG Using PSO The PSO algorithm isused to find out the size of DG to be placed on the systemAt each bus solar DG penetration is allowed and size ofsolar power is taken as a control variable The size is limitedbetween 2834MW and 11336MWwhich is 1 to 5 of totalload

Table 1 illustrates the optimal size of the solar DG at eachbus to be included satisfying the multiobjective function ofminimizing the real power losses reactive power losses andvoltage deviations

Then the size of the DG is tested for negative loadapproach That is NR power flow is run after placing DG ateach bus In the literature listed in [40] Kansal et alrsquos workis limited to reverse power flow and they have not includednegative load approach But this paper provides best resultsand does not have reverse power flow

The buses that withstand negative load effect are 2 4 5 78 12 15 19 21 24 and 30 Among these buses the best locationand the best combination for DG placement are selected as inTables 1 and 2

8 The Scientific World Journal

Table 1 Size of DGs from PSO

Bus number Optimal size (MW)1 49847682 43584673 5217384 64699435 54963056 82662427 10215498 71671979 750025610 678641411 377825412 756259313 105648114 704602615 600733616 742512517 109480418 691723119 461379820 755944721 103330822 512553223 396942324 629310125 64554426 416699227 563055328 745293629 548090130 7336586

52 Priority List Creation The priority list is created to orderthe DG placement (shown in Table 2) It is done by twomethods (1) Loss Sensitivity Factor (LSF) method and (2)weak (WK) bus placement method

(1) The LSF method of placement is used because ofthe sensitivity towards loss Since this paper aimsfor loss minimization with voltage enhancement LSFmethod of finding DG location is most appropriateThe buses are placed in decreasing order of losssensitivity that is the highly sensitive bus is first givenwith DG sitingThus the order of buses is obtained asin Table 2

(2) The weak (WK) bus voltage method is the simplestand easy method for bus placement The buses withweak voltage profile are provided with the DG Thisis in order to improve the voltage profile of thesystem and to obtain minimum voltage deviationsThe ordering is done as in Table 2

The bus number in which solar DG can be fit ishighlighted that is the DGs which sustain negative load

Table 2 Sequence of priority list by WK and LSF method

Bus number LSF Priority list Bus voltage (119881) Priority list1 605119890 minus 14 30 1050 302 107e minus 01 29 1040 273 235119890 minus 01 27 1028 264 244e minus 01 26 1022 255 247e minus 01 25 1010 216 263119890 minus 01 24 1017 247 283e minus 01 23 1006 208 230e minus 01 28 1010 229 449119890 minus 01 19 0976 1710 502119890 minus 01 16 0955 1411 429119890 minus 01 20 1050 2912 476e minus 01 18 0998 1913 412119890 minus 01 21 1050 2814 561119890 minus 01 13 0997 1815 575e minus 01 12 0968 1516 519119890 minus 01 17 0972 1617 526119890 minus 01 15 0954 1318 595119890 minus 01 7 0950 1219 588e minus 01 8 0943 920 576119890 minus 01 11 0945 1021 576e minus 01 10 0941 722 577119890 minus 01 9 0941 823 623119890 minus 01 4 0947 1124 614e minus 01 5 0927 625 590119890 minus 01 6 0920 426 664119890 minus 01 3 0901 227 556119890 minus 01 14 0926 528 304119890 minus 01 22 1012 2329 695119890 minus 01 2 0903 330 738e minus 01 1 0891 1

approachTherefore those buses are provided with DG in theorder of LSF or WK method Now the LSF method bringsout the priority order as 30 24 19 21 15 12 7 5 4 8 and 2whereas by WKmethod the priority order is 30 24 21 19 1512 7 5 8 4 and 2

53 Location and Sizing After finding out the order andlocation of buses the number of DGs and type of DG are tobe found The cases may be 3 DGs 4 DGs and 5 DGs basedon number of placements Here the size of DG is same andis subjected to the same limits And type of DGs conceptis provided in this paper to answer the following questionldquoWhat purpose is DG placement meant forrdquo According tothis concept four cases are dealt with ldquono DG 119875 alone 119876alone and both 119875 and119876 combinedrdquo All these cases are testedwith both LSF and WK bus ordering

531 Control Variables and Its Ranges Consider the follow-ing

(1) Real power supplied by DG 119875SG 2834 to 11336MW

The Scientific World Journal 9

Table 3 Best outcome among all cases

5 DGsrsquo LSFAlgorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

FIT 30851 4260241 4165504 2309231 4260241 2358722 1696819 4295037 1920378119875119871(MW) 3930302 5447339 5004235 2973936 5447339 3022499 2149479 5496311 2407849

119876119871(MVAR) 1706019 2353672 2141433 1268353 2353672 1300848 9444453 2370543 1057728

VD 0006318 511119864 minus 10 0495616 163119864 minus 10 511119864 minus 10 615119864 minus 11 102E minus 04 0001427 0036186119875 solar (MW) 3501449 mdash 3457517 3523621 mdash 3572001 4664924 mdash 4169984 119875 solar 1235515 mdash 1220013 1243338 mdash 126041 1646056 mdash 1471413119876 solar (MVAR) mdash 1541866 1355552 mdash 1541866 1543187 mdash 1540234 1453383 119876 solar mdash 1221764 107413 mdash 1221764 1222811 mdash 122047 1151651

(2) Reactive power supplied by DG 119876SG 1262 to631MVAR

(3) Voltage magnitude of PV buses 09 to 11 pu(4) Tap settings 09 to 11 pu(5) Shunt capacitors 0 to 10 MVAR

Allowed total solar power generation is from 5 to 25of the total load of the system For an IEEE 30-bus system thetotal real power demand is 2834MW and reactive power is1262MWTherefore eachDG is subjected to 1 to 5 of totaldemand andmaximum of 5 DGs are considered to satisfy 5to 25 of total demand

Four types of system cases are evaluated

(1) No DG In this case there is no renewable penetrationand NR load flow is run with the basic case withconventional generators Here the values of fitnessfunction are evaluated for the base case using GSAPSO and hybrid PSOGSA algorithms And the resultsare tabulated in Table 4

(2) Three DGs placed In this case totally three DGs areplaced in the system By LSF method DG locationis prioritized at bus numbers 30 24 and 19 By WKmethod it is found to be at 30 24 and 21 This bringsout reduced loss results than no DG case

(3) Four DGs placed In this case 4 DGs are placed atlocation of bus numbers 30 24 19 and 21 under LSFand 30 24 21 and 19 under WK This case exhibitsmore reduced losses than previous case

(4) Five DGs placed (as shown in Figure 3) Under LSFDG location is found to be at bus numbers 30 24 1921 and 15 and under WK it is at 30 24 21 19 and 15Five-DG case brings out the most wondering resultsas in Table 3

It is to be noted that there is only slight variation in theorder of placement by both methods but the results obtainedhave shown ridiculous performance The DG supplyingvarious 119875119876 capacities concept is discussed next

(1) 119875 aloneThis system consists of multiple DGs supply-ing real power alone located under LSF and WK busmethods The system with multiple DGs supplying 119875

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

sim

simsim

simsim

sim

Figure 3 Test system after addition of DGs at 5 locations

alone is optimized using 3 algorithms and the resultsare tabulated in Table 4

(2) 119876 aloneThis scheme consists ofmultipleDGs supply-ing reactive power alone located under LSF and WKbus method is solved under all three algorithmsThatis the DG is placed as a reactive power compensatingdevice This is a worst case with high power lossincurred compared to other cases Also it is noteconomical to include DG just for providing reactivepower alone So this case will be the worst case amongall and the results are tabulated in the Appendix

(3) Both 119875 and 119876 Under this scheme multiple DGs areplaced satisfying LSF and WK methods meant forproviding both real and reactive power This case isanalyzed with 3 4 and 5 DGs using 3 algorithmsThis provides a trustworthy way of DG installationHere the real and reactive power needs of the systemare compensated at the same time The inverters ofthe solar DG act as reactive power sources Thiscondition suits the main objective of the paper andit aims to compensate both the powers of the system

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

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FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

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StructuresJournal of

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RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EnergyJournal of

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Journal ofEngineeringVolume 2014

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Solar EnergyJournal of

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

6 The Scientific World Journal

4 Proposed Work

Integrating the renewable source to grid or the conversionof the traditional grid into smart grid involves severalessential steps The placement of renewable sources theirsize and amount of penetration into the system are all tobe considered The smart grid possesses five most significantcharacteristics like being adaptive predictive integratedinteractive optimized and secured [30] In this paper the gridachieves all the five perspectives by integrating solar energyplacement of solar energy and performing reactive poweroptimization to maintain voltage profile and thus havingsecured and reliable power system

Solar DG is found to be the best DG that adapts thegrid among several renewable sources And also this researchincludes the grid interactive solar inverters and their behaviorwhen operated in a grid [31] The grid tied solar inverters actas a reactive power source to balance the reactive power ofthe system [7] In the [32] optimal siting and sizing of DGsare found by combined GAPSO algorithms

The proposed work consists of two scenarios

Scenario 1 number of DG placementsScenario 2 DG supplying various 119875119876 capacities

41 Scenario 1 Number of DG Placements This scenariodiscusses the number of DGs to be placed on the system andeffect of increased number of DGs on the system [18]

The number of DGs to be placed depends on the loaddemand of the system and maximum allowable size of DGThe maximum allowable size of DG is up to 25 to 30 ofthe total load as found in various literatures [18 20] In thispaper the maximum numbers of DGs are taken as 5 So it isproposed to select 5 numbers of buses among 30-bus systemThen for each bus it is advisable to take maximum of 5 loadin order to satisfy the total load of 25 amongst 5 buses Alsominimum numbers of DGs are chosen as 3 In this aspectthis paper does not include constant solar DG penetration of25 That means total 25 of load is not divided amongst3 buses Instead in each bus solar DG size of 1 to 5 loadis maintained independent of number of DGs So this workattains the fact that minimum of 15 load is satisfied byincluding 3 numbers of DGs and maximum of 25 load ismet by including 5 numbers of DGs

Therefore the number of DGs is selected as 3 4 and 5respectively And they are placed on the top priority orderedby LSF and weak bus method Now three nature inspiredmetaheuristics algorithms PSO [33] GSA [34 35] and hybridPSOGSA [36 37] are used to optimize the solar sizing In [38]economic dispatch problem in a microgrid is addressed withcost minimization

(i) Basic Concepts of PSO [33] PSO has been developedthrough simulation of simplified social models The featuresof the method are as follows

(i) Based on swarms like fish schooling and a flock ofbirds

(ii) Simple and less time consuming

(iii) Solving nonlinear optimization problems with con-tinuous variables

The convergence is provided by the acceleration term in(23)

The modified velocity of each agent can be calculatedusing the current velocity and the distances from 119901119887119890119904119905 and119892119887119890119904119905 are as shown below

V119896+1119894

= 119908V119896119894+ 1198881rand (119901119887119890119904119905

119894minus 119904119896

119894)

+ 1198882rand (119892119887119890119904119905 minus 119904

119896

119894)

(23)

where V119896119894is the velocity of agent 119894 at 119896th iteration V119896+1

119894is

the modified velocity of agent rand represent functions thatgenerate independent random numbers which are uniformlydistributed between 0 and 1 119904119896

119894is the current position of agent

at 119896th iteration119901119887119890119904119905119894is the119901119887119890119904119905 of agent 119894119892119887119890119904119905 is the119892119887119890119904119905

of the group119908 is the inertia weight factor used to control theimpact of the previous history of velocities V119896

119894on the current

velocity V119896+1119894

and 1198881and 1198882are the acceleration factors named

cognitive and social parameters determine the influence of119901119887119890119904119905119894and 119892119887119890119904119905 in determining the new solutions

And the updated position can be calculated from thefollowing equation

119904119896+1

119894= 119904119896

119894+ V119896+1119894

119894 = 1 2 3 119873119905119898 (24)

where 119873119905119898

is the number of swarms and 119896 represents theiteration

(ii) Basic Concepts of GSA [34 35] GSA is a novel heuristicoptimization method which has been proposed by Rashediet al in 2009 [35] The basic physical theory from whichGSA is inspired is from Newtonrsquos theory The GSA couldbe considered as an isolated system of masses It is like asmall artificial world of masses obeying the Newtonian lawsof gravitation and motion

The convergence is reached indirectly by the accelerationterm in (25)The acceleration of anymass is equal to the forceacted on the system divided by mass of inertia

The velocity and position of the agents for next (119905 + 1)

iteration are calculated using the following equations

V119889119894(119905 + 1) = rand

119894V119889119894(119905) + 119886

119889

119894(119905) (25)

119909119889

119894(119905 + 1) = 119909

119889

119894(119905) + V119889

119894(119905 + 1) (26)

where V119889119894(119905 + 1) is the updated velocity of the particle at 119894th

position V119889119894(119905) is the previous velocity 119886119889

119894(119905) is the acceleration

of the particle at 119894 and 119909119889119894is the position of the particle

(iii) Basic Concepts of Hybrid PSOGSA [36 37] Two algo-rithms can be hybridized in high level or low level withrelay or coevolutionarymethod as homogeneous or heteroge-neous In this paper low level coevolutionary heterogeneoushybrid method is used The hybrid is low level because ofthe combination of the functionality of both algorithms Itis coevolutionary because this algorithm does not use both

The Scientific World Journal 7

algorithms one after another In other words they run inparallel It is heterogeneous because there are two differentalgorithms that are involved to produce final results Themain idea is to integrate the ability of exploitation in PSOwith the ability of exploration in GSA to synthesize bothalgorithmsrsquo strength

The main objective is to combine the social thinkingability of PSO (119892119887119890119904119905) with the local search capability ofGSA hence achieving a new formula for hybrid PSOGSA forvelocity updating as

V119894 (119905 + 1) = 119908V

119894 (119905) + 1198881015840

1rand ac

119894 (119905)

+ 1198881015840

2rand (119892119887119890119904119905 minus 119909

119894 (119905))

(27)

where V119894(119905) is the velocity of agent 119894 at iteration 119905 1198881015840

119895is a

weighting factor119908 is a weighting function rand is a randomnumber between 0 and 1 ac

119894(119905) is the acceleration of agent 119894

at iteration 119905 and 119892119887119890119904119905 is the best solution so far Positionupdate is done by the following formula

119909119894 (119905 + 1) = 119909

119894 (119905) + V119894 (119905 + 1) (28)

42 Scenario 2 DG Supplying Various 119875119876 Capacities Thispaper deals with solar power and grid tied solar inverters inwhich both real power and reactive power of solar energy areconsidered And this scenario deals with 4 types of cases [918 39]

The four different cases as shown below are separatelyanalyzed with respect to LSF and WK method in order tofind out the location of solar power and they are also analyzedwith respect to number of DGs using different optimizationalgorithms to find out the sizing of solar power and the resultsare discussed in the next section

(i) Type 1 system without DG (initial case)(ii) Type 2 DG supplying real power alone (119875)(iii) Type 3 DG supplying reactive power alone (119876)(iv) Type 4 DG supplying both real and reactive power

All the four types are tested with above-mentioned threealgorithms and results are evaluated

5 Results and Discussions

In this section of the paper the entire work is explainedin a precise manner The standard IEEE 30-bus system[8] as in Figure 2 [37] is used as a test system and theresults are evaluated The test system consists of 30 busesof which 6 generating buses are present including slack busand remaining 24 are load buses 41 lines 4 tap changingtransformers and 9 shunt capacitors are present in this testcase MATPOWER open source power system software isused to run NR power flow The initial real power loss of thesystem is 58316MW and initial voltage deviation is 09819

The proposed work is divided into two steps In thefirst step optimal size of DG at each node is found byPSO algorithm assuming that all the 30 buses have solar

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

simsim

sim

sim

sim

sim

Figure 2 Single line diagram of IEEE 30-bus test system

generation And then results obtained through PSO arechecked for reverse power flow by negative load approachto find the possible bus locations for DG placement Thenthe search for optimal location of DGs is fine-tuned by twomethods namely weak (WK) voltage bus placement and LossSensitivity Factor (LSF) method In order to emphasize pointon the effect of increasing the number of solar generatorsthis paper analyses and compares the result with numbers3 4 and 5 of DGs which are placed using priority locationfound fromWK and LSFmethods Further the 119875119876 capacitiesof solar DGs are also analyzed with DG supplying 119875 alone(real power alone) 119876 alone (reactive power alone) and both119875 and 119876 (both real and reactive power) Thus several aspectslike size of DGs number of DGs location of DGs order ofDG placement and 119875119876 capacities of DGs are all discussedunder one roof in this paper

51 Optimal Size of DG Using PSO The PSO algorithm isused to find out the size of DG to be placed on the systemAt each bus solar DG penetration is allowed and size ofsolar power is taken as a control variable The size is limitedbetween 2834MW and 11336MWwhich is 1 to 5 of totalload

Table 1 illustrates the optimal size of the solar DG at eachbus to be included satisfying the multiobjective function ofminimizing the real power losses reactive power losses andvoltage deviations

Then the size of the DG is tested for negative loadapproach That is NR power flow is run after placing DG ateach bus In the literature listed in [40] Kansal et alrsquos workis limited to reverse power flow and they have not includednegative load approach But this paper provides best resultsand does not have reverse power flow

The buses that withstand negative load effect are 2 4 5 78 12 15 19 21 24 and 30 Among these buses the best locationand the best combination for DG placement are selected as inTables 1 and 2

8 The Scientific World Journal

Table 1 Size of DGs from PSO

Bus number Optimal size (MW)1 49847682 43584673 5217384 64699435 54963056 82662427 10215498 71671979 750025610 678641411 377825412 756259313 105648114 704602615 600733616 742512517 109480418 691723119 461379820 755944721 103330822 512553223 396942324 629310125 64554426 416699227 563055328 745293629 548090130 7336586

52 Priority List Creation The priority list is created to orderthe DG placement (shown in Table 2) It is done by twomethods (1) Loss Sensitivity Factor (LSF) method and (2)weak (WK) bus placement method

(1) The LSF method of placement is used because ofthe sensitivity towards loss Since this paper aimsfor loss minimization with voltage enhancement LSFmethod of finding DG location is most appropriateThe buses are placed in decreasing order of losssensitivity that is the highly sensitive bus is first givenwith DG sitingThus the order of buses is obtained asin Table 2

(2) The weak (WK) bus voltage method is the simplestand easy method for bus placement The buses withweak voltage profile are provided with the DG Thisis in order to improve the voltage profile of thesystem and to obtain minimum voltage deviationsThe ordering is done as in Table 2

The bus number in which solar DG can be fit ishighlighted that is the DGs which sustain negative load

Table 2 Sequence of priority list by WK and LSF method

Bus number LSF Priority list Bus voltage (119881) Priority list1 605119890 minus 14 30 1050 302 107e minus 01 29 1040 273 235119890 minus 01 27 1028 264 244e minus 01 26 1022 255 247e minus 01 25 1010 216 263119890 minus 01 24 1017 247 283e minus 01 23 1006 208 230e minus 01 28 1010 229 449119890 minus 01 19 0976 1710 502119890 minus 01 16 0955 1411 429119890 minus 01 20 1050 2912 476e minus 01 18 0998 1913 412119890 minus 01 21 1050 2814 561119890 minus 01 13 0997 1815 575e minus 01 12 0968 1516 519119890 minus 01 17 0972 1617 526119890 minus 01 15 0954 1318 595119890 minus 01 7 0950 1219 588e minus 01 8 0943 920 576119890 minus 01 11 0945 1021 576e minus 01 10 0941 722 577119890 minus 01 9 0941 823 623119890 minus 01 4 0947 1124 614e minus 01 5 0927 625 590119890 minus 01 6 0920 426 664119890 minus 01 3 0901 227 556119890 minus 01 14 0926 528 304119890 minus 01 22 1012 2329 695119890 minus 01 2 0903 330 738e minus 01 1 0891 1

approachTherefore those buses are provided with DG in theorder of LSF or WK method Now the LSF method bringsout the priority order as 30 24 19 21 15 12 7 5 4 8 and 2whereas by WKmethod the priority order is 30 24 21 19 1512 7 5 8 4 and 2

53 Location and Sizing After finding out the order andlocation of buses the number of DGs and type of DG are tobe found The cases may be 3 DGs 4 DGs and 5 DGs basedon number of placements Here the size of DG is same andis subjected to the same limits And type of DGs conceptis provided in this paper to answer the following questionldquoWhat purpose is DG placement meant forrdquo According tothis concept four cases are dealt with ldquono DG 119875 alone 119876alone and both 119875 and119876 combinedrdquo All these cases are testedwith both LSF and WK bus ordering

531 Control Variables and Its Ranges Consider the follow-ing

(1) Real power supplied by DG 119875SG 2834 to 11336MW

The Scientific World Journal 9

Table 3 Best outcome among all cases

5 DGsrsquo LSFAlgorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

FIT 30851 4260241 4165504 2309231 4260241 2358722 1696819 4295037 1920378119875119871(MW) 3930302 5447339 5004235 2973936 5447339 3022499 2149479 5496311 2407849

119876119871(MVAR) 1706019 2353672 2141433 1268353 2353672 1300848 9444453 2370543 1057728

VD 0006318 511119864 minus 10 0495616 163119864 minus 10 511119864 minus 10 615119864 minus 11 102E minus 04 0001427 0036186119875 solar (MW) 3501449 mdash 3457517 3523621 mdash 3572001 4664924 mdash 4169984 119875 solar 1235515 mdash 1220013 1243338 mdash 126041 1646056 mdash 1471413119876 solar (MVAR) mdash 1541866 1355552 mdash 1541866 1543187 mdash 1540234 1453383 119876 solar mdash 1221764 107413 mdash 1221764 1222811 mdash 122047 1151651

(2) Reactive power supplied by DG 119876SG 1262 to631MVAR

(3) Voltage magnitude of PV buses 09 to 11 pu(4) Tap settings 09 to 11 pu(5) Shunt capacitors 0 to 10 MVAR

Allowed total solar power generation is from 5 to 25of the total load of the system For an IEEE 30-bus system thetotal real power demand is 2834MW and reactive power is1262MWTherefore eachDG is subjected to 1 to 5 of totaldemand andmaximum of 5 DGs are considered to satisfy 5to 25 of total demand

Four types of system cases are evaluated

(1) No DG In this case there is no renewable penetrationand NR load flow is run with the basic case withconventional generators Here the values of fitnessfunction are evaluated for the base case using GSAPSO and hybrid PSOGSA algorithms And the resultsare tabulated in Table 4

(2) Three DGs placed In this case totally three DGs areplaced in the system By LSF method DG locationis prioritized at bus numbers 30 24 and 19 By WKmethod it is found to be at 30 24 and 21 This bringsout reduced loss results than no DG case

(3) Four DGs placed In this case 4 DGs are placed atlocation of bus numbers 30 24 19 and 21 under LSFand 30 24 21 and 19 under WK This case exhibitsmore reduced losses than previous case

(4) Five DGs placed (as shown in Figure 3) Under LSFDG location is found to be at bus numbers 30 24 1921 and 15 and under WK it is at 30 24 21 19 and 15Five-DG case brings out the most wondering resultsas in Table 3

It is to be noted that there is only slight variation in theorder of placement by both methods but the results obtainedhave shown ridiculous performance The DG supplyingvarious 119875119876 capacities concept is discussed next

(1) 119875 aloneThis system consists of multiple DGs supply-ing real power alone located under LSF and WK busmethods The system with multiple DGs supplying 119875

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

sim

simsim

simsim

sim

Figure 3 Test system after addition of DGs at 5 locations

alone is optimized using 3 algorithms and the resultsare tabulated in Table 4

(2) 119876 aloneThis scheme consists ofmultipleDGs supply-ing reactive power alone located under LSF and WKbus method is solved under all three algorithmsThatis the DG is placed as a reactive power compensatingdevice This is a worst case with high power lossincurred compared to other cases Also it is noteconomical to include DG just for providing reactivepower alone So this case will be the worst case amongall and the results are tabulated in the Appendix

(3) Both 119875 and 119876 Under this scheme multiple DGs areplaced satisfying LSF and WK methods meant forproviding both real and reactive power This case isanalyzed with 3 4 and 5 DGs using 3 algorithmsThis provides a trustworthy way of DG installationHere the real and reactive power needs of the systemare compensated at the same time The inverters ofthe solar DG act as reactive power sources Thiscondition suits the main objective of the paper andit aims to compensate both the powers of the system

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World Journal 7

algorithms one after another In other words they run inparallel It is heterogeneous because there are two differentalgorithms that are involved to produce final results Themain idea is to integrate the ability of exploitation in PSOwith the ability of exploration in GSA to synthesize bothalgorithmsrsquo strength

The main objective is to combine the social thinkingability of PSO (119892119887119890119904119905) with the local search capability ofGSA hence achieving a new formula for hybrid PSOGSA forvelocity updating as

V119894 (119905 + 1) = 119908V

119894 (119905) + 1198881015840

1rand ac

119894 (119905)

+ 1198881015840

2rand (119892119887119890119904119905 minus 119909

119894 (119905))

(27)

where V119894(119905) is the velocity of agent 119894 at iteration 119905 1198881015840

119895is a

weighting factor119908 is a weighting function rand is a randomnumber between 0 and 1 ac

119894(119905) is the acceleration of agent 119894

at iteration 119905 and 119892119887119890119904119905 is the best solution so far Positionupdate is done by the following formula

119909119894 (119905 + 1) = 119909

119894 (119905) + V119894 (119905 + 1) (28)

42 Scenario 2 DG Supplying Various 119875119876 Capacities Thispaper deals with solar power and grid tied solar inverters inwhich both real power and reactive power of solar energy areconsidered And this scenario deals with 4 types of cases [918 39]

The four different cases as shown below are separatelyanalyzed with respect to LSF and WK method in order tofind out the location of solar power and they are also analyzedwith respect to number of DGs using different optimizationalgorithms to find out the sizing of solar power and the resultsare discussed in the next section

(i) Type 1 system without DG (initial case)(ii) Type 2 DG supplying real power alone (119875)(iii) Type 3 DG supplying reactive power alone (119876)(iv) Type 4 DG supplying both real and reactive power

All the four types are tested with above-mentioned threealgorithms and results are evaluated

5 Results and Discussions

In this section of the paper the entire work is explainedin a precise manner The standard IEEE 30-bus system[8] as in Figure 2 [37] is used as a test system and theresults are evaluated The test system consists of 30 busesof which 6 generating buses are present including slack busand remaining 24 are load buses 41 lines 4 tap changingtransformers and 9 shunt capacitors are present in this testcase MATPOWER open source power system software isused to run NR power flow The initial real power loss of thesystem is 58316MW and initial voltage deviation is 09819

The proposed work is divided into two steps In thefirst step optimal size of DG at each node is found byPSO algorithm assuming that all the 30 buses have solar

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

simsim

sim

sim

sim

sim

Figure 2 Single line diagram of IEEE 30-bus test system

generation And then results obtained through PSO arechecked for reverse power flow by negative load approachto find the possible bus locations for DG placement Thenthe search for optimal location of DGs is fine-tuned by twomethods namely weak (WK) voltage bus placement and LossSensitivity Factor (LSF) method In order to emphasize pointon the effect of increasing the number of solar generatorsthis paper analyses and compares the result with numbers3 4 and 5 of DGs which are placed using priority locationfound fromWK and LSFmethods Further the 119875119876 capacitiesof solar DGs are also analyzed with DG supplying 119875 alone(real power alone) 119876 alone (reactive power alone) and both119875 and 119876 (both real and reactive power) Thus several aspectslike size of DGs number of DGs location of DGs order ofDG placement and 119875119876 capacities of DGs are all discussedunder one roof in this paper

51 Optimal Size of DG Using PSO The PSO algorithm isused to find out the size of DG to be placed on the systemAt each bus solar DG penetration is allowed and size ofsolar power is taken as a control variable The size is limitedbetween 2834MW and 11336MWwhich is 1 to 5 of totalload

Table 1 illustrates the optimal size of the solar DG at eachbus to be included satisfying the multiobjective function ofminimizing the real power losses reactive power losses andvoltage deviations

Then the size of the DG is tested for negative loadapproach That is NR power flow is run after placing DG ateach bus In the literature listed in [40] Kansal et alrsquos workis limited to reverse power flow and they have not includednegative load approach But this paper provides best resultsand does not have reverse power flow

The buses that withstand negative load effect are 2 4 5 78 12 15 19 21 24 and 30 Among these buses the best locationand the best combination for DG placement are selected as inTables 1 and 2

8 The Scientific World Journal

Table 1 Size of DGs from PSO

Bus number Optimal size (MW)1 49847682 43584673 5217384 64699435 54963056 82662427 10215498 71671979 750025610 678641411 377825412 756259313 105648114 704602615 600733616 742512517 109480418 691723119 461379820 755944721 103330822 512553223 396942324 629310125 64554426 416699227 563055328 745293629 548090130 7336586

52 Priority List Creation The priority list is created to orderthe DG placement (shown in Table 2) It is done by twomethods (1) Loss Sensitivity Factor (LSF) method and (2)weak (WK) bus placement method

(1) The LSF method of placement is used because ofthe sensitivity towards loss Since this paper aimsfor loss minimization with voltage enhancement LSFmethod of finding DG location is most appropriateThe buses are placed in decreasing order of losssensitivity that is the highly sensitive bus is first givenwith DG sitingThus the order of buses is obtained asin Table 2

(2) The weak (WK) bus voltage method is the simplestand easy method for bus placement The buses withweak voltage profile are provided with the DG Thisis in order to improve the voltage profile of thesystem and to obtain minimum voltage deviationsThe ordering is done as in Table 2

The bus number in which solar DG can be fit ishighlighted that is the DGs which sustain negative load

Table 2 Sequence of priority list by WK and LSF method

Bus number LSF Priority list Bus voltage (119881) Priority list1 605119890 minus 14 30 1050 302 107e minus 01 29 1040 273 235119890 minus 01 27 1028 264 244e minus 01 26 1022 255 247e minus 01 25 1010 216 263119890 minus 01 24 1017 247 283e minus 01 23 1006 208 230e minus 01 28 1010 229 449119890 minus 01 19 0976 1710 502119890 minus 01 16 0955 1411 429119890 minus 01 20 1050 2912 476e minus 01 18 0998 1913 412119890 minus 01 21 1050 2814 561119890 minus 01 13 0997 1815 575e minus 01 12 0968 1516 519119890 minus 01 17 0972 1617 526119890 minus 01 15 0954 1318 595119890 minus 01 7 0950 1219 588e minus 01 8 0943 920 576119890 minus 01 11 0945 1021 576e minus 01 10 0941 722 577119890 minus 01 9 0941 823 623119890 minus 01 4 0947 1124 614e minus 01 5 0927 625 590119890 minus 01 6 0920 426 664119890 minus 01 3 0901 227 556119890 minus 01 14 0926 528 304119890 minus 01 22 1012 2329 695119890 minus 01 2 0903 330 738e minus 01 1 0891 1

approachTherefore those buses are provided with DG in theorder of LSF or WK method Now the LSF method bringsout the priority order as 30 24 19 21 15 12 7 5 4 8 and 2whereas by WKmethod the priority order is 30 24 21 19 1512 7 5 8 4 and 2

53 Location and Sizing After finding out the order andlocation of buses the number of DGs and type of DG are tobe found The cases may be 3 DGs 4 DGs and 5 DGs basedon number of placements Here the size of DG is same andis subjected to the same limits And type of DGs conceptis provided in this paper to answer the following questionldquoWhat purpose is DG placement meant forrdquo According tothis concept four cases are dealt with ldquono DG 119875 alone 119876alone and both 119875 and119876 combinedrdquo All these cases are testedwith both LSF and WK bus ordering

531 Control Variables and Its Ranges Consider the follow-ing

(1) Real power supplied by DG 119875SG 2834 to 11336MW

The Scientific World Journal 9

Table 3 Best outcome among all cases

5 DGsrsquo LSFAlgorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

FIT 30851 4260241 4165504 2309231 4260241 2358722 1696819 4295037 1920378119875119871(MW) 3930302 5447339 5004235 2973936 5447339 3022499 2149479 5496311 2407849

119876119871(MVAR) 1706019 2353672 2141433 1268353 2353672 1300848 9444453 2370543 1057728

VD 0006318 511119864 minus 10 0495616 163119864 minus 10 511119864 minus 10 615119864 minus 11 102E minus 04 0001427 0036186119875 solar (MW) 3501449 mdash 3457517 3523621 mdash 3572001 4664924 mdash 4169984 119875 solar 1235515 mdash 1220013 1243338 mdash 126041 1646056 mdash 1471413119876 solar (MVAR) mdash 1541866 1355552 mdash 1541866 1543187 mdash 1540234 1453383 119876 solar mdash 1221764 107413 mdash 1221764 1222811 mdash 122047 1151651

(2) Reactive power supplied by DG 119876SG 1262 to631MVAR

(3) Voltage magnitude of PV buses 09 to 11 pu(4) Tap settings 09 to 11 pu(5) Shunt capacitors 0 to 10 MVAR

Allowed total solar power generation is from 5 to 25of the total load of the system For an IEEE 30-bus system thetotal real power demand is 2834MW and reactive power is1262MWTherefore eachDG is subjected to 1 to 5 of totaldemand andmaximum of 5 DGs are considered to satisfy 5to 25 of total demand

Four types of system cases are evaluated

(1) No DG In this case there is no renewable penetrationand NR load flow is run with the basic case withconventional generators Here the values of fitnessfunction are evaluated for the base case using GSAPSO and hybrid PSOGSA algorithms And the resultsare tabulated in Table 4

(2) Three DGs placed In this case totally three DGs areplaced in the system By LSF method DG locationis prioritized at bus numbers 30 24 and 19 By WKmethod it is found to be at 30 24 and 21 This bringsout reduced loss results than no DG case

(3) Four DGs placed In this case 4 DGs are placed atlocation of bus numbers 30 24 19 and 21 under LSFand 30 24 21 and 19 under WK This case exhibitsmore reduced losses than previous case

(4) Five DGs placed (as shown in Figure 3) Under LSFDG location is found to be at bus numbers 30 24 1921 and 15 and under WK it is at 30 24 21 19 and 15Five-DG case brings out the most wondering resultsas in Table 3

It is to be noted that there is only slight variation in theorder of placement by both methods but the results obtainedhave shown ridiculous performance The DG supplyingvarious 119875119876 capacities concept is discussed next

(1) 119875 aloneThis system consists of multiple DGs supply-ing real power alone located under LSF and WK busmethods The system with multiple DGs supplying 119875

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

sim

simsim

simsim

sim

Figure 3 Test system after addition of DGs at 5 locations

alone is optimized using 3 algorithms and the resultsare tabulated in Table 4

(2) 119876 aloneThis scheme consists ofmultipleDGs supply-ing reactive power alone located under LSF and WKbus method is solved under all three algorithmsThatis the DG is placed as a reactive power compensatingdevice This is a worst case with high power lossincurred compared to other cases Also it is noteconomical to include DG just for providing reactivepower alone So this case will be the worst case amongall and the results are tabulated in the Appendix

(3) Both 119875 and 119876 Under this scheme multiple DGs areplaced satisfying LSF and WK methods meant forproviding both real and reactive power This case isanalyzed with 3 4 and 5 DGs using 3 algorithmsThis provides a trustworthy way of DG installationHere the real and reactive power needs of the systemare compensated at the same time The inverters ofthe solar DG act as reactive power sources Thiscondition suits the main objective of the paper andit aims to compensate both the powers of the system

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

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FuelsJournal of

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Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

8 The Scientific World Journal

Table 1 Size of DGs from PSO

Bus number Optimal size (MW)1 49847682 43584673 5217384 64699435 54963056 82662427 10215498 71671979 750025610 678641411 377825412 756259313 105648114 704602615 600733616 742512517 109480418 691723119 461379820 755944721 103330822 512553223 396942324 629310125 64554426 416699227 563055328 745293629 548090130 7336586

52 Priority List Creation The priority list is created to orderthe DG placement (shown in Table 2) It is done by twomethods (1) Loss Sensitivity Factor (LSF) method and (2)weak (WK) bus placement method

(1) The LSF method of placement is used because ofthe sensitivity towards loss Since this paper aimsfor loss minimization with voltage enhancement LSFmethod of finding DG location is most appropriateThe buses are placed in decreasing order of losssensitivity that is the highly sensitive bus is first givenwith DG sitingThus the order of buses is obtained asin Table 2

(2) The weak (WK) bus voltage method is the simplestand easy method for bus placement The buses withweak voltage profile are provided with the DG Thisis in order to improve the voltage profile of thesystem and to obtain minimum voltage deviationsThe ordering is done as in Table 2

The bus number in which solar DG can be fit ishighlighted that is the DGs which sustain negative load

Table 2 Sequence of priority list by WK and LSF method

Bus number LSF Priority list Bus voltage (119881) Priority list1 605119890 minus 14 30 1050 302 107e minus 01 29 1040 273 235119890 minus 01 27 1028 264 244e minus 01 26 1022 255 247e minus 01 25 1010 216 263119890 minus 01 24 1017 247 283e minus 01 23 1006 208 230e minus 01 28 1010 229 449119890 minus 01 19 0976 1710 502119890 minus 01 16 0955 1411 429119890 minus 01 20 1050 2912 476e minus 01 18 0998 1913 412119890 minus 01 21 1050 2814 561119890 minus 01 13 0997 1815 575e minus 01 12 0968 1516 519119890 minus 01 17 0972 1617 526119890 minus 01 15 0954 1318 595119890 minus 01 7 0950 1219 588e minus 01 8 0943 920 576119890 minus 01 11 0945 1021 576e minus 01 10 0941 722 577119890 minus 01 9 0941 823 623119890 minus 01 4 0947 1124 614e minus 01 5 0927 625 590119890 minus 01 6 0920 426 664119890 minus 01 3 0901 227 556119890 minus 01 14 0926 528 304119890 minus 01 22 1012 2329 695119890 minus 01 2 0903 330 738e minus 01 1 0891 1

approachTherefore those buses are provided with DG in theorder of LSF or WK method Now the LSF method bringsout the priority order as 30 24 19 21 15 12 7 5 4 8 and 2whereas by WKmethod the priority order is 30 24 21 19 1512 7 5 8 4 and 2

53 Location and Sizing After finding out the order andlocation of buses the number of DGs and type of DG are tobe found The cases may be 3 DGs 4 DGs and 5 DGs basedon number of placements Here the size of DG is same andis subjected to the same limits And type of DGs conceptis provided in this paper to answer the following questionldquoWhat purpose is DG placement meant forrdquo According tothis concept four cases are dealt with ldquono DG 119875 alone 119876alone and both 119875 and119876 combinedrdquo All these cases are testedwith both LSF and WK bus ordering

531 Control Variables and Its Ranges Consider the follow-ing

(1) Real power supplied by DG 119875SG 2834 to 11336MW

The Scientific World Journal 9

Table 3 Best outcome among all cases

5 DGsrsquo LSFAlgorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

FIT 30851 4260241 4165504 2309231 4260241 2358722 1696819 4295037 1920378119875119871(MW) 3930302 5447339 5004235 2973936 5447339 3022499 2149479 5496311 2407849

119876119871(MVAR) 1706019 2353672 2141433 1268353 2353672 1300848 9444453 2370543 1057728

VD 0006318 511119864 minus 10 0495616 163119864 minus 10 511119864 minus 10 615119864 minus 11 102E minus 04 0001427 0036186119875 solar (MW) 3501449 mdash 3457517 3523621 mdash 3572001 4664924 mdash 4169984 119875 solar 1235515 mdash 1220013 1243338 mdash 126041 1646056 mdash 1471413119876 solar (MVAR) mdash 1541866 1355552 mdash 1541866 1543187 mdash 1540234 1453383 119876 solar mdash 1221764 107413 mdash 1221764 1222811 mdash 122047 1151651

(2) Reactive power supplied by DG 119876SG 1262 to631MVAR

(3) Voltage magnitude of PV buses 09 to 11 pu(4) Tap settings 09 to 11 pu(5) Shunt capacitors 0 to 10 MVAR

Allowed total solar power generation is from 5 to 25of the total load of the system For an IEEE 30-bus system thetotal real power demand is 2834MW and reactive power is1262MWTherefore eachDG is subjected to 1 to 5 of totaldemand andmaximum of 5 DGs are considered to satisfy 5to 25 of total demand

Four types of system cases are evaluated

(1) No DG In this case there is no renewable penetrationand NR load flow is run with the basic case withconventional generators Here the values of fitnessfunction are evaluated for the base case using GSAPSO and hybrid PSOGSA algorithms And the resultsare tabulated in Table 4

(2) Three DGs placed In this case totally three DGs areplaced in the system By LSF method DG locationis prioritized at bus numbers 30 24 and 19 By WKmethod it is found to be at 30 24 and 21 This bringsout reduced loss results than no DG case

(3) Four DGs placed In this case 4 DGs are placed atlocation of bus numbers 30 24 19 and 21 under LSFand 30 24 21 and 19 under WK This case exhibitsmore reduced losses than previous case

(4) Five DGs placed (as shown in Figure 3) Under LSFDG location is found to be at bus numbers 30 24 1921 and 15 and under WK it is at 30 24 21 19 and 15Five-DG case brings out the most wondering resultsas in Table 3

It is to be noted that there is only slight variation in theorder of placement by both methods but the results obtainedhave shown ridiculous performance The DG supplyingvarious 119875119876 capacities concept is discussed next

(1) 119875 aloneThis system consists of multiple DGs supply-ing real power alone located under LSF and WK busmethods The system with multiple DGs supplying 119875

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

sim

simsim

simsim

sim

Figure 3 Test system after addition of DGs at 5 locations

alone is optimized using 3 algorithms and the resultsare tabulated in Table 4

(2) 119876 aloneThis scheme consists ofmultipleDGs supply-ing reactive power alone located under LSF and WKbus method is solved under all three algorithmsThatis the DG is placed as a reactive power compensatingdevice This is a worst case with high power lossincurred compared to other cases Also it is noteconomical to include DG just for providing reactivepower alone So this case will be the worst case amongall and the results are tabulated in the Appendix

(3) Both 119875 and 119876 Under this scheme multiple DGs areplaced satisfying LSF and WK methods meant forproviding both real and reactive power This case isanalyzed with 3 4 and 5 DGs using 3 algorithmsThis provides a trustworthy way of DG installationHere the real and reactive power needs of the systemare compensated at the same time The inverters ofthe solar DG act as reactive power sources Thiscondition suits the main objective of the paper andit aims to compensate both the powers of the system

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

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The Scientific World Journal 9

Table 3 Best outcome among all cases

5 DGsrsquo LSFAlgorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

FIT 30851 4260241 4165504 2309231 4260241 2358722 1696819 4295037 1920378119875119871(MW) 3930302 5447339 5004235 2973936 5447339 3022499 2149479 5496311 2407849

119876119871(MVAR) 1706019 2353672 2141433 1268353 2353672 1300848 9444453 2370543 1057728

VD 0006318 511119864 minus 10 0495616 163119864 minus 10 511119864 minus 10 615119864 minus 11 102E minus 04 0001427 0036186119875 solar (MW) 3501449 mdash 3457517 3523621 mdash 3572001 4664924 mdash 4169984 119875 solar 1235515 mdash 1220013 1243338 mdash 126041 1646056 mdash 1471413119876 solar (MVAR) mdash 1541866 1355552 mdash 1541866 1543187 mdash 1540234 1453383 119876 solar mdash 1221764 107413 mdash 1221764 1222811 mdash 122047 1151651

(2) Reactive power supplied by DG 119876SG 1262 to631MVAR

(3) Voltage magnitude of PV buses 09 to 11 pu(4) Tap settings 09 to 11 pu(5) Shunt capacitors 0 to 10 MVAR

Allowed total solar power generation is from 5 to 25of the total load of the system For an IEEE 30-bus system thetotal real power demand is 2834MW and reactive power is1262MWTherefore eachDG is subjected to 1 to 5 of totaldemand andmaximum of 5 DGs are considered to satisfy 5to 25 of total demand

Four types of system cases are evaluated

(1) No DG In this case there is no renewable penetrationand NR load flow is run with the basic case withconventional generators Here the values of fitnessfunction are evaluated for the base case using GSAPSO and hybrid PSOGSA algorithms And the resultsare tabulated in Table 4

(2) Three DGs placed In this case totally three DGs areplaced in the system By LSF method DG locationis prioritized at bus numbers 30 24 and 19 By WKmethod it is found to be at 30 24 and 21 This bringsout reduced loss results than no DG case

(3) Four DGs placed In this case 4 DGs are placed atlocation of bus numbers 30 24 19 and 21 under LSFand 30 24 21 and 19 under WK This case exhibitsmore reduced losses than previous case

(4) Five DGs placed (as shown in Figure 3) Under LSFDG location is found to be at bus numbers 30 24 1921 and 15 and under WK it is at 30 24 21 19 and 15Five-DG case brings out the most wondering resultsas in Table 3

It is to be noted that there is only slight variation in theorder of placement by both methods but the results obtainedhave shown ridiculous performance The DG supplyingvarious 119875119876 capacities concept is discussed next

(1) 119875 aloneThis system consists of multiple DGs supply-ing real power alone located under LSF and WK busmethods The system with multiple DGs supplying 119875

29

302426

25

2827

1915

18

10

17

1113

8

76

52

1

3 4

9

2021

22

14 16

23

12

sim

simsim

simsim

sim

Figure 3 Test system after addition of DGs at 5 locations

alone is optimized using 3 algorithms and the resultsare tabulated in Table 4

(2) 119876 aloneThis scheme consists ofmultipleDGs supply-ing reactive power alone located under LSF and WKbus method is solved under all three algorithmsThatis the DG is placed as a reactive power compensatingdevice This is a worst case with high power lossincurred compared to other cases Also it is noteconomical to include DG just for providing reactivepower alone So this case will be the worst case amongall and the results are tabulated in the Appendix

(3) Both 119875 and 119876 Under this scheme multiple DGs areplaced satisfying LSF and WK methods meant forproviding both real and reactive power This case isanalyzed with 3 4 and 5 DGs using 3 algorithmsThis provides a trustworthy way of DG installationHere the real and reactive power needs of the systemare compensated at the same time The inverters ofthe solar DG act as reactive power sources Thiscondition suits the main objective of the paper andit aims to compensate both the powers of the system

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

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Submit your manuscripts athttpwwwhindawicom

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

10 The Scientific World Journal

Table 4 Optimal values obtained for best result cases

Control variablesNo DG 5 DGsrsquo LSF

PSO GSA Hybrid PSO GSA Hybrid119875 119875119876 119875 119875119876 119875 119875119876

1198811198661

1089917 1043908 1099979 1069308 0953278 1027006 1018297 108685 10744541198811198662

1082366 1034023 1091815 1088762 0943393 1021787 1012454 1082779 10686751198811198665

1062927 100884 1069204 11 0969738 10009 0991337 1062865 10476961198811198668

1039571 1011822 1068482 1058558 0994771 101119864 + 00 994119864 minus 01 1067363 105325911988111986611

1050594 1022672 0985823 1070703 0974405 1024211 1011202 09792 097068811988111986613

101784 1032264 0988852 0946661 103617 1023102 100859 0980954 09798231198796ndash9 1007764 0992142 1081123 1065734 096849 0985923 0990601 1094339 1085628

1198796ndash10 1013321 0991287 1079838 1016578 1006752 0982872 0999366 1087027 107876

1198794ndash12 0974232 0988311 1090329 1096735 1010581 0988205 0983372 109214 1075721

11987927-28 0998855 0996503 1018112 11 1005154 1003624 099675 1005436 1006862

11987610

0 5085415 6710543 1013802 102632 4587534 5458556 6135134 5565911987612

5910549 4815389 6514192 5623678 5629074 4953474 4783481 6015178 563130611987615

9409826 5035168 6211642 9501841 918421 5153297 5083737 6233104 555851611987617

5640918 5263779 6238567 5019682 5075339 5216868 5443653 613873 537271811987620

3559025 5482562 6625873 3710988 3733716 5562014 5448493 5979799 549872711987621

5846265 5747952 6653561 5959905 5733319 5359788 5347674 6197945 560075211987623

10 6245089 6680471 9704982 9699361 5659589 5957551 6072773 547151611987624

5489716 4813535 6524242 512668 5156616 4408785 487739 6059574 551549411987629

3073788 6131744 6515898 2492969 2435573 6338891 6457947 6089195 552988811987511986611987830

mdash mdash mdash 7845027 7710418 6914579 7578882 8035948 754458611987511986611987824

mdash mdash mdash 105418 1054352 7564736 7497634 8003554 752326611987511986611987819

mdash mdash mdash 6197235 5876226 6588308 6687485 7937735 1133611987511986611987821

mdash mdash mdash 4147673 4009441 6719616 6538963 11336 759786311987511986611987815

mdash mdash mdash 6430637 6435573 744897 7417048 11336 76981211987611986611987830

mdash mdash mdash mdash 3052677 mdash 3311614 mdash 334869411987611986611987824

mdash mdash mdash mdash 2054185 mdash 2841042 mdash 326183211987611986611987819

mdash mdash mdash mdash 2623982 mdash 2817567 mdash 331315311987611986611987815

mdash mdash mdash mdash 2464426 mdash 353734 mdash 126246811987611986611987821

mdash mdash mdash mdash 3360252 mdash 2924316 mdash 3347687The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

Even though it has slightly higher fitness values than119875 alone case this condition is best considering itseffectiveness and will provide a new approach torenewable energy integration

In order to reduce the length of paper all the results aretabulated in Appendix except the best case (5-DG LSF) For5-DG LSF case the results are tabulated in Table 3 using 3algorithms It is found that the 5-DG placement gives themost optimal loss under LSF order of placement with hybridPSOGSA algorithm with DG supplying 119875 alone AnywayDGs supplying both 119875 and 119876 type are also equally effective

54 Selection of Best Algorithm with Increased Solar PowerGeneration To explain the results in an easy and quick way3D plots are drawn (Figures 4(a)ndash4(r)) The graphs shownin Figures 4(a)ndash4(r) describe the values of real power loss(119875119897) reactive power loss (119876

119897) and voltage deviations (VD)

for 3 4 and 5 DGsrsquo placements under all 3 algorithms

The results bring out a conclusion that 5 DGsrsquo case is bestcompared to 3 and 4 DGsrsquo placements And this gives usa hope that increased level of solar penetration increasesthe system stability thereby reducing losses And this caseis further improved when it is solved by hybrid PSOGSAalgorithm

55 Selection of Best ApproachwithDGType So far it is foundthat 5-DG placement under hybrid is the best result Thebest DG placement approach and best DG type are discussedin this section Therefore the fitness function value of 5-DG placement under 119875 alone 119876 alone and both 119875 and 119876

is plotted with LSF and WK bus method The results areillustrated in Figures 5(a)ndash5(c)

From the graphs obtained it is obvious that the 119875 alonecase is best under LSF placement method That is if a solarDG is placed in the system with constant power factor and issupplying real power alone this yields best results

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

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International Journal of

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FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

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Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Renewable Energy

Submit your manuscripts athttpwwwhindawicom

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International Journal of

RotatingMachinery

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EnergyJournal of

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Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

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International Journal ofPhotoenergy

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Solar EnergyJournal of

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Nuclear EnergyInternational Journal of

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High Energy PhysicsAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World Journal 11

12

33

45

Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(a) Weak bus placement 119875 alone

12 3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(b) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(c) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(d) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(e) Weak bus placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(f) Weak bus placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(g) Weak bus placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tion

(h) Weak bus placement119876 alone

12 3

34

5Algorithm

Number of DGs

0

05

1

15

Volta

ge d

evia

tion

(i) Weak bus placement both 119875 and119876

12

3

34

5Algorithm

Number of DGs

012345

Real

pow

er lo

ss

(j) LSF placement 119875 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(k) LSF placement119876 alone

12

3

34

5Algorithm

Number of DGs

02468

Real

pow

er lo

ss

(l) LSF placement both 119875 and119876

12

33

45

Algorithm

Number of DGs

05

10152025

Reac

tive p

ower

loss

(m) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

010203040

Reac

tive p

ower

loss

(n) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

0

10

20

30

Reac

tive p

ower

loss

(o) LSF placement both 119875 and119876

Figure 4 Continued

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

StructuresJournal of

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear InstallationsScience and Technology of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Solar EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Wind EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear EnergyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

High Energy PhysicsAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

12 The Scientific World Journal

12

33

45

Algorithm

Number of DGs

0

05

1

15Vo

ltage

dev

iatio

ns

(p) LSF placement 119875 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(q) LSF placement119876 alone

12

33

45

Algorithm

Number of DGs

002040608

1

Volta

ge d

evia

tions

(r) LSF placement both 119875 and119876

Figure 4 Algorithms used versus number of DGs placed for 119875119897 119876119897 and VD

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

3

35

4

45

5

55

Fitn

ess

(a) Comparison of 119875 119876 and 119875119876 in PSO under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

25

3

35

4

45

Fitn

ess

(b) Comparison of 119875 119876 and 119875119876 in GSA under LSF and weak busmethod

5 Q(W)

5 PQ(W)

5 P(W)

5 Q(L)

5 PQ(L)

5 P(L)

50 100 150 200 250 300 350 400 450 5000Iterations

152

253

354

455

Fitn

ess

(c) Comparison of 5 DGs supplying 119875 119876 and 119875119876 in hybrid PSOGSAunder LSF and weak bus method

Figure 5 Fitness values of 119875 alone 119876 alone and both 119875 and 119876 cases under LSF and WK bus methods

But this would not be a wanted result because the aim isto make the solar DG integration for reactive power demandTherefore DG supplying both 119875 and 119876 type yields betterresults than the initial case Therefore the best solar DGplacement is with 119875 alone and better placement is with both119875 and 119876 and the worst placement is with 119876 alone But oneinteresting point to note is that the LSF (green blue andyellow lines in Figure 5) turns out to be the best ordering inall three algorithms Since the fitness function is to minimizethe total loss the LSF method gives the best order comparedto WK bus method

The entire research consists of huge number of datacalculations and various comparisons to prove the originalityand efficiency of the work done To reduce length onlyimportant and best results among the achieved results aretabulated and highlighted The comparison of no DG with5-DGs case under three nature inspired algorithms is shownin Figure 6 All the no DG cases have high fitness value andall 5-DGs cases have minimum fitness value Also the resultsshow that hybrid PSOGSA possesses a better capability toescape from local optimums with faster convergence than thestandard PSO and GSA Table 4 shows the comparison of no

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

StructuresJournal of

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear InstallationsScience and Technology of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Solar EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Wind EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear EnergyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

High Energy PhysicsAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World Journal 13

Table 5

Control variables5 DGsrsquo WK bus

PSO GSA Hybrid119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

1198811198661

1069308 1069308 0953278 1019529 1031666 1018056 1085504 5 10853581198811198662

1088762 1088762 0943393 1014656 102243 1012203 1081343 102243 10805591198811198665

11 11 0969738 0994113 0999328 0991059 1061518 0999328 10605911198811198668

106119864 + 00 1058558 995119864 minus 01 998119864 minus 01 100119864 + 00 994119864 minus 01 1065719 1000208 106260611988111986611

1070703 1070703 0974405 1029595 1013144 1011856 0978619 1013144 096596911988111986613

0946661 0946661 103617 1030549 1023832 1006507 098228 1023832 09677851198796ndash9 1065734 1065734 096849 0975017 0990016 098121 1088784 0990016 11

1198796ndash10 1016578 1016578 1006752 0992393 0993312 0997504 1089788 0993312 11

1198794ndash12 1096735 1096735 1010581 0977333 0984654 0986355 1087525 0984654 11

11987927-28 11 11 1005154 0996589 1001132 0999475 1004579 1001132 1010224

11987610

1013802 1013802 102632 4580024 4724464 5449149 6102514 4724464 570671911987612

5623678 5623678 5629074 4959731 5004031 4782598 5936364 5004031 546917411987615

9501841 9501841 918421 5172064 5169386 5073348 6327842 5169386 541716611987617

5019682 5019682 5075339 5216286 526975 5476113 6193466 526975 559136111987620

3710988 3710988 3733716 5562951 5543824 5421283 6209261 5543824 562474411987621

5959905 5959905 5733319 5347173 5346397 5328151 5975524 5346397 572752611987623

9704982 9704982 9699361 5653493 5625959 5959699 6149491 5625959 537545511987624

512668 512668 5156616 4394021 4349062 4898907 6190531 4349062 553848211987629

2492969 2492969 2435573 6346391 6290148 6442072 6072827 6290148 560268911987511986611987830

7845027 7710418 6901596 7568974 8192465 755675811987511986611987824

105418 1054352 7545859 7472385 7997332 1133611987511986611987819

6197235 5876226 6574641 669055 8783369 757759411987511986611987821

4147673 4009441 6728393 6555974 8022007 1133611987511986611987815

6430637 6435573 7464848 7424602 113355 757492811987611986611987830

3551065 3052677 3027613 3315499 3027613 325307711987611986611987824

4691007 2054185 3297964 2830617 3297964 334404911987611986611987819

2969467 2623982 2889797 2813561 2889797 338799411987611986611987821

1945957 3360252 2942343 2936526 2942343 331940511987611986611987815

2860872 2464426 325268 3540268 325268 3326865The units of 119881119866 119879119876 119875119866 and119876119866 in the table are in pu pu MW and MVAR

No DG PSO5 DGsrsquo PSONo DG GSA

5 DGsrsquo GSANo DG Hybrid5 DGsrsquo Hybrid

Fitn

ess v

alue

12345678

100 150 200 250 300 350 400 450 50050Iterations

Figure 6 Comparison of no DG case with 5 DGs under LSF in 119875

alone

DG case with 5 DGs placed case under LSF ordering for solarDG supplying 119875 alone and DG supplying both 119875 and 119876 typeAlso the values of all the control variables are listed in Table 4

6 Conclusion

The research carried out in this paper is worthwhile andincludes several important points to be noted Basicallyoptimal siting and sizing of solar DG in IEEE 30-bus systemare the main concept but there are several parallel researcheswhich are carried out to enrich the results

Initially PSO algorithm is used to find out the optimal sizeof DG and the size of DG is tested for negative load impact (toavoid reverse power flow) Now the candidate bus for placingDGs is narrowed Further the order of DG placement is doneunder LSF and WK bus method of ordering Then the workwith number of DGsrsquo placement is elaborated with 3 4 and 5DGsrsquo placements AndDG supplying various119875119876 capacities isalso discussed with 119875 alone119876 alone and both 119875 and119876 casesAll the results are evaluated using 3 different algorithms (PSOGSA and hybrid PSOGSA) Below are the final conclusionsthat are visibly found from the research

(1) PSO gives the best result for optimal DG sizing in avery short span of time

(2) Negative load approach check prevents reverse powerflow condition throughout the process

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

StructuresJournal of

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear InstallationsScience and Technology of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Solar EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Wind EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear EnergyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

High Energy PhysicsAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

14 The Scientific World Journal

Table 6

Algorithm PSO GSA HybridDG type 119875 119876 119875119876 119875 119876 119875119876 119875 119876 119875119876

5 DGsrsquo WK busFIT 3096217 5114391 4177373 236139 42768 2364621 1764437 42768 1800886119875119871(MW) 3948507 648354 5023242 3047111 5474296 3029391 2234555 5474296 2291437

119876119871(MVAR) 170867 2791454 2145659 1294902 2360796 1304334 9823366 2360796 9988834

VD 0010127 0097633 0497416 379119864 minus 07 916119864 minus 07 220119864 minus 09 102119864 minus 05 916119864 minus 07 212119864 minus 07

119875 solar (MW) 3516237 3457517 3521534 3571248 4433067 4538128 119875 solar 1235515 1220013 1242602 1260144 1564244 1601315119876 solar (MVAR) 1601837 1355552 154104 1543647 154104 1663139 119876 solar 1269284 107413 1221109 1223175 1221109 131786

3 DGsrsquo WK busFIT 4264306 5883197 5437968 2894293 423752 2993451 2319683 4335688 2489634119875119871(MW) 4330255 6442337 6252752 3687836 5411755 3820455 2918493 5469429 3154463

119876119871(MVAR) 2190277 3106185 2693665 160355 2343406 1656292 1298041 2368064 1385381

VD 102119864 + 00 0949443 1010617 315119864 minus 11 216119864 minus 10 645119864 minus 11 307119864 minus 04 0096953 348119864 minus 04

119875 solar (MW) 2411545 2410796 213087 2126735 2835612 2709767 119875 solar 8509333 8506692 7518949 7504356 1000569 9561632119876 solar (MVAR) 1067152 7689319 9323842 9659002 0096953 1082097 119876 solar 8456037 6092963 7388148 7653726 7297119 8574461

3 DGsrsquo LSFFIT 4212065 5883197 5397336 281944 4251188 2989552 2212357 3921414 2450767119875119871(MW) 4251328 6442337 6206426 3569999 5437177 3802894 2776769 5484566 3046115

119876119871(MVAR) 217197 3106185 2678419 156994 2348176 1658536 1238338 2371063 1384625

VD 1003874 0949443 099394 112119864 minus 09 389119864 minus 08 637119864 minus 06 0003908 0091727 297119864 minus 06

119875 solar (MW) 2411545 2410796 21341 2129026 3100476 239273 119875 solar 8509333 7689319 7530345 7512442 1094028 8442944119876 solar (MVAR) 1067152 8506692 9313316 9662229 9208964 5900359 119876 solar 8456037 6092963 7379807 7656283 7297119 4675403

4 DGsrsquo WK busFIT 3590047 4310123 4204349 2692499 4204649 2574001 1820807 4310123 2163678119875119871(MW) 3943014 5477871 4608111 3458567 5360118 3222565 2284012 5477871 2728538

119876119871(MVAR) 1772519 236799 2097923 1481996 2328607 1434279 1008463 236799 1208686

VD 0795406 0045233 0897432 834119864 minus 06 246119864 minus 06 215119864 minus 02 0023527 0045233 688119864 minus 06

119875 solar (MW) 2827499 2813829 282066 3182575 4207584 3421096 119875 solar 9962176 9928825 9952928 1122998 148468 1207162119876 solar (MVAR) 1215449 1065566 1238437 1242506 1215449 1352161 119876 solar 9631134 8443475 9813289 984553 9631134 1071443

4 DGsrsquo LSFFIT 3576975 4651419 4188804 2579767 4209986 2567546 1925261 4309961 2001535119875119871(MW) 3924537 5831084 4584043 3298612 536723 3227293 2429493 5477474 253162

119876119871(MVAR) 1767696 2599136 2092145 1425248 2331448 1433873 1074905 2367911 1115468

VD 0792166 0020734 089499 804119864 minus 06 143119864 minus 05 749119864 minus 03 601119864 minus 05 0045335 906119864 minus 07

119875 solar (MW) 2827499 2813829 2819092 3184273 3883614 3821796 119875 solar 9977059 9928825 9947396 1123597 1370365 1348552119876 solar (MVAR) 1301001 1065567 1238567 1239378 1215449 1244819 119876 solar 1030904 8443475 9814316 9820745 9631134 9863862

(3) LSF ordering serves best compared to WK busmethod ensuring that the total losses are minimized

(4) 5 DGsrsquo placement case is the best proving thatincreased level of DG penetration decreases the totalpower losses in the system and maintains flattervoltage profile

(5) DG supplying real power (119875) alone is the best suitablecase for the system denoting that when DG is underunity power factor environment it gives best results

(6) DGs supplying both real (119875) and reactive (119876) poweralso give better results and pave way for a newtechnology of ldquogrid interactive solar power for real

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

StructuresJournal of

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear InstallationsScience and Technology of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Solar EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Wind EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear EnergyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

High Energy PhysicsAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World Journal 15

and reactive power sourcerdquo Here there is no needfor maintaining the power factor at unity and anychange in voltage or reactive power of the system canbe gently met with multiple DGs which will providemore stability to the system

(7) Algorithm-wise the hybrid PSOGSA performs in asuper way by combining the advantages of both PSOand GSA

Thus the best optimal values of fitness function realpower losses reactive power losses voltage deviations andcontrol variables are obtained from hybrid PSOGSA algo-rithm solving 5 DGsrsquo case placed in LSF order with DGssupplying real power (119875) alone

Thus the conventional system is reconfigured by opti-mally integrating the solar DG at globally best locations withglobally best size This makes the grid work smarter whichcomes under smart grid environment Future works involvefurther improvisation that allows increased solar penetrationby different ways to achieve better results

Appendix

See Tables 5 and 6

Conflict of Interests

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

References

[1] A A Aquino-Lugo R Klump and T J Overbye ldquoA controlframework for the smart grid for voltage support using agent-based technologiesrdquo IEEE Transactions on Smart Grid vol 2no 1 pp 161ndash168 2011

[2] V Loia A Vaccaro and K Vaisakh ldquoA self-organizing architec-ture based on cooperative fuzzy agents for smart grid voltagecontrolrdquo IEEE Transactions on Industrial Informatics vol 9 no3 pp 1415ndash1422 2013

[3] A I Nikolaidis F M Gonzalez-Longatt and C A Char-alambous ldquoIndices to assess the integration of renewableenergy resources on transmission systemsrdquo Conference Papersin Energy vol 2013 Article ID 324562 8 pages 2013

[4] Swaminathan and Umashankar ldquoInfluence of Solarpower inSmart Gridsrdquo Energetica India Magazine NovemberDecem-ber Issue

[5] C Timpe D Bauknecht M Koch and C Lynch ldquoIntegrationof electricity from renewable energy sources into Europeanelectricity gridsrdquo ETCACC Technical Paper 201018 2010

[6] R M Kamel and B Kermanshahi ldquoOptimal size and locationof distributed generations for minimizing power losses in aprimary distribution networkrdquoComputer Scienceamp Engineeringand Electrical Engineering vol 16 no 2 pp 137ndash144 2009

[7] KM Rogers R KlumpHKhurana AAAquino-Lugo andTJ Overbye ldquoAn authenticated control framework for distributedvoltage support on the smart gridrdquo IEEE Transactions on SmartGrid vol 1 no 1 pp 40ndash47 2010

[8] K Y Lee Y M Park and J L Ortiz ldquoA united approach tooptimal real and reactive power dispatchrdquo IEEE Transactions onPower Apparatus and Systems vol 104 no 5 pp 1147ndash1153 1985

[9] W Prommee and W Ongsakul ldquoOptimal multiple distributedgeneration placement in microgrid system by improved reini-tialized social structures particle swarm optimizationrdquo Euro-pean Transactions on Electrical Power vol 21 no 1 pp 489ndash5042011

[10] A Ameli S Bahrami F Khazaeli and M-R Haghifam ldquoAmultiobjective particle swarm optimization for sizing andplacement of DGs fromDGownerrsquos and distribution companyrsquosviewpointsrdquo IEEE Transactions on Power Delivery vol 29 no 4pp 1831ndash1840 2014

[11] K Iba ldquoReactive power optimization by genetic algorithmrdquoIEEE Transactions on Power Systems vol 9 no 2 pp 685ndash6921994

[12] L L Lai and J T Ma ldquoApplication of evolutionary program-ming to reactive power planning-comparison with nonlinearprogramming approachrdquo IEEE Transactions on Power Systemsvol 12 no 1 pp 198ndash206 1997

[13] M A Abido and J M Bakhashwain ldquoOptimal VAR dispatchusing a multiobjective evolutionary algorithmrdquo InternationalJournal of Electrical Power amp Energy Systems vol 27 no 1 pp13ndash20 2005

[14] S P Ghoshal A Chatterjee and V Mukherjee ldquoBio-inspiredfuzzy logic based tuning of power system stabilizerrdquo ExpertSystems with Applications vol 36 no 5 pp 9281ndash9292 2009

[15] S Pothiya I Ngamroo and W Kongprawechnon ldquoAnt colonyoptimisation for economic dispatch problem with non-smoothcost functionsrdquo International Journal of Electrical Power ampEnergy Systems vol 32 no 5 pp 478ndash487 2010

[16] H I Shaheen G I Rashed and S J Cheng ldquoOptimal locationand parameter setting of UPFC for enhancing power systemsecurity based on Differential Evolution algorithmrdquo Interna-tional Journal of Electrical Power amp Energy Systems vol 33 no1 pp 94ndash105 2011

[17] R Suresh C Kumar S Sakthivel and S Jaisiva ldquoApplication ofgravitational search algorithm for real power loss and voltagedeviation optimizationrdquo International Journal of EngineeringScience and Innovative Technology vol 2 no 1 pp 283ndash2912013

[18] M F Kotb K M Shebl M El Khazendar and A El HusseinyldquoGenetic algorithm for optimum siting and sizing of distributedgenerationrdquo in Proceedings of the 14th International Middle EastPower Systems Conference (MEPCON rsquo10) Paper ID 196 CairoUniversity December 2010

[19] S Deshmukh B Natarajan and A Pahwa ldquoVoltageVARcontrol in distribution networks via reactive power injectionthrough distributed generatorsrdquo IEEE Transactions on SmartGrid vol 3 no 3 pp 1226ndash1234 2012

[20] M Conley Electricity Market Framework for Renewable andDistributed Generation School of Electrical Computer andTelecommunications Engineering 2013

[21] G Celli E Ghiani S Mocci and F Pilo ldquoA multiobjectiveevolutionary algorithm for the sizing and siting of distributedgenerationrdquo IEEE Transactions on Power Systems vol 20 no 2pp 750ndash757 2005

[22] S Mishra D Das and S Paul ldquoA simple algorithm for distri-bution system load flow with distributed generationrdquo in Pro-ceedings of the Recent Advances and Innovations in Engineering(ICRAIE rsquo14) pp 1ndash5 IEEE Jaipur India May 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

StructuresJournal of

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear InstallationsScience and Technology of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Solar EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Wind EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear EnergyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

High Energy PhysicsAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

16 The Scientific World Journal

[23] K Prakash and M Sydulu ldquoParticle swarm optimization basedcapacitor placement on radial distribution systemsrdquo in Proceed-ings of the IEEE Power Engineering Society General Meeting pp1ndash5 IEEE Tampa Fla USA June 2007

[24] P Sharma J Mehra and V Kumar ldquoLine flow analysis of IEEEbus systemwith the load sensitivity factorrdquo International Journalof Emerging Technology and Advanced Engineering vol 4 no 52014

[25] 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

[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] D Q Hung and N Mithulananthan ldquoMultiple distributedgenerator placement in primary distribution networks for lossreductionrdquo IEEE Transactions on Industrial Electronics vol 60no 4 pp 1700ndash1708 2013

[28] A Parizad A Khazali and M Kalantar ldquoOptimal placementof distributed generation with sensitivity factors consideringvoltage stability and losses indicesrdquo in Proceedings of the 18thIranianConference on Electrical Engineering (ICEE rsquo10) pp 848ndash855 Isfahan Iran May 2010

[29] A Elmitwally ldquoA new algorithm for allocating multiple dis-tributed generation units based on load centroid conceptrdquoAlexandria Engineering Journal vol 52 no 4 pp 655ndash663 2013

[30] K Iniewski Smart Grid Infrastructure and Networking TataMcGraw-Hill Noida India 2012

[31] A R Malekpour and A Pahwa ldquoReactive power and voltagecontrol in distribution systems with photovoltaic generationrdquoin Proceedings of the North American Power Symposium (NAPSrsquo12) pp 1ndash6 IEEE Champaign Ill USA September 2012

[32] M H Moradi and M Abedini ldquoA combination of genetic algo-rithm and particle swarm optimization for optimal DG locationand sizing in distribution systemsrdquo International Journal ofElectrical Power and Energy Systems vol 34 no 1 pp 66ndash742012

[33] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 Perth Wash USA November1995

[34] S Duman Y Sonmez U Guvenc and N Yorukeren ldquoAppli-cation of gravitational search algorithm for optimal reactivepower dispatch problemrdquo in Proceedings of the InternationalSymposium on Innovations in Intelligent Systems and Applica-tions (INISTA rsquo11) pp 519ndash523 IEEE Istanbul Turkey June2011

[35] E Rashedi H Nezamabadi-pour and S Saryazdi ldquoGSA agravitational search algorithmrdquo Information Sciences vol 179no 13 pp 2232ndash2248 2009

[36] S Mirjalili and S Z M Hashim ldquoA new hybrid PSOGSAalgorithm for function optimizationrdquo in Proceedings of the Inter-national Conference on Computer and Information Application(ICCIA rsquo10) pp 374ndash377 Tianjin China November 2010

[37] K Lenin B Ravindranath Reddy and M Surya Kalavathi ldquoAnew hybrid PSOGSA algorithm for solving optimal reactivepower dispatch problemrdquo International Journal ofMechatronicsElectrical and Computer Technology vol 4 no 10 pp 111ndash1252014

[38] N Augustine S Suresh P Moghe and K Sheikh ldquoEconomicdispatch for a microgrid considering renewable energy costfunctionsrdquo in Proceedings of the IEEE PES Innovative Smart GridTechnologies (ISGT rsquo12) pp 1ndash7 IEEE Washington DC USAJanuary 2012

[39] R S Al Abri E F El-Saadany and Y M Atwa ldquoOptimalplacement and sizing method to improve the voltage stabilitymargin in a distribution system using distributed generationrdquoIEEE Transactions on Power Systems vol 28 no 1 pp 326ndash3342013

[40] S Kansal B B R Sai B Tyagi and V Kumar ldquoOptimalplacement of distributed generation in distribution networksrdquoInternational Journal of Engineering Science and Technologyvol 3 no 3 pp 47ndash55 2011

TribologyAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

StructuresJournal of

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear InstallationsScience and Technology of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Solar EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Wind EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear EnergyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

High Energy PhysicsAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

TribologyAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

FuelsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal ofPetroleum Engineering

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Power ElectronicsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Advances in

CombustionJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Renewable Energy

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

StructuresJournal of

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear InstallationsScience and Technology of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Solar EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Wind EnergyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Nuclear EnergyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

High Energy PhysicsAdvances in

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014