2251-6832-5-3

Sedighizadeh et al. International Journal of Energy and EnvironmentalEngineering 2014, 5:3http://www.journal-ijeee.com/content/5/1/3

ORIGINAL RESEARCH Open Access

Optimal reconfiguration and capacitor placementfor power loss reduction of distribution systemusing improved binary particle swarmoptimizationMostafa Sedighizadeh1*, Marzieh Dakhem2, Mohammad Sarvi2 and Hadi Hosseini Kordkheili3,4

Abstract

Optimal reconfiguration and capacitor placement are used to reduce power losses and keep the voltage within itsallowable interval in power distribution systems considering voltage, current, and radial condition constraints. It isneeded to solve two nonlinear discrete optimization problems simultaneously, so an intelligent algorithm is usedto reach an optimum solution for network power losses. An effective method and a new optimization algorithmusing ‘improved binary PSO’ is presented and discussed to minimize power losses in distribution network bysimultaneous network reconfiguration and capacitor placement. The proposed model uses binary strings whichrepresent the state of the network switches and capacitors. The algorithm is applied and tested on 16- and 33-busIEEE test systems to find the optimum configuration of the network with regard to power losses. Five different casesare considered, and the effectiveness of the proposed technique is also demonstrated with improvements in powerloss reduction compared to other previously researched methods, through MATLAB under steady-state conditions.

Keywords: Optimal capacitor placement; Optimal reconfiguration; Power loss reduction; Power distributionnetwork; Improved binary particle swarm optimization

BackgroundA power distribution network consists of a group of radialfeeders which can be connected together by several tieswitches and tie lines. Power loss reduction in the networkis a major concern of electric distribution utilities. Amongconventional methods, optimal reconfiguration and cap-acitor placement are two effective methods which can beapplied on the network.Optimization methods have been used to find the opti-

mal location and size of different devices such as capacitors,FACTS, and distributed generations, in power distributionsystems [1-3]. In this paper, optimization methods are usedfor optimal reconfiguration and capacitor allocation inpower systems.Feeder reconfiguration is the process of changing the

distribution network topology by changing the status of

* Correspondence: [email protected] of Electrical and Computer Engineering, Shahid Beheshti University,G. C., Tehran, 1983963113, IranFull list of author information is available at the end of the article

© Sedighizadeh et al.; licensee Springer. TCommons Attribution License (http://creativecoreproduction in any medium, provided the orig

2014

its switches. There are two kinds of switches based ontheir open or close conditions: normally open (N.O.)and normally close (N.C.). N.O. switches are consideredas tie switches in the network. During a reconfigurationprocess, the status of these switches will be changedoptimally based on the proposed objective function.Opening and closing of these switches can change theamount of power losses.On the other hand, capacitors are mostly used for react-

ive power compensation in distribution networks. Theyare also used for power loss reduction and improvementof voltage profiles. The advantages of this kind of compen-sation depend on how and where to place the capacitorsin the network. In recent years, according to properresults of optimal capacitor placement and optimal distri-bution network reconfiguration for power loss reduction,the idea of using these two methods simultaneously hasbeen considered to maximize the amount of power lossreduction. The combination of these approaches makesthe optimization process more complex.

his is an open access article distributed under the terms of the Creativemmons.org/licenses/by/2.0), which permits unrestricted use, distribution, andinal work is properly cited.

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Optimal capacitor placement and feeder reconfigur-ation have been investigated in many papers separately,and various approaches have been used with differentobjective functions to model the network and optimizethe problem solution.In [4], the branch and bound heuristic method has

been used to solve the reconfiguration problem withminimum power losses. In this method, all networkswitches become closed and will be opened one by oneto obtain a new radial configuration. In [5], the problemof power loss reduction and load balancing reconfigur-ation is formulated as an integer programming method.The branch exchange heuristic method is proposed in[6]. In this method, power loss reduction is obtained byclosing a switch and opening another one. In [7], analgorithm has been proposed to obtain switch patternsas a function of time. A load flow method based on aheuristic algorithm has been introduced in [8] to deter-mine a configuration with minimal power losses. Animplementation using a genetic algorithm (GA) hasbeen proposed in [9]. An improved genetic algorithmbased on fuzzy multi-objective approach has been sug-gested in [10] to solve the problem. In [11], a methodbased on binary particle swarm optimization (BPSO)algorithm is presented to balance network loads. In[12], the authors have used ant colony search algorithm(ACSA) to solve the reconfiguration problem. More-over, they have performed a comparison which showsbetter results for ACSA compared to GA and simulatedannealing (SA). A solution procedure employing simu-lated annealing is proposed in [13] and [14] to searchfor an acceptable noninferior solution. In [15], the net-work reconfiguration problem has been solved by aharmony search algorithm, and an optimal switchingcombination in the network is obtained which resultsin minimum power loss.Many investigations have also been performed to solve

the optimal capacitor placement problem. Grainger andLee [16-18] formulated the problem using a nonlinearprogramming model and solved it by simple iterative pro-cedures based on gradient search. In [19] and [20], thecapacitor placement problem is modeled as a mixed inte-ger nonlinear programming problem and is solved using adecomposition method and a power flow algorithm. In[21], tabu search (TS) algorithm is used and a sensitivityanalysis is performed to reduce the search space. It is

Figure 1 A string of random typical particle.

shown that TS has a better performance compared to SA.In [22], the placement problem is solved together withfinding the amount of proper capacitors. An approachusing genetic algorithm and a new type of sensitivity ana-lysis method is used. In [23], the implementation of inte-grated evolutionary algorithms is investigated for solvingthe capacitor placement optimization problem withreduced annual operating cost. Differential evolution andpattern search are used as metaheuristic optimizationtools to solve optimal capacitor placement problem.Some papers considered these two problems simultan-

eously. In [24], optimal capacitor placement and recon-figuration problems are considered as two consecutivestages, and the branch exchange method is used forreconfiguration process. In [25], SA is used to solveoptimal capacitor placement and reconfiguration. Theamounts of capacitances are determined by a discreteoptimization algorithm. Simultaneous solution of thesetwo problems has been approached with the objective ofpower loss minimization in [26]; the ACSA has beencompared to SA and GA, and its superior results havebeen presented. In [27], a modified PSO algorithm isused to solve the optimal capacitor placement and re-configuration problems. The proposed objective functioncontains power loss minimization and cost reduction inspecific periods of time. In [28], a heuristic algorithm isused for reliability evaluation considering protectionschemes and isolation points in the distribution network.The problems are solved by harmony search algorithm.In [29], a deterministic approach for network reconfigur-ation and a heuristic technique for optimal capacitorplacement for power loss reduction and voltage profileimprovement in distribution networks are presented. Aminimum spanning tree algorithm is utilized to deter-mine the configuration of minimum power losses alongwith a GA for optimal capacitor placement.The most optimum or a global optimum solution is still

to be found of all previous reports, regardless of theirobjective function. Meanwhile, each proposed methodtries to reach a more optimal or near optimal solutioncompared to previous works.In this paper, some planning issues for the priority of re-

configuration and capacitor placement problems in powerdistribution networks are investigated based on a new im-proved BPSO (IBPSO) algorithm. The algorithm is used tosolve the simultaneous reconfiguration and optimal


Figure 2 The flowchart of the proposed algorithm.



Table 1 Binary equivalent of capacitors

Types of capacitors Binary equivalent

0: no capacitor (000)

1: 300-KVAR capacitor (001)



4: 1,200-KVAR capacitor (100)



Table 2 Results of optimal reconfiguration and capacitorplacement in a 16-bus system

Originalconfigurationof system

After reconfigurationand capacitorplacement

Tie switches 15, 21, 26 19, 7, 26

Power loss (kW) 511.4 448.07

Maximum voltage (pu) Umax = 1 Umax = 1

Buses 1, 2, 3 Buses 1, 2, 3

Minimum voltage (pu) Umin = 0.9693 Umin = 0.9757

Buses 12 Buses 12

Power loss reduction (%) - 12.38


capacitor placement problem. The proposed method em-ploys a different structure for the optimization algorithm(using logical operators AND, OR, and XOR). A near-global optimum feeder reconfiguration and capacitor set-ting is obtained, which shows the best results among otherprevious similar works. The proposed IBPSO algorithm iscompared with other intelligent algorithms (SA, GA, antcolony optimization (ACO), and ACSA), and its betterperformance is presented. Then, the priority of reconfigur-ation and the capacitor placement cases are presented inthe ‘Discussion’ subsection.

MethodsProblem formulationThe objective is to minimize network power losses con-sidering constraints under a specified load pattern. Themathematical model of the problem can be expressed asfollows [26]:

Min F ¼ Min PT ;Loss þ λV � SCV þ λI � SCI� �

; ð1Þ

where PT, Loss is the total real power loss of the network.The parameters λV and λI are penalty constants, SCV is

Figure 3 The 16-bus distribution system.

squared sum of the violated voltage constraints, and SCIis the squared sum of the violated current constraints[26]. The equation, λV × SCV + λI × SCI, is used to applyconstraints on the objective function. The voltage mag-nitude at each bus must remain within its allowableinterval. Besides, the current of each branch must satisfythe branch current limitations. These intervals areexpressed as follows:

Umin≤ Uij j≤Umax ð2Þ

Iij j≤Ii;max ð3Þ

where is |Ui| the voltage magnitude of bus I, and Umin

and Umax are minimum and maximum allowable volt-ages, respectively, which are considered as Umin = 0.95pu and Umax = 1.05 pu. |Ii| is the current magnitude andIi,max is the maximum current limit of the branch i.Thus, the penalty constants are determined as follows:

1. λV and/or λI equal to 0 if the associated voltage/current constraint is not violated.

2. A significant value is given to λV and/or λI when theassociated voltage/current constraint is violated; thismakes the objective function move away from theundesirable solution [26]. This significant value isconsidered equal to the amount of objectivefunction before the optimization process (in thisstudy, the initial power loss of the test network)multiplied by a large number (for example 104). Thisprocess is performed automatically by the developedprogram.

To obtain the network's power losses and voltagedrops, a load flow calculation should be performed dur-ing the optimization process. Conventional methods ofload flow are Gauss-Seidel and Newton-Rafson whichlead to unreliable solutions in radial distribution net-works due to the radial structure and the high R/X ratio.


Figure 4 Comparison of voltage profile before and after optimal reconfiguration and capacitor placement in 16-bus test system.Dashed line, before optimization; solid line, after optimization.


Therefore, a backward-forward load flow method (BF-load flow) is used in this paper. Other previous reportschose the same load flow method, too [27,30]. The BF-load flow method is fully presented in [31], which isexplained briefly in the following section.

Backward-forward load flow methodA BF-load flow has been used to evaluate the objectivefunction of the problem. In BF-load flow, the first step is

Figure 5 The 33-bus test system.

to introduce the topology of distribution network to thecomputer program as meaningful matrices using graphtheory [31]. Then, the main process will begin as follows:

1. The vector (Un1×n) containing the voltages of thenodes is defined. At first, we assume that all theelements of the vector are equal to 1 p.u. (or 1.05p.u.) with zero angle.

2. The currents of nodes (In1×n) are calculated by thefollowing equation:

IL ið Þ½ � ¼ SL ið Þ=U ið Þ½ �� ð4Þwhere IL(i), SL(i), and U(i) are current, apparentpower, and voltage of the load in the node i,respectively.

3. The current of the last node is the current of thelast branch. Thus, the currents of network branches

Table 3 Results of optimal reconfiguration and capacitorin a 33-bus system

Originalconfigurationof system

After reconfigurationand capacitorplacement

Tie-switches 33, 34, 35, 36, 37 7, 9, 14, 32, 37

Power losses (kW) 202.68 93.061

Maximum voltage (pu) Umax = 1 Umax = 1

Buses 0 Buses 0

Minimum voltage (pu) Umin = 0.9131 Umin = 0.9587

Power loss reduction (%) - 54.08


Figure 6 Comparison of voltage profile before and after optimal reconfiguration and capacitor placement in 33-bus test system.Dashed line, before optimization; solid line, after optimization.


(J1×f ) are calculated starting from the last node tothe first one using Kirchhoff's current law (backwardsweep); f is the number of network branches.

4. The voltage drop is calculated by the equation LU =ZJ. Z is the impedance and LU1×f is the voltage dropof each branch.

5. The voltage of reference node is equal to 1 or 1.05p.u. The voltage of the next node is calculated bysubtracting the corresponding voltage drop from thevoltage of previous node (forward sweep) and Un1×nwill be updated.

6. The procedure is restarted from step 2 with recentlycalculated Un1×n. The convergence condition is

ΔUn ¼ Un kð Þ−Un k−1ð Þ < ∈ ð5Þwhere K is the iteration number and ∈ is the calculationerror (precision).

Improved binary particle swarm optimizationOverviewBPSO algorithms were introduced by Kennedy andEberhart in 1997. In a standard BPSO algorithm, realnumbers are used in the optimization process. The pro-posed algorithm uses a method similar to the standardbasic PSO, and a random method produces a newswarm. The particles' velocities are calculated using theestablished relationships in the standard mode. The newamounts of particle position are 0 or 1 which is retrievedfrom the comparison of calculated velocity with a ran-dom number. This method has several disadvantages.

The information of particles obtained from previousiterations is not used in the next iteration, and thepositions of particles are updated almost randomly whichleads to inappropriate results. Therefore, IBPSO algorithmhas been introduced in [32].

Improved binary particle swarm optimizationIn IBPSO method, the information of every particle initerations is used to determine new particles. Thus, ifeach particle contains d dimensions (variables), in theiteration k + 1, we will have

vkþ1i;d ¼ w1⊗ pbestki;d⊕xki;d

� �þ w2⊗ gbestkd⊕xki;d

� �;

ð6Þwhere ‘⨂’, ‘⨁’, and ‘+’ denote ‘XOR’, ‘AND’, and ‘OR’ ope-rators, respectively. Variables w1 and w2 are inertia weightfactors which are random binary strings. vkþ1

i;d is the dth

dimension velocity of particle i at iteration k + 1. pbestki;dand gbestkd are the dth dimension of the personal bestposition of particle i until iteration k and the dth dimen-sion of the best position of all particles in the swarm atiteration k, respectively. The new position of each particlecan be updated by Equation 7:

xkþ1i;d ¼ xki;d⊕vkþ1

i;d ð7Þ

where xki;d is the dth dimension position of particle i at

iteration k [32]. The problem will be formulated based onthis optimization process.


Table 4 Comparison of different cases for optimal reconfiguration and capacitor placement in 16-bus system

Parameters Original case Case 1 Case 2 Case 3 Case 4 Case 5

Tie switches 15 17 15 17 17 17

21 19 21 19 19 19

26 26 26 26 26 26

Maximum voltage (pu) 1 1 1 1 1 1

Minimum voltage (pu) 0.9693 0.9716 0.973 0.9763 0.9736 0.9757

Power losses (kW) 511.44 466.13 487.2 448.7 453.10 448.07

Placed capacitors (KVAR) (node number)

1 - - - - - -

2 - - - - - -

3 - - - - - -

4 - - 1,800 1,200 1,800 1,500

5 - - 300 - 300 -

6 - - - - - -

7 - - 900 900 900 900

8 - - 1,800 1,800 1,800 1,800

9 - - - 1,200 - 900

10 - - 1,500 - 1,500 -

11 - - - - - -

12 - - - - - -

13 - - - - - 900

14 - - - - - -

15 - - - - - -

16 - - - 300 - -


Implementation of the optimization algorithmImplementation steps of the IBPSO algorithm to solvethe optimal reconfiguration and capacitor placementproblem are as follows:

1. The inputs of the procedure are w1 and w2, swarmsize (P), objective function, and maximum iterations.

2. To start the procedure, the initial swarm is generatedcontaining random binary strings. Each particle of theswarm indicates the ON/OFF condition of switches,the condition of capacitor installation on each node,and the size of located capacitors. Thus, if the numberof decision variables (dimension of the search space) isconsidered N, then the initial swarm will be a P ×Nmatrix as follows:

S ¼

S11S12…S1NS21S22…S2NS31S32…S3N

:::

2666666664

3777777775

SP1SP2…SPN P�N

Each row of S is a particle flying in the search space. Aspecial coding process is used to build the matrix S.As mentioned above, each particle consists of three

parts: the ON/OFF condition of switches, the conditionof capacitor installation on each node, and the size of lo-cated capacitors which are denoted by three variables:switch_condition, cap_condition, and cap_size, respectively.In the switch_condition, the length of binary string is

r1 + r2 where r1 is the number of normally closedswitches and r2 is the number of tie switches. Everybranch is considered as a potential switch which can beopen or closed. Open and closed switches are shown by0 and 1, respectively.In the cap_condition, the number of required bits (r3)

equals the number of system nodes. The installation of acapacitor on a node is shown by 1. If there is no capacitoron a node, the corresponding bit will be 0 in the string.In the third part of the string, the cap_size, the length

of the string equals the product of the number of nodes(r3) and the number of capacitors' encoding bits. For ex-ample, (001)2 to (110)2 is the binary number of capacitortypes 1 to 6 for six different capacitances, and the num-ber of bits is 3. Thus, in a 16-node network, the cap_sizewill be a 48-bit string.


Table 5 Comparison of different cases for optimal reconfiguration and capacitor placement in 33-bus system

Parameters Original case Case 1 Case 2 Case 3 Case 4 Case 5

Tie switches 33, 34, 35, 36, 37 7, 9, 14, 32, 37 33, 34, 35, 36, 37 7, 9, 14, 32, 37 7, 10, 34, 36, 37 7, 9, 14, 32, 37

Maximum voltage (pu) 1 1 1 1 1 1

Minimum voltage (pu) 0.9131 0.9378 0.9389 0.9612 0.9658 0.9585

Power losses (kW) 202.68 139.55 134.2 94.26 95.91 93.06

Placed capacitors (KVAR) (node number)

1 - - 900 - 900 -

2 - - - - - -

3 - - 300 - 300 -

4 - - - 1,200 - -

5 - - - - - -

6 - - - - - -

7 - - - 900 - 600

8 - - - 1,800 - -

9 - - - 1,200 - -

10 - - - - - -

11 - - - - - -

12 - - - - - 300

13 - - - - - -

14 - - 300 - 300 -

15 - - - - - -

16 - - - 300 - -

17 - - - - - -

18 - - - - - -

19 - - - - - -

20 - - - - - -

21 - - - - - -

22 - - 300 - 300 -

23 - - - - - -

24 - - 300 - 300 -

25 - - - - - 300

26 - - - - - -

27 - - - - - -

28 - - - - - -

29 - - - - - -

30 - - 600 - 600 600

31 - - 600 - 600 -

32 - - - - - -

33 - - - - - 300


Finally, cap_size and cap_condition bits are multi-plied together to determine the location and the sizeof the capacitors. Figure 1 shows a string of a typicalparticle.

3. Network radial condition is verified in each iteration.This function checks two main conditions: (1) thenetwork must contain no loops, and (2) all of thenodes must be energized (connected the supply).


Table 6 Comparison of intelligent algorithms to solveoptimal reconfiguration and capacitor placementproblem in 16-bus system

Used intelligent algorithm Power losses (kW)

IBPSO 448.07

GA [26] 448.2

SA [26] 448.3

ACSA [26] 448.1

ACO [30] 450


Thus, the program considers the radial network as atree of the initial graph, and the above-mentionedconditions are verified as the network remains a treein different states. In other words, a network re-mains radial if the graph of the network remains atree. If it is confirmed, then the load flow will be runon the new generated network, and the nodevoltages and the branch currents will be recalculated.In this step, the constraints are also checked andapplied to the objective function according toEquation 1.

4. pbest and gbest are calculated and updated in eachiteration. They are the strings which result to theleast amount of the objective function. The programwill continue to run in order to reach the maximumpredefined number of iterations. The last calculatedgbest is the best status of the network, and itscorresponding power loss is the optimum solution.

Figure 2 shows the flowchart of the proposed process.

Results and discussionOptimal reconfiguration and capacitor placement areperformed using the proposed algorithm on two IEEEtest systems. The systems are well-known benchmarksystems which are selected to compare the obtained re-sults to previously reported optimization attempts on thesame networks. The algorithm has been implemented inMATLAB R2010.Six types of capacitors 300, 600, 900, 1,200, 1,500

and, 1,800 kilovolt-amperes reactive (KVAR) are usedin capacitor placement. The capacitors are numberedfrom 1 to 6, respectively, and 0 denotes a node with-out any capacitor. The selected ranges of capacitors

Table 7 Comparison of intelligent algorithms to solveoptimal reconfiguration and capacitor placementproblem in 33-bus system

Used intelligent algorithm Power losses (kW)

IBPSO 93.061

ACO [30] 95.79

are the same as previous reports for comparison purposes[26,27]. Their binary equivalent is defined according toTable 1.

16-Bus systemThe first study is performed on a 16-bus system withthree feeders which is shown in Figure 3. Thirteen con-tinuous lines indicate normally closed switches, whilenormally open switches (tie switches) 15, 21, and 26 arerepresented by dotted lines. The initial network powerloss is 511.44 kW [6].The results of the optimization on a 16-bus test sys-

tem are shown in Table 2. Power loss has been reducedto 12.38% compared to the initial condition of the net-work. Five capacitors (1,500, 900, 1,800, 900, and 900KVAR) are used in the optimal case which are placed onbuses 4, 7, 8, 9, and 13, respectively.Figure 4 shows the voltage profiles before and after opti-

mal reconfiguration and capacitor placement. The voltageprofile is obviously improved after the optimization.

33-Bus systemThe other case is the Baran and Wu test system [19,20]shown in Figure 5 [19]. The normally open switches(s33, s34, s35, s36, s37) are represented by dotted linesand normally closed ones (s1 to s32) are illustrated bysolid lines. The initial power loss is 202.68 kW [33].The results of the IBPSO algorithm on 33-bus test net-

work are shown in Table 3. Power loss reaches to 93.061kW which shows a 54.08% reduction. Five capacitors(600, 300, 300, 600, and 300 KVAR) are used in the opti-mal case which are placed on buses 6, 11, 24, 29, and 32,respectively.Figure 6 shows the voltage profiles before and after

the optimal reconfiguration and capacitor placement.Again, it is observed that the voltage profile is improvedafter applying the optimization.

DiscussionTables 4 and 5 compare five different cases which employoptimal reconfiguration and capacitor placement separatelyand simultaneously. The following cases are considered:

Case 1. Only network reconfigurationCase 2. Only capacitor placementCase 3. First, network reconfiguration and thencapacitor placementCase 4. First, capacitor placement and then networkreconfigurationCase 5. Network reconfiguration and optimal capacitorplacement, simultaneously

In case 5, optimal reconfiguration and capacitor place-ment simultaneously leads to the most optimum network



condition. The comparison is mainly performed betweencases 3, 4, and 5 which both reconfiguration and capacitorplacements are carried out. However, there are differencesbetween priorities of these cases. According to the amountof power loss reduction that resulted from the simulation,optimization cases are categorized in distribution net-works respectively as follows:

1. In case 5, the optimal network reconfiguration andcapacitor placement is simultaneous.

2. In case 3, there is network reconfiguration initially,and then capacitor placement follows.

3. In case 4, there is capacitor placement initially, andthen network reconfiguration follows.

The other conclusion is obtained by comparing the re-sults of IBPSO algorithm to other intelligent methodsthat were previously applied in different references withsimilar conditions (constant power loads and the consid-eration of voltage and current constraints) in 16-bus sys-tem. Table 6 shows this comparison. It is concluded thatthe proposed algorithm in this paper leads to a betterresult than other previously proposed methods.The comparison is also done in the 33-bus case.

Table 7 compares the result of applied methods to pre-vious studies in a similar condition on the 33-bussystem. Significant improvement is obtained using theproposed algorithm compared to previous reports ofother researchers.

Environmental benefitsOne of the most important benefits of power and energyloss reduction is its positive environmental impacts. Thissubject is so important especially in countries like Iranwhere a large amount of power generation is from fossilfuel power plants.In such countries, the serious consequence of in-

crease in conventional power generation is the increaseof CO2, SOx, and NOx emissions. Since the power andenergy losses reduce power plants' useful generation,any effective method to decrease the losses is so im-portant. On the other hand, power distribution net-works are responsible for a significant percentage ofpower and energy losses.The proposed method in this paper is able to decrease

the amount of losses in power distribution networks

Table 8 Amount of emission reduction due to reduction of en

Annual energy losses (MWh) CO2 re

16-Bus test network 166.431 119,830

33-Bus test network 288.078 207,416

It is done by applying optimal reconfiguration and optimal capacitor placement sim

considerably. In conventional power plants, the gener-ated emissions are equal to 720 kg CO2, 0.1 kg NOx, and0.0036 kg SO2 per megawatt hour. Using the proposedalgorithm, the amount of emission can be decreasedconsequently [34]. The amount of annual energy lossreduction can be calculated as follows:

Energy losses ¼ 8; 760� Power losses � LF ;

where LF is the loss factor. In this study, all loads areconsidered to be residential with LF = 0.3. Table 8 showsthe amount of emission reduction due to the reductionof energy losses in the test networks. It is clear fromTable 8 that the amount of CO2, SO2 and NOx emissionsare significantly decreased.

ConclusionsIn this paper, simultaneous optimal reconfiguration andcapacitor placement problems are solved in distributionnetworks to achieve the minimum power loss in the net-work. In the optimization process, the applied con-straints are voltages of nodes, currents of branches, andradial condition of the network. The minimum powerlosses with improved voltage profile can be achievedsimply. Optimal capacitor placement can control theflow of reactive power in distribution lines which in turnleads to power loss reduction. The proposed ‘improvedbinary PSO’ algorithm has been tested on two, 16- and33-bus, networks. It is concluded that simultaneoussolving of these two problems will lead to better resultsthan their separated solution. It is also shown thatIBPSO leads to better results compared to other previ-ous similar studies using other intelligent methods. Sim-ulations and comparison results show that optimalreconfiguration and capacitor placement simultaneouslyresult in the most optimum condition of network.According to the amount of power loss reduction, thepriority of optimization cases is proposed as followswhich can be used by utilities to plan their networksoptimally: (1) optimal network reconfiguration and capaci-tor placement are simultaneous; (2) there is network recon-figuration initially, and then capacitor placement follows;and (3) there is capacitor placement initially, and thennetwork reconfiguration follows. The power distributionutilities can prioritize their network improvements opti-mally using the above results. The environmental benefitsof energy loss reduction were briefly presented.

ergy losses in the test networks

duction (kg) SO2 reduction (kg) NOx reduction (kg)

.32 0.6 16.643

.16 1.037 28.807

ultaneously.



Authors’ contributionsMS has given the techniques and ideas of the paper (corresponding author).He also removed technical as well as grammatical mistakes from the paper.MS suggested the good notes for improving paper. HK helped our toanalyze the data using Matlab software. MD performed simulations, analyzedthe data, handled all the comments from reviewer side and compiled thepaper by getting suggestions from coauthors. All authors read and approvedthe final manuscript.

AcknowledgementsThe authors were supported in part by a research grant from shahidbeheshti university, G. C., the authors are indebted to the editor andreferees for greatly improving this paper.

Author details1Faculty of Electrical and Computer Engineering, Shahid Beheshti University,G. C., Tehran, 1983963113, Iran. 2Faculty of Engineering and Technology,Imam Khomeini International University, Qazvin, 1681834149, Iran. 3School ofElectrical, Electronics and Robotics Engineering, Shahrood University ofTechnology, Shahrood, 3619995161, Iran. 4Mazandaran Electric DistributionCo., Sari, 4816669535, Iran.

Received: 18 May 2013 Accepted: 15 November 2013Published: 02 Jan 2014

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Cite this article as: Sedighizadeh et al.: Optimal reconfiguration andcapacitor placement for power loss reduction of distribution systemusing improved binary particle swarm optimization. International Journalof Energy and Environmental Engineering

10.1186/2251-6832-5-3

2014, 5:3


2251-6832-5-3

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