Optimal Capacitor Placement for IEEE 14 bus system using Genetic Algorithm

International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2763
Issue 09, Volume 3 (September 2016) www.ijirae.com
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Optimal Capacitor Placement for IEEE 14 bus system using
Genetic Algorithm
1
Dnyaneshvar Y. Watpade, 2
P. M. Sonwane
1
Maharashtra State Electricity Distribution Company Limited, Nasik
2
K. K. Wagh Institute of Engineering Education & Research, Nasik
Abstract — Genetic Algorithm (GA) is a non-parametric optimization technique that is frequently used in problems of
combinatory nature with discrete or continuous variables. Depending on the evaluation function used this optimization
technique may be applied to solve problems containing more than one objective. In treating with multi-objective evaluation
functions it is important to have an adequate methodology to solve the multiple objectives problem so that each partial
objective composing the evaluation function is adequately treated in the overall optimal solution. In this paper the multi-
objective optimization problem is treated in details and a typical example concerning the allocation of capacitor banks in a
real distribution grid is presented. The allocation of capacitor banks corresponds to one of the most important problems
related to the planning of electrical distribution networks. This problem consists of determining, with the smallest possible
cost, the placement and the dimension of each capacitor bank to be installed in the electrical distribution grid with the
additional objectives of minimizing the voltage deviations and power losses. As many other problems of planning electrical
distribution networks, the allocation of capacitor banks are characterized by the high complexity in the search of the
optimum solution. In this context, the GA comes as a viable tool to obtaining practical solutions to this problem. Simulation
results obtained with a electrical distribution grid are presented and demonstrate the effectiveness of the methodology used.
Keywords— Genetic Algorithm, Multiple Objectives, Allocation of Capacitor Banks and Electric Distribution Grid.
I. INTRODUCTION
This Appropriate planning and maintenance of distribution systems are crucial for an efficient operation, with high quality
services being provided to consumers. In this context, power quality becomes a very important issue, since it defines the final
product delivered to consumers. Voltages must be maintained within the limits specified by regulatory agencies, without
introducing harmonics, and the service should not suffer any interruptions. The maintenance of power quality regarding the
voltage profile is taken into effect by using several measures, including preventive, corrective and emergency tools. Substation
transformers equipped with tap-changing control can limit voltage variations within a certain range, thus reducing voltage
deviations. However, with the load increase (for instance, at peak hours), tap position adjustments may happen to be
insufficient for maintaining the voltage within the desired range, resulting in low voltages at the secondary windings of
distribution transformers. Installing capacitor banks can be an interesting strategy for decreasing reactive power flows through
the network, thus reducing voltage drops and real power losses. Several other benefits can be obtained with the appropriate
allocation of capacitor banks, such as released feeder capacity, released distribution substation capacity, and financial benefits
due to voltage improvement and loss reduction.
The majority of power systems operate at a lagging power factor due to inductive loads and delivery apparatus (lines and
transformers). Power systems are inductive in nature, and require additional reactive power flow from the power grid. But
excessive reactive power demands result in reduced system capacity, increased losses, and decreased voltage, as well as higher
operating costs. Shunt capacitor banks are able to compensate for var requirements, but bank size, location, the capacitor
control method, and cost considerations are important issues that need to be optimized during the design phase. An ideal
solution would be a capacitor placement tool able to weigh all these factors and that considers load levels. This solution should
also be able to place capacitors for voltage support and power factor correction, while minimizing the total cost of installation
and operation.

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AS DESCRIBED IN THE IEEE STANDARD 1036-1992 (IEEE GUIDE FOR APPLICATION OF SHUNT POWER CAPACITORS), THE
PURPOSES OF SHUNT CAPACITOR APPLICATIONS ARE:
PURPOSE BENEFITS
Var support Yields a primary benefit for transmission systems and a secondary benefit for
distribution systems.
Voltage control Yields a primary benefit for both transmission and distribution systems.
System capacity increase Yields a secondary benefit for transmission systems and a primary benefit for
System power loss reduction Yields a secondary benefit for transmission systems and a primary benefit for
Billing charge reduction Does not apply to transmission systems, but yields a primary benefit for
To place shunt capacitors in power systems, it is necessary to:
 Determine bank size in kvar
 Determine connection location
 Determine a control method
 Determine a connection type (wye or delta)
The capacitor size and the appropriate location for voltage support and power factor correction can be determined in different
ways. A common method applies “rules of thumb” techniques, and then runs multiple load flow studies to fine-tune the size
and location. This method may not yield the optimal solution. And it can also be very time consuming and impractical for large
systems. It is also important to minimize cost, while mathematically determining the capacitor size and location. Because this
is an optimization issue, an optimization approach should be employed.
A. OBJECTIVE :
In the Objective group select the capacitor placement objective. This allows the OCP module to place capacitors to perform
voltage support, power factor correction, or perform both at the same time.
• VOLTAGE SUPPORT : The OCP module checks voltage limits and places capacitors to meet the voltage limits when
minimizing the cost.
• POWER FACTOR CORRECTION : The OCP module checks load power factor limits and places capacitors to meet the load
power factor limits when minimizing the cost.
• BOTH: The OCP module checks voltage limits and load power factor limits, and places capacitors to meet the voltage
limits and load power factor limits when minimizing the cost.
GENETIC ALGORITHM FOR OCP
GA is a search algorithm based on the mechanic of natural selection. Basically, a GA makes a population that evolves through
time using reproduction and mutation process. Only individuals representing good solutions of the capacitor placement
problem will survive longer, and their genetic information will be present in the next generation. At the end, after several
generations, the interaction between these high quality individuals will produce a final population which represent the best
solutions set of the problem. Three most important aspects of using GA are:
• Definition of objective function
• Definition and implementation of genetic representation
• Definition and representation of genetic operators
Before of the genetic algorithm procedure, the real parameters of the problem must be represented in genetic algorithm
language. It means that location and size of the capacitors used are codified as a chromosome. The representation chosen for
this application is a chromosome divided in two parts. First part indexes location of the capacitors.
The second part indicates the size of the capacitors used. In reproduction process, first we randomly select a pair of
chromosomes, with the same structure. In the next step, chromosomes are treated separately; one for binary part and another
for integer part. In binary part, for a given position, if two parents share value, the chromosome produced by reproduction will
keep it. If values are different, the result for new chromosome is selected at random. In integer part, for a given position, result
will be the average of values found in the parents. If result is not an integer value, it will be approximated until closer value at
random.

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In mutation process, chromosome structure is modified. This change is performed at random, but there is a difference between
binary and integer part. The GA was able to improve the quality of the randomly generated population very fast, and created
good solutions in a very short time. In the selection of individuals for recombination, selection of a leader uniformly at random
is required. The next step is to choose which one of the three supporters will take part in the recombination. This choice is also
uniformly at random. Following this selection strategy, any pair of parents will belong to the same cluster. That makes the
population act similarly to a multiple-population approach with a high migration rate. After the parents were selected,
following the criterion described before, they are utilized as input parameters in the recombination operator. The
recombination returns a new individual the offspring. Since the chromosome is composed of two distinct parts, they should be
treated separately during the recombination process. The mutation operator aims to add diversity to the population of
individuals. Similarly to the crossover, the mutation is divided into two parts. The first modifies the binary portion of the
chromosome by choosing a position of the individual at random. The second part acts on the integer values by adding or
subtracting a unity from its value. The choice of whether to add or subtract is also decided at random. Mutation is applied to
10% of the offspring. In general, higher mutation rates may slow down evaluation speed and hence should be avoided.
After recombination and mutation, GA submits all or some of the new individuals to a local search procedure for the purpose
of improving their fitness function. This local search acts at the first part of the chromosome, i.e., trying to improve capacitor
location. If a specific location already has a capacitor, the local search tests the possibility of dropping that capacitor (‘drop’).
In case of deterioration of the solution, the position returns to the original value and the local search proceeds to the next one.
This local search acts on the second part of the chromosome. It adjusts the sizes of the capacitors already present in the
solution, trying to find the best size for each location. Only the sizes immediately above and below the present capacitor’s size
are tested. For instance, if a 600 kVAr capacitor is installed in a given position, the procedure tries the capacitors with sizes
400 and 800 kVAr, looking for any improvement. Such tries are executed in a similar manner to the add/drop procedure, in one
capacitor at a time; accepting any change that improves the fitness. The fitness function quantifies the quality of the individual.
Therefore, it will keep a close relation with the objective function of the problem. The first factor to be observed is the cost of
the power losses, which takes into account the maximum voltage deviation observed in the distribution network’s nodes for a
given solution. Calculation of the power losses requires the execution of a load-flow algorithm. The objective of optimal
capacitor placement is to minimize the cost of the system. This cost is measured in four ways:
1. FIXED CAPACITOR INSTALLATION COST
2. CAPACITOR PURCHASE COST
3. CAPACITOR BANK OPERATING COST (MAINTENANCE AND DEPRECIATION)
4. COST OF REAL POWER LOSSES
Figure 1 The Genetic Algorithm Procedure

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To understand in a clear way the applied methodology for equally considering the different evaluation functions (objectives) in
a multi-objective fitness function it is initially made use of the fundamental concepts of the basic GA for a single evaluation
function and, subsequently, this same principles are used so that each evaluation function behaves in the selection process as if
the others didn’t exist. The GA processing begins with an initial population in which the genetic material for each individual of
the population is established randomly. The Evaluation Process calculates the evaluation values for each chromosome
(solution). To avoid problems related to super-individuals or when the evaluation values of the individuals of the population
are very close amongst themselves and very distant from the reference, fitness evaluation techniques are adopted such as
Linear Normalization and Windowing. For this work the Linear Normalization is used as the fitness evaluation technique. This
avoids not only the two mentioned aspects as well as it allows the evaluation values to be negative.
THE GA PROCEDURE IS SUMMARIZED BY THE STEPS ILLUSTRATED IN FIGURE 1.
In other words, the Linear Normalization is equal to establishing fitness values uniformly distributed in an interval and linearly
related with the rank of the best fit individuals. Therefore, what defines the selection frequency of each individual in the
population is its position in the rank of the best fit individuals. In the end of the evaluation process, the fitness values are
passed to the selection process. The selection process characterizes the most important step of the GA, because it is this
process that imitates the Natural Selection. The selection process is governed by selection techniques such as the Roulette-
Wheel Selection, the Tournament Selection and the Stochastic Universal Sampling .
The genetic operators of crossover and mutation act after the selection process and they determine the balance among the
exploitation and exploration elements during the evalution of the Genetic Algorithm. The crossover is commonly
accomplished by techniques such as the crossover of One-Point, Two-Points and Uniform. After the application of the genetic
operators, a new population is obtained. According to Figure 1, all of the individuals of the population are changed by their
descendants. But, with the purpose of adding components of memory of previous evolutions, individuals of the old population
can be added to the new population. That is accomplished through techniques such as Elitism and Stead State.
The methodology proposed in this work doesn't interfere in the way of processing the phases of the basic GA, described
previously. It just adds one more particularity to the process that will be presented in the next section. The Lab WASF
software, used to simulate the results presented in this paper, incorporate the basic GA configuration as well as the proposed
methodology.
B. CALCULATION OF THE TOTAL FITNESS VALUE
In general, one aspect that distinguishes an evaluation function from another is its metric, such as MW, Volts, $, among others.
It is common to observe the use of constant factors to penalize the values of the different evaluation functions. However, the
values of these constants must be defined by the user in a trial and error approach and may cause additional difficulty in the
interaction between the user and GA. In the following analyses it is considered only the evaluation process without losing of
view the impacts in the selection process. It is supposed a population with M chromosomes and that each chromosome of the
population must be evaluated by N evaluation functions.
The value corresponding to the evaluation function i of the chromosome k is defined as:
Evaluation ik , Where 1≤ k ≤ M and 1≤ i ≤ N .
In this point, no effort should be made to join the evaluation functions. It is calculated the individuals aptitudes according to
the adopted fitness evaluation technique (Linear Normalization). However, to apply this technique it is necessary to establish
the rank values of the most capable individuals for each evaluation function. In this context, each one of the evaluation
function i is used without considering the other functions, as what happens in a GA that makes use of a single evaluation
function. Then, each evaluation function i will create a rank i. Figure 2 allows to observe the classification of the individuals of
a hypothetical population for each rank i. In this figure, j is the placement of the chromosomes in the rank, i indicates the
evaluation function to which the rank is associated and k is the index of the chromosome. Then, Figure 2 illustrates a rank
matrix whose elements are the indexes k of the chromosomes, the lines are given by j and the columns by i.
Each rank i creates a fitness value for each chromosome of the population and, therefore, for the N ranks there is, for each
chromosome, N fitness values. Considering the technique of Linear Normalization the aptitude value of chromosome k with
placement j in the rank i is given for:
Fitnesski = max-[(max-min)/(1-M)].(j-1)
Where k = rank[ j][i] , 1≤ j ≤ M and 1≤ i ≤ N .

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Figure 2 – Ranks associated to each evaluation function.
The values of min and max are defined by the user. However, if the selection method is the Roulette-Wheel Selection and the
size of the population is not very big, a good choice would be min=1 and max=M. This choice gives to the less fit chromosome
a chance of survival equal to the difference among the chances of two any consecutive chromosomes. However, if the selection
method is the Tournament, the less fit individual will be dead for any value min. In this case a convenient value would be
min=0 and max=100.
SYSTEM IDENTIFICATION
INTRODUCTION
A new and efficient approach for optimal capacitor configuration in IEEE 14 bus system is selected as case study.
Determination of optimal locations and size of capacitor with an objective function for the constraints like maintaining the
voltage profile and power factor as per standard. Other benefits of optimal capacitor configuration are reduction in power loss
and enhancement of reliability of the system. The solution methodology genetic algorithm is used to optimal capacitor
placement using ETAP 11.1.1 software. The Capacitor Location or Placement for low voltage systems determines capacitor
size, location and control schemes. Optimal capacitor placement is generally a hard combinatorial optimization problem that
can be formulated as a nonlinear multiobjective problem. Many researchers had carried out work on optimal capacitor
placement including Neural Network, Partial Swarm Optimization and Fuzzy theory. This dissertation work is based on
Genetic Algorithm based optimal capacitor placement and sizing. ETAP software 11.1.1 is used to evaluate the capacitor size
and place in the system network. IEEE 14 bus system is selected as test system for OCP using ETAP software. This system
consist of 14 bus in addition to 20 branches and 11 Load points. It is observed that capacitor placement in this system improves
voltage profile attain the marginal power factor and reduces active and reactive losses. This also supports in capacity release.
CAPACITOR PLACEMENT PROBLEMS CAN BE SOLVED IN TWO STEPS:
Use of load flow model and find system parameters including feeder losses Minimize the cost function-min f- subject to
constraints, like practical limits of voltage and capacitor size available, power factor.
OBJECTIVE FUNCTION
Min COST = ∑ni=0 KpLP + KE LE + CC
Where, KpLP= Peak Power Loss,KE LE = Energy Loss,CC = Cost of capacitor, N= No. of buses
For constraint- 0.95 <= V=<1.05; pf=>0.95
ASSUMPTIONS CONSIDERED IN THE DEVELOPMENT OF THE OBJECTIVE FUNCTION ARE
(a) Balanced network considered for simplicity
(b) Capacitors are available in step size and
(c) Capacitor placement affects only the flow of reactive power in the feeder.
II. SYSTEM DESCRIPTION OF IEEE 14 BUS
Optimal capacitor placement and sizing problem is formulated based on the requirements of beneﬁts due to reliability cost, cost
of capacitor, purchase cost, operating cost, maintenance cost and savings due to transmission and distribution loss for IEEE 14
bus system. The one line diagram of an IEEE-14 bus system is shown in Fig.1. The System data is taken from IEEE PES
Society and discussed in Appendix A. In this system five generators placed at bus numbers 1,2,3,6 and 8. Trans- formers are
placed in between buses 4-7; 4-9; 5-9. IEEE 14 bus system is benchmark system selected for the case study. The system is also
graphically represented and tabulated in Appendix A. In short, this network consists of 14 buses, 20 branches, and 12 loads.

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TABLE 1: SYSTEM DESCRIPTION
Figure 3: IEEE 14 bus system
III. LOAD FLOW ANALYSIS FOR IEEE 14 BUS SYSTEM
During load Flow study, this research work is carried out for the system as it is for evaluation of power factor, capacity of
transmission line, voltage profile and other parameters. As this research is oriented for optimal capacitor placement, the
existing fixed capacitors in the network is removed first and then load flow is carried out before capacitor placement as well as
with capacitor placement.
TABLE 2: LINE DATA FROM LOAD FLOW IN ETAP
ID RATING RATE KV KW KVAR AMP % PF % LOADING V TERMAL
BUS SHUNT 9 19000 KVA 1 2.118 -21183 11583 -0.01 105.6 105.59
LOAD 2 25143 KVA 1 21700 12699 13891 86.31 95.7 104.5
LOAD 3 96097 KVA 1 94200 19000 54932 98.03 99 101
LOAD 4 47959 KVA 1 47800 -3900 27208 -99.67 98.3 101.77
LOAD 5 7767 KVA 1 7600 1600 4398 97.85 98.1 101.95
LOAD 6 13479 KVA 1 11200 7500 7273 83.09 93.5 107
LOAD 9 33850 KVA 1 29500 16599 18508 87.15 94.7 105.59
LOAD 10 10707 KVA 1 9000 5800 5882 84.06 95.2 105.1
LOAD 11 3936 KVA 1 3500 1800 2150 88.93 94.6 105.69
LOAD 12 6306 KVA 1 6100 1600 3451 96.73 94.8 105.52
LOAD 13 14693 KVA 1 13500 5800 8076 91.88 95.2 105.04
LOAD 14 15717 KVA 1 14900 5000 8763 94.8 96.6 103.55
BUSES 14
BRANCHES 20
GENERATORS 5
POWER GRIDS 0
LOADS 12

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TABLE 3:GENERATOR DATA FOR LOAD FLOW IN ETAP
ID RATING RATED KV MW MVAR AMP % PF % GENE RATION
GEN_1 232.4 MW 1 232.404 -16.561 126904 -99.75 100
GEN_2 40 MW 1 40 43.533 32663 67.66 100
GEN_3 0 MW 1 0 25.063 14327 0
GEN_6 0 MW 1 0 12.77 6891 0
GEN_8 0 MW 1 0 17.635 9341 0
LOAD FLOW OF IEEE 14 BUS
ETAP based load flow study is carried out using ETAP 11.1.1 and given in Table 2 Fig. 3 shows IEEE 14 bus system. The
details of this system is also illustrated in Appendix A. OCP-PSO software initialize with ETAP based load flow data.
Necessary information relating to bus and line is passed through OCP treatment in two modules. First module simulates
objective function without capacitor and second module introduces PSO treatment with capacitor placement with Local and
Global minima.
IV. ETAP SIMULATION PROCESS FOR LOAD FLOW
Figure 4: IEEE 14 bus system Load Flow Analysis
Table 2 shows bus data simulations for bus voltage in per unit, rated voltage, active and reactive power loading and ampere
loading on every bus. Table 3 indicates load data information regarding active and reactive power drawn by load, power factor
load current, percentage loading and terminal voltage. All these information is essential in OCP simulation process which is
developed in dot net framework. Table 2 shows load data without capacitor while Table 3 represents generator data. This
useful data is then provided as input to OCP software module. Five generators are connected to bus 1, 2, 3,6, 8. Generator 1 is
connected to swing bus and rated with 232.40 MW as shown in Table 5.5. This generator is running with 100 % generation
with 232.40MW, -16.56 MVAr with 99.75 leading power factor. Generator 2 is producing 40MW active and 43.53 MVAr
reactive powers. Whereas genera tors placed at buses 3,6, 8 are standby and used to absorb reactive power. IEEE 14 bus line
data is shown in Table 2 with line number, active and reactive power flow, current flow, voltage drop in line and active and
reactive power losses due to resistance and inductance of transmission line or transformer device. This line data is provided to
OCP software for further evaluation process through two treatments in the module, one without capacitor and other with
capacitor placement module.
V. OCP MODULE IN ETAP
Optimal Capacitor Placement module in ETAP consist,
• Optimal location & bank size
• Minimize installation & operation costs
• Individual source or average energy cost
• Voltage & power factor objectives
• Minimum, maximum, & average loading
• Branch capacity release & cost savings
• Review capacitor impact on the system
• Capacitor control method To place shunt capacitors in power systems.

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It is necessary to determine bank size in kvar, determine connection location, determine a control method and determine a
connection type. The capacitor size and the appropriate location for voltage support and power factor correction can be
determined in different ways. A common method applies “rules of thumb” techniques, and then runs multiple load flow studies
to fine-tune the size and location. Unfortunately, this method may not yield the optimal solution and it can also be very time
consuming and impractical for large systems.
A. KEY FEATURES OF ETAP
• Calculate the most cost-effective installation locations and best bank size
• Minimize total installation and operation cost
• Consider voltage support and power factor correction
• Evaluate Capacitor control method
• Allow review of capacitor impact on the system
• Employ most advanced optimum techniques
Figure 5: IEEE 14 bus system optimal capacitor placement
B. FLEXIBLE OPERATION
• Show available locations
• Apply user-selected load categories
• Utilize individual and global constraints
• Handle unlimited network configurations
• Use only user selected installation locations
• Constrain maximum capacitors installed at a location to user specified quantity
C. CAPABILITY OF ETAP OCP MODULE
• Advanced graphic user interface
• User friendly input and output
• Instantly view new capacitors
• Speed and precision control
• Integrated load flow results
• Standard Crystal reports
D. PLOTTING IN ETAP
• Loss reduction savings during the planning period
• Capacitor operation cost during the planning period
• Profit during the planning period

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E. REPORTING
• Capacitor properties
• Capacitor locations and sizes
• Load flow results for maximum, average and minimum loads
• Branch capacity release • Cost summary
VI. CALCULATION METHOD
ETAP currently utilizes the genetic algorithm for optimal capacitor placement. The genetic algorithm is an optimization
technique based on the theory of natural selection. A genetic algorithm starts with a generation of solutions with wide diversity
to represent characteristics of the whole search space. By mutation and crossover, good characteristics are selected and carried
to the next generation. The optimal solution can be reached through repeated generations. OCP uses the present worth method
to perform alternative comparisons. It considers initial installation and operating costs, which include maintenance,
depreciation, and loss reduction savings.
OBJECTIVE FUNCTION OF OCP IN ETAP
The objective of optimal capacitor placement is to minimize the cost of the system. This cost is measured in four ways:
• Fixed capacitor installation cost
• Capacitor purchase cost
• Capacitor bank operating cost (maintenance and depreciation)
• Cost of real power losses
Cost can be represented mathematically as:
minf =∑xiC0i + QciC1i + BiC2iT + C2∑TlPLi
Nbus - No of bus candidate
Xi - 0/1,0 means no cap installed at bus i
C0i - Installation cost
C1i - per kVAr cost of capacitor bank
Qci - Capacitor bank size in KVAr
Bi - Number of capacitor banks
C2i - Operating cost of per bank, per year
T - Planning period(years)
C2 - Cost of each kWh loss in Rs/kWh
L - Load levels, maximum, average and minimum
Tl - Time duration, in hours, of load level
lPLi - Total system loss at load level l Constraints
The main constraints for capacitor placement are to meet the load flow constraints. In addition, all voltage magnitudes of load
(PQ) buses should be within the lower and upper bars. Load Power Factor (PF) should be greater than the minimum. It may be
a maximum power factor bar at unity. The constraints can be represented mathematically as:
Vmin < V < Vmax PFmin ≤ PF VTHD ≤ V max THD Qci ≤ Bi × KVAR
ETAP based optimal capacitor placement for IEEE 14 bus system is studied.
VII. SIMULATION AND RESULTS
For this research work, a case study of IEEE 14 bus system is selected to implement optimal capacitor placement. ETAP has
different module for reliability and optimal capacitor placement. This research work is carried out for ETAP based optimal
capacitor placement for IEEE 14 bus . Adding KVAr through capacitor placement can/may improve voltage profile, modify
power factor, decrease losses, improve reliability indices, decrease cost associated to reliability and lot of benefits. Most
importantly capacitor placement should be profit oriented in addition to above benefits. all above parameters which modifies as
number of capacitor increases while placing capacitor is evaluated. All these parameters are then compared with the results
when no capacitor is placed. This comparison study is required to take decision whether to place a capacitor or not. This study
includes voltage constraint, power factor constraint and both constraint separately At the same time, voltage constraint is
studied for two different limits (i) 0.95 < V < 1.05 and (ii) 0.9 < V < 1.1 for 10 cases for this study.
In ETAP software, OCP and reliability is evaluated separately and cost function is added such that the objective function
developed should match with objectives of genetic algorithm technique used in ETAP. Before optimal capacitor placement in
ETAP following parameters are set as bus constraints

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TABLE 4 CONSTRAINTS APPLIED
BUS ID %VOLTAGE (MIN) %VOLTAGE (MAX) P.F. (MIN) P.F. (MAX)
BUS 4 95 105 90
BUS 5 95 105 90
BUS 7 95 105 90
BUS 9 95 105 90
BUS 10 95 105 90
BUS 11 90
BUS 12 90
After applying these constraints ETAP proposed capacitor bank at following buses
TABLE 5 CAPACITOR PLACEMENT AFTER OCP
BUS NO CAPCITOR SIZE NO. OF CAPACITORS PLACED
BUS 4 20 KVAR 8
BUS 5 20 KVAR 2
BUS 7 20 KVAR 14
BUS 10 20 KVAR 100
BUS 11 20 KVAR 37
BUS 12 20 KVAR 11
BUS 14 20 KVAR 88
Results are compared for loss reduction, p.f. improvement, capacity release in dissertation work before and after OCP
implementation
TABLE 6 BRANCH/CKT LOSS COMPARISON BEFORE OCP AND AFTER OCP
BRANCH LOSSES BEFORE OCP
(KW)
LOSSES BEFORE OCP
(KVAR)
LOSSES AFTER OCP
(KW)
LOSSES AFTER OCP
(KVAR)
1-2 7527.1 17180.2 4297.9 7272.8
1-5 4628.4 13841.7 2763.2 6085.6
2-3 3739.5 11215.8 2323.3 5162.4
2-4 2657.0 4517.6 1676.8 1470.6
2-5 1406.9 679.4 903.8 -927.9
3-4 623.3 304.5 373.4 -362.7
4-5 837.6 2642.1 514.3 1622.3
4-7 2.7 2734.3 1.7 1703.1
4-9 2.1 2080.4 1.3 1304.9
5-6 6.8 6768.5 4.4 4421.3
6-11 84.3 176.5 55.4 116.1
6-12 111.9 232.8 71.8 149.5
6-13 331.1 652.1 212.1 417.8
7-8 0.0 658.9 0.0 461.1
7-9 0.0 1236.2 0.0 801.9
9-10 14.3 38.1 12.9 34.1
9-14 170.4 362.5 116.1 246.9
10-11 21.1 49.4 12.6 29.5
12-13 10.1 9.1 6.3 5.7
13-14 84.3 171.6 54.1 110.2
TOTAL 22258.8 65551.6 13410.5 30125.3
TABLE 7 BUS % VTG COMPARISON BEFORE OCP AND AFTER OCP
BUS NO. % VOLTAGE BEFORE OCP % VOLTAGE AFTER OCP
BUS 1 106 104.8
BUS 2 104.5 104.5
BUS 3 101 101

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BUS 4 101.77 101.77
BUS 5 101.95 101.95
BUS 6 107 104.9
BUS 7 106.15 104.1
BUS 8 109 104.3
BUS 9 105.59 105
BUS 10 105.1 105
BUS 11 105.69 104.8
BUS 12 105.52 104.7
BUS 13 105.04 105
BUS 14 103.55 103.50
TABLE 8 BUS % P.F. COMPARISON BEFORE OCP AND AFTER OCP
BUS NO % P. F. BEFORE OCP % P. F. AFTER OCP
BUS 1 99.6 -99.6
BUS 2 97.4 -99.5
BUS 3 96.6 99.8
BUS 4 98.7 -99.9
BUS 5 99.0 100
BUS 6 90.5 99
BUS 7 85.3 0
BUS 8 0 0
BUS 9 86.0 99.5
BUS 10 84.1 89.0
BUS 11 90.4 99.1
BUS 12 95.6 97.3
BUS 13 93.0 95.7
BUS 14 94.8 96.2
VIII. CONCLUSION
In this paper, a new method for solving capacitor placement problem was described. It can provide specific location of fixed
capacitors in order to reduce energy system's losses. The load variation, the energy cost and capacitors sizes easily found in
market were considered in the model. The program developed can be used in radial systems with different topologies and load
variation because it has flexible parameters. Also it can be used as an analysis tool to make planning studies or to take
decisions about the convenience of a specific reactive compensation plan. Then is a powerful tool in the design of an electrical
distribution system.So, the work presented is possible, cheap and reliable to find the optimal capacitor placement in a radial
system.
REFERENCES
[1] Carpinelli, G. ; Celli, G. ; Mocci, S. ; Mottola, F.; Pilo, F.; Proto, D., Optimal Integration of Distributed Energy Storage
Devices in Smart Grids, Smart Grid, IEEE Transactions on Volume: 4, Issue: 2 DOI: Year: 2013,
[2] Biswas, S. ; Goswami, S.K. ; Chatterjee, A., Optimal distributed generation placement in shuntcapacitor compensated
distribution systems considering voltage sag and harmonics distortions, Generation, Transmission & Distribution, IET
Volume: 8 , Is- sue: 5 Publication Year: 2014,
[3] Hamid Reza Esmaeilian, Omid Darijany, Mohsen Mohammadian, Optimal placement and sizing of DG units and
capacitors simultaneously in radial distribution networks based on the voltage stability security margin, Turkish Journal of
Electrical Engineering & Computer Sciences, Published Online,2014,
[4] Calderaro, V. ; Galdi, V. ; Piccolo, A. ; Conio, G. ; Fusco, R. , Wind farm power plant: Optimal capacitor placement for
reactive power compensation, Innovative Smart Grid Technologies Europe (ISGT EUROPE), 2013 4th IEEE/PES Year:
2013 ,
[5] Sathya Siva Chandan. G, Optimal Placement of Capacitor and Sizing in a Radial Distribution Network to Reduce Real
Power Losses, International Journal of Scien- tiﬁc Engineering and Research (IJSER) ISSN (Online): 2347-3878 Volume
2 Issue 9, September 2014.

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