Efficient Optimal Sizing And Allocation Of Capacitors In Radial Distribution Systems Using Drdlf And Differential Evolution

ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 2011

Efficient Optimal Sizing And Allocation Of Capacitors
In Radial Distribution Systems Using Drdlf And
Differential Evolution
S.Neelima#1, Dr. P.S.Subramanyam*2
#1
Associate Professor, Department of Electrical and Electronics Engineering, MVJCE, Whitefield Bangalore-560067, India
1
s.neelimarakesh@gmail.com
*2
Professor, Department of Electrical and Electronics Engineering, VBIT, Ghatkesar,
Hyderabad-501301, India
2
subramanyamps@gmail.com

Abstract— A distribution system is an interface between the changes which are very vital in capacitor location was not
bulk power system and the consumers. The radial distribution considered. Other techniques have considered load changes
system is popular among these because of its low cost and only in three different levels. A few proposals were schemes
simple design. The voltage instability in the power system is for determining the optimal design and control of switched
characterized by a monotonic voltage drop, which is slow at capacitors with non-simultaneous switching [4]. It is also
first and becomes abrupt after some time when the system is very important to consider the problem solution methods
unable to meet the increasing power demand. Therefore to
employed to solve the capacitor placement problem, such as
overcome these problems capacitors are used. The installation
of the shunt capacitors on the radial distribution system is gradient search optimization, local variation method,
essential for power flow control, improving system stability, optimization of equal area criteria method for fixed capacitors
pf correction, voltage profile management and losses and dynamic programs [4], [5], [6]. Although these techniques
minimization. But the placement of the capacitors with have solved the problem, most of the early works used
appropriate size is always a challenge. Therefore for this analytical methods with some kind of heuristics. In doing so,
purpose, in this paper along with Differential Evolution (DE) the problem formulation was oversimplified with certain
Algorithm, Dimension Reducing Distribution Load Flow assumptions, which was lacking generality. There is also a
(DRDLF) is used. This load flow identifies the location of the problem of local minimal in some of these methods.
capacitors and the Differential Algorithm determines the size
Furthermore, since the capacitor banks are non continuous
of the capacitors such that the cost of the energy loss and the
capacitor to be minimum. In this problem the installation variables, taking them as continuous compensation, by some
cost of the capacitors is also included. The above method is authors, can cause very high inaccuracy with the obtained
tested on IEEE 69 bus system and was found to be better results. A differential evolution algorithm (DEA) is an
compared to other methods like Genetic Algorithm and PSO. evolutionary computation method that was originally
introduced by Storn and Price in 1995 [18]. Furthermore, they
Index Terms — Electrical Distribution Network, Optimal developed DEA to be a reliable and versatile function
Capacitors Placement, Dimension reducing distribution load optimizer that is also readily applicable to a wide range of
flow (DRDLF), Differential Evolution (DE) Algorithm. optimization problems [19]. DEA uses rather greedy selection
and less stochastic approach to solve optimization problems
I. INTRODUCTION than other classical EAs. There are also a number of significant
Capacitors are generally used for reactive power advantages when using DEA, which were summarized by
compensation in distribution systems. The purpose of Price in [20]. Most of the initial researches were conducted
capacitors is to minimize the power and energy losses and to by the differential evolution algorithm inventors (Storn and
maintain better voltage regulation for load buses and to Price) with several papers [18], [21], [22], [23] which explained
improve system security. The amount of compensation the basis of differential evolution algorithm and how the
provided with the capacitors that are placed in the distribution optimization process is carried out. In this respect, it is very
network depends upon the location, size and type of the suitable to solve the capacitor placement or location problem.
capacitors placed in the system [1]. A lot of research has IEEE 69 bus distribution system is considered for case study.
been made on the location of capacitors in the recent past The test system is a 12.66 KV, 10 KVA, 69-bus radial
[2], [3] without including the installation cost of the capacitors. distribution feeder consisting of one main branch and seven
All the approaches differ from each other by the way of their laterals containing different number of load buses. Buses 1
problem formulation and the problem solution method to 27 lie on the main branch. Bus #1 represents the substation
employed. Some of the early works could not take into feeding the distribution system.
account of capacitor cost. In some approaches the objective
function considered was for control of voltage. In some of
the techniques, only fixed capacitors are adopted and load

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II. DISTRIBUTION POWER FLOW 5. The bus currents are determined
*
The distribution systems are characterized by their Pi  Qi  P ij  iQij  V (V i i  V j* )Yij* (6)
prevailing radial nature and high R/X ratio. This renders the ik ( i ) ik ( i )
load flow problem ill conditioned. So many methods [24-29] And from (6) bus powers are calculated. Since the
have been developed and tested ranging from sweep transmission losses are neglected in the first iteration there
methods, to conic programming formulation. Early research will be mismatch between the specified powers and calculated
indicated that standard load flow methods fail to converge powers. The mismatch is a part of the transmission loss. TLijr
for ill-conditioned test systems [30]. Esposito and Ramos is the transmission loss part for ‘ij’th element for ‘r’th iteration.
[28] have proposed a radial load flow technique based on Transmission loss of each element is the summation of the
solving a system of equations in terms of new variables and transmission loss portions of all previous iterations.
using the Newton approach. The relationship between the
complex branch powers and complex bus powers is derived TLij   rT Lij
as a non singular square matrix known as element incidence (7)
matrix. ‘r‘ Where is the Iteration count
The power flow equations are rewritten in terms of a new
variable as linear recursive equations. The linear equations TLr  S spec  r 1V j .r 1I *
ij j j (8)
are solved to determine the bus voltages and branch currents
in terms of new variable as complex numbers. The advantage S ji  S ij  TL ij
of this algorithm is that it does not need any initial value and receiving sending
easier to develop the code since all the equations are S branch  S branch  TL loss
(9)
expressed in matrix format. This proposed method could be max( TL r )  
ij
applied to distribution systems having voltage-controlled
buses also. Treatment of voltage controlled buses
Notations If power is fed from multiple ends of the radial system, other
N-no of buses feeding buses except slack bus are treated as voltage
I ij -Branch current flowing through element ij controlled buses. The equation is as follows.
I j -Bus current of node j
V j -Bus voltage of node j
Rij  Vi (Vi *  V j* ) (10)
th
S ij -Complex power flowing from node i to node j Equation 10 is modified for the j voltage controlled bus.
S ji-Complex power flowing received at node j from node i
S j-Specified Bus power at bus j real ( Sij )  Pij  real ( RijYij* )
(11)
Z ij-Impedance of element ij Rij  X ij  iYij
TL ij-Transmission loss of element ij (12)
The power flow method is summarized as follows:
Pij  real (( X ij  iYij )(Gij  iBij ))
1. For the first iteration transmission losses are initialized as (13)
zero for each element. 2
Pij G ij ( Vi  Vi V j cos(12 ))  Bij Vi V j sin(12 )
2. From the bus powers specified the branch powers are
determined as per equation (1&2). 2
Gij Vi  Pij (14)
1 G ij cos(12 )  Bij sin(12 )
I branch  K I bus (1) Vi V j (15)
S bus  K S  sending
branch  TLbranch  (2)
The trigonometric equations are to be solved to get the phase
3. The variable Rij. is determined for each element using angle of each PV bus j and the reactive power can be updated
equation 3. As
S ij  Pij  iQij  RijYij* (3) 2
Qij  B ij ( Vi  Vi V j cos(12 ))  Gij Vi V j sin(12 )
4. The bus voltage, branch current and bus current are
determined from Rij. (16)
* Then the same procedure described for the PQ buses is carried
Rij (4) out till the convergence.
V j  Vi 
Vi *
*
III. DIFFERENTIAL EVOLUTION
Rij (5)
I ij  Yij Differential evolution (DE) is a population-based
Vi* stochastic optimization algorithm for real-valued optimization

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problems. In DE each design variable is represented in the
chromosome by a real number. The DE algorithm is simple
and requires only three control parameters: weight factor (F),
crossover rates (CR), and population size (NP). The initial
population is randomly generated by uniformly distributed
random numbers using the upper and lower limitation of each
design variable. Then the objective function values of all the
individuals of population are calculated to find out the best
individual xbest,G of current generation, where G is the index of
generation. Three main steps of DE, mutation, crossover, and
selection were performed sequentially and were repeated
during the optimization cycle. Figure.1 The Schematic diagram of crossover operation
A. Mutation Where Cr represents the crossover probability and j is the
For each individual vector xi,G in the population, mutation design variable component number. If random number R is
operation was used to generate mutated vectors in DE larger than Cr value, the component of mutation vector, vi,G+1
according to the following scheme equation: will be chose to the trial vector. Otherwise, the component of
target vector is selected to the trial vectors. The mutation
 i ,G 1  xbest ,G  F ( xr 1,G  xr 2,G ), i  1, 2,3...NP (17) and crossover operators are used to diversify the search
In the Eq. 17, vector indices r1 and r2 are distinct and different area of optimization problems.
population index and they are randomly selected. The
C. Selection operator
selected two vectors, xr1,G and xr2,G are used as differential
variation for mutation. The vector xbest,G is the best solution After the mutation and crossover operator, all trial vectors
of current generation. And vi,G+1 is the best target vector and ui,G+1 have found. The trial vector ui,G+1 are compared with the
mutation vector of current generation. Weight factor F is the individual vector xi,G for selection into the next generation.
real value between 0 to 1 and it controls the amplification The selection operator is listed in the following description:
of the differential variation between the two random x i , G  1  u i , G  1 , if . f ( u i , G  1 )  f ( x i , G )
vectors. There are different mutation mechanisms available (18)
x i , G  1  x i , G , if . f ( u i , G  1 )  f ( x i , G ), i  1, 2 ... NP
for DE, as shown Table. The individual vectors xr1,G, xr2,G, xr3,G,
xr4,G, xr5,G are randomly selected from current generation and If the objective function value of trial vector is better than
these random number are different from each other. So the the value of individual vector, the trial vector will be chosen
population size must be greater then the number of randomly as the new individual vector xi,G+1 of next generation. On the
selected ion if choosing Rand/2/exp mechanism of DE contrary, the original individual vector xi,G will be kept as the
mutation, the NP should be bigger than 5 to allow mutation. individual vector xi,G+1 in next generation. The optimization
loop of DE runs iteratively until the stop criteria are met.
TABLE I. THE MUTATION MECHANISM OF DE

B. Crossover
In the crossover operator, the trial vector ui,G+1 is generated
by choosing some arts of mutation vector, vi,G+1 and other
parts come from the target vector xiG. The crossover operator
of DE is shown in Figure 1. Figure 2. The flowchart of differential evolution

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IV. IMPLEMENTATION OF DE
Algorithm to find capacitor sizes using DE:
The basic procedure of DE is summarized as follows.
Step 1: Randomly initialize population of individual for DE.
Step 2: Evaluate the objective values of all individuals, and
determine the best individual.
Step 3: Perform mutation operation for each individual
according to Eq. 17 in order to obtain each individual’s
corresponding mutant vector.
Step 4: Perform crossover operation between each
individual and its corresponding mutant vector in order to
obtain each individual’s trial vector.
Step 5: Evaluate the objective values of the trial vectors.
Step 6: Perform selection operation between each individual
and its corresponding trial Vector according to Eq.18 so as to
generate the new individual for the next generation.
Step 7: Determine the best individual of the current new
population with the best Objective value then updates best
individual and its objective value. Figure 3: IEEE 69 bus system
Step 8: If a stopping criterion is met, then output gives its
bests and its objective value. VII. RESULTS AND DISCUSSIONS
Otherwise go back to step 3. Prior to capacitor installation, a load flow program based
on complex power flow method is run to obtain the present
V. MATHEMATICAL FORMULATION system conditions. System conditions are shown in Table 2.
Objective Function for capacitor Sizing: The table specifies the minimum per-unit bus voltage,
DE estimates the size of the capacitor to be installed by maximum per-unit bus voltage, real power losses in KW and
minimizing the following objective function. the cost of energy losses during all load levels. It is clear
from the table that the minimum bus voltages during the
L ncap simulation are less than the pre-specified minimum allowable
S  K e  T j Pj   ( K cf  K c Qci ) bus voltage. Therefore, capacitors shall be installed to
j 1 i 1 provide the required voltage correction and to reduce the
overall energy losses in the system. The proposed solution
Where,
methodologies have been implemented in MATLAB 7.10.0.
Pj = Power loss at jth load level.
The solution algorithms based on DE algorithm and tested
Qci = Reactive power injection from capacitor to node i
on IEEE 69 Bus System in Fig.3 which has been designed to
S = Savings in ‘$’
find the optimal solution for this problem. In this case, only
Tj = Load Duration (8760 hrs)
fixed type capacitors are installed in the system and all the
ncap = Number of Capacitor locations
loads are assumed to be linear. Computer programs have been
L = Number of Load levels
written for these algorithms based on the respective
Ke = Capacitor Energy Cost of Losses (0.06$/kWh)
procedures highlighted earlier. The parameters are defined
Kcf = Capacitor Installation Cost (1000$)
as shown below:
Kc = Capacitor Marginal Cost (3$/KVAR)
Elapsed time: 92.38 Sec
Gmax = 800; F: 0.8; CR: 0.8; NP: 100
VI. TEST CASE CONSIDERED
Again, the parameters are set empirically by trial and error
Main Feeder Test System Specification: procedure. Parameters that have resulted in the best solution
IEEE Standard 12.66 KV, 69 Bus Systems were chosen. A Differential Evolution based on steady-state
RADIAL FEEDER : 12.66 KV, 69 Bus Systems replacement usually converges faster than the one designed
LOAD : 1 P.U based on generational replacement. Due to this, steady-state
NO OF LOAD LEVEL (L): 1 replacement method requires less number of generations
LOAD DURATION (T) : 8760 before it converges to the optimal solution.
Table 2 shows the capacitor size obtained from DE method.
From this table, we infer that total capacitor size in KVAR
obtained by DE algorithm is quite less compared to the GA
and PSO methods. From Table 2 it can be also observed that
the results obtained using DE are compared and found to be
better than the results obtained in the work under [17] & [33]
© 2011 ACEEE 59
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regarding net savings. The optimal placement and KVAR
rating of shunt capacitor banks had been best determined for
the studied distribution network using the proposed
‘dimension reducing distribution load flow algorithm
(DRDLFA)’and Differential Evolution.
Fig 4 shows the comparative results of average voltage
profile before and after capacitor placement for both DE and
GA methods. From Fig 4 it is observed that the voltage profile
is improved after capacitor placement by 2.24% by DE
compared to 2.38% by GA. From Fig 5, we infer that Net
Savings increased in DE compared to GA & PSO. Although
DE algorithm does not show a much increase in voltage profile,
but provides a significant Savings.
Fig 5: Savings Analysis Chart
TABLE II: SYSTEM CONDITIONS WITHOUT AND WITH CAPACITORS PLACEMENT FOR
IEEE 69 BUS SYSTEM
VIII. CONCLUSION
It is concluded that savings in cost is maximum for DE
than GA & PSO. Differential Evolution is the best method
when compared to GA and PSO for optimal capacitor
placement in Radial Distribution systems. This study presents
DE method for Multi-objective programming to solve the IEEE
69 Bus Problem regarding Capacitor placement in the
distribution system. The determined optimal location has
reduced the system energy losses and consequently
increased the net savings.

XI. FUTURE SCOPE
The advanced tools like Differential Evolution (DE) can
be applied to same multi- objective problem of IEEE 69 Bus
system for faster execution and the better results. The
maintenance cost of the capacitor may be included in the
cost function. However, the voltage profile can also be
improved by considering the voltage constraints for each
bus as a future Scope of the work.

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Efficient Optimal Sizing And Allocation Of Capacitors In Radial Distribution Systems Using Drdlf And Differential Evolution

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Efficient Optimal Sizing And Allocation Of Capacitors In Radial Distribution Systems Using Drdlf And Differential Evolution