 Open Access
 Total Downloads : 1587
 Authors : Ankita Mishra, Arti Bhandakkar
 Paper ID : IJERTV3IS10399
 Volume & Issue : Volume 03, Issue 01 (January 2014)
 Published (First Online): 25012014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Selection of Optimal Location and Size of Distributed Generation in Distribution System Using Particle Swarm Optimization
Ankita Mishra1, Arti Bhandakkar2
1(PG Scholar, Department of Electrical & Electronics Engineering, SRIT, Jabalpur) 2(Associate Professor, Department of Electrical & Electronics Engineering, SRIT, Jabalpur)
Abstract: Distributed generations (DGs) play an important role in distribution networks. Distributed generation (DG) exists in distribution systems and is installed by either the utility or the customers. Distributed Generators (DGs) are now commonly used in distribution systems to reduce the power disruption in the power system network. Due to the installation of DGs in the system, the total power loss can be reduced and voltage profile of the buses can be improved due to this power quality of the distribution system is improved. Studies show that nonoptimal locations and nonoptimal sizes of DG units may lead to losses increase, together with bad effect on voltage profile. So, this paper aims at determining optimal DG allocation and sizing. To do so, the optimization technique named Particle Swarm Optimization (PSO) is used .This Particle Swarm Optimization (PSO) approach), capable to establish the optimal DG allocation and sizing on a distribution network. This paper presents optimal placement and estimation of distributed generation (DG) capacity using Particle Swarm Optimization (PSO) approach in the radial distribution systems to reduce the real power losses and to gain voltage profile improvement. The proposed (PSO)based approach is tested on an IEEE 30bus radial distribution system.
KeywordsDG, PSO, Power Loss, Voltage Profile

Introduction
Distributed generation is any electricity generating technology installed by a customer or independent electricity producer that is connected at the distribution system level of the electric grid [1].
With the increasing expanding of network construction in the modern power system and the rapid development of renewable energy resource, distributed generation (DG) has become an important form of electrical source. More and more DGs are connected into the power distribution system. It is predicted that DG would have a share of about 20% of new generating units being on lined [2]. DG effects in distribution network depend on several factors such as the DG place, technology issues, Capacity and the way it operates in the network. DG can significantly increase reliability, reduce losses and save energy while is cost effective, though it
Suffers from some disadvantages because of the isolated power quality functioning, and voltage control problems. Generally, planners assess DG functioning in two respects: costs and benefits. Cost is one of the most important factors that should be considered regarding DG application [3]. So, to reach to these targets, loss reduction and voltage profile improvement of the electric system with the presence of DG requires the definition of several factors such as, the best technology to be used, the number and the capacity of the units, the best location, the type of network connection and etc. The problem of DG allocation and sizing is of great importance. The installation of DG units at nonoptimal places and with nonoptimal sizing can result in an increase in system losses, damaging voltage state, voltage flicker, protection, harmonic, stability and implying in an increase in costs and, therefore, having an effect opposite to the desired [4,5]. Several optimization techniques have been applied to DG placement and sizing, such as genetic algorithm [6], tabu search [7], heuristic algorithms [8,9] and analytical based methods [10], analytical method to place DG in radial as well as meshed systems to minimize power loss of the system is presented. In this method separate expressions for radial and network system are derived and a complex procedure based on phasor current is proposed to solve the location problem. However, this method only optimizes location and considers size of DG as fixed. In this paper, Particle Swarm Optimization algorithm (PSO) is presented as the optimization technique for the allocation and sizing of DG in distribution networks in order to loss reduction in distribution network with minimum economic cost test system. The 30 bus test feeder is selected to test proposed method [11].
A lot of technologies are used for DG sources such as photovoltaic cells, wind generation, combustion engines, fuel cells etc.[12][13]. Usually, DGs are attached with the already existing distribution system and lot of studies are performed to find out the best location and size of DGs to produce highest benefits [14],[15]. The different characteristics that are considered to identify an optimal DG location and size are the minimization of transmission loss, maximization of supply reliability, maximization of profit of the distribution companies etc [16].
Due to wideranging costs, the DGs are to be allocated properly with best size to enhance the performance of the system in order to minimize the loss in the system and to improve different voltage profiles, while maintaining the stability of the system [17]. The effect of placing a DG on network indices
the GA chromosome). They contain the variable values and are not binary encoded. Each particle moves about the cost surface with a velocity. The particles update their velocities and positions based on the local and global best solutions:
will be different based upon its type and location and
Vnew = Vold + Ã—
Ã— (plocal best pold ) + Ã—
m ,n
m .n 1 1
m .n
m ,n 2 2
(predict) load at the connection point [18]. There are
Ã— ( pglobal best pold ) (1)
lot of variety of potential benefits to DG systems both
m ,n
m ,n
to the consumer and the electrical supplier that allow
pnew = pold + Vold
for both greater electrical flexibility and energy
m ,n
m ,n
m ,n
security [19].

Voltage Sag
Vm ,n
Particle velocity;
Most of the power quality problems that experienced by consumers are voltage sags. IEEE Standard 1159 1995 states that voltage sag is a brief decrease in the rms line voltage of 10 to 90 percent of the nominal RMS voltage. The duration of voltage sag is 0.5 cycle to 1 minute. Voltage sag is not a complete interruption of power; it is a temporary drop of supply voltage made the voltage delivered to the consumer is not at the rated voltage. The European standard 50160 defines voltage sag as a sudden reduction of the supply voltage to a value between 90% and 10% of the declared supply voltage, followed by a voltage recovery after a short period of time. The MS IEC 61000 series define voltage sag as a sudden reduction in voltage to a value between 90% to 10% of nominal voltage for duration of 10 ms to 60 second. Risky of voltage sag will depend on the magnitude and duration of it and on the sensitivity of electronic equipments. Voltage sags typically appear when there is an abrupt increase in load such as starting large motor loads. It also appears after symmetrical and asymmetrical faults, motor starting, load switching or transformer energizing. Weather such as lightning, animal contact, contamination of insulators, construction accidents, motor vehicle accidents, falling or contact with tree limbs also contribute in voltage sags. A short circuit fault is a typical cause of voltage sag. Single line to ground faults on the utility system is the most common cause of voltage sags in an industrial plant [20].

Particl Swarm Optimization (PSO)
Pm,n Particle variables ;
1=2 Independent uniform random numbers;
G1 = G2 Learning factors
m ,n
plocal best Best local solution
m ,n
pglobal best Best global solution
for each particle then adds that velocity to the particle position or values. Velocity updates are influenced by both the best global solution associated with the lowest cost ever found by a particle and the best local solution associated with the lowest cost in the present population. If the best local solution has a cost less than the cost of the current global solution, then the best local solution replaces the best global solution. The particle velocity is reminiscent of local minimizes that use derivative information, because velocity is the derivative of position. The constant G1 is called the cognitive parameter. The constant G2 is called the social parameter. The advantages of PSO are that it is easy to implement and there are few parameters to adjust [21][22].
The particle swarming becomes evident as the generations pass. The largest group of particles ends up in the vicinity of the global minimum and the next largest group is near the next lowest minimum. A few other particles are roaming the cost surface at some distance away from the two groups. Figure (1) shows
plots of and as well as the
PSO was formulated by Edward and Kennedy in
,
,
1995. The thought process behind the algorithm was inspired by the social behavior of animals, such as bird flocking or fish schooling. PSO is similar to the continuous GA in that it begins with a random population matrix. Unlike the GA, PSO has no evolution operators such as crossover and mutation. The rows in the matrix are called particles (same as
population average as a function of generation. The particle serves the same function as elite chromosome in the GA. The chaotic swarming process is best illustrated by following the path of
,
one of the particles until it reaches the global
minimum in this implementation the particles frequently bounce off the boundaries.
Fig.1. Convergence of the PSO algorithm.
PSO is a optimization technique to evaluate the optimal solution. here pso algorithm is used to calculate optimal power flow in each bus of IEEE 30 bus system and also calculate the losses in each bus
,based on the pso result we select the optimal location of DG and its capacity.

Problem Formulation
Optimal DG placement and sizing problem is formulated as a constrained nonlinear integer optimization problem.
Objective Function: The objective function aimed at best location of DG in order to minimize economic losses of buses due to interruption caused with voltage sag and that of the DG installation and sum of active power of DG injected to system.
Total real power is defined by
programs such as Newton Raphson method and GaussSiedel method are widely used. In this work, the power flow representation is based on Backward Forward sweep algorithm [23]. The equality constraints are expressed in a vector form as follows:
F ( , ) =0
vector of state variables like voltage magnitude
vector of DG size
Be equal to zero of F, is associated with satisfying all of the load flow of network.
B. Inequality Constraints
The inequality constraints are those associated with the bus voltages and DG to be installed.

: Bus Voltage Limits: The bus voltage magnitudes are to be kept within acceptable operating limits throughout the optimization process.
Vmin Vi Vmax (3)
Where
Vmin lower bound of bus voltage limits;
Vmax upper bound of bus voltage limits;
Vi rms value of the th bus voltage

: Number and Sizes of DGs:
There are constraints associated with the DGs themselves. DGs that are commercially available come in discrete sizes. That is, the DGs to be deal with are multiple integers of the smallest capacitor size available and this matter itself is because of coordination between sizes of DGs to be installed with what is available in practical method. This constraint is as follows:
Ploss =
(2)
L P , L=1, 2 . . . n (4)
=1
o
It should be pointed out that the cost of the real power loss per unit is fixed. Also, the cost of the active power injection per unit is constant.
Constraints: Another significant part of the optimization model that needs to be defined is the constraints. There are two types of constraints: equality and inequality.

Equality Constraints
These constraints are related to the nonlinear power flow equations. In many published papers, the power flow equations are the real and reactive power mismatch equations. The reason for this is that modified versions of conventional power flow
Po smallest DG size available
Also, the total active power injection is not to exceed the total active power demand in radial distribution system
<
Where
total active power demand
This paper has two major goals: 1) Improvement of voltage profile, 2) Loss reduction. There are also some limitations based on which the destination function should be defined.

(Loss with DG) < (Loss without DG) 2)

Step 8: If the iteration number reaches the maximum limit, go to Step 9. Otherwise, set iteration index k=k+1, and go back to Step 4.
Step 9: Print out the optimal solution to the target problem. The best position includes the optimal locations and size of, DG, and the corresponding
fitness value representing the minimum total real
power loss.
According to the first limitation the loss reduces when DG exists. Also, second limitation states that the authorized voltage of a certain bus depends on the minimum and maximum voltages of the bus [24]
In the proposed work, in order to observe and compare the results with those of the specified destination function, an IEEE 30bus distribution network has been selected as a sample. It should be noted that the specified destination function can be generalized to be used for all distribution networks with any number of buses. Moreover, the optimization algorithm of the destination function is a PSO Algorithm. The single line diagram of the network is illustrated in Fig. 3
. 5. The PSO Algorithm Procedure
The particle swarm optimizer (PSO) algorithm is a random evolution method based on intelligent search of the group birds. It has quick convergence speed and optimal searching ability for solving large scale optimization problems.
The PSObased approach for solving OPDG problem to minimize the loss takes the following steps:
Step 1: Input line and bus data, and bus voltage limits.
Step 2: Step 2: Calculate the loss using distribution load flow based on backwardforward sweep.
Step 3: Randomly generates an initial population (array) of particles with random positions and velocities on dimensions in the solution space. Set the iteration counter k=0.
Step 4: For each particle if the bus voltage is within the limits, calculate the total loss. Otherwise, that particle is infeasible.
Step 5: For each particle, compare its objective value with the individual best. If the objective value is lower than Pbest, set this value as the current Pbest, and record the corresponding particle position.
Step 6: Choose the particle associated with the minimum individual best Pbest of all particles, and set the value of this Pbest as the current overall best Gbest.
Step 7: Update the velocity and position of particle.
Fig. 2. PSO Computational Procedure.
In this paper the optimization algorithm of the destination function is a PSO Algorithm whose population size=100, Maxium generation ( )= 500.

Result and Discussion
The proposed method is implemented using MATLAB 2010a and tested for IEEE 30 bus system. which is shown in Figure 3.The optimization algorithm in the present study is a PSO Algorithm. 1 is considered as the slack bus, buses 2, 13, 22, 23 & 27 are generator buses and there are 41 transmission lines in total. There are loads in 20 nodes, i.e.bus 2, 3, 4, 7, 8, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 23,
24,26, 29 and 30, The buses are connected in loop thus it is considered as a power system that operates under 11 kV levels. The total power loss in IEEE30 bus test system using PSO algorithm we found that
13.563 MW due to the losses system has voltage sag in voltage wave form .This voltage sag can compensate using Distribution Generation.
According to losses we can assume that the active and reactive power of DG unit is randomly generated within the power limits of 0 P 20 MW.the DG unit is connected in the buses where the voltage dip is higher which is shown in fig 4.the buses having higher sag i.e. bus no 18,19and 30.
1.2
1.15
bus voltages in p.u
1.1
1.05
1
0.95
0.9
0.85
Voltage magnitude in p.u before DG
0 5 10 15 20 25 30
no of buses
Fig.4: voltage magnitude in p.u before DG

Conclusion
The result shows that PSO technique is more efficient than the other conventional load flow methods. The losses obtain by the PSO is more optimum and time taken is very less to calculate the losses.
In this paper, optimal location of DG using Particle Swarm Optimization was tested for a IEEE 30 bus system. We studied the effects of the significant parameters to optimally enhance the parameters (such as loss reduction, voltage profile improvement) the proposed method PSO uses many possible significant parameters into account to be formulized and optimized. The total power loss in the system without DG is 13.563 MW and after connecting DG in the system, the power loss will reduce. Hence, the power quality of the system is improved.
Fig.3: Single line Diagram for IEEE 30bus Distribution Network.
The fig4 shows the bus voltages in p.u and no of buses.

References

S.M. MoghadasTafreshi, Electrical Energy Generation Resources in 21st century, k.N. Toosi University of Technology Press, 1st Edition, Tehran, 2005.

(Khattam & Salama, 2004; Brown et al., 2001; Seyed Ali Mohammad Javadian & Maryam Massaeli, 2011a,b,c; Navid Khalesi & Seyed Ali Mohammad Javadian, 2011).

Rau N. S. and Wan Y. H. , "Optimum location of resources in distributed planning," IEEE Trans. on Power Systems, vol. 9, pp. 20142020, 1994.

M. Gandomkar, M. Vakili an, M. Ehsan, "Optimal distributedgeneration allocation in distribution network using Hereford Ranch algorithm ", Proceedings of the Eighth International Conference onElectrical Machines and Systems,
Sept. 2005.

K.H. Kim, y'1. Lee, S.B. Rhee, S.K. Lee, and S.K. You,"Dispersed generator placement using fuzzyGA in distribution systems," in Proc. 2002 IEEE Power Engineering Society. Summer Meeting, vol. 3, Chicago, IL, pp. 11481153, July 2002.

K. Nara, Y. Hayashi, K. Ikeda, and T. Ashizawa, "Application oftabu search to optimal placement of distributed generators," IEEE Power Engineering Society Winter Meeting, pp. 918923, 2001.

1. O. Kim, S. W. Nam, S. K. Park, and C. Singh, "Dispersed generation planning using improved Hereford ranch algorithm," Electric Power System Research, vol. 47, no. I, pp. 4755, Oct. 1998.

G. Celli, E. Ghaiani, S. Mocci, and F. Pilo, "A multiobjective evolutionary algorithm for the sizing and sitting of distributed generation," IEEE Transactions on Power Systems, vol. 20, 2005.

H. L. Willis, "Analytical methods and rules of thumb for modeling DG distribution interaction," in Proc. 2000 IEEE Power Engineering Society Summer Meeting, vol. 3, Seattle, WA, pp. 16431644,2000.

D. Das, D. P. Kothari, A. Kalam, " Simple and efficient method for load flow solution of radial distribution networks," Electrical Power & Energy Systems, Vol. 17, No.5, pp. 335 346,1995

K. Manjunatha Sharma and K. P. Vittal, "A Heuristic Approach to Distributed Generation Source Allocation for Electrical Power Distribution Systems", Iranian Journal of Electrical & Electronic Engineering, Vol. 6, No. 4, pp. 224231,
Dec. 2010

M.F.Kotb, K.M.Shebl, M. El Khazendar and A. El Husseiny, "Genetic Algorithm for Optimum Siting and Sizing of Distributed Generation", In Proceedings of 14th International Middle East Power Systems Conference, pp. 433440, Egypt, 2010.

Deependra Singh, Devender Singh and K S Verma, "Distributed Generation Planning Strategy with Load Models in Radial Distribution System", International Journal of Computer and Electrical Engineering,Vol. 1, No. 3, pp. 362375, Aug 2009.

Wichit Krueasuk and Weerakorn Ongsakul, "Optimal Placement of Distributed Generation using Particle Swarm Optimization", In Proceedings of Power Engineering Conference in Australasian Universities, Australia, 2006.

Soma Biswas and S.K. Goswami, "Genetic Algorithm based Optimal Placement of Distributed Generation Reducing Loss and Improving Voltage Sag Performance", Proc. of Int. Conf. on Advances in Electrical & Electronics, pp. 4951, 2010.

Naveen Jain, S.N. Singh and S.C. Srivastava, "Particle Swarm Optimization Based Method for Optimal Siting and Sizing of Multiple Distributed Generators", In Proceedings of 16th National Power Systems Conference, pp. 669674, Hyderabad, 2010.

Amin Hajizadeh, Ehsan Hajizadeh, "PSOBased Planning of Distribution Systems with Distributed Generations", International Journal of Electrical and Electronics Engineering, Vol. 2, No. 1, pp. 3338, 2008.

Benjamin Kroposki, Christopher Pink, Richard DeBlasio, Holly Thomas, Marcelo Simoes and Pankaj K. Sen, "Benefits of Power Electronic Interfaces for Distributed Energy Systems", IEEE Transactions on Energy Conversion, Vol. 25, No. 3, pp. 901 908, Sep 2010.

McGranaghan, R, Mueller, D, "Effects of Voltage Sag in Process Industry Applications," available on http://www.dranetz mi.com, October 2010.

Y.Shi, Eberhart, "A modified particle swarm optimization," IEEE Conference on computer engineering, pp. 6973, May 1998.

Y.Shi, Eberhart, "Empirical study of particle swarm optimization," IEEE Conf on computer engineering,pp. 19451950, 1999.

JenHao Teng, ChuoYean Chang, "BackwardIForward SweepBased Harmonic Analysis Method for Distribution Systems ", IEEE Transactions on Power Delivery, pp. 16651672,2007

J.H. Teng, Y.H. Liu, C.Y. Chen, and C.F, Chen, Value Based Distributed Generator Placements for Service Quality Improvements, International Journal of Electrical Power and Energy Systems, Vol. 29, No. 3, 2007, pp. 268274. [25] M. Kaplan, Optimization of Number, Loation, Size, Control Type, and Control Setting of Shunt Capacitors on Radial Distribution Feeders, IEEE Power & Energy Society , Vol. 103, Sept. 1984, pp.2659 2665.