 Open Access
 Total Downloads : 312
 Authors : Deepesh Agarwal, S. A. Siddiqui, N. K. Swarnkar
 Paper ID : IJERTV5IS040844
 Volume & Issue : Volume 05, Issue 04 (April 2016)
 DOI : http://dx.doi.org/10.17577/IJERTV5IS040844
 Published (First Online): 25042016
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
GA based Optimal DG Placement for Power Loss Reduction and Voltage Stability Improvement
Deepesh Agarwal

Tech. Scholar AIET, Jaipur

K. Swarnkar Professor
Dept. of Electrical Engineering AIET, Jaipur

A. Siddiqui Associate Professor
Dept. of Electrical Engineering Manipal University
Abstract–The voltage deviation from the nominal value is a major problem in the distribution system during operation of the system. Normally voltage profile of load buses decreases from source to loads at far end. With the deviation in load connected to the system, voltage profile of the load buses increases/decreases and may lead to the collapse of the system and subsequent loss of economy. Another problem in distribution system is line losses which reduces the efficiency of the system. Among the possible solutions for these problems, DG allocation is a promising one which feeds the system with additional benefits. However a nonoptimal allocation of DG can adversely affect the performance of the system. This paper proposes GA based optimization algorithm to improve voltage profile of the system and simultaneously reduces the total real and reactive power losses.
Index Terms (DG) Distributed Generator, (GA) Genetic Algorithm, (RDS) Radial Distribution System.

INTRODUCTION
Line losses have a major impact on efficiency of power system as a considerable amount of power continuously getting lost due to line resistance [1]. Distribution systems in India are usually radial in nature due to their operational simplicity. The Radial Distribution Systems (RDS) are fed at only one point and the power flow in the RDS is unidirectional. Due to high ratio of R/X in distribution system compared to transmission system, distribution lines constitute large bus voltage deviation, high power losses and low system stability [25]. Hence, line loss reduction and voltage profile improvement are major challenges for the distribution utilities. Several attempts have been made by researchers to improve the voltage profile and efficiency of distribution system.
There are many schemes are proposed in the literature to achieve the mentioned objectives by embedding small resources of electricity with distribution network called Distributed Generator (DG).Modern advancement in renewable technology has pushed DG in as a probable solution for these issues [68]. DG is small size generators or any power source sited close to the load being served. It is also known as an onsite or decentralizes generation [9 10].With the rapid advancement in the new technological era, utilization of DGs in power system is continuously increasing. Use of renewable resource saves the conventional resources and provides clean and green energy [9], [11]. Ministry of new and renewable energy resources, India has targeted to increase the renewable generation by five times by the end of 2022[12].
Location and capacity of DG plays a very important role as if it is optimal, it can increase the benefits many fold. Installation of DG at nonoptimal location may increase the line losses and can further reduce the voltage profile of the system. Therefore, to find the optimal location and size of DG in the system is a major challenge for any system planner [9], [10]. There are various methods and approaches proposed in the literature for optimal allocation of DG to the distribution system. In [13], Hereford Search Algorithm is proposed based on artificial intelligent technique for optimal location of the DG. In [14], authors proposed analytical approach using a quantitative index to optimize DG location which is calculated through continuous power flow technique. In [15], a multi objective GA is proposed in combination with MultiAttribute Decision Making Method to optimize the location and sizing of DG in distribution system.GA shows various advantages over other proposed techniques and is a powerful approach for the optimization problem. GA is a global search approach which analyzes the solution among a random set of possible solution and have an advantage that this do not require any prior information of gradient surface. Evolution concept of genetic algorithm inherits the fundamental concepts found in nature [16][17].
In this paper GA is used in the proposed algorithm to optimize DG site and size to provide active power support and improve the voltage profile of the system. The main objective of the present work is to find the location and capacity of DG to reduce the system line losses and improve the cumulative voltage deviation in distribution system through DG allocation. The proposed method is tested on standard IEEE
33 radial distribution system under varying operating conditions and the results obtained are promising is achieving the goals.

PROBLEM FORMULATION
Interconnection of DG improves the performance of the system by reducing the line losses and bus voltage deviation, only when allocated optimally. It also serves the system with various other advantages like voltage stability enhancement, improvement in system loadability etc., voltage stability can be defined as the ability of the system to retain the bus voltage constant even in varying load conditions. Advantages of DGs can be maximized by connecting DG at most suitable location among all candidate buses. Line losses and bus voltage profile are the most important parameter which affects the performance and stability of the system.
The losses in the system are continuous through heat dissipation from the lines, which reduces the efficiency of the system. Line loss increases with the increase in load demand; power loss in supply line is evaluated by load flow analysis and can be represented as follows:
The combinations of Ws which are giving the best solution are considered for the present work. The weights assigned to real and reactive power loss is considered equal always in different combinations so that the both the losses are optimize equally.
For the minimization of the problem fitness function is as
= =1 2
(1)
follows:
= { 1
} (7)
= =1 2 (2)
Where PL, QL are the total active and reactive power loss of

Constraints
1 + ( , , )
the system respectively.
In distribution system bus voltage continuously reduces away from source end. With the increment in load of the system, this problem becomes more predominant. This causes reduction in the voltage stability margin and the system become more vulnerable to the disturbance. Hence voltage profile improvement is major aspect of this analysis. Bus voltages of the system can also be obtained from load flow analysis of the system. To keep the optimization problem minimizing, voltage profile of the system is replaced by cumulative voltage deviation (CVD), which can be represented by following formula:
Normally in optimization problem solution is achieved by considering certain constraints. In the present work, the above objective function is optimized by considering the following constraints:

Power balance constraint
+ (8)
+ (9)
Here PG And QG are the net active and reactive power generated in the system, PD and QD are the active and reactive
= =1(1 )
Where (I = 1, 2, 3n) bus of the system
(3)
power demand of the load connected to the system and PL and
QL are the active and reactive line losses of the distribution system. For the stable oeration of the system net power generated should be equal or greater than the sum of net
DGs can provide potential solution for the above problems. DGs are classified by the kind of support which they provide to the system. Either active, reactive or both kind of power support may be expected from DGs. DG injects the power at the load end, by virtue of that it reduces the demand of the
power demand and power loss of the system.

Voltage constraint
(10)
power at respective bus location. This reduction in demand
reduces the overall line losses of the system. This injection of power also improves the balance of active to reactive power which improves the voltage profile.
The main objective of the proposed work is to maximize the benefits of the DGs by optimizing the size and location simultaneously. For solving any problem for optimization objective function or fitness function is to be formulated for determining the feasibility of the solution. In the proposed work, an objective function for real and reactive power loss minimization and to improve the overall voltage profile of the system is formulated. Mathematically it can be given as:
F (PL, QL, CVD)= max{ + + ()}
(4)
Where WP, WQ and WV are the weights assigned to the variables depending upon the objectives. The weights to the objective variables are assigned as follows:
1,2,3[0,1] (5)
WP + WQ + WV = 1 (6)
Stability of the system is highly affected by bus voltage hence bus voltage should lie in between maximum and minimum limits defied as and respectively.

DG size constraint

To obtain maximum benefits, DG size is considered in between 10% to 40% of the source capacity. DGs capacity lesser than the minimum limit provides negligible benefits and capacity over maximum limits reduces the system stability.
10% 40% (11)


GENETIC ALGORITHM
In the proposed work the optimal location and capacity of DGs is obtained using Genetic Algorithm (GA). GA uses the concept of genetic evolution to achieve convergence and it can be utilized for both constrained and unconstrained optimization problem. GA has advantage over other conventional and modern optimization approaches is that it does not require any prior information of objective function. Also it does not deal directly with the parameters of optimization problem.GA propagates in a search space containing random sets of N possible solutions, collectively called population. Each candidate solution contains a random
set of n possible location for DG connection and their corresponding random DG ratings, individually called genes. GA selects the candidates for operation by their biological selection of most fit candidate by the help of fitness function.GA converge the solution in iterative way by using genetic operators Reproduction Crossover and Mutation inspired by natural evolution process. GA modifies the population of candidate solutions for every iteration as per the genetic operators; this modified population is called generation [19].

Population
To initialize the algorithm GA requires an initial set of probable solutions called initial population. This is completely a random group of candidate solution generated by random number generator and for these candidates no prior knowledge exists.
These candidate solution consist subset consisting properties of candidate related to the DG location and sizes, known as genes. These candidates can be constructed in following ways

In binary representation of chromosomes (candidates) value of each gene is given in their binary equivalent. The major issue is to choose the count of bits in which genes are formulated.

In real coding of chromosome, property of each gene is coded in their relevant decimal values.
In this paper real number coding is used for the representation of chromosomes. With respect to the number of DGs connected, twice no of genes are inherited in each chromosome. Half of the genes carried the location properties and rest carried the respective sizes of DG.


Genetic operators
After evaluating the fitness of each candidate using fitness function, GA converge the solution by their genetic operators which are Reproduction (Selection), Crossover & Mutation. This complete evolution process is nature inspired, although its not necessary to use all the operators. Use of operators can be modified as per requirement of the problem.

Reproduction
The reproduction operator reduces the search space for achieving convergence by selecting the parents in descending fitness order. This operator transfer the pair of parent chromosome for the next step of evolution by giving higher priority to higher fit candidate and removes those candidates which do not satisfies the minimum fitness criteria. In this way selection ensures the propagation of best genetic material to the next upcoming generation.

Crossover
The crossover is one of the most important genetic operator as it produces the new generation by performing the crossover of genetic material between two selected parents. Crossover can be performed to generate one child chromosome inherited best from both the parents. However two child chromosomes can be generated after random transfer of genetic material
between parent chromosomes. Generally transfer of genes is performed by one point crossover, two point crossovers or multipoint crossover.

Mutation
This operator introduces diversity in population. This operator works at gene level for each candidate. It randomly selects a gene from chromosome and modifies it by a specific rate. This produces the diversity in new population from older one. This helps to avoid premature convergence and leads towards better solutions.


Algorithm control parameters
Control parameters are applied at every step of algorithm to control the execution of the algorithm. This is necessary to control because uncontrolled evolution may lead the algorithm towards nonoptimal results or may keep algorithm un converged.
The common parameters for the genetic algorithm are Initial population size, selection rate, crossover rate and mutation rate. Other parameters can be added as per requirement of the problem. Population size defines the area of search space. Large population size has the advantage of better results but may increases the time of execution.
Selection rate is defined by the fitness below which candidate marked as unfit for optimization. This helps in selection of candidate with better fitness. A higher fitness level reduces the execution time of algorithm as it selects the candidates with high value of fitness. But this may lead to premature convergence as it may drop a candidate with potential fitness which will reduce the probability to achieve global solution.
Crossover and Mutation are the most important steps of evolution. Crossover rate control the frequency of crossover operation whereas mutation rate controls the percentage of diversity introduced by operator in child chromosome. Higher mutation rate may distinct the child from rest of population.
Fig.1.IEEE 33 Bus radial distribution systems


SOLUTION METHODOLOGY
GA evaluates the fitness of each candidate by fitness function. To initiate the algorithm, population of candidate solution is obtained using load flow of considered test system and is randomly generated. The total 500 candidate solutions are generated randomly to optimize 3 DGs in IEEE 33 bus radial distribution system. Fitness of these candidates is obtained by fitness function.
Fitness functions variables which are active power loss, reactive power loss and cumulative voltage deviation (CVD)are obtained by load flow analysis for every andidate solution. Reproduction operator is then chooses the fit candidates and forward them for further evolution process. Average fitness is observed for the reproduction operator by manual observation. Candidates below this fitness level are eliminated from the population.
Random N point crossover is performed to generate offspring. Genes of candidate chromosomes are interchanged by this operator and new population formed. This operation is controlled by crossover rate (0.05). Now the offspring is forwarded to mutation operator. Mutation operator randomly selects a gene and alters it by multiplying a random number. This operation is controlled by mutation rate (0.05).This operation maintains the diversity in population and prevent the premature convergence which may occur due to elimination of possible candidate at earlier stages. Fitness of this population is again evaluated and the operation is again followed in iterative way until convergence achieved. The flow chart of the proposed method is shown in Fig. 2.
START
Set parameters; GA, Distribution system data
Set parameters; GA, Distribution system data
Generate random initial population
Generate random initial population
GEN=0
GEN = GEN + 1
[1;] = (1,2,3) (1,2,3) Entered in distribution systemRun load flow
Run load flow
Fetch fitness of candidate using fitness function Select chromosomes for mating pool
Perform crossover between parents and generate offspring

SIMULATION RESULT
Location and sizing of DG for the objectives considered are obtained. For each topology and locations results for distribution system performance is analyzed by load flow of the system. The GA gives the optimal bus locations (3, 17, and 28) with optimal rating of (0.8256 p.u., 0.2134 p.u. and 0.4571 p.u.) respectively for IEEE 33bus radial distribution system. Initially system performance analysis is performed for base loading condition. It is observed that with the increment in load of the system, performance of the system reduces in the form of increment in system losses and reduction in voltage profile. The load is increased in steps of 5% and for each load profile the system analysis is performed to evaluate the effects of DG placement by the proposed method.
TableI presents reduction in cumulative voltage deviation of the system. The analysis is done for the 100, 105, 110, 115, and 120 percent of loading condition. Reduction in voltage deviation with increase in load conditions is indexed in table shown below. Fig.3 shows the comparative bus voltage profiles for all the buses with and without DG system for various loading condition. This graph shows the significance of DG interconnection for improvement in bus voltage profile of the system.
TABLE I: PERFORMANCE EVALUVATION OF DGFOR CVDPROFILE OF
THE SYSTEM
Loading condition
System status
Optimal Bus Location
Respective DG ratings (PU)
CVD (PU)
CVDR (%)
100%
Without DG
–
–
1.0137
48.60
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.5210
105%
Without DG
–
–
1.0499
47.09
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.5555
110%
Without DG
–
–
1.0862
45.67
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.5901
115%
Without DG
–
–
1.1226
44.34
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.6248
120%
Without DG
–
–
1.1592
43.09
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.6596
Loading condition
System status
Optimal Bus Location
Respective DG ratings (PU)
CVD (PU)
CVDR (%)
100%
Without DG
–
–
1.0137
48.60
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.5210
105%
Without DG
–
–
1.0499
47.09
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.5555
110%
Without DG
–
–
1.0862
45.67
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.5901
115%
Without DG
–
–
1.1226
44.34
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.6248
120%
Without DG
–
–
1.1592
43.09
With DG
3,17,28
0.8256, 0.2134,
0.4571
0.6596
Perform random mutation on each offspring
Assigned this set of offspring as new population and replace it with initial population
NO
Converged?
YES
YES
1.01
Voltage Megnitude
Voltage Megnitude
1
0.99
0.98
0.97
0.96
0.95
0.94
0.93
Optimal solution is best fit chromosome of population
Optimal solution is best fit chromosome of population
0.92
1 3 5 7 9 111315171921232527293133
System Buses
BL without DG
BL with DG 105% with DG
110% with DG
115% with DG
STOP
Fig. 2 Flow chart of proposed Algorithm
Fig.3. Bus voltage profile of the system under different loading conditions
Reduction in cumulative voltage deviation of the system by connecting DG at optimal locations is shown in Fig. 4. This figure shows benefits of DG even in increase load conditions. TableII shows the reduction in active power losses of the system operating with DGs. Over 50% reduction in loss profile is achieved by the proposed algorithm. It is evident that
reduction in loss profile of the system is consistent with different loading conditions. Under base load condition, system shows active losses 0.123 p.u. (without DGs), 0.057
p.u. (with DGs). Reduction in losses of the system achieved is 53.66%. Fig. 5 graphically represents the results of table II. The figure shows the bar graph of loss profile improvement for all the cases.
applied for different loading conditions and the results shows the effectiveness of the optimal placement of the DGs. Reactive losses of system are graphically shown in fig.5 and improvement in reactive power loss profile is evident for all the cases.
TABLE III
PERFORMANCE EVELUVATION OF DGOVER REACTIVE LOSS PROFILE OF
THE SYSTEM
Syatem Cumulative voltage
Deviation
Syatem Cumulative voltage
Deviation
1.2
1
0.8/p>
0.6
0.4
0.2
0
CVD
without DG, 1.1592
CVD with
Loading conditio n
System status
Optimal Bus Location
Respective DG ratings (PU)
Reactive Power Loss (QL)
QL
Profile Improve ment (%)
100%
Without DG
–
–
0.088
51.13
With DG
3,17,28
0.8256,
0.043
0.2134,
0.4571
105%
Without DG
–
–
0.094
52.12
With DG
3,17,28
0.8256,
0.045
0.2134,
0.4571
110%
Without DG
–
–
0.101
52.47
With DG
3,17,28
0.8256,
0.048
0.2134,
0.4571
115%
Without DG
–
–
0.109
53.21
With DG
3,17,28
0.8256,
0.051
0.2134,
0.4571
120%
Without DG
–
–
0.116
53.54
With DG
3,17,28
0.8256,
0.054
0.2134,
0.4571
Loading conditio n
System status
Optimal Bus Location
Respective DG ratings (PU)
Reactive Power Loss (QL)
QL
Profile Improve ment (%)
100%
Without DG
–
–
0.088
51.13
With DG
3,17,28
0.8256,
0.043
0.2134,
0.4571
105%
Without DG
–
–
0.094
52.12
With DG
3,17,28
0.8256,
0.045
0.2134,
0.4571
110%
Without DG
–
–
0.101
52.47
With DG
3,17,28
0.8256,
0.048
0.2134,
0.4571
115%
Without DG
–
–
0.109
53.21
With DG
3,17,28
0.8256,
0.051
0.2134,
0.4571
120%
Without DG
–
–
0.116
53.54
With DG
3,17,28
0.8256,
0.054
0.2134,
0.4571
DG , 0.6596
Base Load
105% 110% 115% 120%
Fig.4. Cumulative voltage profile of the system: Comparative analysis between without DG and with DG
TABLE II
PERFORMANCE EVELUVATION OF DGOVER ATIVE LOSS PROFILE OF THE SYSTEM
Loading condition
System status Optimal Bus Location
Respective DG ratings (PU)
Active Power Loss (PL)
PL Profile Improve ment (%)
0.12
0.1
0.08
0.06
0.04
0.02
0
0.12
0.1
0.08
0.06
0.04
0.02
0
QL
withouQtL with
QL
withouQtL with
DG,
DG,
DG,
DG,
0.1160.054
Base Load
0.1160.054
Base Load
105% 110% 115% 120%
105% 110% 115% 120%
0.15
0.15
DG, 0.163
DG, 0.163
0.1
0.1
QL with
DG, 0.071
QL with
DG, 0.071
System Active Loss
System Active Loss
System Reactive Loss
System Reactive Loss
Without DG
–
–
0.123
100%
With DG
3,17,28
0.8256,
0.057
53.66
0.2134,
0.4571
Without DG
–
–
0.132
With DG
3,17,28
0.8256,
0.060
54.54
105%
0.2134,
0.4571
Without DG
–
–
0.142
With DG
3,17,28
0.8256,
0.063
55.63
110%
0.2134,
0.4571
Without DG
–
–
0.152
With DG
3,17,28
0.8256,
0.067
55.92
115%
0.2134,
0.4571
Without DG
–
–
0.163
With DG
3,17,28
0.8256,
0.071
56.44
120%
0.2134,
0.4571
Without DG
–
–
0.123
100%
With DG
3,17,28
0.8256,
0.057
53.66
0.2134,
0.4571
Without DG
–
–
0.132
With DG
3,17,28
0.8256,
0.060
54.54
105%
0.2134,
0.4571
Without DG
–
–
0.142
With DG
3,17,28
0.8256,
0.063
55.63
110%
0.2134,
0.4571
Without DG
–
–
0.152
With DG
3,17,28
0.8256,
0.067
55.92
115%
0.2134,
0.4571
Without DG
–
–
0.163
With DG
3,17,28
0.8256,
0.071
56.44
120%
0.2134,
0.4571
Fig.6. Reactive loss profile of the system: Comparative analysis between without DG and with DG condition of the system
0.2
PL
without
0.2
PL
without
Base
Load
105%
110% 115% 120%
Base
Load
105%
110% 115% 120%
0.05
0
0.05
0
Fig.5. Active loss profile of the system: Comparative analysis between without DG and with DG condition of the system
In Fig. 5 it can be observed that the losses are less when the DGs are connected in the system as compared to the system without DGs. Improvement in reactive loss profile of the system is shown in table III. The proposed method is also
Line losses and cumulative voltage deviation in the system are reduced effectively by optimizing the DGs location and ratings. Results and graphs mentioned above are presenting the effectiveness of the methodology.

CONCLUSION
This paper presents a heuristic approach (Genetic Algorithm) to carry out optimal placement of DG in distribution network. The analysis of the proposed method is tested on the IEEE 33bus radial distribution network, and the results show the significance of the proposed method in improving the voltage profile and loss reduction of the system. DG location and size in distribution system was bounded by some inequality constraints and this algorithm successfully attends the convergence with satisfying all the constraints.GA is advantageous as it have less computation time and possess high level of convergence. Analysis of the results performed by load flow analysis depicts the effectiveness of the approach. Voltage profile and Power losses of the system have
improved considerably by placing and sizing the DGs by the proposed GA based optimization method. The proposed method is also tested under varying operating conditions by increasing the real and reactive loads of the system in steps. The results obtained shows that even under stressed operating conditions the proposed method is able to improve the voltage profile and power loss of the system. Search space of this technique is random and works on principal of genetic evolution which provides solution near to global optima.

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