 Open Access
 Total Downloads : 248
 Authors : Mojtaba Jamiati, Hamidreza Houshiyar, Behzad Ziloee, Alireza Jelodarian
 Paper ID : IJERTV4IS010146
 Volume & Issue : Volume 04, Issue 01 (January 2015)
 Published (First Online): 29012015
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Determining Optimal Location and Size of Diesel Generator and Wind Turbine in Simultaneous Mode
Mojtaba Jamiati
Faculty member, Department of Physics, Naragh Branch, Islamic Azad University, Naragh,
Iran
Hamidreza Houshiyar, Behzad ziloee, Alireza Jelodarian
Department of Electrical Engineering,Naragh Branch, Islamic Azad University, Naragh, Iran
Abstract In this paper, Group Search Optimization (GSO) algorithm has been proposed to determine optimal location and size of diesel generator and wind turbine. For this propose, a novel multiobjective function has been suggested based on power loss cost and Cost of Energy Not Supplied (CENS) as well as costs of installation and operating resource. Case study has been performed on 37 bus test system and four scenarios and four cases introduced in this system. The studied parameters are: total system cost, installation cost, CENS, power loss cost and location and size of the placed units.
Keywords Hybrid system, Diesel Generator, Wind Turbine, GSO algorithm.

INTRODUCTION
A wind/diesel or hybrid power system presents an opportunity to combine the conventional attributes of autonomous diesel electric generators with the advantages of renewable energy resources [1]. Economical electric generation with wind energy in remote areas has been under investigation for many years. For example, small battery charging wind plants were often used to provide electricity in many parts of the United States in the 1930s. In most cases, these units were replaced by electricity from a central grid. In other cases, diesel generators, which could provide power more reliably, in greater quantity and at reasonable cost, displaced the wind machines. With the rising cost and uncertainty of supply of oil in the mid1970s, attention turned again to using wind machines to reduce fuel costs [2].
Detailed studies has been performed on hybrid system. In this section, the published works of hybrid system have been categorized in three groups; which are: diesel/other renewable sources, wind turbine/other renewable sources and wind/diesel.
In [35], researchers have designed hybrid system by considering diesel and other renewable sources. Ref.[3] presents an optimal design of a solar PVdiesel hybrid mini grid system for a fishing community in an isolated island Sandwip in Bangladesh using genetic algorithm. Ref.[4] reports on the investigating economic feasibility of a PV/diesel hybrid power systems in various climatic zones within South Africa. Ref.[5] is devoted to a renewable hybrid PVdiesel generator developed to supply power to a designated remote controlled FM transmitters located in remote locations.
In [69], placement of wind turbine and other renewable sources have been performed. The objective of Ref.[6] is to propose a seriesparallel resonant high frequency inverter for standalone hybrid photovoltaic (PV)/wind power system in order to simplify the power system and reduce the cost. In [7], a laboratory study has been performed by combining wind and solar energies to generate electrical energy in the climate in Jordan. Ref.[8] presents the results of a wind/PV/Battery Energy Storage System (BESS) hybrid power system simulation analysis undertaken to improve the smoothing performance of wind and PhotoVoltaic (PV) power generation. In [9], a prefeasibility of windPVbattery hybrid system has been performed for a small community in the east southern part of Bangladesh.
In [1013], the studies have been performed based on allocation of diesel generator and wind turbine. Ref.[10] presents a comparative study of reactive power control for isolated winddiesel hybrid power system in three different cases with wind power generation by induction generator (IG), permanentmagnet induction generator (PMIG) and permanentmagnet synchronous generator. A dynamic programming method is used in [11] to generate the optimum operational option by maximizing the net cash flow of the plant. Results show that operational options can provide additional value to the hybrid power system when this operational flexibility is correctly utilized. The collection and analysis of 6 months of continuously recorded field data from a small remote winddiesel power system at a coastal farm site has been reported in [12].
In this paper, designing hybrid system have been done based on diesel and wind energies by Group Search Optimization (GSO) algorithm. This context has been organized in five sections. The multiobjective function has been formulated in Section 2. Concept of GSO algorithm has been discussed in Section 3. Simulation results have been listed in several tables in Section 4. This work has been concluded in Section 5.

OBJECTIVE FUNCTION
Improving reliability and reduce power loss are the main challenges of distribution system designers. Thus in this paper, the multiobjective function has been formulated based on maximizing reliability and minimizing power loss cost (CLOSS). For this, the Cost of Energy Not Supplied (CENS) has been used index as reliability index. Unit installation are
applied cost of installation and operation (CDG) to system. Then the proposed multiobjective function is as following,
OF CENS CLOSS CDG (1)
To calculate reliability indices, SAIDI and CENS, analytical method based on error modes and their effects (FMEA) is used [13]. Accordingly the mentioned parameters are calculated using Eqs. (2) to (3).
assumes all scroungers will join the resource found by the producer, is used. In optimization problems, unknown optima can be regarded as open patches randomly distributed in a search space. Group members therefore search for the patches by moving over the search space. It is also assumed that the producer and the scroungers do not differ in their relevant phenotypic characteristics. Therefore, they can switch between the two roles.
CENS i
C nssys
l i
r loc
l loc ,i
P
loc ,i

r rep P rep ,i

Producer
l t
l t
(2)
At the kth iteration, let the producers position denoted by
X k=(xk ,, xk ). It scans three points around it to find a
K p p1 pn
CENS sys CENS i
better position. First, the producer scans a point in front of it:
I 1 (3)
X X k r l D k k
where:
CENSi: Cost of Energy Not Supplied due to an error in the ith region
sys: Annual failure rate of system
li: Length of the ith region
lt: Total length of feeder
F P 1 max p
(7)
Second, it scans a point on its righthand side:
F P
1 max
p
2 max
X X k r l D k k r 2
F P
1 max
p
2 max
Third, it scans a point on its lefthand side:
(8)
loc
r : Average time for locating the fault
X X k r l D k k r 2
(9)
lloc, i: The length of region which is deenergized for locating the fault due to an error in the ith region
where, r1 is a random number normally distributed with mean 0 and standard deviation 1, r2 is a random number uniformly
Nloc,i: Total number of customers who are deenergized for
locating the fault due to an error in the ith region
Nt: Total number of system customers
rrep: Average time to repair a fault
distributed in[0,1].The y
is maxpursuit distance:
n
max
is maxpursuit angle, and the lmax
2
Nrep, i: The number ofcustomers who are deenergized for repairing the fault due to an error in the ith region
l max U

L
U j L j
j 1
(10)
Cns: The average cost of a 1 KWh outage
Ploc, i: The average outage active power for repairing the fault due to an error in the ith region
Prep, i: The average outage reactive power for repairing the fault due to an error in the ith region.



GROUP SEARCH OPTIMIZATION ALGORITHM [1415] The population of the GSO algorithm is called a group and
where, Uj and Lj are the upper bound and lower bound of the search range.
If the producer finds that the best position in the three points is better than its current position, it moves to the best position and change its head angle as Eq.(9),where max is the maxturning angle. Otherwise, it stays at original position. If the producer fails to find a better point in a iterations, it scans front again as Eq.(12):
i
each individual in the population is called a member. In an n dimensional search space, the ith member at the kth searching bout (iteration) has a current position X k Rn , a head angle
k 1 k r
2 max
k a k
(11)
k=( k,, k ) Rn1. The search direction of the ith
(12)
i i1 i(n1)
member, which is a unit vector D k( k)=( d k,, dk ) Rn
i i i1 i(n1)
that can be calculated from ki via a polar to Cartesian coordinate transformation.
d
iq
i1
k cos k
q 1
(4)

Scrounger
In the computation, most of the members are chosen as scroungers. If the ith member is chosen as a scrounger at the
d
sin
k k
i1 i j 1
.cos k j 2,…, n 1
kth iteration, it moves toward the producer with a random distance,
iq
q 1
(5)
X k 1 X k r .X k X k
(13)
d k sin k
i i 3 p i
i1 i j 1
(6)
where, r3 is a random sequence uniformly distribution in
In GSO, a group consists of three types of members: producers and scroungers whose behaviors are based on the PS model; and dispersed members who perform random walk motions. For convenience of computation, we simplify the PS model by assuming that there is only one producer at each searching bout and the remaining members are scroungers and dispersed members. The simplest joining policy, which
[0,1]. 
Ranger
The rest members in the group are rangers. If the ith member is chosen as a ranger at the kth iteration, it turns its head to a random angle as Eq.(9), and calculates the search
direction using Eqs. (45), then moves to that direction with a random distance as the following:
Start
li a.r1l max
Generate and evaluate initial members
(14)
Randomly generated feasible discrete particles with position vectors

Solving the problem by the GSO algorithm
Load flow program
In Sections IIIII, concepts of optimal diesel generator and wind turbine placement problem and GSO algorithm has been presented. In this section, the problem solution by GSO algorithm is discussed. The capacitor placement problem solution by GSO algorithm has been performed in nine steps:
Step 1. Generating initial members
Choose a member as producer
Step 2. Randomly generated feasible discrete particles with position vectors
The producer performs producing
Step 3. Running load flow program Step 4. Choosing a member as producer Step 5. Performing the producer
Increase iteration
Choose scroungers
Step 6. Choosing scroungers
Step 7. Performing the scroungers scrounging
Scroungers perform
Step 8. Dispersing the rest members to perform ranging Step 9. Evaluating members
Dispersed the rest members to perform
Fig.1 shows flowchart of optimal capacitor placement solution by the GSO algorithm
Evaluate members
No Termination Criterion
Satisfied?
Yes
Print Optimal values
End
Fig. 1. Flowchart of the solving problem by the GSO algorithm


CASE STUDY
IEEE 37bus distribution network is selected as test system. Single line diagram of this system has been illustrated in Fig.2.
35
36
34
33
20
32
21 26
30
29
28
27
19
25
24
23
22
18
17
7
6
5
4
3
2
1
Source
12
11
16
14 8
15 9
13
10
Fig. 2. Single line diagram of test system
Four scenarios have been introduced for the system: Scenario I: Placement of one diesel generator Scenario II: Placement of two diesel generators Scenario III: Placement of three diesel generators Scenario IV: Placement of four diesel generators Four Cases have been defined for each scenario: Case i: Placement of one wind turbine
Case ii: Placement of two wind turbines Case iii: Placement of three wind turbines Case iv: Placement of four wind turbines

Scenario I: Placement of one diesel generator
In first scenario, one diesel generator have been placed in the presence of one to four wind turbine. Results of first scenario have been listed in Table I.
TABLE I. RESULTS OF FIRST SCENARIO
Case
CENS
CDG
CLOSS
OF
i
27669232
1587608000
2394183800
4009461032
ii
27666419
1757182000
2194144724
3978993143
iii
27679056
1882546000
2074349995
3984575051
iv
27680491
1934319000
1974367759
3936367250
By considering results if Table I, it can be claimed that fourth case is the best solution. Table II ahows optimal location and size of the placed units.
TABLE II. OPTIMAL LOCATION AND SIZE OF UNITS IN FIRST SCENARIO
Case
1
2
3
4
DGen
Location
12
12
12
13
Size
2000
2500
300
150
WT
Location
11
11 14
34 18 11
11 15 18 34
Size
750
150 750
4590 105
90 90 60 45
By considering results of above table, diesel generator is tendency to install in Bus 12 however is installed in bus 13 in fourth case. While usually wind turbine is placed in bus 11.

Scenario II: Placement of two diesel generators
In second scenario, two diesel generators are placed simultaneous with changing the number of wind turbine from one to four and their results have been presented in Table 3.
TABLE III. RESULTS OF SECOND SCENARIO
Case
CENS
CDG
CLOSS
OF
i
27678693
1775217000
2394257284
4446261221
ii
27677063
1969720000
2294336510
4291733573
iii
27680411
2082148000
2094367932
4204196343
iv
27675518
2204147000
1993966506
4225789024
By considering results of Table III, third and first cases present the best and worst solution, respectively. Cost of third case is 242064878, 87537230 and 21592681 $ less than related value of first, second and fourth cases. Location and size of the installed diesel generator and wind turbine are visible in Table IV.
TABLE IV. OPTIMAL LOCATION AND SIZE OF UNITS IN SECOND SCENARIO
Case
1
2
3
4
DGen
Location
12 18
11
12
13 12
13 35
Size
50 100
5050
100 50
250 2000
WT
Location
34
11
12
11 12
34
11 12 15
34
Size
750
90750
75 15
60
90 15 15
300
In this scenario, diesel generator tends to install in bus 13 in more cases however in two cases are installed in bus 12. Wind turbine in more case (expect case i) tends to presence in bus 11.

Placement of three diesel generators
Table V consists of results of placement of three diesel generator for changing the number of wind turbine from one to four units.
TABLE V. RESULTS OF THIRD SCENARIO
Case
CENS
CDG
CLOSS
OF
i
27667501
2081285000
2394278112
4503230613
ii
27678228
2204147000
2194266740
4426091968
iii
27676662
2465654000
2094334289
4587664951
iv
27674835
2611441000
1894328667
4533444502
By considering results if Table V, second case presents the best solution for objective function while in none of the three studied parameters, this case do not offer the best solution. The cost of second case is 77138645, 161572983 and 107352534 $ less than related value of first, third and fourth cases, respectively. In this scenario, third case presents the best solution. Table VI illustrates the location and size of the placed units in third scenario.
TABLE VI. OPTIMAL LOCATION AND SIZE OF UNITS IN THIRD SCENARIO
Case
1
2
3
4
DGen
Loc.
11 12 13
31 13
12
11 12 20
12 13 31
Size
2000 400
400
250
500
500
300 150
200
100 50 150
WT
Loc.
11
34 15
11 12 15
11 12 15
34
Size
750
60 45
240 60
300
60 105 240
750

Scenario IV: Placement of four diesel generators
In the last scenario, allocation of four diesel generators and one to four wind turbines have been done and their results are visible in Table VII.
TABLE VII. RESULTS OF FOURTH SCENARIO
Case
CENS
CDG
CLOSS
OF
i
27678261
2022136000
2394257284
4444071545
ii
27677960
2389547000
2004328285
4421553245
iii
27680135
2411284000
1794392329
4233356464
iv
27677614
2612324000
1614243813
4254245427
By considering results of Table 7, third and first cases present the best and worst solution, respectively. Cost of third case is 210715081, 188196781 and 20888963 $ less than related value of first, second and fourth cases. Location and size of the installed diesel generator and wind turbine are visible in Table VIII.
TABLE VIII. OPTIMAL LOCATION AND SIZE OF UNITS IN FOURTH SCENARIO
Case
1
2
3
4
DGen
Loc.
11 13
18 31
11
1220 34
11 15
31 34
10 12 13 20
Size
5050 50
100
100
100
250200
100
100250
200
50 600 300
300
WT
Loc.
34
15 34
11 12 34
11 15 21 34
Size
750
15 15
75 15 60
375 240 300
90


CONCLUSION
In this paper, the placement of diesel generator and wind turbine has been performed by GSO algorithm. In case study, four scenarios and four cases have been introduced. From simulation results, we can be claimed:
Increase the number of units, technical feasibility, but may not necessarily be economically justified. Except in the first scenario in the rest scenarios, increasing the number of units may not be the best answer possible.
In three scenarios, first case has the worst solution. This fact indicates that the number of placement units is reasonably close relationship must exist and the number of diesel and wind are should not be statistically significant.
Third case in three scenarios present the best solution. The optimal solution obtains from simultaneous optimization of three objective function parameters.
REFERENCE

J.G.McGowan, J.F.Manwell, and S.R.Connors, Wind/diesel energy systems: review of design options and recent developments, Energy, vol.41, no.6, pp. 561575, 1988.

J.G.Mcgowan, and J.F.Manwell, Hybrid wind/PV/diesel system experiences, Renewable Energy, vol.16, pp.928933, 1999.

B.K.Bala, and S.A.Siddique, Optimal design of a PVdiesel hybrid system for electrification of an isolated islandSandwip in Bangladesh using genetic algorithm, Energy for Sustainable Development, vol.13, no. 3, , pp.137142, 2009.

J.Dekker, M.Nthontho, S.Chowdhury, and S.P.Chowdhury, Economic analysis of PV/diesel hybrid power systems in different climatic zones of South Africa, International Journal of Electrical Power & Energy Systems, vol.40, no.1, pp.104112, 2012.

M.Moghavvemi, M.S.Ismail, B.Murali, S.S.Yang, A.Attaran, and S.Moghavvemi, Development and optimization of a PV/diesel hybrid supply system for remote controlled commercial large scale FM transmitters, Energy Conversion and Management, Vol.75, pp.542 551, 2013.

P.Kong, J.Zhao, and Y.Xing, Seriesparallel resonant high frequency inverter for standalone hybrid PV/wind power system, Energy Procedia, vol.12, pp.10901097, 2011.

S.Essalaimeh, A.AlSalaymeh, and Y.Abdullat, Electrical production for domestic andindustrial applications using hybrid PVwind system, Energy Conversion and Management, vol.65, pp.736743, 2013.

X.Li, Y.Li, X.Han, and D.Hui, Application of fuzzy wavelet transform to smooth wind/PV hybrid power system output with battery energy storage system, Energy Procedia, vol.12, pp.9941001, 2011.

S.Kumar Nandi, H.Ranjan Ghosh, Prospect of windPVbattery hybrid power system as an alternative to grid extension in Bangladesh, Energy, vol.35, no.7, pp.30403047, 2010.

P.Sharma, W.Sulkowski, and B.Hoff, Dynamic stability study of an isolated winddiesel hybrid power system with wind power generation using IG, PMIG and PMSG: A comparison, International Journal of Electrical Power & Energy Systems, vol.53, pp.857866, 2013.

Y.Hu, and P.Solana, Optimization of a hybrid dieselwind generation plant with operational options, Renewable Energy, vol.51, pp.364 372, 2013.

A.J. Bowen, M.Cowie, N.Zakay, The performance of a remote wind diesel power system, Renewable Energy, vol.22, no.4, pp.429445, 2001.

H.A.Abbas, Marriage in honey bees optimization(HMBO): a haplometrosis polygynous swarming approach the congress on evolutinary computation, CEC2001, Seoul,Korea, pp.207214, 2001.

X.Yan, W.Yang, and H.Shi, A group search optimization based on improved small world and its application on neural network training in ammonia synthesis, Neurocomputing, 97, pp.94107, 2012.

S.He, Q.H.Wu, and J.R. Saunders, Group search optimizer: an optimization algorithm inspired by animal searching behavior, IEEE Transactions on Evolutionary Computation, vol.13, no.5, pp. 973990, 2009.