Energy and Power Engineering, 2013, 5, 683-688
doi:10.4236/epe.2013.54B132 Published Online July 2013 (http://www.scirp.org/journal/epe)
Optimal Placement of Distributed Generation for
Reliability Benefit in Distribution Systems
N. Rugthaicharoencheep, A. Chalangsut
Department of Electrical Engineering, Faculty of Engineering, Rajamangala University of Technology Phra Nakhon,
Bangkok, Thailand
Received February, 2013
ABSTRACT
A distributed generator is a small-scaled active generating unit located on or near the site where it is to be used. Several
benefits have been realized by installing distributed generators in a distribution network. Among them is reliability im-
provement if their locations and sizes are appropriately determined. For this reason, reliability benefit is investigated in
this paper with the main objective for the optimal placement and sizing of distributed generators in a distribution system
to minimize the customer interruption cost subject to the maximum number of distributed generators, total capacity of
distributed generators, bus voltage limits, current transfer capability of the feeders and only one distributed generator
for one installation position. The technique employed to solve the minimization problem is based on a developed Tabu
search algorithm and reliability worth analysis. The Tabu search algorithm is a local search that uses memory to avoid
being trapped around a local neighborhood and help to move away from a local optimum solution. The reliability worth
analysis provides an indirect measure for cost implication associated with power failure. The developed methodology is
tested with a distribution system of Provincial Electricity Authority (PEA). Numerical results from the tests demonstrate
that distributed generators can be used to promote the reliability of the distribution system.
Keywords: Distributed Generation; Reliability; Tabu Search; Distribution System
1. Introduction
Electricity has always been the major part of human de-
velopment and it has gone through various changes with
time. Traditionally, much of the electricity generated has
been produced by large-scaled, centralized power plants
using fossil fuels (e.g., coal, oil and gas), hydropower or
nuclear power. The electrical energy is transmitted over
long distances by extra high voltage (EHV) or ultra high
voltage (UHV) transmission lines and from there the
high voltage levels are converted to low voltage levels
through distribution lines in the distribution system to
end-use customers [1].
Such a centralized generation pattern, however, suffers
a number of drawbacks, such as a high level of depend-
ence on imported fuels that are very vulnerable, trans-
mission losses, the necessity for continuous upgrading
and replacement of the transmission and distribution fa-
cilities and therefore high operating cost, and environ-
mental impact. In addition, as electric demand is substan-
tially increasing as a result of economic and social
growths, the construction of a large sized power plant is
running into financial and technical difficulties, because
it is capital intensive and needs considerable amount of
time.
An ideal alternative on electric distributions to electric
users is the installation of a small sized generator or
commonly known as distributed generator (DG). DG is a
small-scale active generating unit located on or near the
site where it is to be used (i.e., in distribution systems).
The primary energy resources of DG could be wind, so-
lar, biomass, fuel cells and hydrogen, etc [2].
Although DGs have gained many positive effects, they
still have some economic and technical issues to be ad-
dressed before their applications in the distribution sys-
tem can be realized. The main objective of this paper is
to investigate the impact of distributed generation on
distribution system reliability. It is expected that reliabil-
ity on the installation of DGs can be improved because
they can be served as backup generation when a utility
supply interruption occurs. In other words, some of the
load points can still be electrically supplied by the DGs
and therefore economic loss as a result of the power out-
age can be reduced. However, amount of reliability im-
provement depends on location and size of the DGs to be
installed. It is therefore proposed in this paper a method
to determine the optimal placement and sizing of DGs in
a distribution system to minimize the customer interrup-
tion cost subject to system operational constraints [3].

N. RUGTHAICHAROENCHEEP, A. CHALANGSUT
684
The technique employed to solve the minimization
problem is based on a developed Tabu search algorithm
and reliabilty worth analysis. The Tabu algorithm sys-
tematically searches solutions expressed in forms of the
location and size of DGs. The solution obtained will then
be passed to reliability worth analysis to evaluate the
quality of the solution. The process is repeated until the
best solution has been found. The developed methodol-
ogy is tested with a distribution system of Provincial
Electricity Authority (PEA) with 26 load points.
2. Tabu Search
Tabu search is a meta-heuristic that guides a local heuris-
tic search strategy to explore the solution space beyond
local optimality [4]. The basic idea behind the search is a
move from a current solution to its neighborhood by ef-
fectively utilizing a memory to provide an efficient
search for optimality. The memory is called “Tabu list”,
which stores attributes of solutions. In the search process,
the solutions are in the Tabu list cannot be a candidate of
the next iteration. As a result, it helps inhibit choosing
the same solution many times and avoid being trapped
into cycling of the solutions [5]. The quality of a move in
solution space is assessed by aspiration criteria that pro-
vide a mechanism for overriding the Tabu list. Aspiration
criteria are analogous to a fitness function of the genetic
algorithm and the Bolzman function in the simulated
annealing.
In the search process, a move to the best solution in
the neighborhood, although its quality is worse than the
current solution, is allowed. This strategy helps escape
from local optimal and explore wider in the search space.
A Tabu list includes recently selected solutions that are
forbidden to prevent cycling. If the move is present in the
Tabu list, it is accepted only if it has a better aspiration
level than the minimal level so far. Figure 1 shows the
main concept of a search direction in Tabu search [6].
3. Reliability Indices
The basic distributed system reliability indices at a load
point are average failure rate λ, average outage duration r,
and annual outage duration U. With these three basic
load point indices, the following system reliability indi-
ces can be calculated [7].
System average interruption frequency index (SAIFI)
=ii
i
N
SAIFI N

 (1)
System average interruption duration index (SAIDI)
=ii
i
UN
SAIDI N
 (2)
Customer average interruption duration index (CAIDI)
=ii
ii
UN
CAIDI N

 (3)
Average service availability index (ASAI)
8760
=8760
i
i
N
ASAI N


ii
UN


 (4)
Average service unavailability index (ASUI)
=1 8760
ii
i
UN
ASUIASAI N 

 (5)
Energy not supplied index (ENS)
()
=ai i ENSL U

(6)
Average energy not supplied index (AENS)
()
=ai i
i
LU
AENS N

 (7)
where
i
l =failure rate of load point
i
i
N =number of customers of load point
i
i
U =annual outage time of load point
i
()ai
L=average load connected to load point
i
h
l =failure rate of contingency
h
h
r =average outage time of contingency
h
A basic approach to quantifying the worth of electric
service reliability is to estimate customer interruption
costs due to electric power supply interruptions. One
convenient way is an interpretation of customer interrup-
tion costs in terms of customer damage functions. The
customer damage functions can be determined for given
customer types and aggregated to make sector customer
damage functions (SCDF), which reflect economic con-
sequences of supply interruption as a function of cost in
different groups of customers [8].
Figure 1. Search direction of Tabu search.

N. RUGTHAICHAROENCHEEP, A. CHALANGSUT 685
4. Problem Formulation
Objective f unction:
11
Minimize ()
hi
nn
ihih h
hi
ECOSTLC r


 (8)
Constraints:
Power flow equations:
1
cos( )
B
N
kikikikk
i
PYVV i




 (9)
1
sin( )
B
N
kikikikk
i
QYVV i



 
 (10)
Voltage of each bus must be within specified limits:
min max
kkk
VVV (11)
Current transfer capability of feeders:
max , {1,2,...}
ll l
I
Il N (12)
Maximum number of DGs to be installed:
1
{1,2,...}
B
N
j
kDG C
k
enj N


 (13)
Maximum installed capacity of DGs:
11
C BN
N
jjk
kj
Ce G



 (14)
Decision variables for the installation of a DG:
0 if the DG is not installed at bus
1 if the DG is installed at bus
with the capacity at step
jk
k
e
j





k




(15)
Only one DG can be installed at one position:
1
1 {1,2,...}
C
N
j
k
j
ek N


B
(16)
where
hi
C = Outage cost ($/kW) of customer due to con-
tingency with an outage duration of hh r
h
L = load at load point
i
i
n = total number of load points
h
n = number of contingencies
k
P = power active power at bus
k
k
Q = power reactive power at bus
k
ik
Y = element (i,k) in bus admittance matrix
ik
q = angle of
ik
Y
k
d = voltage angle at bus
k
min
k
V=minimum voltage at bus k
max
k
V=maximum voltage at bus k
l
N =number of feeders
l
I =current flow in feeder
l
max
l
I=maximum current capability of feeder l
B
N =number of buses
j
k
e =decision variable for installation of a DG at
bus with the capacity at step
kj
DG
n =total maximum of distributed generation
C
N =number of capacity steps of a DG
j
C =capacity at stepof distributed generation
j
G =
maximum total installed capacity
5. Solution Algorithm
The solution algorithm for the problem is described step
by step as follows:
Step 1:Randomly select a feasible solution from the
search space: S0ÎΩ. Set the size of a Tabu list,
maximum iteration and iteration index m=1.
Step 2:Let the initial solution obtained in step 1 be the
current solution and the best solution: Sbest = S0,
and Scurrent = S0.
Step 3:Perform a power flow analysis to determine
whether the current solution satisfies the con-
straints defined in (9) and (10). A penalty factor
is applied for constraint violation.
Step 4:Calculate EC using (8) with consideration of
load point restoration.
OST
Step 5:Calculate the aspiration level of Sbest : fbest =
f(Sbest). The aspiration level is the sum of
and a penalty function.
ECOST
Step 6:G enerate a set of solutions in the neighborhood of
Scurrent. This set of solutions is designated as
Sneighbor.
Step 7:Calculate the aspiration level for each member
of Sneighbor , and choose the one that has the
highest aspiration level, Sneighbor_best.
Step 8:Check whether the attribute of the solution
obtained in step 7 is in the Tabu list. If yes, go
to step 9, or else Scurrent = Sneighbor_best and go to
step 10.

N. RUGTHAICHAROENCHEEP, A. CHALANGSUT
686
Step 9: Accept Sneighbor_best if has a better aspiration level
than fbest and set Scurrent = Sneighbor_best , or else select
a next-best solution that is not in the Tabu list to
become the current solution.
Step 10: Update the Tabu list and set m = m+1.
Step 11: Repeat steps 6 to 10 until the specified maxi-
mum iteration has been reached and report the
best solution.
where
0
S = initial solution
Ω = search space
best
S = best solution in search space
current
S = current solution in search space
best
f
= objective function of
best
S
neighbor
S = neighborhood solutions of
current
S
_neighborbest
S = best solution of
neighbor
S
6. Case Study
The developed Tabu search algorithm was tested with a
distribution system of PEA consisting of two feeders
KWA01 and KWA06. The system is modified [9] to in-
clude disconnecting switches and fuses so that the benefit
of DGs can be realized. There are 6 load points in feeder
KWA01 and 20 load points in feeder KWA06. The con-
figuration of the system is shown in Figure 2. The
maximum iteration for Tabu search is 1,000. The mini-
mum and maximum voltages for each bus are 0.95 p.u.
and 1.05 p.u. The sizes of DGs are 100 kW, 200 kW, 300
kW, 400 kW and 500 kW. The failure of a transformer is
recovered by repair. All the protective devices and DGs
are assumed to be fully reliable. Seven cases are investi-
gated in this case study.
Case 1: No DG is installed in the system.
Case 2:
N
o more than one DG can be installed in the
system.
Case 3: No more than two DGs can be installed in the
system.
Case 4:
N
o more than three DGs can be installed in the
system.
Case 5: Total installed capacity of DGs cannot be
greater than 600 kW and no more than fou
r
DGs can be installed in the system
Case 6: The same as case 5 except that total installe
d
capacity of DGs cannot be greater than 800 kW.
Case 7: The same as case 5 except that total installed
capacity of DGs cannot be greater than 1,000 kW
Figure 2. Single line diagram of two feeders of PEA.
The results from the case study are shown in Tab le s 1,
2 and 3. All the cases have the same SAIFI because this
index depends only on the reliability of components (e.g.,
lines, transformers) and is not affected distributed gen-
erators to be installed.
We can see that the overall reliability indices of cases
2 to 7 are improved compared with that of case 1 (base
case). In cases 2, 3, and 4, where the number of DGs is
limited at 1, 2, and 3 respectively, see reductions in the
system ECOST. It is very interesting to note that the
constraint given in (13) is binding for these three cases.
Table 1. Location and capacity of distributed generators.
Location of DG (bus) Capacity of DG (kW)
Case
KWA01 KWA06 KWA01 KWA06
1 - - - -
2 - 24 - 500
3 6 24 300 500
4 6, 9 24 300, 300 500
5 7 24 100 500
6 9 24 300 500
7 9 18, 24 300 200, 500

N. RUGTHAICHAROENCHEEP, A. CHALANGSUT 687
Table 2. Reliability indices of case study 1-4.
Reliabil-
ity Case
indices 1 2 3 4
SAIFI 7.33998 7.33998 7.33998 7.33998
SAIDI 17.8899 14.7669 14.7593 14.7484
CAIDI 2.43733 2.01184 2.01080 2.00932
ASAI 0.997958 0.998314 0.998316 0.998317
ASUI 0.002042 0.001686 0.001684 0.001683
ENS 45,746.8 42,008.7 41,347.6 40,833.1
AENS 116.404 106.892 105.210 103.007
ECOST 1,787,061 1,622,746 1,592,748 1,569,397
Table 3. Reliability indices of case study 5-7.
Case
Reliability
indices 5 6 7
SAIFI 7.33998 7.33998 7.33998
SAIDI 14.7633 14.7521 14.3976
CAIDI 2.011356 2.009821 1.961523
ASAI 0.998315 0.998315 0.998356
ASUI 0.001685 0.001684 0.001643
ENS 41,958.9 41,494.1 41,313.7
AENS 106.766 105.583 105.124
ECOST 1,620,491 1,599,395 1,592,721
The reason is that to minimize the system ECOST, as
many DGs as possible should be installed. However, for
example, in case 3, a 300 kW unit, instead of a 400 kW
or a 500 kW unit, is placed at bus 6. An explanation for
this is that the 300 kW unit is sufficient for the demand at
bus 6. Had the 400 or 500 kW unit been placed at bus 6
the system ECOST would have been the same. Likewise,
a 300 kW in case 4 installed at bus 9 can sufficiently
cover the demands of LP4, LP5, and LP6.
With regard to cases 5, 6, and 7, the constraint on total
capacity of DGs is binding but the constraint on maxi-
mum number of DGs is not. The same reason given in
cases 2, 3, and 4 are also used to explain the binding of
these three cases. It is observed that a DG, if its size is
large enough, tends to be installed at the end of a feeder.
Such a placement is reasonable because the load point at
the end of feeder has the highest failure rate and there-
fore most frequently needs a backup generation. In addi-
tion, the DG is able to supply power to upstream load
points.
7. Conclusions
This paper has presented a Tabu search-based method for
optimal placement of distributed generation in distribu-
tion systems with the main objective to maximize reli-
ability benefits described in forms of the customer inter-
ruption cost. From reliability point of view, distributed
generators are served as back up generation for load
points that would otherwise have been left disconnected
until the repair of a faulted component had been com-
pleted. The effectiveness of the proposed method was
demonstrated by a case study of a distribution network of
PEA with 26 load points. It can be seen from the case
study that distributed generators can reduce the customer
interruption cost and therefore improve the reliability of
the system.
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N. RUGTHAICHAROENCHEEP, A. CHALANGSUT
688
Appendix
Table A1. Customer data of feeder KWA01. Table A4. Reliability parameters of feeders KWA01 and
KWA06.
Demand
Load
Point
Number of
Customer Type P (kW) Average Q (kVAR)
LP1 1 Large Business 700 433.83
LP2 1 Large Business 700 433.83
LP3 1 Medium Business220.5 136.65
LP4 1 Medium Business35 21.69
LP5 1 Medium Business105 65.07
LP6 1 Medium Business105 65.07
Component l (f/yr) r(hr) sw (hr)
Transformers 0.0150 200 -
Line 0.3700 5 1.06
where = failure rate of component; r = repair time; l
s
w=
switching time
Table A5. Type and length of feeder KWA01.
Line No. Type Length (km)
1 SAC 185 1.0760
2 PIC 185 0.9740
3 PIC 185 0.0066
4 PIC 185 0.1960
5 SAC 185 2.1750
6 SAC 185 0.4150
7 SAC 185 0.0610
8 SAC 185 0.0130
9 SAC 185 0.9800
Table A2. Customer data of feeder KWA06.
Demand
Load
Point
Number of
Customer Type P (kW)
Average Q (kVAR)
LP1 1 Large Business 3,130.75 1,940
LP2 105 Residence 32.50 20.14
LP3 31 Residence 9.75 6.04
LP4 1 Medium Business 110.25 68.33
LP5 31 Residence 9.75 6.04
LP6 31 Residence 9.75 6.04
LP7 21 Residence 6.50 4.03
LP8 1 Government 45.50 28.20
LP9 21 Residence 6.50 4.03
LP10 1 Small Business 10.50 6.51
LP11 1 Medium Business 175 108.46
LP12 31 Residence 9.75 6.04
LP13 84 Residence 26 16.11
LP14 1 Medium Business 56 34.71
LP15 1 Medium Business 175 108.46
LP16 1 Government 22.75 14.10
LP17 1 Government 17.50 10.85
LP18 1 Government 35 21.69
LP19 21 Residence 6.50 4.03
LP20 1 Government 9.75 6.04
Table A6. Type and length of feeder KWA06.
Line No. Type Length (km)
1 SAC 185 8.7400
2 SAC 185 0.3830
3 SAC 185 0.4290
4 SAC 185 0.2890
5 SAC 185 3.0060
6 ACSR 50 0.1900
7 ACSR 50 1.0690
8 ACSR 50 0.8540
9 ACSR 50 0.0170
10 ACSR 50 0.2220
11 ACSR 50 0.1580
12 ACSR 50 0.0810
13 ACSR 50 0.5080
14 ACSR 50 0.0640
15 ACSR 50 0.3120
16 ACSR 50 0.0510
17 ACSR 50 0.4660
18 ACSR 50 0.0910
19 ACSR 50 0.4100
20 ACSR 50 0.1660
21 ACSR 50 0.3190
22 ACSR 50 0.5050
23 ACSR 50 0.1300
24 ACSR 50 0.3940
25 ACSR 50 0.6930
26 ACSR 50 0.4300
27 ACSR 50 0.2910
28 ACSR 50 0.0910
Table A3. Customer damage function.
Duration in Hours and Interruption Cost
(Baht/kW)
Type
1 hr 2 hr 4 hr 8 hr
Residence 8.694 19.050 39.762 80.716
Small Business 166.172 288.467 591.748 1,054.216
Medium Business 55.006 92.647 193.661 363.221
Large Business 50.877 79.913 145.614 251.938
Government 20.025 28.827 40.175 50.941