A New Method for Loss Reduction Based on Simultaneous DG
Placement and Network Reconfiguration
H.–R. Mirjalili1,2, A.–R. Sedighianaraki1, and M.–R. Haghifam3
1-Yazd University, h_mirjalili@stu.yazduni.ac.ir
2-Yazd Electrical Power Distribution Company
3-Tarbiat Modares University, haghifam@modares.ac.ir
Abstract: This Distributed generations (DGs) represent an
efficient option for overcoming problems and challenges of
power utilities like loss reduction, load growth, reliability
improvement and green house gas effect reduction. Appropriate
sitting and sizing of DGs play a remarkable role on loss
reduction. In this paper a new effective method for DG
allocation is proposed that has more ability for loss reduction
during system operation. In this method reconfiguration as new
subject has been added to former DG allocation algorithms, so
different possible configurations are considered for DG
placement problem. In the other words DG allocation and
reconfiguration are done simultaneously. Also to overcoming
time-consuming of reconfiguration TLL-based reconfiguration
is presented in this paper. In this reconfiguration method a
profitable solution for finding all possible loops is suggested,
that is based on two TLL and CT/M matrices calculation. This
proposed approach is applied to both test and real networks. All
proposed procedures are programmed in DIgSILENT program
using DPL. The Simulation results demonstrated the practical
benefits of proposed approach.
Keywords:
Distributed
generation
allocation,
Reconfiguration, genetic algorithm, active and reactive
loss reduction.
1.
Introduction
Growth of power demand is a critical problem of every
power utilities as they must always supply all customers
with the least cost and interruption. Substation capacity
increase is a solution to supply all requested power.
However, to performing this solution some problems like
disability of substation due to generation and
transmission limits or necessity of distribution feeder
reinforcement could be appeared. Integration of
distributed generation units to distribution networks can
be a more appropriate solution that postpones
investments of upgrading existing power systems.
Moreover, the other DG benefits include loss reduction,
peak shaving, increase overall energy efficiency, voltage
improvement, congestion relief of transmission and
distribution, reduce environmental impacts [1], and
reliability improvement [2].
Loss reduction is one of the main goals of power
utilities. Specially, with the impending deregulated
environment, electric utilities are seeking new
technologies to provide cheap power with suitable
reliability and power quality because of retail competitive
structure. There are many methods for loss reduction like
capacitor placement, high voltage distribution system,
conductor
grading,
DG
units
and
network
reconfiguration. One of the recent most remarkable
techniques for loss reduction is DG placement in
distribution systems. The connection of generators on
distribution feeders causes significant impact on power
loss reduction [3] but may cause operation and safety
problems [4-5]. As DG units generate power locally to
fulfill customers demand, appropriate size and placement
of DG can drastically reduce power losses in the systems
[2]. Determining Characteristics such as the size, location
and operation mode of distributed generation are vital to
gain DGs benefits as much as possible. The allocation of
this energy sources has been investigated in several
references using classic methods and evolutionary
programs. In [1, 3, 6] DGs allocation has been formulated
analytically. Authors in [7] has been employed
combination tabu search and genetic algorithm to
maximize loss reduction,
fuzzy approach for
optimization of its algorithm [8], heuristic approach [9]
and analytical sensitive-based with a power loss index for
each DG [3], a sequential Quadratic programming upon
the level of power losses and DG cost compatibility of
different generation [10]. But all of researchers have been
allocated DGs in an individual configuration that could
be considered as original configuration. Indeed, in
distribution system, configuration of network changes to
feeder balancing, loss reduction, improvement of
reliability and voltage, etc [11]. In this paper a new
method for DG allocating is considered. In this approach
DG allocation is done simultaneously with network
reconfiguration for maximize loss reduction. In addition a
heuristic method for speeding up reconfiguration
proposes in this paper. Altogether, the main searches
about reconfiguration are included two main heuristic and
evolutionary programming. [12-14] has proposed
heuristic method to find best configuration in a short time
1384
but with no guarantee of global optimality. Although
some authors used genetic algorithm [15], simulated
annealing [16], artificial neural network, fuzzy logic, etc
for reconfiguration with better global optimality, most of
them are more time-consuming than heuristic methods. In
this paper a hybrid heuristic and evolutionary
programming is introduced to solving dichotomy of “best
topology” and “time-consuming” for reconfiguration
problems. The proposed methodology is successfully
applied to a test network and a real distribution network.
All process of this algorithm has been programmed in
DIgSILENT power factory 13.2 using DPL. Simulation
results prove that proposed idea is effective and practical
for real networks with numerous objects.
2.
DG Placement Simultaneous With Network
Reconfiguration
Different goals have been considered for DG
allocation. In this paper for loss reduction in electric
distribution network, reconfiguration as a new effective
parameter for DG sitting and sizing is introduced.
Different topology of studying network should be
considered in DG placement. This paper intent to
evaluate impact of feeders reconfiguration on distributed
generation allocation. For overcoming to complexity of
optimal
reconfiguration
and
DG
placement
simultaneously, an evolutionary program using genetic
algorithm is applied for optimization process. Therefore
in each generation of GA both of reconfiguration and DG
location is represented using each chromosome. This
methodology is illustrated in Fig. 1. The length of
chromosomes is equal to summation of tie switches,
manoeuvrable liens, DGs candidate places and DGs
ratings. As it shown in Fig. 1 for every chromosome
reconfiguration is done firstly, and then according to this
new feasible network topology, DG allocation is done. It
should be mentioned that genetic operators is done for
above process separately.
Vmin < Vi < Vmax
(1)
I l < I l ,max
(2)
S g < S g ,max
(3)
∑ S g < S pen ,max
(4)
n
g =1
where Vi : voltage of i-th terminal, Vmin and
Vmax standard voltage limits, I l : l-th line rated current,
S g ,max : maximum capacity of g-th DG and S pen ,max :
maximum allowable penetration of DGs in system. This
paper focuses on active and reactive loss reduction of
lines as below:
NL
Fobj = ∑ Rl × I l
l =1
2
+
NL
∑ X l × Il
l =1
2
(5)
where Rl : line resistance, X l line inductance and I l is
line current.
4.
Representation of New Heuristic Network
Reconfiguration
In a practical network by increasing the number of
switches the number of possible switching operation will
be tremendous. Consequently network reconfiguration
becomes a complex decision-making and time-consuming
procedure
for
system
operators.
Furthermore
reconfiguration problem will be indeed more difficult
task as the electric systems are mostly configured radial
for proper relay coordination.
Therefore it needs a
criterion to investigate feasibility of topology. According
to graph theorem, network doesn’t have any loop if it
satisfies two following conditions:
N l = N t - Sub
(6)
N iso = Sub
(7)
where N l is the number of connected lines, N t is the
number of terminals, N iso is the number of isolated area
and Sub is the number of substations. In this paper a new
approach for network reconfiguration is proposed that
have acceptable computing time in spite of total search
space.
Fig 1: Proposed process for DG allocation algorithm via GA
3.
and power are considered as gens of chromosomes. For
every DG allocation process some important constraints
should be considered:
4.1
DG Allocation
Although power losses can be reduced by DGs
inclusion, suitable place and size of them should be
computed to obtain most benefit as much as possible.
Evolutionary program can solve the complexity and
nonlinear equations of DGs allocation in real systems. As
it mentioned formerly, genetic algorithm is used for this
optimization problem. Binary equivalent of DGs place
TLL Matrix Extraction
When a tie-line is closed, a suitable line must be
opened to refuse forming a loop. In this part, a new
method is suggested that investigates all lines can omit
the loop due to closed tie-line. These lines can be either
maneuverable lines or ordinary lines. The following
algorithm presents procedure of all tie loop lines
extraction.
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1) At first, all lines that belong to a loop due to
closing a tie line are investigated and saved in tie loop
lines (TLL) matrix. This process is done for each tie-line.
2) During previous step, maneuverable lines related
to each tie-line loop are saved in another matrix. Then
based on this, all tie-lines and maneuverable lines that are
common with each tie line are calculated. This common
tie/maneuverable matrix is called CTM matrix.
When a special configuration is obtained after some
genetic operation, if by relevant TLL matrix, network
cannot be returned to a radial network without any
unsupplied area, CTM matrix can help the program to
investigate adjacent loops. TLL and CTM matrices are
used in genetic algorithm. These matrices help to correct
network topology after changes due to GA; in the other
words these matrices reduce search space and time of
computation.
and 3- DG placement simultaneous with reconfiguration.
Form results of Table II maximum active and reactive
loss reduction has been obtained from third scenario. It
should be noticed that one of the DGs place has been
changed through different scenarios.
5.2
Real Network
The second example as it shown in Fig. 3 is a practical
distribution network of Yazd electric power distribution
company, Iran. The main characteristics of this network
are expressed in Table III. For expanding the search
space three short tie-lines (<0.05km) were considered.
These lines were inspected by Electrical Power
Distribution company of Yazd whether they are possible
in accomplishment or not. Also 14 available
maneuverable lines have been selected.
4.2 Application of Genetic Algorithm
Each chromosome represents a feasible solution of
problem. In this paper chromosome length is assumed
equal to sum of the number of tie switches and
maneuverable lines. Crossover and mutation operators
change some genes in stochastic procedure, so it may
results unfeasible configuration. To correcting unfeasible
topology, a subprogram as a complementary of GA is
considered that’s called network correction. In order to
overcome the complexity of this process, a vector with
length of chromosomes length is considered for each
chromosome that called TM states. When a
tie/maneuverable line is connected due to genetic
operators, According to TLL matrix a line must be
disconnected to refuse forming a loop. The line’s number
of this disconnected line is saved in TM states vector in
relevant array of connected line. Also if
a
tie/maneuverable line should be disconnected due to
genetic operators, network correction subprogram
according to TM states vector, CTM and TLL matrices
modify network to satisfy Equation (6) and (7).
5.
Case Studies
Proposed methodology is tested on both test network
and a real distribution network. At first, a typical test
network has been selected to demonstrate the adequacy of
presented approach. Then to proving the effectiveness of
proposed method a real large scale distribution network
has been chosen.
5.1
Test Network
A distribution network with two feeders as it is shown
in Fig. 2 is considered as test network. Characteristics of
this typical network are shown in Table I. The system
consist of two feeder, 25 normally sectionalizing
switches, 2 normally open tie switches and 22 loads with
Sbase = 100 MVA and Vbase = 20kV. The results of
proposed algorithm simulation are expressed in Table II.
Three different scenarios were chosen for DG placement
problem that include 1- DG placement for the original
configuration, 2- DG placement for best configuration
1386
Fig. 2: Test Network with two substations
TABLE I: Characteristics of Test Network
Section
Section
End bus
Section
resistance reactance active load
bus to bus
(p.u.)
(p.u.)
(MW)
T1-11
11-12
12-13
13-14
11-15
15-16
16-17
17-18
18-19
T2-20
20-21
20-22
22-23
34-24
24-25
20-26
26-27
27-28
28-29
29-30
26-31
31-32
31-33
33-34
34-35
14-18
14-30
0.3
0.3
0.16
0.48
0.36
0.16
0.44
0.44
0.44
0.45
0.11
0.3
0.44
0.12
0.12
0.24
0.36
0.18
0.18
0.08
0.32
0.44
0.24
0.44
0.36
0.56
0.2
0.4
0.4
0.16
0.48
0.72
0.16
0.44
0.44
0.44
0.6
0.11
0.4
0.44
0.12
0.12
0.24
0.48
0.24
0.24
0.08
0.44
0.44
0.24
0.44
0.72
0.77
0.2
1.
1.
2.
2.
2.
1.5
1.
2.
1.
---2.
1.
2.
1.
1.
2.
1.
2.5
1.
1.5
2.
2.
---1.3
4.
--------
End bus
reactive load
(MVAr)
End bus
fixed
capacitor
(MVAr)
0.2
0.3
0.5
0.6
0.8
0.3
0.3
0.4
0.5
---1.2
0.5
1.3
0.5
0.7
0.4
0.2
0.75
0.1
0.3
1.3
1.2
---0.4
1.7
-------
2.3
---------2.4
------------------------------1.25
0.6
----------3.7
-------------------
TABLE II: Simulation results before and after reconfiguration and DG placement for test network
After DG placement
Original
configuration
Best configuration
Open Switches
Tie1,Tie2
DG location
Main items
For original config.
For best config.
Simultaneous with
Reconfiguration
Tie1, L 29
Tie1,Tie2
Tie1, L 29
Tie1,L 28
----
----
T18, T35
T14 ,T35
T 29,T 35
Active Power Loss
1291
1182
780
698
659
Reactive Power Loss
1689
1538
1003
916
866
Active loss reduction
(%)
----
8.4
39.6
45.9
48.9
----
8.9
40.6
45.8
48.7
0.937
0.944
0.959
0.964
0.969
Reactive loss reduction
(%)
Minimum Terminal
voltage(p.u.)
It shall be mentioned that all of lines can take apart in
optimization process in an intelligence way via proposed
TLL-based reconfiguration. In order to compare the
effect of DGs number but with totally same capacity on
loss reduction two strategies is selected for DG
allocation.
At first strategy two DG with Pnom= 3.5MW and
PF=0.8Lag were considered in first strategy. Like to
former simulation, three scenarios were chosen for DG
placement problem that include 1- DG placement for the
original configuration, 2- DG placement for best
configuration and 3- DG placement simultaneous with
reconfiguration. Numerical results of simulation are listed
in Table IV. It should be noted that in the first scenario,
DG allocation approach is based on loss reduction for
current network configuration using usual genetic
algorithm as [17] and [18]. Thus, same as other
researchers’ methodologies, other possible configuration
have never been considered for DG placement. The
results show that when DG placement problem is done
simultaneously with network reconfiguration, better
response for active and reactive loss reduction could be
obtained.
Maybe it was supposed that DG allocation for the best
configuration (with lowest losses) can reduce losses
good, but this paper different configuration are
considered for DG allocation and it demonstrates if it
done simultaneously with reconfiguration, it will reduce
losses more than other recent conventional introduced
methods.
For second strategy, four DGs with Pnom= 1.75MW
and PF=0.8Lag were considered that was equal with total
DGs capacity of first strategy. Simulation results of this
strategy are listed in Table IV. As it seen from
comparison of different scenarios, loss reduction of active
and reactive power obtained from third scenario is more
than the others. Furthermore it shows that by increasing
the number of DGs but with same aggregated capacity
losses can be reduced more than inserting DGs with much
capacity (same as first strategy results). For instance
active loss reduction with 2 DG after running third
scenario was obtained 84%, whereas this reduction with 4
DGs with same aggregated capacity was obtained 86%.
This effect for second scenario is more noticeable (75%
against 83.9%). It could be noticed that this strategy
improve reliability, but it should be economically
evaluated according the type of DGs and network
construction.
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TABLE III: Characteristics Of Yazd City Network
Characteristic
Number of lines
Number of loads (Tr. 20/0.4)
Total active load
Total reactive load
Conductor type
value
446
80
11.09 MW
6.87 Mvar
Dog, Mink,
Fox
Fig. 3: Real network of Yazd city
TABLE IV: Simulation results before reconfiguration, after reconfiguration and simultaneous reconfiguration and DG placement for real
distribution network of Yazd city.
Main items
Original
configuration
Best
configuration
After DG placement
For original configuration
DG Capacity
----
----
Open
Switches
176,428,430
,433,438,
439,443
50,129,153,
431,434,
439,441
DG location
-----
------
258, 268
108
76
103
Active Power
Loss
Reactive
Power Loss
Active loss
reduction
Reactive loss
reduction
Minimum
Terminal
voltage (pu)
(4*1.75MW)
(2*3.5MW)
(4*1.75W)
(2*3.5MW)
(4*1.75MW)
50,129,153,
431, 434,
439,441
50,129,153,
431,434,
439,441
26,50,71,
52,180,
437,439
28, 50, 68,
155,183,
279, 440
112,151,
278,408
150, 280
53, 117,
345, 521
279, 490
152,276,
427314
22.9
21.4
27
17.4
17.4
15
71
6.8
5.8
12.2
2.7
2.6
0.1
----
29.6 %
78.8 %
80.2 %
75 %
83.9 %
84 %
86%
----
31 %
93.3 %
94.4 %
88 %
97.4 %
97.5 %
99.9 %
0.933
0.987
0.996
0.996
0.995
0.996
0.996
0.997
[2]
Conclusion
In this paper a new methodology for DG allocation in
distribution systems has been proposed. This
methodology adds a new consideration to determine DG
placement and size. It claims that if DG allocation is
computed simultaneously with reconfiguration, more loss
reduction can be achieved. In the most research an
individual configuration was selected for DG allocation
whereas because of other purposes like loss reduction or
voltage profile improvement, configuration of system
changes by distribution network operators. Because of
complexity of each these two task especially
reconfiguration; genetic algorithm with dynamic
mutation rate was used for proving suggested method. A
TLL-based reconfiguration method has been proposed
too that is done in two stages: extraction of TLL and
CTM matrices and optimization using genetic algorithm.
All possible radial configurations can be investigated by
these two matrices. Thus after each change due to genetic
operators search space is just limited to all possible
configuration and vast unfeasible configurations will be
omitted.
Acknowledgements
The authors wish to thank Eng. M.-R. Sehati, Eng. M.H. Shariatnasab, and Eng. Ali Mohammad Entezari, the
general manager and vice managers of Yazd Electrical
Power Distribution Company, Iran, for data supporting.
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176,428,430, 176,428,430,
433, 438,
433,438,
439,443
439,443
6.
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