1. Zuhusna Adilla Binti Ibrahim
B011110121
Supervisor : Encik Mohamad Fani bin Sulaima
Distribution Network Reconfiguration (DNR) Using
Improved Artificial Bee Colony (IABC) For Energy Saving
1

2. Motivation
In Malaysia, the growing industrialization and increasing standard of living has
considerably increased the usage of energy.
The increasing demand of the electrical energy is quietly related to the power
demand.
In order to cope the demand of the electricity, the distribution system has
become more complex and causing power loss always occurred while distributing
the electric.
To reduce the power loss, the network distribution system needs to be
reconfigured.
2

3. • The demand for the electricity is rising due to the
increasing population group.
• The distribution system has become more complex.
• The current drawn increasing during the distribution of
electricity which lead to the instability.
• As the system unstable, the power losses will occur.
Problem Statements
3

4. Research Background
Power System
Generation Transmission Distribution
LoopMeshRadial
DNR
Act of
opening and
closing
switches
Easy to analyze
and isolate fault
4

5. Research Background
Optimization Technique
Heuristic Artificial Intelligence
ABCGAANN
Works by
mimicking bee
behavior of
finding food
source
Optimal
Flow
Pattern
(OFP)
Branch
Exchange
Method
(BEM)
5

6. 6

7. Scope
7

8. Previous Work
Author Project Title Method Used Description Comment
R.J Safri, M.M.A
Salama, A.Y
Chikhani
Distribution
System
Reconfiguration
for Loss
Reduction : A
New Algorithm
based on a set
of Quantified
Heuristic Rules
Quantified
Heuristic Rules
 Aim to reduce
power losses
 The method
serves as pre-
processor by
removing the
undesirable
switching
 Does not
perform the
complex
analysis load
flow.
 This
proposed
method does
not perform
the load flow
analysis
 A new
artificial
intelligence
technique is
proposed
8

9. Author Project Title Method
Used
Description Comment
S. Ganesh Network
Reconfiguration of
Distribution
System Using
Artificial Bee
Colony Algorithm
ABC
algorithm
technique
 Aim to minimize
power losses
 The ABC is tested
on the 33-bus
system
 Compared with
Refined Generic
Algorithm (RGA)
and Tabu Search
Algorithm (TSA)
 ABC has the best
performance in
minimizing power
losses.
 Does not
apply the
improved
ABC
algorithm
 Does not
improve the
voltage
profile
9

10. Author Project Title Method
Used
Description Comment
M.
Assadian,
M.M
Farsangi,
Hossein
GCPSO in
cooperation with
graph theory to
distribution
network
reconfiguration
for energy
saving
Guaranteed
Convergence
Particle
Swarm
Optimization
(GCPSO)
and Particle
Swarm
Optimization
(PSO)
 Objectives are to
reduce power loss
and enhancement
of voltage profile
 Compared with
applied GA +
GCPSO
 Results show that
the GA and
GCPSO are better
than conventional
PSO in term of
energy saving.
 The paper
does not
show the cost
saving
 The
proposed
method does
not show the
value of
energy
saved.
10

11. METHODOLOGY
11

12. Methodology
Start
Initialization Phase
Employed Bee Phase
Onlooker Bee Phase
Scout Bee Phase
Memorize the best solution
Exceed
maximum
cycle?
Stop
No
Yes
Flowchart of ABC
12

13. Improved Artificial Bee Colony (IABC) Technique
• Inspired by the improved strategies of Particle Swarm Optimization (PSO)
• An inertial weight w inspired by PSO evolution equation and its improving
strategies are added.
• The benefits of using this technique are:
 Maximize the exploitation capacity
 Balanced the exploitation and exploration phase
13

14. Start
Initialization Phase
Employed Bee Phase
(Weight is added here)
Onlooker Bee Phase
Scout Bee Phase
Memorize the best solution
Exceed
maximum
cycle?
Stop
No
Yes
Flowchart of IABC
14

15. Energy Saving Formulation
15

16. RESULTS AND
DISCUSSION
16

17. Test System
Analysis
17

18. • In this system, the 33-bus initial
configuration are consists of:
• 1 feeder, 32 normally closed tie
line and 5 normally open tie
lines.
• The normally open tie lines are
represented by 33, 34, 35, 36
and 37 branches.
Sectionalizing Switch
Tie Switch
Figure 1: IEEE 33-bus radial original network configuration
Test System Analysis
18

19. • The IABC algorithm is tested on 33-bus
network system for 30 times.
• From the 30 run times, only 12 of them
are radial.
• The best combination of switches that
has been chosen is at 20 because
value of power loss at this 20th
running
times is the lowest which is 107.1 kW
and has the fastest computational time
(1222.6623s).
• The best combination switches are
opened at S31, S6, S21, S13 and, S37
Test System Analysis
19

20. Figure 4.2: The Power Loss after IABC Network Reconfiguration
20

21. Power Losses
21

22. Test System Analysis
Figure 5.1: Power Loss (kW) Comparison between the Network Reconfiguration
22

23. Figure 5.2: Loss Reduction Comparison between the Network Reconfiguration
23

24. Voltage Profile
24

25. Figure 4.5: Voltage Profile of the Three Network Reconfiguration System
25

26. Energy Saving &
Cost Saving
26

27. Company SAIDI (Minute)
2008 2009 2010 2011 2012 2013
TNB 68.31 56.72 88.1 63.25 49.30 56.20
Data from SAIDI (TNB)
Table 4.3: The Average SAIDI data in Peninsular Malaysia [22]
Region Electricity Average Selling Price
(sen/kWh)
Peninsular Malaysia 33.88
Table 4.4: The Electricity Average Selling Price (sen/kWh) [22]
27

28. Energy Saving
Network
Reconfiguration
Initial Network ABC IABC
Total Power Loss
(kW)
202.71 134.26 107.10
Energy (kWh) 4 833.82 3201.56 2553.90
Total loss Cost for
one day (RM)
1 637.70 1084.69 865.26
Table 5.2: The total energy and total cost loss in one day
28

29. Total Cost Loss
Figure 5.3: The Monthly Cost Loss of the Network Reconfiguration
29

30. Figure 5.4: Total Cost Loss for a Year
Total Cost Loss
30

31. Conclusion
• IABC algorithm technique has shown a good performance in minimizing the
power loss when it is compared to the ABC and other optimization method
• Succeeded in reducing the energy losses in the distribution network system
• The objectives of this study have been achieved successfully
31

32. Recommendation
• Tested on 14-kV and 69-kV IEEE test bus system in
order to get better outcomes and analysis.
• To consider the Distribution Generators (DGs) in the
future.
• To consider the power quality.
32

33. References
[1] R.J Safri, M.M.A Salama and A.Y Chickani, “Distribution system reconfiguration for
loss reduction: a new algorithm based on a set of quantified heuristic rules”, Proceedings
of Electrical and Computer Engineering, Vol. 1, Canada , pp. 125-130,1994.
[2] S. Ganesh, “Network Reconfiguration of Distribution System Using Artificial Bee
Colony Algorithm”, International Journal of Electrical, Robotics, Electronics and
Communication Engineering, Vol.8, No. 2, pp. 403-409, 2014.
[3] M. Assadian, M. M. Farsangi, Hossein Nezamabadi, “GCPSO in cooperation with
graph theory to distribution network reconfiguration for energy saving”, Energy
Conversion and Management vol. 51,pp. 418-417, 2010.
[22] Suruhanjaya Tenaga, Performance and Statistical Information on Electricity Supply
Industry in Malaysia, pp. 22-24, 2013.
[14] M. Rohani, H. Tabatabaee & A. Rohani, “Reconfiguration Optimization for Loss
reduction in Distribution Networks using Hybrid PSO Algorithm and Fuzzy Logic”,
MAGNT Research Report, Vol. 2(5), pp. 903-911, 2011
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