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International Journal of Electrical Engineering and Technology (IJEET)
Volume 13, Issue 7, July 2022, pp. 1-13, Article ID: IJEET_13_07_001
Available online at https://iaeme.com/Home/issue/IJEET?Volume=13&Issue=7
ISSN Print: 0976-6545 and ISSN Online: 0976-6553
© IAEME Publication Scopus Indexed
OPTIMAL RECONFIGURATION OF POWER
DISTRIBUTION RADIAL NETWORK USING
HYBRID META-HEURISTIC ALGORITHMS
Jamali A. Nagamora1*, Reuel C. Pallugna2, Noel R. Estoperez3
1,2
College of Engineering and Architecture, University of Science and Technology of
Southern Philippines, Cagayan de Oro City, Philippines
3
College of Engineering and Technology, Mindanao State University-Iligan Institute of
Technology, Iligan City, Philippines
*Corresponding Author
ABSTRACT
Distribution system is a critical link between the electric power distributor and the
consumers. Most of the distribution networks commonly used by the electric utility is
the radial distribution network. However in this type of network, it has technical issues
such as enormous power losses which affect the quality of the supply. Nowadays, the
introduction of Distributed Generation (DG) units in the system help improve and
support the voltage profile of the network as well as the performance of the system
components through power loss mitigation. In this study network reconfiguration was
done using two meta-heuristic algorithms Particle Swarm Optimization and
Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage
profile in the system when simultaneously applied with the DG units.
Backward/Forward Sweep Method was used in the load flow analysis and simulated
using the MATLAB program. Five cases were considered in the Reconfiguration based
on the contribution of DG units. The proposed method was tested using IEEE 33 bus
system. Based on the results, there was a voltage profile improvement in the system from
0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power
losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the
performance of each case.
Key words: Particle Swarm Optimization, Gravitational Search Algorithm,
Metaheuristic, Radial Distribution, Optimization, Load Flow Analysis,
Reconfiguration, Distributed Generation, Voltage Profile.
Cite this Article: Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez,
Optimal Reconfiguration of Power Distribution Radial Network using Hybrid Meta-
heuristic Algorithms, International Journal of Electrical Engineering and Technology
(IJEET). 13(7), 2022, pp. 1-10.
https://iaeme.com/Home/issue/IJEET?Volume=13&Issue=7

2. Optimal Reconfiguration of Power Distribution Radial Network using Hybrid Meta-heuristic Algorithms
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1. INTRODUCTION
Electrical distribution system plays a vital part in power system. It has to be designed efficiently
to fulfill economic, reliability and even environmental objectives. While there were a bunch of
studies devoted to generation stations and transmission system, the distribution level seems to
have less attention. For the past years, most innovations were focused to the devices or
equipment because the electrical distribution networks do not require special research and
development. With the rapid construction of new infrastructures, the investment and update
for distribution network is relatively backward. In effect the quality of electric supply to the
user is being compromised. Loss of supply (outage) can be considered as cost factor for
operation, especially in commercial or industrial entity. The distribution network must be
designed efficiently to keep the voltage value within a given range for the correct operation of
appliances and minimize the voltage drop on the network.
Nowadays, our electrical networks are undergoing tremendous changes. The introduction
of Renewable Energy Sources (RES), Plug-in Hybrid Electric Vehicle (PHEV) as well as the
smart end-users systems makes our distribution system very dynamic, changing the new
paradigm of energy. Some of the countries like France, 95% of these sources are located in
distribution level while the concept for smart grid were applied to their system (Hung et al,.
(2013). Thereupon, classical distribution network should also evolve, and accurate
Reconfiguration must be done to optimize capital investment programs, efficient operations,
reduce outages times and maintenance of equipment. These needs to be considered during the
designing and planning of distribution network.
The integration also of Distribution Generators (DG) units introduces uncertainties and
dynamic operation in the system. To this aim, some optimization algorithms are utilized to find
the optimal placement and size of distribution generators (DG) as well as the opening and
closing of tie switches on buses. According to (Shahnia, Arefi, & Ledwich, 2018), the
commonly used for solving mixed-integer nonlinear programming problem, includes
mathematical and heuristic-based algorithms. Determining the optimal Distribution Network
Reconfiguration (DNR) account for different constraint such as voltage limits, maximum
permissible line current carrying capacity and network structure of the system.
2. METHODOLOGY
In this study the standard IEEE 33 bus system in figure 1 will be used as the test network for
validation of reconfiguration method and results. The test network has 32 lines, 33 buses and 5
tie switches (Baran & Wu, 1989). Distribution networks are usually built as interconnected
networks, while in operation, they are arranged into a radial tree-like structure. This means that
distribution systems are divided into subsystems of radial feeders, which contain a number of
normally closed switches and normally open switches. Initially, the open line switches before
Reconfiguration are 33, 34, 35, 36 and 37. The test network has 3,715 kW and 2300 kVAR of
load with a base voltage of 12.66 kV.
In the Reconfiguration of the system, the proposed hybrid algorithms were used to
determine the optimum switching sequence and size of DG units in the network. Sensitivity
factor and index method (Belwin & Suresh, 2018) was applied to determine the optimal
locations of DG units in the network. The number of DG units integrated in the radial system
was assumed to have unity power factor. Voltage constraints were within ±5% of the nominal
value. DG capacities were limited from 20% to 85% of the total power load demands of the
radial distribution networks to avoid the reverse power flow in the network. The following
cases were considered in this study:

3. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez
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Case 1: The base network before Reconfiguration
Case 2: After Reconfiguration of tie switches by PSO without DG’s.
Case 3: After Reconfiguration by of tie switches by PSO with DG’s.
Case 4: After Reconfiguration of tie switches by PSOGSA without DG’s.
Case 5: After Reconfiguration of tie switches by PSOGSA with DG’s
Figure 1 IEEE 33 Bus Test Network
2.1. Network Reconfiguration
The general reconfiguration flowchart is shown in Figure 2 where the sequence is presented.
The first step is to read the IEEE 33 bus system data in the MATLAB program such as the line
impedance, loads and voltage. Second is to locate the fault (opened switch) in the system and
initial configuration of tie and sectionalizing switches. Third, the backward and forward load
flow analysis (Rupa & Ganesh, 2014) will be done on the system to compute for current flow,
the voltage profile and power system loss. Fourth, the PSO-GS algorithm (Mirjalili & Mohd
Hashim, 2010) program will be run to determine the optimal closing/opening of tie switches as
well as the size and placement of distributed generator (DG) in the network.

4. Optimal Reconfiguration of Power Distribution Radial Network using Hybrid Meta-heuristic Algorithms
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(1)
Figure 2 General Reconfiguration Flowchart
2.3. Formulation of Objective Functions
The objectives for reconfiguring the radial network are to minimize power loss and improve
voltage profile. In network reconfiguration, some constraints have to be identified in order to
maintain the topology of the network. For 33-bus system, it should always have 5 tie switches
and 32 sectionalizing switch. Some of the functions were utilized.
Minimize the total power losses ( f ), P(Tloss)
Determine the feeder’s power limits for each nth branch (Atteya, Ashour,
Fahmi, & Strickland, 2016);
Read data of distribution system
(bus, loads, branch data)
Identify fault and initial
configuration of the
systems
(tie/ sectionalizing
Run Load Flow
Program for the system
Compute current flow, the
voltage profile, power system
loss.
Print output
result
Change of tie/sectionalizing
switches and DG in the
distribution system using the
proposed algorithms
Objectives
&
constraints
attained?
Yes
No

5. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez
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(2)
(3)
(4)
(5)
(6)
The radial network topology;
where;
Rn : line resistance.
Qn : Reactive power value.
Pn : Real power value.
Vm : Node voltage value.
kn : Status for each branches (if kn = 1 means closed and if kn=0 means open).
NL: Number of branches
Nbus: Number of Buses
3. RESULTS AND DISCUSSION
3.1. Reconfigurations without DG Units
The network reconfigurations in the absence of DG units are represented by case 2 and case 4.
Table 1 shows the summary of load flow results for Case 1, 2 and 4. The initial value of Case
1 was used as the reference for the comparison of two algorithms. At 100% loading the initial
total power loss of the system is found to be 210.9876 kW. After the Reconfiguration by Case
2 (PSO), the optimal switch sequence for new topology were 28, 32, 14, 9 and 7. The power
loss had been reduced to 139.9814, amounting 33.65% of reduction from the initial network.
For Case 4 (PSOGSA), the switch sequence were the bus 37, 32, 14, 9 and 7. The power loss
from the initial network had been reduced further to 139.5301 kW amounting 33.87%.
Table 1 Network reconfiguration without DG units
Parameters
Case 1
(Initial)
Case 2
(PSO)
Case 4
(PSOGSA)
Opened Switches 33, 34, 35, 36, 37 28, 32, 14, 9, 7 37, 32, 14, 9, 7
Real power Loss (kW) 210.9876 139.9814 139.5301
Loss reduction (%) ---- 33.65 33.87
Time (s) --- 18.32 6.73
Lowest Voltage (p.u) 0.9044 (node : 20) 0.9385 (node : 32) 0.9419 (node : 32)
Figure 3 shows the summary of power losses for each bus in the network at different case
scenarios. The simulation was performed at 100% loading without DG units. Notice in the
figure that the majority of buses of Case 1 have significant power loss. But when case 2 and
case 4 were applied, almost 85% of buses had been reduced of power loss except for Bus 3, 5,
22, 23, 25, 30, 31, and 32 wherein there were a slight increase in power loss due to transfer of
load to other feeders.

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Figure 3 Comparison of power losses in each bus for different Cases
Figure 4 shows the summary of the voltage profile of the network before and after
reconfigurations without DG units. There is a significant improvement of voltages from bus 1
to bus 19 when Case 2 and Case 4 were applied to the network. But due to the changes made in
the network topology, certain buses like bus 20, 21, and 22 had a slight voltage drop from these
two cases. However, in bus 23 to 33, the two algorithms were able to recover and improve the
voltages between these buses.
Figure 4 Summary Voltage profile of the network for different Cases
0.0
10.0
20.0
30.0
40.0
50.0
60.0
1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132
Power
Loss
(kW)
Bus Number
Power Loss of each bus
CASE 1
CASE 2
CASE 4
0.8400
0.8600
0.8800
0.9000
0.9200
0.9400
0.9600
0.9800
1.0000
1.0200
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
Voltage
(p.u.)
Bus Number
Voltage profile of network after reconfiguration
CASE 1
CASE 2
CASE 4

7. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez
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3.2. Results for Optimal Placement of DG Units
The optimal positions of Distributed Generator was determined using sensitivity index method.
Table 2 shows the results of optimal placement and size of DG unit without the Reconfiguration
of the network. Using the PSO algorithm, the optimal placements for DG units were in bus 8,
13, and 32 having a capacities of 981.6 kW, 982.7 kW and 1176.8 kW respectively. In PSOGSA
algorithm the optimal placements for DG units were in bus 8, 13 and 31 with a capacities of
500 kW, 600 kW and 810 kW respectively. Figure 4.7 shows the performance summary of two
algorithms when DG units were installed in the system. PSOGSA algorithm had the total power
loss reduction of 83.8401 kW amounting 60.26% of initial power loss in the network. While
the PSO algorithm had the total power loss in the system of 105.35 kW amounting 50.06% of
reduction of loss.
Table 2 Summary of Optimal size and placement of DG units
Parameters
Before Optimization After Optimization
Initial PSO PSOGSA
Total Power Loss (kW) 210.9876 105.3524 83.8401
Loss Reduction % --- 50.0612 60.2616
Worst bus voltage 0.9038 (node : 20) 0.9806 (30) 0.9661 (18)
Optimal location (bus#)
and size of DGs (kW) ---
(13) 981.6
(32) 982.7
(8) 1176.8
(8) 500
(13) 600
(31) 810
Total DG (kW) --- 2988.1 1910
DG power factor --- unity unity
Figure 5 also shows the comparison of two algorithms when DG units only are considered
in the network. The introduction of DG units contribute significantly in the reduction of power
loss in each branch. Almost every branch in the network had been reduced except for bus 10,
15, 30, 31, wherein the PSO algorithm did not perform well from these buses.
Figure 5 Power Loss of each bus by optimal placement and sizing of DG units
The optimal placement and sizing also of DG units had improved the voltage profile of the
network as depicted in figure 6. The PSO algorithm did a satisfying improvement in voltage
profile of network which had recorded a lowest bus voltage of 0.9806 p.u at bus 30. While the

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PSOGSA algorithm, had recorded the lowest voltage of 0.9661 p.u at bus 18. Though big
difference of voltage profile can be observed in the figure, the PSOGSA utilized only 1,910 kW
from DG units while the PSO had utilized 2988.1 kW of DG units.
Figure 6 Comparison of Voltage Profile of Network considering DG units.
3.3. Results for Reconfigurations with DG units
In this section multi-objective optimization was performed to determine the optimal switching
and allocation of DG units for loss minimization in the network. As shown in Table 3, the
results for the proposed algorithm and PSO algorithm had reduced the initial power loss of
network by 67.11% and 66.03% respectively. Both had the same placements for DG units (6,
12, 16, and 31) and opened switches (9, 14, 28, 32, and 33).
Table 3 Comparison of two algorithms in network reconfiguration considering DG units
Parameters
Before Optimization Reconfiguration + DG
Initial
(Case 1)
PSO
(Case 3)
PSOGSA
(Case 5)
Switch 33, 34, 35, 36, 37 9, 14, 28, 32, 33 9, 14, 28, 32, 33
Total Loss (kW) 210.9876 71.6689 69.3963
Loss Reduction % --- 66.03 67.11
Worst bus voltage 0.9038 (node : 20) 0.9590 (33) 0.9594 (33)
Optimal location
(bus#) and size of DGs
(kW)
---
(6) 499.5
(12) 499.5
(16) 400.5
(31) 499.5
(6) 499.5
(12) 499.5
(16) 499.5
(31) 499.5
Total DG (kW) --- 1899 1998
DG power factor --- unity unity
In figure 7, all branches except for branch 22, 25, 26 and 30 had reduced the initial power
loss in the system when Reconfiguration with DG units were applied. The total power loss in
the system for Case 3 and Case 5 are 71.6689 and 69.3963kW respectively.

9. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez
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Figure 7 Comparison of power losses in each branch of two cases
The improvement of voltage profile from the initial network topology is shown in figure 8.
It can be observed that two algorithms had almost the same profile having the bus 33 as the
worst voltage dropped.
Figure 8 Comparison of Case 3 and Case 5 voltage profile
3.4. Results for Convergence Rate
The comparisons of convergence rate for PSO and a hybrid PSOGSA is shown in figure 9.
Notice that proposed algorithm (PSOGSA) converge much faster than the widely known PSO
algorithm. Simulation iterations were set to 50 to allow the algorithms to find the better solution
and the optimum fitness. Based on the figure, the PSOGSA algorithm converged at 8 iteration
only while PSO settled at 32 iterations.

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Figure 9 Convergence rate of two Algorithms
4. CONCLUSION AND RECOMMENDATIONS
4.1. General Conclusion
Load flow analysis using Backward-Forward Sweep method was successfully implemented and
tested in different cases of network. Based on the results, the proposed hybrid algorithm has
been proven to be effective in determining the optimal Reconfiguration of power distribution
network. The combination of both meta-heuristic algorithms can be more powerful compared
to using a single algorithm only. The speed of convergence rate must be considered during the
process, as these could be the criteria in making modern switching devices. The advantage of
PSOGSA from other algorithm is that as the particles increases in size, it becomes more
aggressive in finding optimum values.
The optimal placement and sizing of DG units, helped also in the improvement of voltage
profile and power quality of the network. However, optimal position of DG units should be
considered in this study to obtain an efficient operation of the system. As shown in results Case
5 had 50.26% more power loss reduction from Case 4, while Case 3 had only 48.80% power
loss reduction from Case 2. From graphical presentations of results, we can deduce that the
proposed algorithm outperformed in voltage profile and power quality enhancement of test
network.
4.2. Recommendations
In this study, it is recommended to consider the optimal placement for capacitor bank, dynamic
loads, DG operation modes, penetration level of DG and economic analysis of the system. It is
recommended also to perform reliability study for this reconfigured network and test the
algorithm into existing utility distribution network. To evaluate also the actual performance of
the algorithm, the succeeding researcher shall create miniature smart distribution network
which will mimic the modern utility distribution network. The proposed algorithm can be
developed into firmware which will be embedded into the controller unit of the system. In
addition, there are still more metaheuristic algorithms published which can be further explore,
study its characteristics and determine the possibility of combining it into other algorithm to
produce more efficient and powerful algorithm.

11. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez
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ACKNOWLEDGMENT
The researchers would like to their gratitude to the University of Science and Technology of
Southern Philippines, Cagayan de Oro City, Philippines.
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