Load Balancing in Distribution Networks based on SSA Optimized
Algorithm
IBRAHIM A. I. ALTAWIL, MOHAMMAD AWAD MOMANI*
Department of Electrical Power Engineering, Hajjawi Faculty for Engineering Technology,
Yarmouk University, 21163, Irbid,
JORDAN
*Corresponding Author
Abstract: - The low voltage power distribution networks are three-phase networks where most of the clients are
supplied from a different single phase with different load behavior. This way of supply causes several problems
that imply the unbalance in the voltage percentage (UV %) in the three phases, and unbalanced increase in the
neutral currents and power loss limits. One of the mitigation techniques for such problems is through swamping
customers between phases and applying much simpler and more reliable smart systems to resolve the balancing
problems. The introduction of an automated method based on a re-phasing technique for swamping customers
between phases solves the balancing problem directly and effectively. The Salp Swarm Algorithm (SSA) with a
set of constraints and limitations in the objective function to ensure the lower number of switching processes
was proposed in this paper. It is found that phase balancing using SSA is considered an effective and direct
method to decrease power losses, minimizing the problem of voltage or current unbalance, and enhance the
system stability and efficiency of the power system. This technique was examined on 19 different low-voltage
customers. A comparison between the neutral current before and after the treatments shows that it has been
reduced from about 24 A to 1.15 A. The power loss was also reduced from 1.8 to 1.2 kW respectively and
finally, UV % was reduced from 11-15% to about 1.2%.
Key-Words: - Salp Swarm Algorithm, Load Unbalance, Unbalance Voltage, Neutral Current, Losses,
Rearrangement, Re-phasing, Low Voltage, Distribution System.
Received: April 26, 2023. Revised: May 4, 2024. Accepted: June 14, 2024. Published: July 30, 2024.
1 Introduction
Most of the low voltage distribution (LV) networks
in Jordan are radial networks where many
customers are supplied from the same feeder or
primary electrical substation. A single phase and a
neutral wire are used to connect most residential
loads to the feeder. It is possible to select any phase
to connect a load to three-phase transformers to
power four wire systems in LV networks, [1].
Single-phase clients are the customers whose
load profiles differ from one another. The
variations in load behavior are expected to cause
power imbalances between phases, which decrease
the system's hosting capacity and increase power
losses as well as imbalanced current and voltage. A
comparison between medium voltage (MV) or high
voltage (H.V.) networks with the low voltage (LV)
distribution networks. The highest losses and the
voltage regulation in LV networks are more
challenging, [2]. A common cause of unbalanced
systems includes imbalanced loads, faults in the
system, unequal distribution of power, and poorly
designed distribution networks, [1], [3], [4]. The
unbalance problem sometimes can limit the amount
of power transferred through feeders when one of
the phases may reach the maximum carrying
capacity measured in amperes while the other two
phases are unable to carry the full amount of
current. This case may result in unnecessary feeder
expansion and upgrades.
In modern power distribution systems, the
efficient management of loads is of top importance
to ensure a stable and reliable supply of electricity.
In this context, the three-phase low-voltage
distribution networks play a crucial role in
delivering power to end-users. With a considerable
portion of loads being single-phase in nature and
having specific references, the challenge lies in
achieving optimal load balancing to enhance
system performance and minimize losses, [5].
Load balancing in three-phase low-voltage
distribution networks is a complex task, primarily
due to the varying characteristics and demands of
individual loads. Traditional methods of load
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DOI: 10.37394/232016.2024.19.23
Ibrahim A. I. Altawil, Mohammad Awad Momani
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balancing often fall short in addressing the specific
requirements of single-phase loads, leading to
suboptimal distribution and potential overloading of
certain phases. Recognizing the need for a more
sophisticated approach, researchers and engineers
have turned to nature-inspired algorithms for
optimization, among which the Salp Swarm
Algorithm (SSA) stands out as a promising
candidate.
The radial distribution system examined in this
paper as it is the simplest network configuration
standpoint, easy to ride through faults, has the
cheapest construction cost, and requires fewer
security measures. However, it is fed by a single
power supply, and in the event of an outage, there
is no alternate source of power to serve the demand,
as seen in Figure 1. Through a comprehensive
analysis of the SSA application in load balancing,
this study tries to find valuable contribution insights
to the field of power distribution engineering. The
goal of this research is to provide a robust and
adaptive solution that caters to the difficulties of
three-phase low-voltage networks, paving the way
for more resilient and optimized electricity supply
systems in the face of evolving energy demands.
Phase balancing is considered effective and direct
method to decrease power losses [6] and minimize
voltage or current unbalance index [7] and leads to
increased system stability and efficiency of power
system, [8].
Fig. 1: Typical radial distribution system, [9]
This paper proposes the SSA algorithm to
mitigate and solve the problem of load balancing in
three-phase LV distribution networks. The
algorithm is selected as it offers an intelligent and
adaptive approach to redistributing loads
efficiently, [1].
In [1], the consumer load arrangement on three-
phase systems was made based on switching
techniques for feeders and unit levels. In [2] the
heuristic algorithm was used to study the effect of
different numbers of contactors in each house and
all the cases showed high performance in load
balancing and reduced the imbalance index. In [3] a
balancing approach that uses a Genetic Algorithm
was applied with the aim of minimizing the active
power losses over an interval of 24 hours in
Romania. The method is tested in a real Romanian
low-voltage distribution network with Smart
Metering load data. based on the literature, most of
the previous work was made on the feeder level or
based on smart meters using a heuristic algorithm,
but in this paper, we focus on load phase switching
based on rephrasing techniques. The paper is
organized as follows, Section 1 is the introduction
of the paper, Section 2 is an unbalancing problem,
mitigation techniques, and re-phasing technique in
distribution system. Section 3 presents the system
under study. Section 4 is the SSA followed by
Results discussion and conclusion in Sections 5 and
6 respectively.
2 The Unbalance Problem in LV the
Distribution System
The unbalanced loads arise when the power
consumption on one or more phases differs
significantly from the others, leading to potential
issues such as voltage deviations, increased losses,
and reduced system efficiency. Understanding and
mitigating the effects of unbalanced loads are
essential for ensuring the reliability and optimal
performance of three-phase low-voltage
distribution networks. The unbalanced voltage
(UV) distribution in electrical systems can result
from various factors, including uneven loads,
unequal distribution of power, or faults in the
system, [10]. This imbalance can lead to several
problems, such as voltage fluctuations, decreased
system efficiency, and potential damage to
equipment. A common cause of unbalanced
systems includes inadequate design of the
distribution network can lead to voltage
imbalances, for example, asymmetrical
transmission impedances; this may include issues
with transformer connections, conductor sizes, or
improper planning, [4].
It is important for utilities and operators to have
effective monitoring and control systems in place to
detect and address the UV in the distribution
network promptly. Therefore, to solve UV voltage
distribution, utilities and system operators typically
employ strategies such as load balancing, regular
maintenance, and monitoring, implementing
voltage regulation devices, and the re-phasing
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Ibrahim A. I. Altawil, Mohammad Awad Momani
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technique, [4], [11]. The re-phasing technique is
employed in this paper to reduce the problem of
imbalanced load on specific feeders.
National Electrical Manufacturers Association
(NEMA) defined the UV problem and current
unbalance equations as follows, [12]:
(1)
Where Vmax is the maximum voltage and Vavg is the
average voltage magnitude of the three-phase
voltages given by:
(2)
Where the acceptable voltage unbalance ratio is
about ±2% in Jordan. The unbalanced occurrence
has a negative impact on the power system, which
lowers system stability and security, [1], [11]. The
low voltage distribution feeder's overall losses can
be expressed as follows, [7]:
(3)
Where: n is the number of load buses, P is the
active power in Watt, Q is the reactive power in
VAR, r is the resistance in ohm and V is the bus
voltage. The losses in electrical systems are often
represented by the term Ii2r, where I is the current
and. In the case of unbalanced loads, the higher
currents in certain phases lead to increased Ii2r
losses in those phases. The power dissipated as heat
in the conductors is proportional to the square of
the current.
2.1 Load Balancing Problem and Mitigation
Techniques
Low voltage distribution network may have a
balanced three-phase system if [5]: the three phases
have approximately the same power, voltages, and
current. Moreover, the load flow variation although
24 hours is the same in the three phases. Different
load balancing mitigation strategies employed in
power system areas such as:
a) Load Balancing Algorithms that employ
advanced load balancing algorithms are crucial for
mitigating the impact of unbalanced loads.
Algorithms such as the SSA offer an intelligent and
adaptive approach to redistributing loads
efficiently, [1].
b) Smart Grid Technologies that integrate smart
grid technologies enable real-time monitoring and
control of distribution networks. Smart grid
solutions, such as advanced metering infrastructure
(AMI) and intelligent load controllers, contribute to
dynamic load management, [2].
c) Distributed Energy Resources (DERs) that
introduce DERs, such as solar photovoltaic systems
and energy storage, provide a decentralized
approach to load balancing. These resources can
help mitigate the effects of unbalanced loads and
contribute to overall network stability, [3]. Phase
switching is the most efficient and best way to
balance low-voltage networks. As a result, it lowers
the imbalance index. Phase swapping needs to have
a minimum number of executions to ensure system
security while lowering system load.
The process of swamping customers between
phases, usually occurs at a load which causes
disturbances in the electrical system therefore,
reducing the number of switches is an essential goal
in this research. To guarantee the lower number of
switching processes and avoid or reduce the effect
of electrical system disturbances the following
constraints and limitation in the objective function
in the SSA algorithm have been proposed. The
range of the objective function in SSA to be Vm
was set to be ±2% Vm. Set the objective function in
the SSA to be the average current of three-phase
current measurements. These special SSA objective
functions are used to expand the range of switching
periods of the load between phases to reduce the
cost of the switching process and reduce
disturbance in the system. Moreover, it decreases
the running time consumed for the SSA algorithm.
2.2 Re-phasing Technique
Phase swapping is a direct and effective way to
balance a feeder in terms of phases, and this
method based on load-balancing algorithms will be
implemented in this work. Re-phasing of the single-
phase customers can lead to more reduction of
unbalanced indexes and losses on feeders.
Nevertheless, increasing the number of re-phased
customers is impractical due to practical difficulties
and increasing costs, [13]. Applying a limitation on
the number of customers to experience phase
changes in SSA is vital. The number of swapping,
time to do swapping, and determining the customer
that must be swapping between phases is essential.
By changing the phases of a few customers, the
performance of the network power quality and
power losses can be improved, [14].
In this work, the SSA algorithm is employed to
find the best distribution for single-phase customers
on three-phase networks by moving heavy single-
phase loads to lightly loaded phases through a
switching system connected to 19 different
customers. Various hardware phase switch selector
devices are used to switch customers between
phases; all these devices have the same layout, as
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seen in Figure 2, which combines a single-phase
output with a three-phase input.
Fig. 2: Re-phasing Technique, [9]
3 System under Study
The three-phase distribution feeder under testing as
seen in Figure 3, served 19 single-phase clients
(customers).
Fig. 3: Part of the feeder under study
The feeder parameters, Aluminium 95 mm2
cross-section, 700-meter length, supplied from a
three-phase transformer rated 250 kVA. Table 1
illustrates the distribution of the 19 customers
(loads) among the three phases system.
Table 1. Distribution of loads among three-phase
feeder
Phase
Number of Customers
connected to the phase
Customers
A
9
2, 3, 7, 10, 13, 15, 17,
18, 19
B
5
1, 4, 5, 9, 14
C
5
6, 8, 11, 12, 16
Before load balance treatment, a
measurement of a six-day time window (6 cases)
has been taken and recorded in med of July 2021.
A 19 smart meters have been installed in the
houses of each customer, also, three smart meters
installed on the sending end of the selected
feeder. After balancing, the recorded data needs
to be compared with the recorded data again. The
comparison includes the sending end (three-phase
currents) and the neutral current shown in Figure
4.
Fig. 4: The three-phase currents and neutral current
for the feeder under study
4 Salp Swarm Algorithm
The SSA is a new swarm intelligence algorithm
that is used to simulate the foraging behavior of the
sea swarm slap. The SSA has advantages that imply
fewer parameter requirements and effectiveness for
both continuous and discrete problems, [8], [15].
When moving and feeding in the water, SSA
mimics the actions of salps, a kind of marine
animal. Inspired by common methods found in
nature, such as salps, which can prevent the
optimum local, flexibility, simplicity, and behavior
to identify a feasible solution to a real-world
problem, [15], [16], this algorithm was created.
The SSA Algorithm suggests that the load will be
spread across the feeder as efficiently as possible to
reduce feeder losses, balance voltage, and current,
eliminate current imbalance (neutral current), and
keep it close to zero. The multi-objective function
incorporates both the voltage balance equation and
the current balance equation, as seen below [15]:
(4)
(5)
(6)
where: Ia, Ib and Ic are the phases currents a, b, c.
: nominal rating value of current.
Va, Vb and Vc : voltages per phase a,b ,c
32,8
35,3
35,2
35,7
31
34
7,19
19
9,53
7,02
10,7
10,7
2,54
12,9
11,7
3,67
10,5
8,26
28,5
23,9
19,6
30
18,3
24,1
C A S E 1C A S E 2C A S E 3C A S E 4C A S E 5C A S E 6
Ia Ib Ic In
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is nominal value for secondary voltages
of the transformer.
The outcome is zero without balancing when
the square in Equations (4) – (6) used to eliminate
the negative value in the goal functions. Some
limitations must be considered while using the SSA
technique to lower the switch status numbers.
Therefore, it is best to transfer loads as seldom as
possible to reduce the feeder's current load and
enhance system security. Similar to earlier
approaches based on swarm techniques, the Salps'
position in the n-dimensional search space, where n
is the variable number for the task, is precisely
determined. The locations of each Salps are then
stored in a two-dimensional matrix called x. It is
also believed that the swarm's intended goal is to
find a food supply with the name f that is present in
the search area. It is recommended [8] to use the
following equation to update the position of the
leader:
(7)
Where:
defines the position of the food supply
in the dth dimension, the upper bound of the dth
dimension, and the lower bound of the dth
dimension.
fd is the position of the first salp (leader) in the dth
dimension.
According to Equation (7), the leading salps
adjust their positions in order to track the food
supply. The coefficient C1, which is time-varying or
dependent on the number of iterations, is the most
important parameter in the SSA because it is the
only one that regulates the balance between
exploration and exploitation. It has the following
definition, [15], [16], [17], [18]:
(8)
Where: T is the maximum iteration numbers and
t is the current iteration.
Random values between 0 and 1 are uniformly
generated for the parameters c2 and c3. Indeed, they
indicate the step size and whether or not the next
location in the dth-dimensional should point in the
direction of +∞ or -∞. The following equation, [15],
[16], [17], [18] is used to update the followers'
position:
(9)
Figure 5 provides a summary of the application
of SSA for voltage and current balance in the
distribution system.
5 Results and Discussion
This section presents the main paper results as
follows:
5.1 Voltage Assessment
The end user's voltage is regulated by the
government Energy and Minerals Commission to
be approximately ±2% of the nominal voltage. UV,
overvoltages, or undervoltages may cause home
appliance failure on the user's end. Moreover, the
modern smart kWh meters are designed to be
sensitive to UV. Once balanced loads are obtained
by utilizing SSA with the lowest number of load
swaps, all voltage magnitudes fall within the multi-
objective function constraints.
Fig. 5: SSA utilized for the best balance of voltage
and current flow chart, [19]
Figure 6, clearly displays the three-phase
voltage before and after the SSA application with
the lowest number of switching (6 switching) and
how the situation improves, case 6 represents the
best results obtained. It is necessary to illustrate
how the voltage drop in the feeder is causing the
customer's end voltages to decrease.
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Fig. 6: Three-phase voltage of the feeder before and
after the switching
After balancing, all magnitudes fall within
allowable ranges. The minimum voltage percentage
occurs at Case 6, just six consumers need to switch
for the voltage magnitude to fall within an
acceptable range. The percentage results of other
cases need more than 6 switching to get the voltage
within an acceptable range. As feeder end voltage
magnitudes increase, the fraction of UV will
decrease even further, as indicated in Table 2.
Table 2. Percentage of UV% for all study cases
UV %
Case 1
Case 2
Case 3
Case 4
Case 5
Case 6
Before
11.9%
11.2%
14.3%
14.7%
11.7%
13.5%
After
1.3%
2.3%
1.5%
2.0%
2.1%
0.9%
5.2 Currents Assessment
A balanced state is one in which there is an equal
current in every phase during the loading phase.
Figure 7 compares the total currents in each phase
of the two situations before and after the load-
balancing treatments. The SSA results of a
minimum switching number have been consider
which indicate that load balancing current is nearly
complete because SSA can distribute the current
over the subject feeder. The results shown in Figure
7 represent the currents after balancing for the
minimum number of swaps. Case 6, which displays
the SSA results (best) of measurements as input
data, demonstrates excellent current balancing of
the three-phase system according to the SSA
results.
Fig. 7: Three-phase currents of the feeder before
and after the switching
Figure 8 shows the neutral current before and
after SSA treatments. It is clear from the figure that
the neutral current has dropped significantly. The
balancing for the lowest swapping numbers can
result in a lower neutral current rather than
balancing for the highest switching numbers. Thus,
employing the maximum swapping numbers may
not always be necessary to achieve load balancing.
It can be sufficient to swap only the smallest
number of end users to get a good result and reduce
the load that voltage and current place on the
distribution networks. There is no discernible
difference in the values of neutral current in all
cases because all magnitudes after balancing are
within acceptable bounds.
Fig 8: The neutral current before and after the
treatments
5.3 Losses Assessment
The number of kWh of power lost in the system is
one of the most significant indicators for the
financial health of distribution companies. Loss
Va
0
50
100
150
200
250
300
Before
After
Before
After
Before
After
Before
After
Before
After
Before
After
Case 1Case 2Case 3Case 4Case 5 Case 6
Va Vb Vc
Ia
0
20
40
Before
After
Before
After
Before
After
Before
After
Before
After
Before
After
Case 1Case 2Case 3Case 4Case 5 Case 6
Ia Ib Ic
0
5
10
15
20
25
30
35
Before
After
Before
After
Before
After
Before
After
Before
After
Before
After
Case 1Case 2Case 3Case 4Case 5 Case 6
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minimization is a difficult practice in the electrical
engineering field, load balancing employing the
SSA minimizes losses. Load balancing reduces
power losses by achieving a current balance among
feeders; the net consequence is total power losses
reduced. Before and after balancing as shown in
Figure 9 shows a decrease in total losses. The
balancing had been established and the number of
swaps had a critical role in reducing losses.
As shown in Figure 9, the power losses over the
feeder under investigation have been greatly
reduced by transferring just six customers between
phases. Furthermore, when most customers migrate
from phase A to phases B and C the biggest loss
reduction is achieved when a maximum number of
swamps occur.
Fig. 9: Total power losses assessments
5.4 Swamping Assessment
Referring to Table 1 displays the number of
customers connected to each phase before the
treatments. Figure 10 shows how the 19 customers
are distributed among the three phases that depend
on the load of each customer.
It is obvious that phase A used to have the
highest number of connected customers, which may
be due to working team consideration. The SSA
results showedshow the get the best load balancing
therefore less balancing trouble than to have 5
customers connected to phase A, while other
customers are distributed between phases B and C
(Figure 11). Therefore, to resolve the balancing
trouble, six swamping processes must be performed
in between load phases to get the best balancing
state which provides the lowest neutral current.
Fig. 10: Number of customers connected to each
phase
Fig. 11: Number of switching for each SSA
treatment
6 Conclusions
This research investigates the use of SSA to
mitigate the unbalance problem in the LV radial
distribution system. The power losses, the
imbalance in voltage and current in phases, and
neutral wires on a low-voltage feeder supplying 19
customers are examined. A comparison between the
neutral current before and after the treatments
shows that the neutral current has been reduced
from (For instance; in Case 6 the neutral was
reduced significantly from about 24A to 1.15 A;
loss reduced from 1.8 to 1.2 kW). The results
obtained from the fewest number of customer
switching are compared with the SSA-using
solutions. The results show that balance is reached
1,64
0,85
1,9
1,8
2,13
1,33
2
0,95
1,71
1,16
1,876
1,218
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
C A S E 1C A S E 2C A S E 3C A S E 4C A S E 5C A S E 6
9
5
9
5
9
6
9
5
9
5
9
6
5
7
5
6
5
7
5
6
5
7
5
6
5
7
5
8
5
6
5
8
5
7
5
7
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
C A S E 1C A S E 2C A S E 3C A S E 4C A S E 5C A S E 6
# Loads on A # Loads on B # Loads on C
0
8
0
8
0
7
0
8
0
6
0
6
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
B E F O R E
A F T E R
C A S E 1C A S E 2C A S E 3C A S E 4C A S E 5C A S E 6
Number of Switching
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for a range of parameters, including voltages,
currents, neutral currents, power losses, and voltage
imbalance percentage, regardless of the number of
swaps. Furthermore, there is currently less burden
on the system. Consequently, low-voltage networks
experience a reduction in operating expenses. Once
the perfect balance is reached, the incidence of
electric current disturbances at the consumers
reduces, maintaining the security and safety of the
system.
Declaration of Generative AI and AI-assisted
Technologies in the Writing Process
During the preparation of this work, the authors
sometimes used ChatGPT AI tool in order to
improve the readability and language of our
manuscript especially in the Section 1 and 2
respectively. It is mainly used for sentence
rephrasing. Our own software particularly the
Matlab2023 produces all the figures in the paper.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Ibrahim Altawil carried out the simulation and
optimization, and he implemented the Algorithm
of Section 4.
- M. A. Momani Organized and executed the
results of all experiments that are made in Section
5 and he was a responsible for the data
manipulation and programming.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This Research is partially funded by Yarmouk
University
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
However, this research will be useful for the power
utilites such as Irbid District Electricity Company
(IDECO).
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.e
n_US
WSEAS TRANSACTIONS on POWER SYSTEMS
DOI: 10.37394/232016.2024.19.23
Ibrahim A. I. Altawil, Mohammad Awad Momani
E-ISSN: 2224-350X
264
Volume 19, 2024
