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International Journal of Applied Power Engineering (IJAPE)
Vol. 12, No. 1, March 2023, pp. 83~89
ISSN: 2252-8792, DOI: 10.11591/ijape.v12.i1.pp83-89 83
Journal homepage: http://ijape.iaescore.com
Reliable and efficient operation of distribution network by
connecting solar distributed generation
Simarla Vijender Reddy, Mane Manjula
Department of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad, India
Article Info
ABSTRACT
Article history:
Received Oct 27, 2022
Revised Jan 9, 2023
Accepted Jan 25, 2023
One of the major issues in the distribution network (DN) is ensuring that
power systems operate optimally in light of the effects of distributed
generation (DG). In a broader sense, optimal operation in a power system
refers to the most efficient use of all active and reactive power generation
and control equipment that adheres to physical and technical constraints.
Most studies focused on DG size and location in the DN, using various
optimization techniques for loss reduction. But in a practical distribution
network, reliable operation is dependent on the demand and power supply at
any given moment. Solar DGs provide variable power throughout the day,
and loads are similarly variable. It is difficult for the DN to function
efficiently and reliably while handling variable loads and DG power
supplies. Voltages and power losses are measured as loads change by
connecting solar DGs to assess the performance of the DN.
Keywords:
Distributed generation
Distribution network
MOSMA
Power loss
Solar energy
Voltage regulation
This is an open access article under the CC BY-SA license.
Corresponding Author:
Simarla Vijender Reddy
Department of Electrical Engineering, University College of Engineering, Osmania University
Hyderabad, Telangana 500007, India
Email: simarlavijender@gmail.com
1. INTRODUCTION
Electrical power demand is increasing day by day to meet the escalating electrical power usage. The
majority of electricity sources currently are conventional. The availability of conventional energy sources is
dwindling by the day. Non-conventional energy sources, such as wind and solar, are plentiful. Renewable
energy sources are the best option for meeting power demand. The optimal allocation problems are solved
using optimization techniques such as the genetic algorithm (GA), particle swarm optimization (PSO), ant
colony search algorithm (ACSA), slime mould algorithm (SMA), and multi-objective slime mould algorithm
(MOSMA).
Almabsout et al. [1] addresses controlling active and reactive power in distribution networks has a
significant impact on their performance. The most common strategies for improving distribution system
performance are the connection of distributed generation (DG) and shunt capacitors (SCs). The reactive
power optimization algorithm for distribution network (DN) with solar (PV) generation is provided in [2].
The penetration of renewable distributed generation (RDG) into traditional distribution systems (TDSs) is
examined in [3] and has been found to address many of its flaws and shortcomings. According to [4],
installing local micro-level power generating sources such as fuel cells, micro turbines, and energy storage
devices is a current trend that aids in the intermittent impacts of renewable energy sources while making
micro grids less reliant on the main grid.
According to [5], the significant level of penetration of such renewable energy sources has an effect
on the dynamic performance of power systems. The regulations for wind power plants are to protect the
boundaries that maintain the reliable operation of the power system. The stochastic optimal power flow
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(OPF) problem is addressed in [6] by a robust and effective method inspired by the slime mould. As per [7],
[8], the installation of a DG at a grid node minimizes real power losses in the network. The slime mould
optimization approach is used to manage the reactive power (Volt/VAr) of smart inverters for PVs in order to
optimize PVHC in DN [9]. Premkumar et al. [10] covers the use of MOSMA in the industry to solve multi-
objective optimization problems. Ouyang et al. [11] proposed the theory and method of active and reactive
power coordinated control before wind speed changes on the basis of model predictive control theory.
According to [12], it is a slow occurrence that can be handled utilizing slow manual reserves. If these
reserves must be obtained from nearby regions, they should be kept at a sufficient reserve capacity in the AC
tie-lines. According to [13], the frequency regulation strategy that merely responds to traditional power is
progressively reduced as wind power becomes more integrated into the electric system.
Singh et al. [14] presents a multimode single-stage solar photovoltaic (PV) energy generation system
(SPEGS) interfaced to a distribution feeder using a reliable DS-based control technique that has been created for
enhancing power quality. Kim and Lee [15] presents a two-stage probabilistic forecasting system for solar
power that makes use of observations of solar irradiation taken at various sites. A two-stage robust optimal
inverter dispatch model has been presented to manage the uncertainties of PV generation in active distribution
networks [16], [17]. By combining solar and wind turbines in addition to the current grid, it is possible to
provide consumers with an uninterrupted power supply at a low cost [18]. Ghiassi-Farrokhfal et al. [19]
examined the challenge of allocating a capital budget to solar panels stored in the context of a large-scale
solar farm engaging in an energy market in order to optimize predicted revenue.
Chang et al. [20] presents an improved backward/forward sweep methodology for the three-phase
load-flow analysis of radial distribution systems. A strategy for solving the distributed power flow problem
that is adaptive and relies on compensation. This approach was examined under a variety of scenarios,
including load imbalance, an abrupt increase in 1-phase loads, the degree of meshing in the loops, and the
number of generating nodes. It is quick, reliable, and keeps the required accuracy [21], [22]. A linear three-
phase power flow model for a DN is covered in [23], [24].
2. RENEWABLE ENERGY SOURCES
There are numerous renewable energy sources available, including solar, wind, biomass,
geothermal, biodiesel, biogas, tidal energy, and others. Solar energy is the best choice for large-scale power
generation. Solar power applications are used in this paper. Solar panels produce changeable power as the
temperature varies throughout the day, from sunrise to sunset. Solar panels generate maximum power at a
temperature of 25 C. The solar power generation is nil during the nighttime.
3. LOAD FLOW ANALYSIS
Load flow is used to evaluate voltages at each node and power losses at each branch of the
distribution network. The effective power of each node is evaluated using the bus injected node power
(BINP) matrix. The effective line resistance of each node is determined using the line loss node power
(LLNP) matrix [25], [26]. The equivalent two-node DN is shown in Figure 1.
Figure 1. 2-bus distribution network
The voltage at any node for sending end ‘M’ and receiving end ‘N’ is given by (1).
(1)
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Where PNeff and QNeff are active and reactive powers at ‘N’ nodes respectively. The power loss of ‘N’ buses
DN is shown in (2).
Where ’k’ represents the kth node of DN and
.
(2)
The IEEE 85-bus radial DN is shown in Figure 2. The line impedances and node powers are given in [26].
The total load power of DN is 3640 kVA at a power factor of 0.8 lag.
Figure 2. 85-Bus radial distribution network
4. SLIME MOULD AND MULTI-OBJECTIVE SLIME MOULD ALGORITHM
Slime mould algorithm (SMA), predicted the existence of a novel inhabitant of a location-based
metaheuristic seemingly moved by supernatural powers by the swinging manner of conduct of slime mould
fashionable nature. The input positions of slime mould are declared as X=(X1, X2, X3…. . . XN). Where ’N’
is the population size, the population is judged by utilizing an objective function. The staging process of the
SMA algorithms, which involves grasping edible material, wrapping food, and approaching edible material,
can be specified mathematically as (3).
(3)
“W” represents the smell index, “VA” is the vibration limit, and the lower and upper limits of the
search area are represented by “L” and “U”. The ‘rand’ and “ra” are represented as random values. The slime
mould has been represented as a DG location and size in this slime mould algorithm.
MOSMA is a variant of SMA that incorporates non-dominated sorting and crowding distance [6]. In
this paper, the multi-objective slime mould method is used to focus on power loss minimization and reduce
voltage deviation, whereas the slime mould algorithm is used to focus on single objectives such as power loss
minimization. Power loss reduction and voltage deviation are calculated using (4) and (5), respectively.
(4)
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(5)
Algorithm for MOSMA
i) Read initial parameters (Ua, La,N, Max.Iter=100).
ii) Read the Line and load data.
iii) Initially, generate the slime mould (DG location and size).
iv) Run load flow (calculate voltages and power losses from (1) and (2)).
v) Modify the slime mould from (3).
vi) Non-dominated sorting from (4) and (5) (power loss and voltage deviation for different slime moulds).
vii) Crowding distance between power loss and voltage deviation for different slime moulds.
viii) Check to converge? (No to go to step iii), Yes to print results).
5. RESULTS AND DISCUSSION
The performance of the IEEE-85 bus radial DN is determined by calculating voltages and power losses.
5.1. IEEE-85 bus radial distribution network results
The 85 buses of voltages are depicted in Figure 3. On 85 buses, the lowest voltage available is 0.877
per unit (PU), which is less than the 0.95 PU boundary limit. The DN has a power loss of 320.7 kVA. In this
state, the DN has been unreliable and inefficient. The performance of DN is evaluated by linking solar DGs.
5.1.1. Solar results
Solar DGs are placed and sized optimally in 85 bus DN using various optimization techniques such
as SMA and MOSMA. Table 1 shows the placements and sizes of solar DGs for various optimization
techniques. The MOSMA method has higher per-unit voltages for 85 buses than the SMA, as shown in
Figure 4. The power losses of SMA and MOSMA are 95.1 kVA and 97.6 kVA, respectively.
The irradiance of the sun varies throughout the day, from dawn to sunset. The output power of solar
panels varies with temperature. As the power supplied by solar DGs varies, the voltages of DN fluctuate. The
DN supplies are reliable if the voltages on all buses are within the limits. The voltages for several case
studies of solar DGs with variable power supply are assessed.
Solar DGs produce their maximum power when the sun is shining at a temperature of 25 C. Solar
DGs produce the least amount of power at sunrise, then climb to their maximum, and finally decline to their
minimum at dusk. Solar DGs generate power at 50%, 75%, and 100% of their maximum capacity, as shown
in Table 2. The minimum voltages at 75% and 50% of maximum power solar DGs, as shown in Figure 5, are
0.944 and 0.922 per unit, respectively, which are outside of the boundary limits.
Figure 3. Voltages at 85 buses
Table 1. Solar DGs placement and power supply in 85 bus DN
Solar DG Number
MOSMA
SMA
Place at Bus no.
Power supply (kVA)
Place at Bus no.
Power supply (kVA)
DG_1
34
432.6
55
430.0
DG_2
74
413.5
31
421.1
DG_3
51
477.5
10
404.2
DG_4
82
384.6
80
452.9
DG_5
62
457.1
70
459.0
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Reliable and efficient operation of distribution network by … (Simarla Vijender Reddy)
87
Figure 4. Voltages at 85 buses for SMA and MOSMA
Table 2. The various power supply of solar DGs
Solar
DGs
place
Solar DGs at the
maximum power supply
One of 5 DGs at
50% of maximum
power supply
Two of 5 DGs at
50% of the maximum
power supply
5 DGs at 75% of
maximum power
supply
5 DGs at 50% of
maximum power
supply
DGs
number
Power
supply
(kVA)
Change
at DGs
power
supply
(kVA)
Change
at DGs
power
supply
(kVA)
Change
at DGs
power
supply
(kVA)
Change
at DGs
power
supply
(kVA)
34
DG_1
432.6
DG_1
432.6
DG_1
432.6
75% of
DG_1
324.4
50% of
DG_1
216.3
74
DG_2
413.5
DG_2
413.5
DG_2
413.5
75% of
DG_2
310.1
50% of
DG_2
206.7
51
DG_3
477.5
DG_3
477.5
DG_3
477.5
75% of
DG_3
358.1
50% of
DG_3
238.7
82
DG_4
384.6
DG_4
384.6
50% of
DG_4
192.3
75% of
DG_4
288.5
50% of
DG_4
192.3
62
DG_5
457.1
50% of
DG_5
228.5
50% of
DG_5
228.5
75% of
DG_5
342.8
50% of
DG_5
228.5
Figure 5. Voltages at 85 buses with variable power supply of solar DGs
The output power of solar DGs diminishes dramatically under cloudy conditions. Cloudy conditions
in one or two locations cause a significant drop in solar DG power, which has an effect on DN voltages. Due
to cloudy conditions, the DG 4 and DG 5 powers have been reduced by 50% and are connected to 82 and 62
buses, respectively, as shown in Table 2. The corresponding voltages of 85 buses are shown in Figure 5.
Even though the power of two DGs has been reduced by 50% owing to cloudy weather, the voltages are
within the boundary limitations. The power losses of DN with variable power sources of solar DGs are
depicted in Figure 6.
The voltages in the distribution network change as the supply power or the load varies. The voltages
of 85 buses are evaluated with load changes when solar DGs generate maximum power, as shown in Table 2.
Figure 7 shows the voltages of 85 buses with load variations of 50%, 75%, 100%, and 125%, all of which are
within the boundary limits except for the load change of 125%. The lowest voltage at 125% load is 0.932 per
unit, making the power supply for the loads unreliable. Figure 8 shows the power losses with varying loads,
with the lowest losses occurring at 50% load.
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Figure 6. power losses at 85 buses with variable power supply of solar DGs
Figure 7. Voltages at 85 buses with variable loads
Figure 8. Power losses at 85 buses with variable loads
6. CONCLUSION
Solar DGs are placed and sized optimally in 85 buses DN using SMA and MOSMA. The MOSMA
approach performed better than others in terms of the two objectives, which are voltage improvement and
power loss reduction, than others. The DN has been reliable even when the power of one or two solar DGs
has been reduced to 50% of maximum power, but the DN has been unreliable when the power of all solar
DGs has dropped to 75% of maximum power. When solar DGs are connected, voltages decrease as the load
increases. At 125% of full load, power losses are larger and voltages are felt below the boundary limits,
making the distribution network unreliable.
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BIOGRAPHIES OF AUTHORS
Simarla Vijender Reddy was born in Charakonda village, Nagerkarnool district,
Telangana State, India. He received M.Tech. degree from Jawaharlal Nehru Technology
University Hyderabad (JNTUH). He is pursuing P.hD. at Osmania University. Currently, he is
working as an Assistant Professor in the Department of Electrical Engineering, UCE, Osmania
University. He has more than 12 years of experience in teaching. His research area is
application of optimization techniques in power systems. He can be contacted at email:
simarlavijender@gmail.com.
Mane Manjula completed her B.E in Electrical and Electronics Engineering from
Osmania University in the year 1995. She joined the Department of Electrical Engineering,
University College of Engineering, Osmania University in 1997 as a lecturer. Completed her
Ph.D. in the year January 2014. She is having 24 years of teaching experience in the field of
Electrical Engineering. She is presently working as Professor in the Department. She can be
contacted at email: vijendersimarla100@gmail.com.
