Design Analysis of Microgrid Power System for Telecommunication
Industries in Nigeria
DAVID S. KUPONIYI1, MATTHEW B. OLAJIDE2, MICHAEL A. EKO3, CHARITY S.
ODEYEMI4, NAJEEM O. ADELAKUN5
1,3Department of Electrical/Electronic Engineering, Gateway (ICT) Polytechnic, Saapade, Ogun State
NIGERIA
2Department of Electrical/Electronic Engineering, Olabisi Onabanjo University Ago-Iwoye, Ogun
State, NIGERIA
4Department of Electrical/Electronic Engineering, Federal University of Technology Akure, Ondo
State, NIGERIA
5Department of Works and Services, Federal College of Education Iwo, Osun State
NIGERIA
Abstract: - A microgrid power system is an independent power system that provides off-grid power or grid
backup. It consists of a conventional power system, a renewable power system, power storage, load management,
and a control system. However, different microgrid configurations do exist, be it conventional energy sources or
hybrid energy configurations, which have been discussed in this research to achieve an efficient and cost-
competitive power system configuration. The microgrids could improve the quality of service for the
telecommunications industries in Nigeria. The study takes into account the diverse network architecture of eight
possible configured network models, and the topologies were simulated and tested for economic optimisation on
HOMER energy software. The simulation results show that if any of the options were properly studied and
harnessed, a permanent solution to power failure at our base station would be achieved. Similarly, the cost
analysis presented reveals that the installation and operating expenses of any of the options were relatively cheap
when compared to conventional procedures, lowering the tariff cost imposed on customers. Consequently, it will
lead to the development of robust, off-grid power solutions for telecom infrastructure, enabling continuous
connectivity in remote locations, decreasing downtime, and improving the country's digital communication
network.
Key-Words: - microgrid, renewable energy, power system, modeling, network topology, simulation.
Received: November 15, 2022. Revised: July 16, 2023. Accepted: August 19, 2023. Published: October 4, 2023.
1 Introduction
With increased penetration in the country's rural
regions, Nigeria's telecommunications sector has
continued to expand enormously, requiring a stable
energy supply capable of powering mobile base
stations in an environmentally acceptable way. Rural
electrification may be accomplished through three
methods: extension of existing national grids,
minigrids or microgrids, and freestanding power
microgeneration systems [1]. Due to the growing
usage of renewable energy sources (RES) and the
country's inconsistent electricity supply, Nigeria's
electrical infrastructure requires renovation [2–3].
However, the country's erratic power supply has
impeded telecommunications carriers' ability to
deliver high-quality service. This demands the need
for alternative energy sources that can improve power
quality, give rapid access to electricity, promote
reliable, energy-efficient, and renewable energy, and
provide a variety of eco-friendly benefits over existing
utility systems [4]. Thus, renewable energy has
gradually become a viable option for both developed
and developing countries in terms of energy
challenges, with solar energy performing as a major
source of growth [5-8].
Globally, the networking industry has expanded at a
rapid pace in recent years, resulting in a massive
increase in the number of wireless devices. However,
energy consumption is one of the most expensive
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.15
David S. Kuponiyi, Matthew B. Olajide,
Michael A. Eko, Charity S. Odeyemi,
Najeem O. Adelakun
E-ISSN: 2769-2507
135
Volume 5, 2023
elements for telecommunications providers [9], and
the demand for energy will increase as more traffic
load is expected in forthcoming 5G networks [10]. It
is also worth noting that base stations (BSs) are
frequently regarded as key energy consumption
elements of cell sites [11]. During the last two
decades, there has been an increase in demand for base
transceiver stations (BTSs) due to the growth of
mobile communication networks with smaller cells
and BTSs closer to consumers [12].
A microgrid is an interconnected network of
distributed energy sources and loads that function as a
single controlled entity in relation to the grid [13].
Microgrids are grid components that may operate
autonomously and comprise distributed energy
suppliers, distributed sensing, and demand-side
control. The concept of microgrids was introduced by
the Consortium for Electric Reliability Technology
Solutions (CERTS), and many research and test beds
have been done in both industrialised and developing
nations since then [14]. The idea integrates renewable
and conventional power sources to ensure: enhanced
renewable energy contributing to local and grid-wide
power demands; efficient blending of renewable
power sources; and power supplies. Microgrids are
classified as DC, AC, or hybrid microgrids [15], which
play a vital role in the maintenance of the mobile
network, with a benchmark network uptime of 99.98%
to ensure reliability and quality of service [16–17].
Congestion is a fundamental issue in telecom quality
of service (QoS), where congestion simply signals a
shortage of network resources [18–19]. After more
than twenty years of GSM, the quality of service has
not kept pace with the increasing development in the
telecom market, with different consumer complaints
about paying for undelivered messages, cost for
dropped calls, and so on [20].
The total number of base stations owned by mobile
telecommunications companies increased to 30,637 in
December 2018 from 30,598 in December 2017,
representing a 6.02% increase over the previous year
across all states of the Federation. MTN had 14,715
base stations in December 2018, followed by AIRTEL
(7,966), GLO (7,244), NTEL (562), EMTS (148), and
SMILE (2 base stations) [21]. Several telecom
businesses went with diesel generators with inverters
and battery banks as backups to prevent unpredictable
power from the national grid. As a result, carbon
monoxide (Co) pollution developed. Energy
efficiency in communication networks looks to be an
absolute requirement in the battle against global
warming [22].
To increase service quality without interruption,
several attempts have been made to power
telecommunications base stations using hybrid power
systems, micropower systems, and other renewable
sources. It is worth noting that integrating artificial
intelligence (AI) algorithms into the microgrid power
system framework can provide real-time load
forecasting and adaptive energy management,
optimising microgrid performance and providing
uninterrupted power supply to telecom infrastructure
[23]. [24] conducted energy audits on various
telecommunication base stations (BTS) in Cameroon's
Sahel area to assess and create an optimisation
framework that reduces the operational costs of
several BTS power system combinations, including
utility grids with battery backup, utility grids with
battery backup and diesel generators, and utility grids
with battery backup and solar. The data revealed that
the utility grid combination with a diesel generator and
battery bank is more expensive, particularly when the
8-hour window is taken into account, costing up to
$12.86 in comparison to $12.44 and $10.54 for
configurations 1 and 3, respectively.
Despite the bold action of the operators, the power
supply problem remains owing to high diesel costs,
theft, and other socioeconomic concerns associated
with the operation and maintenance of diesel generator
sets, as well as national grid instability. The price of
fuel jumped to N836.81 per litre in March 2023 from
N539.32 in the same month in 2022, according to the
National Bureau of Statistics [25–26]. As a result, this
study used HOMER energy software for the research
analysis. Hybrid Optimisation Model for Electric
Renewable (HOMER) is a simulation model software
for comparing and assessing micro-grid technologies
for a wide range of applications [7], and it may
simulate hundreds of systems depending on how the
problem is framed. The Hybrid Optimisation Model
for Electric Renewable (HOMER) is a simulation
model software for evaluating and analysing micro-
grid technologies for a wide range of applications, and
it can simulate hundreds of systems depending on how
the problem is phrased.
2 Design Principle
The microgrid system components are
interconnected as indicated in Figures 1–8 to allow
modelling and optimisation of the entire system. The
systems also include energy storage in batteries to
extend the length of energy autonomy, as well as a
backup diesel generator connected to the system to
provide electric energy for peak loads that cannot be
handled by the renewable system.
This section discusses modelling and sizing,
which are also dependent on the requirements of the
load and the power supply system. Consequently,
the proper size of microgrid system components is
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.15
David S. Kuponiyi, Matthew B. Olajide,
Michael A. Eko, Charity S. Odeyemi,
Najeem O. Adelakun
E-ISSN: 2769-2507
136
Volume 5, 2023
calculated such that the power system is neither
excessive (expensive without boosting
performance) nor undersized (unable to operate
loads). This study also takes into account the
diverse network architecture of eight possibilities
that have been studied and tested for economic
optimisation.
2.1 Network Topologies
The eight options considered are as follows: the first
option uses only a diesel generator set to provide
power without any other components; the second
option uses a solar energy source, a diesel generator
set, and a bi-directional inverter without a battery; and
the remaining options are highlighted below.
Regarding the characteristics of the primary load, a
suitable size of modelled components was simulated
for each network design for optimised results
discussed later in this study.
Figure 1 - Network with only diesel genset source
Microgrid Network Diagram (option 1)
Figure 2 - Network without wind turbine and storage
battery Microgrid Network Diagram (option 2)
Figure 3 - Network without storage battery only
Diagram (option 3)
Figure 4 - Network without DC bus Microgrid
Network Microgrid Network Diagram (option 4)
Figure 5 - Network without without renewable
source Microgrid Network Diagram (option 5)
Figure 6 - Network without wind energy source only
Microgrid Network Diagram (option 6)
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.15
David S. Kuponiyi, Matthew B. Olajide,
Michael A. Eko, Charity S. Odeyemi,
Najeem O. Adelakun
E-ISSN: 2769-2507
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Volume 5, 2023
Figure 7 - Network without PV solar panel Microgrid
Network Diagram (option 7)
Figure 8 - Network considered Microgrid Network
Diagram (option 8)
2.1.1 Components Modeling and Sizing
Analysis
A. Primary Load and Profile
To avoid unmet demand, the electrical load
known as the primary load must be satisfied
immediately. The system may accept the addition
of two or more separate primary loads via the
Add/Remove window, but only one is considered
for this project, with all DC loads drawing power
from the AC bus via individual power rectifier
units. Every hour of the year, power from the
system's power-producing modules is sent out to
serve the complete primary load.
The daily load profile utilised is based on an
informed estimate, with the maximum load of the
BTS shown in Table 1. Typically, BTS load
profiles peak in the morning near a business
metropolis and in the evening and on weekends
near a residential area. Though it is critical to
have a solid estimate of the load demand since it
will impact the size of the microgrid components
(generators, battery bank, and converter).
B. BTS Energy Demand
The initial step was to determine the energy
requirement of the BTS under consideration.
This was accomplished by evaluating the
influence of several operational systems that
comprise a BTS, with a BTS serving as a model.
A BTS is a tower or mast equipped with
telecommunications technology to broadcast
mobile signals (voice and data), such as an
antenna, radio reception, and transmitters at the
top of the mast [25], and is often referred to as a
primary energy requirement part of mobile
telecommunication networks that facilitates
wireless communications between user
equipment and a network [27]. At the foot of each
tower is a shelter with extra gearbox equipment,
air conditioning, battery banks, and a diesel
generator for BTS in off-grid situations [25]. The
BTS site load profile is influenced by radio
equipment, antennas, power conversion
equipment, and transmission equipment, among
other things. As a result, it is necessary to
develop an accurate power profile before
selecting and sizing energy components. The
categories below indicate how much energy each
component consumes at a typical Radio Base
Station (RBS) location [25].
1. Radio equipment:
Radio Unit (RF Power Amplification
and Radio Frequency (RF) Conversion)
=
4210
W
Base Band ( Processing and Control
Signal)
=
2190
W
2.
Power
equipment:
Power
Supply with
Rectifier
=
1200
W
3. Antenna
e
q
u
i
p
m
en
t:
RF feeder = 120W
Remote Monitoring and
Safety Lamp
=
100W
4. Transmission
e
q
u
ipm
e
n
t:
Transmitter (signal) = 120W
5. Auxiliary
e
q
u
i
p
m
en
t
Security
and
Lighting =
200
W
Temperature control equipment (air
conditioner) = 1500W (2H.P.)
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.15
David S. Kuponiyi, Matthew B. Olajide,
Michael A. Eko, Charity S. Odeyemi,
Najeem O. Adelakun
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Based on the above, a typical hourly-daily load
profile for the base station was evaluated using
the maximum load to guarantee adequate size of
the energy sources. The primary load profile
input for simulation is shown in Table 1.
Table 1: Typical Load Demand For A Base Transceiver Station
BTS HOURLY - DAILY LOAD DEMAND
Hourly
Time
Radio
Unit
(W/h)
Base
Band
Unit
(W/h)
Power
Supply
with
Rectifier
(W/h)
RF
Feeder
Unit
(W/h)
Remote
Monitorin
g and
Safety
Lamp
(W/h)
Signal
Transmitt
er (W/h)
Security
and
Lighting
(W/h)
Temperatur
e Control
Equipment
(W/h)
Total
(W/h)
00-01
4210
2190
1200
120
100
120
200
900
9040
01-02
4210
2190
1200
120
100
120
200
900
9040
02-03
4210
2190
1200
120
100
120
200
900
9040
03-04
4210
2190
1200
120
100
120
200
900
9040
04-05
4210
2190
1200
120
100
120
200
900
9040
05-06
4210
2190
1200
120
100
120
200
900
9040
06-07
4210
2190
1200
120
100
120
200
900
9040
07-08
4210
2190
1200
120
100
120
OFF
900
8840
08-09
4210
2190
1200
120
100
120
OFF
900
8840
09-10
4210
2190
1200
120
100
120
OFF
1125
9065
10-11
4210
2190
1200
120
100
120
OFF
1125
9065
11-12
4210
2190
1200
120
100
120
OFF
1125
9065
12-13
4210
2190
1200
120
100
120
OFF
1500
9440
13-14
4210
2190
1200
120
100
120
OFF
1500
9440
14-15
4210
2190
1200
120
100
120
OFF
1500
9440
15-16
4210
2190
1200
120
100
120
OFF
1500
9440
16-17
4210
2190
1200
120
100
120
OFF
1500
9440
17-18
4210
2190
1200
120
100
120
OFF
1500
9440
18-19
4210
2190
1200
120
100
120
200
1500
9640
19-20
4210
2190
1200
120
100
120
200
1125
9265
20-21
4210
2190
1200
120
100
120
200
1125
9265
21-22
4210
2190
1200
120
100
120
200
1125
9265
22-23
4210
2190
1200
120
100
120
200
1125
9265
23-00
4210
2190
1200
120
100
120
200
1125
9265
Total
101040
52560
28800
2880
2400
2880
2600
27600
20760
C. Energy Resources
The term "resource" refers to everything that comes
from outside the power system and is used by it to
create electric or thermal power. The four basic
renewable energy sources are solar, wind, hydro, and
biomass, and any fuel required by the system's
International Journal of Electrical Engineering and Computer Science
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Najeem O. Adelakun
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components is included. The placement of renewable
resources has a large impact. Large-scale
atmospheric circulation patterns and geographic
effects influence the wind resource; local rainfall
patterns and terrain influence the hydro resource; and
regional biological productivity influences both the
hydro and solar resources. Furthermore, a renewable
resource may demonstrate significant hour-to-hour
and seasonal variations at any one place. Because the
resource determines renewable energy output, the
type of renewable resources available has an impact
on the economics and behaviour of renewable energy
systems. As a result, accurate modelling of renewable
resources has become an essential component of
system modelling. This section explains how
HOMER models fuel and renewable resources.
D. Wind Turbine Modelling and Sizing
Modelling wind turbines with HOMER requires the
input of the type of turbine if it is not already in the
library. Also considered for modelling is the input
cost of turbine sizes, which is very important for
simulation. HOMER provides different drop-down
boxes and tables for these options.
E. Wind Turbine Sizing
i. Wind Turbine type
All of the many varieties of wind turbines that are
kept in the component library are available in this
drop-down box. From this list, a suitable wind turbine
model is picked. Following your choice from this
drop-down box, a brief description of the chosen
wind turbine's characteristics is shown in the area
below, and you may view more information by
clicking the Details button.
Additionally, by selecting the new button, a brand-
new sort of wind turbine may be developed. The new
turbine type will be included in HOMER's
component library. Alternatively, by selecting the
Delete button, any turbine type can be eliminated
from the component collection.
ii. Wind Turbine Properties
The main characteristics of the chosen wind turbine
are displayed in this section. The power curve, which
describes the output of the turbine's power over a
range of wind speeds, is its most important
characteristic. Using a standard wind turbine power
curve, it was determined that the chosen wind turbine
was suitable.
F. Modelling and Sizing of PV Panels
The total peak power of the PV generator needed to
supply a specific load depends on the load, solar
radiation, ambient temperature, power temperature
coefficient, efficiencies of the solar charger regulator
and inverter, as well as the safety factor taken into
account to account for losses and temperature effect.
This total peak power is obtained as follows
=
(1)
Where:
YPV is the PV array's rated capacity, i.e., its power
production under typical test conditions. (kW)
fPV is the PV derating factor (%)
Is solar radiation hitting the PV array at the
present time step? (kW/m2)
Is the radiation incident under typical test
conditions? (1 kW/m2)
is the coefficient of power for temperature (%
/ °C)
Tc is the temperature of PV cell at this moment in
time (°C)
Tc,STC is the PV cell temperature understandard
conditions (25 °C)
As the influence of temperature on the PV array is not
selected in the PV Inputs box for this project, HOMER
assumes that the temperature coefficient of power is zero,
simplifying the preceding equation to:
=
(2)
G. Modeling and Sizing of Battery Bank
As the wind speed changes throughout the day, so does the
wind turbine's output power. Additionally, fluctuations in
solar light and temperature affect the maximum power
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.15
David S. Kuponiyi, Matthew B. Olajide,
Michael A. Eko, Charity S. Odeyemi,
Najeem O. Adelakun
E-ISSN: 2769-2507
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Volume 5, 2023
output of the PV generator. Therefore, it's possible that the
wind turbine and the PV generator won't always be able to
handle the load. During these periods, a battery placed
between the microgrid system's DC bus and the load will
serve as a power supply and compensate.
When the output power from the wind turbine and PV
generator exceeds the amount of power needed for the
load, the excess energy is stored in the battery to provide
power for the load when the wind turbine and PV generator
are unable to do so.
The battery capacity considered in this research is
determined by (3) and (4).
BC =
(3)
Where
BC is the capacity of a battery
F is the the reserve factor
W is the Daily Energy demand
Vbatt is the DC Bus voltage for the system
The battery's Ampere-hour (Ah) rating is calculated as
Ah =
(4)
H. Battery Bank
A grouping of one or more different batteries makes up the
battery bank. A single battery is modelled by HOMER as
a system that can store a specific amount of direct current
electricity at a fixed round-trip energy efficiency, with
restrictions on how quickly it can be charged or
discharged, how deeply it can be discharged without
suffering damage, and how much energy can cycle through
it before it needs to be replaced. HOMER makes the
assumption that a battery's characteristics will not change
over the course of its lifetime due to environmental
conditions like temperature.
The battery's nominal voltage, capacity curve, lifetime
curve, minimum state of charge, and round-trip efficiency
are its primary physical characteristics in HOMER. The
capacity curve compares the battery's ampere-hours of
discharge capacity to its amperes of discharge current. The
amount of ampere-hours that can be discharged out of a
fully charged battery at a steady current is what
manufacturers use to calculate each point on this curve.
Typically, capacity declines as discharge current rises. The
battery's lifetime curve plots the number of discharge-
charge cycles it can sustain against the depth of the cycle.
As cycle depth increases, the number of cycles to failure
often decreases. The minimum state of charge is the state
of charge below which the battery must not be discharged
in order to prevent long-term harm. In the system
simulation, HOMER prevents the battery from being
depleted more deeply than this threshold. The amount of
energy entering the battery that can be pulled out again is
indicated by the round-trip efficiency.
Figure 9 - Kinetic Battery Model [28]
The kinetic battery model [28], which treats the battery as
a two-tank system as depicted in Figure 9, is used by
HOMER to determine the battery's maximum permitted
rate of charge or discharge. The kinetic battery concept
states that while some of the battery's energy storage
capacity is instantaneously accessible for charging or
discharging, the other portion is chemically bonded. The
difference in height between the two tanks affects how
quickly available energy is converted into bound energy.
The battery can be described by three variables. The total
size of the available and bound tanks is the battery's
maximum capacity. The ratio of the size of the available
tank to the total size of the two tanks is referred to as the
capacity ratio. The pipe size between the tanks and the rate
constant are comparable.
The shape of the usual battery capacity curve, such as the
one in Figure 10, is explained by the kinetic battery model.
High discharge rates cause the available tank to quickly
empty, and very little of the bound energy can be released
before the tank is empty. At this point, the battery is no
longer able to sustain the high discharge rate and seems to
be totally depleted. The apparent capacity rises when the
discharge rate slows because more bound energy can be
converted to usable energy before the available tank is
completely depleted. The three components of the kinetic
battery model are calculated by HOMER using a curve fit
on the battery's discharge curve. In accordance with this
curve fit, the line in Figure 10 is drawn.
It contains two effects to model the battery as a two-tank
system rather than a single-tank system. It first implies that
the battery cannot be fully charged or drained
simultaneously; a full charge necessitates an infinite
amount of time at a charge current that asymptotically
approaches zero. Second, it implies that the battery's
capacity to charge and discharge is influenced not only by
its current level of charge but also by its most recent
history of charge and discharge. Since it will have a larger
level in its available tank, a battery that has been rapidly
charged to 80 percent state of charge can discharge at a
quicker pace than the same battery that has been similarly
rapidly discharged to 80 percent. Each hour, HOMER
monitors the levels in the two tanks and models both of
these impacts.
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.15
David S. Kuponiyi, Matthew B. Olajide,
Michael A. Eko, Charity S. Odeyemi,
Najeem O. Adelakun
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Figure 10 - Cappacity Curve Of The Battery
A deep-cycle lead-acid battery lifetime curve is depicted
in Figure 10. With increasing discharge depth, the number
of cycles before failure (shown on the graph as the lighter-
colored spots) rapidly decreases. Finding the product of the
number of cycles, the depth of discharge, the nominal
voltage of the battery, and the aforementioned maximum
capacity of the battery allows one to determine the lifetime
throughput (the amount of energy that passed through the
battery before failure) for each point on this curve. Figure
11's lifetime throughput curve, shown as a series of black
dots, frequently depends on cycle depth far less. HOMER's
simplifying premise is that the depth of discharge has no
impact on lifetime throughput. The average of the points
from the lifespan curve above the minimal state of charge
is the value that HOMER recommends for this lifetime
throughput, although the user can change this number to
be more or less conservative.
Assuming that lifetime throughput is independent of cycle
depth, HOMER may predict the battery bank's remaining
life by observing the quantity of energy passing through it,
without taking the length of the numerous charge-
discharge cycles into account. HOMER determines the
battery bank's lifespan in years as
(5)
where Nbatt is the number of batteries in the
battery bank,
Qlifetime the lifetime throughput of a single
battery,
Qthrpt the annual throughput (the total ammount
of energy that cycles through the battery
bank in one year) and
Rbatt,f the float life of the battery (the
maximum life regardless of throughput).
Figure 11 - Lifetime Curve For The Modelled Battery
The capital cost, replacement cost, and Operations and
maintenance cost of the battery bank are all stated in US
dollars (HOMER standard) each year. As a dispatchable
power source, the battery bank's fixed and marginal energy
costs are calculated for comparison with those of other
dispatchable sources. The fixed cost of energy for the
battery bank is zero because, unlike the generator, it
doesn't cost anything to run it so that it is ready to produce
energy. HOMER calculates the marginal cost of energy as
the product of the battery wear cost (the price per
kilowatthour for cycling energy through the battery bank)
and the battery energy cost (the average cost of the energy
stored in the battery bank). The battery wear cost is
determined by HOMER as follows:
(6)
where
Crep.batt is the replacement cost of the battery bank
Nbatt is the number of batteries in the battery bank,
Qlifetime is the life time throughput of a single battery
(kWh), and
ηrt is the round-trip efficiency.
By dividing the overall annual cost of charging the battery
bank by the total annual quantity of energy put into the
battery bank, HOMER determines the battery energy cost
for each hour of the simulation. Due to the fact that the
battery bank is only ever charged by excess electricity
while using the load following dispatch technique, there is
never any cost involved in doing so. The cost of charging
the battery bank is not zero, however, because the cycle-
charging approach calls for a generator to create extra
energy (and hence use more fuel) specifically for the
purpose of charging the battery bank.
0100 200 300 400 500
200
400
600
800
1,000
1,200
Capacity (Ah)
Discharge Current (A)
Data Points Best Fit
0
3,000
6,000
9,000
12,000
020 40 60 80 100
0
1,000
2,000
3,000
4,000
5,000
6,000
Cycles to Failure
Depth of Discharge (%)
Lifetime Thrpt. (kWh)
Cycles Throughput
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Figure 12 - Battery Sizing Cost Curve
I. Diesel Generator
Fuel is consumed by a diesel generator to generate power,
along with potential heat output. The generator module in
HOMER is adaptable enough to represent a wide range of
generators, including those powered by internal
combustion engines, microturbines, fuel cells, Stirling
engines, thermophotovoltaic generators, and
thermoelectric generators. As many as three generators,
each of which may be ac or dc and use a different fuel,
could be used in a power system that HOMER can
simulate.
The generator's main physical characteristics include its
maximum and minimum electrical power output,
estimated lifetime in operating hours, the kind of fuel it
uses, and its fuel curve, which connects the amount of fuel
used to the amount of electrical power generated. The fuel
consumption of the generator is calculated using the
following equation by HOMER under the assumption that
the fuel curve is a straight line with a y-intercept:
(7)
Where
F0 is the fuel curve intercept coefficient,
F1 is the fuel curve slope,
Ygen the rated capacity of the generator (kW), and
Pgen the electrical output of the generator (kW).
The measuring units for the fuel determine the units of F.
The units of gasoline are L/h if the fuel is measured in
liters. The units of F are m3/h or kg/h depending on
whether the fuel is expressed in m3 or kilogram. Similarly,
the units of F0 and F1 are determined by the fuel's
measurement units. The units of F0 and F1 for fuels with
liter denominators are L/h.kW. The user also specifies the
heat recovery ratio for a generator that produces both heat
and electricity. HOMER makes the assumption that the
generator transforms 100% of the fuel energy into either
waste heat or electricity. The amount of waste heat that can
be recovered to meet the thermal load is known as the heat
recovery ratio. The modeler can also specify the generator
emissions coefficients, which indicate the generator's
emissions of six distinct pollutants in terms of grams of
pollutant emitted per unit of fuel consumed.
The generator's functioning can be scheduled to turn on or
off at predetermined times. When the generator isn't being
pushed on or off, HOMER decides whether it should run
based on the system's requirements and the relative costs
of the alternative power sources. When the generator is
required to run, HOMER chooses the power output level it
will use, which might be anything between its minimum
and maximum power output.
The initial capital cost in dollars, replacement cost in
dollars, and annual Operations and maintenance cost in
dollars per operating hour for the generator are all listed.
Oil changes and other maintenance costs are included in
the generator Operation and maintenance ( o&m, but fuel
prices are not included because fuel costs are determined
separately by HOMER. The fixed and marginal cost of
energy for the generator is determined, as it is for all
dispatchable power sources, and used by HOMER to
model the system operation. The hourly cost of simply
running the generator without generating any electricity is
the fixed cost of energy. The increased cost per
kilowatthour for using that generator to produce power is
known as the marginal cost of energy.
The fixed cost of energy for the generator is determined by
HOMER using the following equation:
(8)
Where Com.gen is the cost of running and managing in
dollars per hour,
Crep.gen the replacement price in money,
Rgen is the generator life expectancy in hours,
F0, the fuel curve intercept coefficient
in terms of the fuel's quality per hour per
kilowatt, and Ygen, the generator's
capacity (kW), and
Cfuel.eff the fuel's actual cost, expressed
as a dollar amount per fuel quantity.
The effective price of fuel include the cost penalties
if any associated with the emissions of pollutant from
the generator.
The following equation is used by HOMER to determine
the generator's marginal cost of energy:
(9)
where
F1 is the fuel curve slope in quantity of fuel
per hour per kilowatthour and Cfuel.eff is the
effective price of fuel (including the cost of any
050 100 150 200
0
50
100
150
200
250
Cost (000 $)
Cost Curve
Quantity
Capital Replacement
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penalties on emissions) in dollars per quantity of
fuel.
3 Simulation and Results
A. Simulation Input For BTS Energy Demand
Table 2: Simulation Input For BTS Energy Demand
Figure13 - Typical Daily Energy Demand Profile of
Base Station
Figure14 - Typical Annual Load Profile of A BTS
A. Simulation Input for Solar PV
Table 3: Simulation Input for Solar PV
Size (kW)
Capital (N)
Replacement (N)
O&M (N/yr)
1
412,500
330,000
0
Sizes considered:
10, 20, 30, 40, 60, 80 kW
Lifetime:
25 yr
Derating factor:
80%
Tracking system:
No Tracking
Slope:
6.57 deg
Azimuth:
0 deg
Ground reflectance:
20%
Solar Resource
Latitude:
6 degrees 42 minutes North
Longitude:
3 degrees 15 minutes East
Time zone:
GMT +1:00
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Synthesized Solar Radition Data
Month
Clearness Index
Average Radiation
(kWh/m2/day)
Jan
0.531
4.949
Feb
0.525
5.184
Mar
0.512
5.303
Apr
0.491
5.131
May
0.468
4.773
Jun
0.436
4.345
Jul
0.384
3.851
Aug
0.384
3.942
Sep
0.408
4.215
Oct
0.487
4.855
Nov
0.577
5.426
Dec
0.54
4.909
Scaled annual average:
4.74 kWh/m²/d
Figure 15 - Typical Global Horizontal Radiation for Sun
B. Simulation Input for AC Wind Turbine: PGE 20/25
Table 4: Simulation Input for AC Wind Turbine: 20/25
Quantity
Capital (N)
Replacement (N)
O&M (N/yr)
1
7,260,000
6,660,000
74,250
Quantities to consider:
0, 1, 2, 3, 4
Lifetime:
25 yr
Hub height:
30 m
Wind Resource
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Table 5: Synthesized Wind Speed Data
Month
Wind Speed
(m/s)
Jan
4.15
Feb
4.3
Mar
4.01
Apr
3.49
May
3
Jun
3.12
Jul
3.7
Aug
3.85
Sep
3.5
Oct
2.83
Nov
3.05
Dec
3.65
Figure 16 - Synthesized Wind Resource Data
Table 6: Other Simulation Input for AC Wind Turbine: 20/25
Weibull k:
2.01
Autocorrelation factor:
0.849
Diurnal pattern strength:
0.249
Hour of peak wind speed:
15
Scaled annual average:
3.55 m/s
Anemometer height:
50 m
Altitude:
30 m
Wind shear profile:
Logarithmic
Surface roughness length:
0.01 m
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Figure 17 - Power Curve for Wind Turbine: 20/25
Wind Turbine Specification
Curve made at 25m hub height
Available towers: 24 / 30 / 36m
Rotor : 20m diameter, 305 m2 swept, 32 rpm
Cold cut-in wind speed: 3.5 m/s
Low wind speed cut-out: 1.7 m/s
Rated power wind speed: 25 kW @ 9 m/s
High wind speed cut-out: 25 m/s
A. Simulation Input for AC Generator: Generator 1
Table 7: Simulation Input for AC Generator
Size (kW)
Capital (N)
Replacement (N)
O&M (N/hr)
22
3,234,000
2,640,000
825,000
44
4,867,500
4,125,000
1,155,000
Table 8: AC Generator Parameters/Specifications
Sizes to consider:
0, 22, 44 kW
Lifetime:
15,000 hrs
Min. load ratio:
30%
Heat recovery ratio:
0%
Fuel used:
Diesel
Fuel curve intercept:
0.08 L/hr/kW
Fuel curve slope:
0.25 L/hr/kW
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Figure 18 - Efficiency Curve for AC Generator 1
Table 9: Simulation Input for AC Generator Fuel: Diesel
Price:
(N),99, 115.5, 132, 148.5, 165/L
Lower heating value:
43.2 MJ/kg
Density:
820 kg/m3
Carbon content:
88.00%
Sulfur content:
0.33%
C. Simulation Input for Battery: Surrette 6CS25P
Table 10: Simulation Input for Battery: Surrette 6CS25P
Quantity
Capital (N)
Replacement (N)
O&M (N/yr)
1
198,000
181,500
8,250
Quantities considered:
0, 20, 40, 60, 80, 100
Voltage:
6 V
Nominal capacity:
1,156 Ah
Lifetime throughput:
9,645 kWh
D. Simulation Input for Converter: Bi-Directional
Table 11: Simulation Input for Bi-Directional Converter
Size (kW)
Capital (N)
Replacement (N)
O&M (N/yr)
10
6,600,000
6,600,000
49,500
Sizes considered:
0, 5, 10, 20, 30, 40 kW
Lifetime:
25 yr
Inverter efficiency:
90%
Inverter can parallel with AC generator:
Yes
Rectifier relative capacity:
100%
Rectifier efficiency:
85%
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E. Simulation Optimised Result
Table 12: Optimization Result of Simulation.
The following table provides the optimisation outcome of all options assessed for this study, and the graphical summary
analysis follows.
Graphical Summary Analysis of the Optimization
Figure 19 - Option 8 Cash Flow Analysis
Figure 20 - Option 7 Cash Flow Analysis
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Figure 21 - Option 5 Cash Flow Analysis
Hourly - Daily Map (January)
Figure 22 - Graphical Analysis For Hourly – Daily Map (January)
H. OTHER RESULTS
A. AC Wind Turbine: PGE 20/25
Table 13: Wind Turbine PGE 20/25 Output Analysis Result
Variable
Value
Units
Total rated capacity
50
kW
Mean output
6.97
kW
Capacity factor
13.9
%
Total production
61,015
kWh/yr
Variable
Value
Units
Minimum output
0
kW
Maximum output
52.4
kW
Wind penetration
78.9
%
Hours of operation
4,629
hr/yr
Levelized cost
0.128
$/kWh
Capital Replacement Operating Fuel Salvage
-100,000
0
100,000
200,000
300,000
400,000
Net Present Cost ($)
Cash Flow Summary
Generator 1
Surrette 6CS25P
Converter
Other
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Figure 23 - Graphical Analysis For PGE 20/25 Output
B. Deisel Generator Table 14: Diesel Generator 1 Output Analysis Result
Quantity
Value
Units
Hours of operation
526
hr/yr
Number of starts
31
starts/yr
Operational life
28.5
yr
Capacity factor
4.49
%
Fixed generation cost
7.47
$/hr
Marginal generation
cost
0.2
$/kWhyr
Quantity
Value
Units
Electrical production
8,662
kWh/yr
Mean electrical output
16.5
kW
Min. electrical output
6.6
kW
Max. electrical output
22
kW
Quantity
Value
Units
Fuel consumption
3,091
L/yr
Specific fuel
consumption
0.357
L/kWh
Fuel energy input
30,419
kWh/yr
Mean electrical
efficiency
28.5
%
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Figure 24 - Generator 1 Output
C. Battery
Table 15: Other Battery Parameters
Quantity
Value
String size
1
Strings in parallel
80
Batteries
80
Bus voltage (V)
6
Table 16: Battery Simulation Result
Figure 25 - Battery State of Charge Frequency Histogram Figure 26 - Battery State of Charge Monthly Statistics
Quantity
Value
Units
Energy in
42,461
kWh/yr
Energy out
34,066
kWh/yr
Storage depletion
108
kWh/yr
Losses
8,288
kWh/yr
Annual throughput
38,086
kWh/yr
Expected life
12
yr
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Figure 27 - Battery Bank State of Charge Annual Result
D. Bi-Directional Converter
Table 17: Bi-Directional Converter Output Analysis
Quantity
Inverter
Rectifier
Units
Capacity
20
20
kW
Mean output
5
1.6
kW
Minimum output
0
0
kW
Maximum output
17
19.7
kW
Capacity factor
25.1
7.8
%
Table 18: Bi-Directional Converter Operational Analysis Per Annual
Quantity
Inverter
Rectifier
Units
Hours of operation
5,920
2,054
hrs/yr
Energy in
48,858
16,020
kWh/yr
Energy out
43,972
13,617
kWh/yr
Losses
4,886
2,403
kWh/yr
Figure 28 - Bi-Directional – Inverter Output Power
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Figure 29 - Bi-Directional Rectifier Output Power
E. Emissions
Table 19: Generator 1 Emission Output
Figure 30 - Generator 1 Emission Output Analysis
Table 20: Optimization Result of Simulation
4 Conclusion
The optimisation summary table for the eight most cost-
effective power systems presented shows the benefits
and drawbacks of each model. A comparison of the eight
modelled topologies reveals that option one, with solely
diesel gensets, has the lowest initial capital cost but the
highest running cost, levelized cost of energy, and total
net present cost. Option five lacks solar PV and wind
turbines but does have storage batteries and inverters,
which are common in all rural areas without a national
grid. The net present value is 134,917,530:00, a
difference of ₦42,162,450:00 from option eight. This
analysis also found that option seven, which includes a
solar PV system but no wind turbine, is less cost-
effective despite having a lower capital cost of nearly
half that of option eight. The simulation results proved
that if any of the options were properly studied and
harnessed, a permanent solution to power failure at our
base station would be achieved. Hence, the cost analysis
presented shows that the implementation and running
costs of any of the options were very low if compared
with existing techniques, which will reduce the tariff
cost placed on customers. Future research should look at
enhanced energy storage technologies, such as next-
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generation batteries or supercapacitors, to improve
microgrid efficiency. Furthermore, studying the
integration of renewable energy sources like solar and
wind might lessen dependency on fossil fuels, making
Nigerian communication networks more sustainable and
environmentally friendly.
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David S. Kuponiyi, Matthew B. Olajide, Michael A. Eko
worked on the methodology.
David S. Kuponiyi, and Michael A. Eko carried out the
simulation of the data.
Matthew B. Olajide, Charity S. Odeyemi, Najeem O.
Adelakun organised and worked on results and
discussion section.
Michael A. Eko, Charity S. Odeyemi worked on the
conclusion.
David S. Kuponiyi, Matthew B. Olajide and Najeem O.
Adelakun was responsible for the proofreading of the
manuscript.
The authors received no financial support for the
research, authorship, and/or publication of this article.
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International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.15
David S. Kuponiyi, Matthew B. Olajide,
Michael A. Eko, Charity S. Odeyemi,
Najeem O. Adelakun
E-ISSN: 2769-2507
156
Volume 5, 2023
