Uninterrupted Electricity Supply using Off-Grid Solar PV Systems for Remote Areas
Published at : 18 Sep 2024
Volume : IJtech
Vol 15, No 5 (2024)
DOI : https://doi.org/10.14716/ijtech.v15i5.6089
Ajiwiguna, T.A., Kirom, M.R., 2024. Uninterrupted Electricity Supply using Off-Grid Solar PV Systems for Remote Areas. International Journal of Technology. Volume 15(5), pp. 1561-1572
| Tri Ayodha Ajiwiguna | 1. Engineering Physics Study Program, Telkom University, Jalan Telekomunikasi, Bandung, 40527, Indonesia. 2. Center of Excellence of Sustainable Energy and Climate Change, Telkom University, Jalan Tel |
| Mukhammad Ramdlan Kirom | Engineering Physics Study Program, Telkom University, Jalan Telekomunikasi, Bandung, 40527, Indonesia |
Due to the intermittent electricity production, the
battery takes an important role in the off-grid PV systems by storing excess
electricity production. The stored electricity is then used when the PV system
generates less electricity than what is demanded. However, the current price
and lifetime of the battery make the electricity cost of the off-grid PV system
expensive. Therefore, the capacity of components must be designed to fulfill
the demand at the lowest electricity cost. In this study, two strategies in the
design of off-grid PV systems to fulfill the same demand are compared. The
first strategy is to employ the PV module with proper capacity, which means the
annual energy production equals the annual energy demand, but it needs a huge
capacity battery to store the accumulation of the excess energy. The second
strategy is to use the large capacity of the PV module, which considers the
lowest energy production day, but it requires the small capacity of the
battery. The results show that the electricity cost of the second strategy is
only 29.0 % of the first strategy. However, it dumps 50.1 % of the annual
produced electricity.
Electricity cost; Off-grid system; Sizing strategy; Solar photovoltaic
1. Introduction
The economic analysis of the off-grid PV system for the
remote area has been presented by various studies (Cuesta, Castillo-Calzadilla,
and Borges, 2020; Jamshidi and Askarzadeh, 2019; Mandal, Das, and Hoque, 2018).
Taufiqurrohman designed and evaluated the off-grid PV system to fulfill the
electricity demand at 1.61 kWh/d for small houses in Indonesia (Taufiqurrohman, 2018). The resulting electricity cost
was 0.30 $/kWh. Awapone presented the feasibility of the off-grid system
consisting of PV, battery, and diesel generators in Ghana (Awopone, 2021). The system was designed for 80
houses with an electricity demand of 224.06 kWh/d. The electricity cost
resulted at 0.4 $/kWh. Sinaga et al., (2019) presented
the off-grid PV system in the area of Kupang, Indonesia
This study presents a novel strategy to minimize the capacity of the battery without interrupting the electricity supply simultaneously. The proposed strategy is to consider the lowest energy production day to determine the PV capacity. By using this strategy, the battery capacity can be minimized to only one autonomous day because the daily electric energy production is always equal to or higher than the daily electric energy demand. However, it also implies that dumping energy is not avoidable. The electricity cost is then estimated as the final parameter of system performance. The proposed design system is also compared technically and economically with the design without dumping energy that was presented by (Ajiwiguna et al. 2022).
2. System
and Case Study Description
The
schematic diagram of an off-grid PV system is shown in Figure 1 (Awasthi et al., 2020; Ghafoor and Munir, 2015).
PV module converts solar irradiation into direct current (DC) electricity.
Solar charge controller (SCC) manages the electricity by sending it to the
demand, storing it in the battery, or both. If the electricity produced by the
PV system is higher than the demand, the excess energy is stored in the battery
(the battery is charged). If the electricity produced by the PV system is lower
than the demand, the deficit energy is supplied from energy stored in the
battery (the battery is discharged). The specification of the PV module and
battery used in this study is shown in Table 1 and Table 2, respectively.
Although the lifetime of PV modules was 25 years, the
off-grid PV system was designed only for 20 years, considering the lifetime of
other components, especially the battery. The lifetime of the components must
be considered because if it is shorter than the system lifetime, the
replacement cost is required. Operational and maintenance cost is mostly for
the PV module for cleaning and checking the connection. For battery, its
O&M cost is negligible since the battery used in this study is
maintenance-free.
Figure 1 Configuration
of off-grid PV system
Table 1
Specifications of the PV module
|
Maximum Power (STC), PSTC |
375 W |
|
Open Circuit Voltage, VOC |
47.6 V |
|
Short Circuit Current, ISC |
9.93 A |
|
Nominal Operating Cell Temperature, NOCT |
41 ± 3oC |
|
Temperature coefficient of power, |
-0.37%3oC |
|
Lifetime |
25 years |
Table 2 Specification of battery per unit
|
Capacity |
1.2 kWh (100 Ah) |
|
Voltage |
12 V |
|
Battery efficiency |
85% |
|
Depth of discharge |
80 % |
|
Lifetime |
7 years |
The
case study was conducted in Cisoka Village, Indonesia, which was selected due
to its potential in the tourism industry, but with limited access to
electricity. The solar PV system was designed to cater to small residential
buildings, with a daily electricity consumption assumption as presented in
Table 3. The total daily energy consumption of 3.23 kWh is reasonable, as it
falls within the range of energy consumption reported by Palaloi for small
buildings in Indonesia (Palaloi, 2014). The
required capacity of system components was estimated based on Typical
Meteorological Year (TMY) weather data obtained from the Repository of Free
Climate Data in 2019.
Table 3 Appliances and estimation of daily energy consumption
|
No |
Appliance |
Quantity |
Effective operation hour (h/d) |
Wattage (W) |
Energy consumption (kWh) |
|
1 |
Lamp 1 (outdoor) |
3 |
12 |
18 |
0.65 |
|
2 |
Lamp 2 (indoor) |
5 |
10 |
12 |
0.60 |
|
3 |
Refrigerator |
1 |
8 |
110 |
0.88 |
|
4 |
Rice cooker |
1 |
2 |
300 |
0.60 |
|
5 |
Pump |
1 |
2 |
100 |
0.20 |
|
6 |
Washing Machine |
1 |
1 |
300 |
0.31 |
|
|
Total daily energy consumption |
|
|
|
3.23 |
where
is DC power produced by PV modules, 
is the power produced by the PV module at standard test conditions, 
is the temperature dependence coefficient of power, 
is a module or cell temperature, 
is the surrounding temperature, and NOCT is the nominal operating cell temperature. This calculation requires data from the specification of PV modules and weather data (especially solar irradiation and ambient temperature).
where 
is battery
efficiency, and L is the overall losses of the system. By using the procedure
above, the hourly data for a year are obtained because the weather data used in
this study is also hourly data.
In
this study, the comparison of two different design strategies to fulfill the
same electricity demand was conducted. The strategies
presented in this study aim to supply electricity to the demand without
interruption while the weather and season are not constant. To obtain this
purpose, two different strategies can be applied. The first strategy is to
minimize the PV capacity thus, the annual electricity production is as same as
the annual electricity demand. This option needs the huge capacity battery to
store the excess electricity production on the consecutive sunny days and to
use it on cloudy or rainy days. The second strategy, our proposed strategy,
is to use the worst weather day of electricity production as the basis for
determining the capacity of the PV system. By using this option, the PV
capacity must be huge because it must produce the electricity as much as demand
on the worst production day. However, the battery capacity can
be reduced to only one day's worth of autonomy, as the system does not need to
store excess electricity production for consecutive days. This allows the
system to efficiently supply electricity to meet demand without encountering
any problems on the following day. The differences between the two design
strategies are summarized in Table 4.
In the first
strategy (Strategy A), the PV module capacity was determined so that the annual
electrical energy production was as same as the annual demand. The flow chart
for determining the PV capacity is shown in Figure 2. Since the weather is not
constant, the battery capacity was determined by considering the accumulation
of storing excess electricity production. Generally, Indonesia has two seasons:
Dry and Rainy. In the dry season, solar irradiation is very high; thus, the
electricity production from the PV system is also high. Most of the excess
electricity production is in this period. To accommodate the accumulation of
stored energy from consecutive days, a huge capacity battery is needed. The methods used to calculate the
capacity of PV modules and batteries were based on the study presented by Ajiwiguna et al.
(2022).
Table 4
Design characteristics of the two strategies
|
|
Strategy
A |
Strategy
B |
|
PV
module capacity |
Determined
by calculating the annual electrical energy production as same as athe nnual
demand |
Determined
by using the worst daily weather in a year to produce electrical energy |
|
Battery
capacity |
Considering
the accumulation of energy storage and/or energy deficit in a year |
One
autonomous day |
|
Dumping
energy |
No
(or minimum) |
Yes |
Figure 2 Flowchart to determine the capacity of PV module for strategy A
where 
is daily energy produced by a PV system using
a single module, and 
is a time interval of data. Next, the minimum
daily energy production,
is chosen
as the basis for determining the capacity of the PV module. The required number
of PV modules was calculated by using Equation (5).
where 
is daily energy demand. Then the PV capacity
for strategy B is calculated by using Equation (6).
By using
that capacity, the electricity demand on the other days was fulfilled because
the weathers were better than that worst day. Since the daily energy production
was always equal to or higher than daily demand, the battery didn’t need to
store the excess energy for consecutive days. It also meant the required
capacity of the battery was only one autonomous day. However, dumping energy in
this strategy was unavoidable. One of the functions of a solar charge
controller is to stop the charging process when the battery is already full.
Therefore, the overcharging of the battery can be avoided.
3.2. Electricity cost estimation method
Electricity
cost was calculated as the ultimate parameter for comparison between the two
strategies. It considered capital cost, operational and maintenance cost, and
replacement cost. The total capital cost 
PV module
capital cost 
Inverter capital cost 
Solar charge controller capital cost
and battery capital cost were calculated
by using Equations (7) to (11).
where 
are the total price of the PV module,
inverter, solar charge controller, and battery, respectively. For the PV
module, it considered 15% of the installation cost (Mohamed
and Papadakis, 2004). This installation cost is
included in the installation of other components. Therefore, the other
components' capital costs were only the total price of components.
Annual
operational and maintenance cost (OnM) was assumed to be 
(Fu, Feldman, and
Margolis, 2018). Since the PV system was designed
for 20 years of operation, the replacement cost must be considered for the
components, which have a lifetime of less than 20 years. The components
replacement cost is the frequency of replacement multiplied by the component
capital cost. In this case, the battery was replaced two times since its
lifetime was only 7 years. Operational and maintenance cost and total
replacement cost 
was
calculated by using Equations (12) and (13).
where 
is the total capacity of PV modules, 
is the replacement cost of the inverter, 
is the replacement of the solar charge
controller, and 
is the replacement of the battery.
The
annualized capital and replacement cost, called annual fixed charge (AFC),
was then calculated by considering the amortization factor 
This factor
considers the inflation rate and lifetime of the PV system. Equations (10) and
(11) were used to calculate the amortization factor and annual fixed charge,
respectively. In these equations, i is the inflation rate, and n
is the lifetime of the PV system.
The electricity cost 
was then calculated by using Equation (12):
where 
is annual electricity demand.
Table 5 shows the assumptions of the prices of each component. The price information was obtained by surveying the market, and their reasonability was checked by comparing it with the report from NREL (Cole and Frazier, 2019; Fu, Feldman, and Margolis, 2018). The specific price is needed to check the reasonability of the price used in this study because most of the statistical report shows specific prices.
Table 5 Price list of main components
|
No |
Component |
Capacity/unit |
Price
($) |
specific
price |
|
1 |
PV
module |
375
W |
307.5
$ |
0.82
$/W |
|
2 |
Battery |
1.2
kWh |
200
$ |
166.67
$/kWh |
|
3 |
Inverter
|
500
W |
30
$ |
0.06
$/W |
|
No |
Parameter |
Strategy A |
Strategy B |
|
1 |
PV module Capacity |
0.75 kW |
1.5 kW |
|
2 |
Battery capacity |
27.2 kWh |
4.0 kWh |
|
3 |
Annual energy demand |
1180 kWh |
1180 kWh |
|
4 |
Annual energy production |
1181.8 kWh |
2363.8 kWh |
|
5 |
Annual dumped energy |
1.8 kWh |
1183.7 kWh |
Strategy A required 0.75 kW of PV module capacity and 26.8 kWh of
battery capacity. The PV module capacity was determined by estimating the
annual energy production so that it was as same as the annual energy demand.
Therefore, the mismatch between daily energy production and demand was
unavoidable. This strategy required a huge capacity of battery to accommodate
the accumulation of excess energy production in the dry season and deficit
energy production in the rainy season. Without considering the recommended DoD,
the required capacity of the battery is 6.6 autonomous days. It meant that the
battery was enough to supply electricity to the demand for 6.6 days without
interruption, even if the production from the PV system was zero. The dumped
energy was very low, which is 1.8 kWh, because almost all the excess daily
energy production is stored in the battery. The PV capacity of the PV module is
slightly oversized because the number of PV modules must be an integer.
In strategy B, the PV module and battery capacities were 1.5 kW and 4.0
kWh, respectively. The PV capacity is determined by choosing the lowest energy
production day as the basis calculation. Therefore, it required a relatively
huge capacity of PV modules. By using this strategy, the daily energy
production was always equal to or higher than the energy demand. This strategy
allowed the system not to store the accumulation of consecutive excess daily
energy production. It implied that the required battery capacity could be
minimized to only one autonomous day. However, dumping energy was not
unavoidable due to the limitation of battery capacity. The annual dumped energy
was 50.1 % of annual energy production. It was reasonable since the energy must
be dumped almost every day (except the lowest energy production day).
Those two strategies discussed above were the only options if the demand
must be fulfilled without interruption for a whole year. Reducing the capacity
of the PV module or battery caused energy shortage conditions for some hours.
From the energy point of view, strategy A is the most optimum strategy because,
theoretically, the dumped energy can be avoided. In other words, all the energy
produced by the PV module is used to supply energy demand.
Table 7
shows the economic comparison between the two strategies. The capital costs of
the components were proportional to their capacity. It is worth noting that the
capital cost is the initial cost for the system to work properly during the
first operation. Therefore, the replacement cost is not included in the capital
cost. It also meant that the capital cost of the battery was only for the first
seven years of operation. For the whole designed operation time (20 years), the
battery must be replaced twice. The replacement costs were considered for
calculating the electricity cost as expressed in Eq. 16. The most expensive
capital cost for strategy A was for the battery, which takes 80.9 % of the
total capital cost. Contrarily, 67.2 % of the total capital cost was for the PV
module in strategy B which made it the highest capital cost of the component.
The total capital cost of strategy B was only 52.5% of the total capital cost
of strategy A. In the case of the electricity cost, strategy B also resulted in
much lower costs which only 29 % of the cost resulting from strategy A. Based
on this economic analysis, it implies that strategy B was more feasible than
Strategy A to fulfill the same electricity demand.
Table 7 Economic aspect comparison
|
|
Strategy A |
Strategy B |
|
Capital
cost of PV module ($) |
707.25 |
1414.5 |
|
Capital
cost of Battery ($) |
4600 |
800 |
|
Capital
cost of Inverter ($) |
60 |
90 |
|
Total
Capital cost ($) |
5367 |
2304.5 |
|
Electricity
cost ($/kWh) |
1.00 |
0.29 |
The
problem of off-grid PV system are the expensiveness of battery and the
interruption electricity supply. To overcome those two problems, certain design
strategy is needed. In this study, two different strategies for designing the
off-grid PV system to fulfill the same demand were compared. The first strategy
uses the optimum number of the PV system, so the dumped energy can be
minimized. However, it needs a large capacity battery to accommodate the
difference between electricity production and demand. The second strategy uses
a minimum capacity of the battery. The capacity of the PV module is determined
by considering the lowest energy production day. Therefore, this strategy dumps
a lot of energy produced by the PV system. Strategy B uses a larger capacity PV
system but a smaller capacity battery than Strategy A. Even though the capacity
of the PV system is larger, strategy B is more feasible than strategy A since
the total capital cost and electricity cost are only 42.9 % and 29.0 % of
strategy B, respectively. However, strategy B dumped 50.2% of the energy
produced by the PV system because the capacity of the battery is small. In the
future, the design concept for harnessing the dumped energy should be
investigated thus the system may have additional economic value.
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