B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

28 

 

 

 

Article 

Feasibility Analysis and Economic Viability of 

Standalone Hybrid Systems for Marudi 

Electrification in Sarawak, Malaysia  
Brendan Wong Zhi Cheng1, Kamyar Mehranzamir1, Hadi Nabipour Afrouzi2*, Ateeb Hassan2 

1Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, University of Nottingham   

Malaysia, Jalan Broga, 43500 Semenyih, Selangor, Malaysia  
2Faculty of Engineering, Computing, and Science, Swinburne University of Technology Sarawak, 93350 Kuching, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 01 April 2022  
Received in revised form 
02 May 2022 
Accepted 07 May 2022 
 
Keywords: 
Hybrid renewable energy system, off-grid,  
mini-grid, techno-economic optimization,  
rural electrification, feasibility analysis 
 
Corresponding author 
Email address:  
Hafrouzi@swinburne.edu.my 
 
 
 
DOI: 10.55670/fpll.fuen.1.2.5 

A B S T R A C T 
 

A hybrid renewable energy system is a feasible solution for off-grid 
electrification where grid electricity is not available due to economic or 
technical limitations. In this study, rural electrification is performed on a small 
longhouse settlement, Long Moh, in Sarawak, Malaysia, with a population of 308 
from 70 households. Initially, a hybrid PV/Hydro/DG/Battery system is 
proposed due to the abundance of solar and hydro resources in the village. 
There have been a lot of studies based on PV/DG/Battery systems in Malaysia 
but less with the inclusion of hydropower. Through simulation and 
optimization process, the most optimal system in terms of Net Present Cost 
(NPC) is found to be a hybrid Hydro/DG/Battery system which provides a total 
NPC of $213,694.90, cost of electricity of $0.08/kWh, and operating cost of 
$9,495.56/year. The most environmentally friendly system is the proposed 
PV/Hydro/DG/Battery system due to less fuel consumption (12,863.63 L/year) 
and its high renewable penetration. The standalone diesel generator (DG) 
system was the least economic and most polluting system. The best system 
overall for rural electrification at the case study location is a hybrid 
Hydro/DG/battery system due to its relatively low NPC and emissions output 
compared to a standalone DG system. 

 

 
1. Introduction  

As of 2019, there has been a record of high global energy 
consumption of 162,189 TWh, an increase of 20.9% 
compared to the energy consumption a decade ago in 2009, 
which was 134,116 TWh. The world’s primary energy 
source is fossil fuels, such as natural gas, oil, and coal, which 
influence greenhouse gas emissions and adversely affect the 
environment [1]. Burning fossil fuel produces large 
quantities of carbon dioxide, and it is the leading cause of 
man-made global warming. Employing renewable energy 
systems (RESs) such as hydro, wind, and solar has been 
gaining attention due to global warming and fossil fuel 
depletion concerns. In 2009, renewable energy 
consumption accounted for 8.1% of the global energy 
consumption, while in 2019, this figure increased to 11.4% 
[1]. However, as of 2018, 10.4% of the global population 
hasn’t had access to electricity [2]. In the state of Sarawak in 
Malaysia, there are a small but significant number of 
communities living in areas that are difficult to access and 

far from the national grid. Due to technical issues, grid 
extension to those communities is too costly and not 
economically feasible. Many of these communities are in 
areas only accessible by boat travel. The state government is 
aiming toward 100% electrification across the state by 
2025, and as a result, various projects have been initiated by 
the government to increase electricity coverage in rural 
areas, such as Sarawak Alternative Rural Electrification 
Scheme (SARES) and the Rural Power Supply Scheme (RPSS) 
[3]. Despite considerable progress made by these schemes, 
as of 2018, 9% of the rural population still does not have an 
electricity supply which translates to an estimated number 
of 20,000 households [4]. Access to electricity is essential for 
the socio-economic development of the state. It will give 
access to the construction of more social infrastructures, 
such as clinics and schools, which will improve the overall 
quality of life (QOL) [5]. Therefore, installing off-grid RESs 
will be very beneficial to such communities. Due to 
Malaysia’s location in the equatorial zone, it receives an 

 

 

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B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

29 

 

abundance of sunlight all year round. Malaysia receives an 
average global horizontal irradiation of 4.5 𝑘𝑊ℎ/𝑚2/𝑑𝑎𝑦 
[6]. Sarawak’s geography is also characterized by having 
many rivers which have the potential of being used for 
hydroelectric power, such as micro hydropower turbines. As 
an example, the Rajang River currently houses Malaysia’s 
largest and tallest hydroelectric project, the Bakun Hydro-
Electric Dam Project (2400MW). Thus, the combination of 
RESs such as photovoltaic (PV) and hydro is a suitable choice 
for the electrification of rural areas in Sarawak. The purpose 
of considering hydropower in the hybrid system as opposed 
to a pure PV-based system is to expectantly supply the off-
grid network in cases where solar energy is not available, for 
example, during sundown and night-time.  

However, one downside of having a standalone RES is 
that renewable sources are very dependent on 
environmental conditions, which directly affects the energy 
production levels of such systems. PV power levels depend 
on the average sun hours, while hydro systems such as micro 
hydropower turbines depend on the average rainfall levels 
and flow rate of rivers. Reliance on a single technology also 
leads to system over-sizing, increasing the initial set-up cost 
of the system [7]. One solution to this problem is to integrate 
the usage of RESs with conventional energy systems (CESs) 
such as diesel generators to produce a hybrid renewable 
energy system (HRES). This way, the energy system can be 
more reliable and consistent, which is a must for powering 
rural households to avoid supply interruptions. Energy 
storage systems (ESSs) can also be integrated with HRESs to 
further harness renewable sources when there is an 
oversupply; for example, when the load demand during a 
sunny day is low, the extrasolar energy can be used to charge 
batteries in ESSs instead of going to waste. ESSs can then be 
discharged when there is a drop in RESs power levels.  

A drawback to using RES is that the initial investment 
required might be very high. However, due to policies for 
encouraging renewable energy growth, solar power is at its 
cheapest point historically. For example, the European 
Union (EU) has set a goal to supply 32% of its energy from 
RESs by 2030 [8]. For utility-scale solar power, the average 
cost of electricity generation is around USD45 per MWh, 
while a decade ago, it was USD300 per MWh, showing an 
85% reduction in price [9]. It is also reported that the cost of 
building coal plants is higher than installing solar farms, 
with the average cost of coal plants being around USD100 
per MWh. 

There have been a lot of studies discussing the usage of 
hybrid PV/diesel and PV/diesel/battery systems in 
Malaysia, but very few researches have discussed the 
techno-economic feasibility of PV/hydro/diesel/battery 
systems, while hydropower is one of the most abundant 
renewable resources in Malaysia. Lau et al. [10] analyzed the 
usage of a hybrid PV/diesel system in Malaysia for a 40-
house rural residential area with a 2 kW peak load per 
house. Their analysis of the PV/diesel system was done in 
comparison to the standalone diesel system and 
PV/diesel/battery. Standalone diesel systems had the 
lowest operation cost but had the most pollutant emissions. 
Hybrid PV/diesel/battery had a lower operating cost than 
excluding batteries in their system. Excess PV power was 
used to charge the batteries, significantly optimizing the 
hybrid system instead of being considered a loss. It was 
noted that the initial set-up cost of the PV system was very 
high, as high as USD5600 per kW. However, their study [10] 
was conducted in 2010, and PV technology prices have 
significantly reduced since that time. 

A study by Rohit and Subhes [11] is referred to justify the 
inclusion of hydropower in a hybrid system. Their study  
was done in an Indian rural village where the load 
requirements were approximately 500 kWh per day. The 
optimal hybrid system configuration is analyzed to be a 
hybrid PV/hydro/biodiesel with an ESS (battery). The 
system architecture compromised a 20 kW PV array, 30 kW 
small hydropower station, 10 kW biodiesel generator, and 
40 batteries rated at 6.94 kWh each. Hydropower has been 
seen to supply 76% of the total energy consumed, while 14% 
was supplied by PV and the remaining 10% by biodiesel. 
Hydropower could supply a large portion of the energy 
required as it could be online 24 hours a day, while PV 
depended on the sun hours of the day. The difference 
between a system with one renewable source and multiple 
renewable sources has also been analyzed in Ref [11]. The 
overall flow for the planning and design process of an HRES 
was then determined. Firstly, an analysis of studies that 
implemented hybrid PV/diesel/battery systems was 
performed. This HRES combination was the most common 
due to its relative simplicity of the low number of 
components and only one renewable energy generation 
source.  

Chong et al. [12] have studied the techno-economic 
viability of a hybrid PV/diesel/battery system for a housing 
estate in Harbin, China. Load profiles were set up for the 4 
seasons with different loads at varying hours of the day, and 
a peak power draw of 500 kW was present during winter 
and summer. Different combinations of the three HRESs 
were simulated, and it was concluded that the full 
combination of a hybrid PV/diesel/battery system with a 
load-following dispatch strategy was the most cost-optimal 
option. The standalone diesel generator system was the 
most expensive due to the high operating cost and the large 
amount of fuel consumed by running the generators 24 
hours daily. Their study also considered different aspects 
such as sensitivity analysis and dispatch strategy and has 
detailed analysis for the calculations of input parameters for 
PV array and diesel generator. 

Halabi et al. [13] have analyzed the performance of 
hybrid PV/diesel/battery systems in Sabah, Malaysia. Two 
different locations have been chosen as the case studies, 
including an island and a rural area where existing hybrid 
PV/diesel/battery systems have been implemented. Their 
study compared the existing system to a standalone diesel 
system and a 100% PV/battery renewables-only system. 
The final proposed system by Halabi et al. [13] was a 
PV/diesel/battery system that had a low capital and 
replacement cost and lower renewables penetration. The 
initial investment for the new system was lower than the 
existing system in Ref [13]. A conclusion was made that a 
standalone diesel system had the least economic benefit and 
highest negative environmental impact while it was vice-
versa for a fully renewable PV/battery system. Including 
batteries in a RES is noted to be essential to store excess 
energy and reduce power loss. A system with 100% 
renewables penetration is economically impractical due to 
high PV and battery capital and replacement cost; however, 
these prices are expected to decrease over the years with the 
introduction of more environment-conscious policies 
around the world. 

The techno-economic feasibility of different 
combinations of hybrid PV/diesel/battery systems for 
replacing existing fossil fuel generators in the outlying 
islands of Taiwan was investigated by Tsai et al. [14]. It was 
reported that PV/diesel system with 15% renewables 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

30 

 

penetration resulted in excess electricity of up to 2.6% a 
year being wasted, which was a significant amount [14]. But 
by employing ESS, the excess electricity was reduced to 
0.5%, showing the importance of ESS such as batteries in the 
HRES. A system with 40% renewables penetration was the 
optimal choice considering the net present cost (NPC) and 
the cost of energy (COE). Higher renewables penetration 
would lead to higher NPC and COE, but those would result in 
reducing the fuel consumption and lower 𝐶𝑂2 emissions. 
Their study also considered the region’s interest and 
inflation rates. These factors were commonly overlooked 
when performing economic analysis. Another similar study 
has been done by Rehman and Al-Hadhrami [15] to replace 
existing fossil fuel generators with a PV/diesel/battery 
system for a rural area in Saudi Arabia. The same outcome 
was observed where an increasing renewables fraction 
leads to lower diesel fuel consumption but higher COE and 
NPC. Their study concluded that a renewable penetration of 
20% was the most optimum configuration. 

Ashraf et al. [16] have completed their analysis of 
PV/diesel/battery combinations using an analysis method 
known as the Elephant Herding Optimisation (EHO) 
algorithm. They compared the results with those obtained 
by another proprietary derivative-free algorithm from a 
microgrid simulation software. Their system had 
implemented objective functions such as low annualized 
cost, unmet load probability, and 𝐶𝑂2 emissions. EHO 
algorithm successfully optimised a system that has the 
lowest 𝐶𝑂2 emissions and lowest capital cost. However, it 
had some limitations such as poor battery system 
optimisation which led to a large portion of wasted energy. 
Thus, the proprietary algorithm was a simple and effective 
way of performing HRES analysis. 

Another study by Salameh et al. [17] has analyzed 
different tracking systems for PV arrays in a hybrid 
PV/diesel/battery system. Their systems contained several 
scenarios, including fixed structures, a continuous 
horizontal axis (elevation), a continuous vertical axis 
(azimuth), and dual-axis solar trackers. Their objectives  
were to obtain a system with the lowest COE and highest 
renewable fraction. Dual-axis solar tracker is noted to 
produce the best results with zero unmet load and a 
reasonable excess power percentage. The power output of 
the dual-axis solar tracker was 15.5% higher than that of the 
system with a fixed structure, showing the importance of the 
tracking systems when designing a PV system. Furthermore, 
by installing the dual-axis trackers, a reduction of 69.7% of 
𝐶𝑂2 emissions was recorded as more solar energy was 
harnessed. Odou et al. [18] analyzed the effectiveness of a 
hybrid PV/hydro/diesel/battery system to power a remote 
area in Africa. The area of interest compromised of 50 
households and several social and commercial 
infrastructures such as schools, clinics, and water pumping 
systems. Load profile was splinted into three categories, 
including household, community, and commercial load, each 
having its own hourly load demands. Their hybrid 
PV/diesel/battery system was reported to be more 
economically feasible compared to a grid extension project 
considered over the project’s lifetime. The hybrid system 
also had a shorter payback period and up to 97% less 𝐶𝑂2 
emissions compared to a standalone diesel system making it 
the best choice for rural electrification. Hydropower 
potential at their location was good; however, the hydro site 
was too far from the village, incurring additional costs from 
grid extension and making it less economically feasible. 
Thus, the effectiveness of certain renewable sources not 

only depends on their availability but also their distance to 
the required load.    

Hoseinzadeh et al. [19] have analyzed the 
implementation of PV arrays, wind turbines, and batteries to 
supply an existing run-of-river plant due to the region of 
interest being water-scarce during hot seasons. The average 
power consumption modeled in the region was 665 
kWh/day. During hot seasons, with just a standalone hydro 
plant, the power deficit could reach up to 125 kWh/day, 
which was 18.9% of the total required power. Power deficit 
would result in households not having electricity or facing 
frequent blackouts. Their study noted that for available 
hydropower of 61 kW, 20 kW of PV arrays and a 7.5 kW wind 
turbine should be added. Batteries were also employed to 
store the excess solar energy produced during the day and 
to be discharged during high loads or at night. Wind and 
hydro were both complementary to PV as those renewable 
sources were available at night. However, their study 
implemented a system with a renewable penetration of 
100%, which was not recommended as renewable power is 
very source dependent. 

The effectiveness of a hybrid PV/wind/battery system 
for an energy-poor rural village in India was studied by 
Krishan and Suhag [20]. In their system, a maximum power 
point tracker (MPPT) was implemented for the PV arrays 
and wind turbines. MPPT helped to maximize power 
extraction of sources with variable power such as PV and 
wind. Similarly, it was observed that the hybrid 
PV/wind/battery system performed the best compared to 
PV/battery and wind/battery showing the importance of 
having complementary renewable sources. However, the 
NPC of the system was quite high ($228,353), considering 
the system was only powering a community of 54 people 
consuming an average of 168.23 kWh/day with a peak of 
36.5 kW. Oladigbolu et al. [21] have proposed a hybrid 
PV/hydro/wind/diesel/battery system to power a remote 
village located in Nigeria. The remote village was grid-
connected, albeit only having 4 hours of electricity daily. The 
village had a total of 250 households and 20 healthcare 
centers. Load profile was divided into the two respective 
categories, including residential and healthcare, with 
varying hourly load demands. Their study made 
comparisons to a hybrid PV/wind/diesel/battery system 
and noted that the amount of disseminated 𝐶𝑂2 by this 
system was four times of a PV/hydro/wind/diesel/battery 
system [21]. The RES without hydro also had a higher 
operating cost, while most of the cost was spent on diesel 
fuel. However, the proposed system had generated 9.4% 
excess electricity, which should be minimized to allow for 
more efficient usage of the power system. The study noted 
that the proposed system had a high investment cost of 
$250,000, which was not economically suitable for 
implementation in a rural village. Thus, the system should 
be downsized to reduce costs. A large-scale analysis was 
performed by Baseer et al. [22] on an industrial city with an 
average load of 11,160 kWh/day while the peak load was 
685 kW. They compared PV/diesel/battery, 
PV/wind/diesel/battery, PV/wind/battery, and 
wind/diesel/battery systems to identify the system with the 
lowest COE and NPC. PV/wind/diesel/battery was the most 
suitable system, followed by the wind/diesel/battery 
system. The inclusion of diesel generators helped to reduce 
the COE and NPC of the system. The capital cost of a 100% 
renewable energy-based system (PV/wind/battery) was 
30% higher than non-renewable energy-based 
configurations in their study. Similar to several studies [14, 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

31 

 

15, 20], a more environmentally friendly system can be 
obtained at the expense of higher initial investment, NPC, 
and COE. Their finding was also supported by another study 
done by Aziz et al. [23], which analyzed 
PV/hydro/diesel/battery systems. They concluded that the 
full hybrid PV/hydro/diesel/battery system was the best 
performing system. PV/battery and PV/hydro/battery 
systems would have a lower emission production but higher 
NPC and COE. 

In a much smaller scale study, Haratian et al. [24] 
proposed a hybrid PV/wind/battery system to power a 
renewable energy laboratory in Iran. The average load 
requirement was noted to be around 4kWh/day. However, 
due to the region’s low average wind speed of 3-4 m/s, 
installing the wind turbines in the system served to increase 
the COE and NPC by 20% and 10 %, respectively. The 
region’s solar irradiation is around 5 𝑘𝑊ℎ/𝑚2/𝑑𝑎𝑦 which 
closely resembles the one in Sarawak. The most economical 
solution was modeled to be a PV/battery system with 1.2 kW 
PV arrays and 6 units of 3 kWh batteries. Even without the 
installation of wind turbines, there was no electricity 
shortage and the power generation could be done at a lower 
cost. This showed the importance of selecting the correct 
renewable sources according to region-specific 
characteristics such as wind speed and solar irradiation. 

Elkadeem et al. [25] have provided a framework to 
ensure the optimal planning and design of HRESs and 
implemented said procedures to determine the feasibility of 
a hybrid PV/wind/diesel/battery system in Sudan. The 
introduced framework was divided into five categories. 
Firstly, the motivations such as environmental policies and 
financial incentives behind choosing HRES as a power 
system were determined. Next, a preliminary study was 
conducted to find which combination of renewable sources 
was suitable for that case study. Different factors such as 
load demand, meteorological data, and existing systems 
were considered, and the most appropriate configuration of 
HRES was found. After that, microgrid simulation software 
was utilized to provide a techno-economic and optimization 
analysis of the proposed HRES. Different variables, including 
the sizing of each renewable power component to obtain an 
optimal solution in terms of NPC and COE, could be 
investigated at this stage. After having an optimized system, 
technical, economic, and environmental assessment was 
done to evaluate the benefits of the proposed system. Lastly, 
a sensitivity analysis was performed to investigate the 
sensitivity of the proposed system against uncertain 
parameters such as variation in fuel price and interest rate. 
Using this framework, the authors in Ref.  [25] successfully 
found a combination of HRES appropriate for the load 
scenario, providing excess energy of 9.65% and reducing 
harmful emissions by 95% compared to standalone diesel 
generator operation. Other authors such as Chauhan and 
Saini [26] also did the same study, including load and 
resource assessment, modeling of systems, problem 
formulation, demand management, and optimization in a 
case study in India. A summary of the related studies is 
tabulated in Table 1 (Appendix). 

The purpose of this study is to design the best 
configuration of PV/hydro/diesel/battery components for a 
small rural town located in the Marudi division of Sarawak 
called Long Moh. Several objectives have been set to be 
achieved in this study. Firstly, the average load profile of the 
rural community in Sarawak is determined by the 
investigation. The available conventional and non-
conventional sources of energy, such as solar irradiation, 

river flow, and diesel fuel cost, are obtained from national 
sources and meteorological references. Having that 
information, a suitable size for the proposed PV array, 
hydroelectric station, diesel generator, and battery bank is 
then determined. The best possible combination of these 
sources in terms of environmental effects, economic, and 
technical performance are analyzed. The aim is to find a 
combination of HRESs that provides an acceptable capital 
cost, replacement cost, and cost of operation and 
maintenance.  

2. Site description 

2.1 Study area 

The case study location is a rural longhouse settlement 

named ‘Long Moh’ in the Marudi district of the state of 

Sarawak, Malaysia. It is located at coordinates 3.0576° N, 

115.0766° E, and approximately 550 km east-northeast of the 

state’s capital city Kuching. The main source of income comes 

from agriculture, such as fruits, vegetables, and meat, where 

the goods are taken to bigger villages nearby to sell. The exact 

population data of Long Moh is unavailable as it is a rural area; 

however, a longhouse settlement can be estimated to have 70 

households [27]. According to a census conducted by the 

Department of Statistics Malaysia, the average household size 

in Sarawak is 4.4; thus, it can be estimated that Long Moh has 

a population of 308 people [28]. The location can be seen in 

the map of Sarawak given in Figure 1 [29].  

 

Figure 1. Long Moh area in Sarawak state 

Long Moh is currently not completely connected to the 

national grid; however, the government has plans in the 

future to do so. For now, the residents get power sparingly 

from portable diesel generators. The village is located next to 

the Baram River, which is the proposed river for a 1200 MW 

hydropower station. However, strong protests from locals 

were made because flooding of the dam would have resulted 

in the displacement of over 20000 locals. Thus, the project has 

since been halted indefinitely [30]. A micro-hydro station is 

one of the suitable renewable energy sources for Long Moh 

due to the strong hydropower potential of the Baram River. 

 

 

 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

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2.2 Solar data and temperature 

The average solar radiation data and clearness index for 
the year 2019 is obtained from NASA’s POWER Data Access 
Viewer [31]. Temperature data for Long Moh is illustrated in 
Figure 2. The recorded temperature had an annual high of 
23.06℃, a low of 21.9℃, and an average of 22.4℃. Solar 
radiation data is the average amount of solar radiation that is 
incident on a horizontal surface on Earth  and has a unit of 
𝑘𝑊ℎ/𝑚2/𝑑𝑎𝑦. The Clearness index is a dimensionless 
measure of the clearness of the atmosphere, i.e., the amount 
of solar radiation that passes through the atmosphere to 
reach the surface of the Earth. Solar radiation ranges from 
4.43 to 6.02 𝑘𝑊ℎ/𝑚2/𝑑𝑎𝑦 with a yearly average of 
5.12 𝑘𝑊ℎ/𝑚2/𝑑𝑎𝑦. The Clearness index ranges from 0.47 to 
0.58 with a yearly average of 0.51. Solar radiation data was 
also obtained from NASA as presented in Figure 3.  

 
Figure 2. Temperature data for Long Moh in the year 2019 

 
Figure 3. Solar radiation data for Long Moh in the year 2019 

2.3 Hydrological data 

A research team, in collaboration with a Sabah-based 

Non-governmental organization (NGO), LEAP (Land, 

Empowerment, Animals, People), provided an analysis of the 

streamflow for Long San, which is a village downstream of 

Long Moh [32]. The streamflow in Long Moh is assumed to be 

the same as in Long San as the two villages are located not too 

far from each other along the same river. Streamflow data is 

shown in Figure 4. The flow rate has a monthly average of as 

high as 98 L/s and as low as 31 L/s, with an annual average of 

65 L/s. 

2.4 Diesel Fuel Price 

In 2020, diesel fuel price was 1.74 Malaysian Ringgit 

(RM) per liter which equals $0.42. The fuel price in rural areas 

was the same as in the cities due to programs launched by the 

Ministry of  Domestic Trade and Consumer Affairs to bring 

down retail prices of essential goods, including diesel fuel, in 

the rural regions in Sarawak [33]. 

 

Figure 4. Baram River Stream Flow Data 

2.5 Load demand assessment 

As estimated earlier, the village of Long Moh contained a 

population of 308 with 70 households. The village also has 

other infrastructures such as a primary school, community 

church, and village store. Thus, the four main contributors to 

load demand will be households, schools, churches, and store 

loads. A breakdown of the individual load demand is listed in 

Table 2 (Appendix) and Figure 5. A normal weekday is 

assumed for the load profile, where they're in a power spike 

(21.48 kW) at 6 AM. At 7 AM, parents work, and children 

attend school until the afternoon, resulting in a low household 

load during that period. Household load then picks up in the 

evening when the family is home, and another spike (36.45 

kW) occurs at 8 PM. The household load then settles at 17.06 

kW. The community church only has gatherings on Sunday for 

3 hours, while the village store is open 12 hours a day. This 

load is expected to be accurate for the whole year as Malaysia 

does not have a winter season where the heating will cause 

the power draw to increase. A day-to-day variability and 

timestep variability of 2% is added to the load profile in the 

simulation software to make the load profile more realistic. 

The day-to-day variability shifts the load profile upwards or 

downwards randomly while maintaining its shape, while 

timestep variability changes the shape of the load profile 

while keeping the size constant. 

 

Figure 5. Load Demand Assessment for Long Moh 

 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

33 

 

3. Methodology 

The following framework was established to successfully 

evaluate the appropriate HRESs configuration. Firstly, a 

suitable location for HRES implementation was determined. 

The required data such as solar radiation, temperature, and 

river flow were obtained from meteorological sources. These 

data have been fed into microgrid simulation tools to analyze 

the generated power by PV arrays and hydropower turbines. 

Next, an hourly load profile was assessed based on the site’s 

population and estimated load demand. Available 

infrastructures at the site also must be taken into 

consideration when the load profile is generated.  

A flowchart presents the implemented optimization 

procedure in this study (Figure 6) [29].  

 

Figure 6. Flowchart showing optimization procedure 

As seen from the ‘Input data’ section, the technical 

specification and cost of each renewable generation 

component in the proposed HRES is input into a simulation 

software together with the load profile and meteorological 

data of the case study. Economic data referred to parameters 

such as project lifetime and expected inflation rate. 

Simulation and optimization are performed by running 

iterations through a search space where the minimum and 

maximum number and size of system components are 

specified. After obtaining an optimized system, a sensitivity 

analysis is performed to gauge how variations in input 

variables such as diesel fuel cost, stream flow rate, and 

interest rate affect the overall performance of the system. 

This step is important if there is uncertainty in any of the 

input variables. For example, diesel fuel price is not a constant 

variable and will fluctuate according to global demand. Thus, 

a sensitivity analysis of diesel fuel prices for a range of values 

helps the designer to analyze the effect of fuel price 

fluctuations on the economic performance of the system. 

4. Modeling of the hybrid RES 

4.1 System Components 

There are four main components in this hybrid system, 

namely, PV modules, micro-hydropower turbines, batteries, 

and diesel generators (DG). A bi-directional converter is also 

required to convert the DC power provided by PV 

modules/batteries to AC power for loads and AC power from 

diesel generators/hydropower to DC power for battery 

charging. A simple schematic of the overall system 

components connection is given in Figure 7. 

 

Figure 7. Schematic of the system 

4.2 Dispatch Strategy 

A control strategy which is called the dispatch strategy is 

applied to diesel generators and battery bank operation when 

enough renewable energy is not available to power the load. 

Three types of dispatch strategies, including Cycle Charging 

(CC), Load Following (LF), and Combined Dispatch (CD), are 

used. In CC strategy, whenever a diesel generator is required 

to be online to serve a load, it runs at full capacity, and the 

excess power generated would be used to charge the 

batteries. In the LF strategy, when a generator is required, it 

only runs to produce enough power to supply the required 

load. CD strategy intelligently moves between CC and LF 

strategies depending on the current netload. For low net 

loads, CC is used, while for high net loads, LF is used.  

4.3 PV modules 

LONGi Solar Hi-MO4m Monofacial Modules (LR4-

72HPH-440M) were selected for this study. The specifications 

of this PV module under standard testing conditions of 1000 

𝑊/𝑚2 irradiance and cell temperature of 25℃ are as follows: 

maximum power (0.44𝑘𝑊𝑝) is 440 W, open circuit voltage is 

48.9 V, short circuit current is 11.46 A, maximum power 

voltage is 41.1 V, maximum power current is 10.71 A, and 

module efficiency is 20.2%. This PV module comes with a 25-

year linear output power warranty; thus, 25 years is selected 

as the lifetime of the modules, and no replacement is needed 

throughout the project’s lifetime. The 440 𝑊𝑝 the module has 

a market price of $230-$250; thus, the cost of the PV system 

is estimated to be $530/k𝑊𝑝 [34]. A tracking system is also 

required to ensure the modules are always facing the sun. 

Tracking systems can be obtained for $100/k𝑊𝑝, while other 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

34 

 

soft costs including installation fee, shipping, import duties, 

and sales tax are estimated to account for 50% of the final cost 

[35], giving a total capital cost of $1260/k𝑊𝑝. Derating factor 

is set to 99.45%/year which is obtained from the 

manufacturer’s datasheet which states the module will 

degrade < 2% in the first year and 0.55% from years 2-25. 

Dust accumulations on the panels might contribute to the 

derating factor, however, the residents should perform 

periodic cleaning of the modules to prevent dust 

accumulation. Periodic cleaning contributes to the annual 

O&M costs which is set to $10/k𝑊𝑝/year. The power output 

of PV modules is calculated using Eq. 1 [36]: 

 

𝑃𝑃𝑉 = 𝑌𝑃𝑉𝑓𝑃𝑉 (
�̅�𝑇

�̅�𝑇,𝑆𝑇𝐶
) [1 + 𝛼𝑃(𝑇𝑐 − 𝑇𝑐,𝑆𝑇𝐶)] (1) 

where 𝑌𝑃𝑉 is the PV module’s output power under standard 
test conditions (STC) (kW), 𝑓𝑃𝑉  is the PV derating factor (%), 
�̅�𝑇 is the solar radiation incident on the PV array in the 
current time step (𝑘𝑊/𝑚2), �̅�𝑇,𝑆𝑇𝐶  is the incident radiation at 

STC (1 𝑘𝑊/𝑚2), 𝛼𝑃 is the temperature coefficient of power 
(%/℃), 𝑇𝑐 is the PV cell temperature in the current time step 
(℃) and 𝑇𝑐,𝑆𝑇𝐶  is the PV cell temperature under STC (25℃). 

PV cell temperature and ambient temperature are different. 
During the day, cell temperature can exceed the ambient 
temperature by 30℃ or more, while during the night, the cell 
temperature is the same as ambient temperature. Thus, the 
temperature can be calculated as follows [36]: 

𝑇𝑐 = 𝑇𝑎 + (𝑇𝑐,𝑁𝑂𝐶𝑇 − 𝑇𝑎,𝑁𝑂𝐶𝑇) (
𝐺𝑇

𝐺𝑇,𝑁𝑂𝐶𝑇
) (1 −

𝜂𝑚𝑝

𝜏𝛼
) (2) 

where 𝑇𝑎 is the ambient temperature (℃), 𝑇𝑐,𝑁𝑂𝐶𝑇 is the 

nominal operating cell temperature (℃), 𝐺𝑇 is the solar 
radiation incident on the PV module (𝑘𝑊/𝑚2),  𝐺𝑇,𝑁𝑂𝐶𝑇 is the 
solar radiation at which nominal operating cell temperature 
is defined (0.8 𝑘𝑊/𝑚2),  𝜂𝑚𝑝  is the PV module’s efficiency at 

its maximum power point (%), 𝜏 is the solar transmittance of 
any covering over the PV module, and 𝛼 is the solar 
absorption factor of the PV module (%).  

4.4 Hydropower 

The micro hydropower turbine under consideration has 

the following specifications [32]:  

The design flow rate is 60 L/s, minimum flow ratio is 25%, 

maximum flow ratio is 150%, pipe head loss is set at zero 

percent, and turbine efficiency is 60%. In addition, the 

available net head is assumed to be 25 m, which is the net 

head available at a dam in Long San, another village 

downstream of Long Moh. The cost of micro-hydropower is 

$1300/kW, which is obtained from previous micro-

hydropower projects conducted by NGOs such as CREATE 

Borneo. The lifetime is 30 years, while replacement costs are 

$1300/kW. O&M costs are negligible and assumed to be zero. 

Power generated from the micro-hydro turbine can be 

calculated using [36]: 

𝑃ℎ𝑦𝑑 =
𝜂ℎ𝑦𝑑⋅𝜌𝑤𝑎𝑡𝑒𝑟⋅𝑔⋅ℎ𝑛𝑒𝑡⋅�̇�𝑡𝑢𝑟𝑏𝑖𝑛𝑒

1000𝑊/𝑘𝑊
 (3) 

where 𝜂ℎ𝑦𝑑  is the hydro turbine efficiency (%), 𝜌𝑤𝑎𝑡𝑒𝑟  is the 

density of water (1000 𝑘𝑔/𝑚3), g is the gravitational 
acceleration (9.81 𝑚/𝑠2), ℎ𝑛𝑒𝑡  is the effective head (m) and 

�̇�𝑡𝑢𝑟𝑏𝑖𝑛𝑒  is the water flow rate through the hydro turbine. For 
the hydro turbine flow rate, it must be more than the 
minimum flow rate specified; otherwise, it will be zero. 

Likewise, the flow rate cannot exceed the specified maximum 
flow rate, and if it does, it will be capped at that value. Nominal 
hydropower represents the size of the hydro system given 
streamflow equal to the design flow rate specified. The 
nominal hydropower for this turbine is 8.83 kW.  

4.5 Diesel Generator 

Diesel generators of a few sizes are considered, namely 

12 kW, 33 kW, and 50 kW. The selected diesel generators are 

from the 50 Hz, 230V Premium Generator series. Diesel fuel 

consumption of the generators is 2.4 L/h for the 12-kW 

model, 6.18 L/h for the 33-kW model, and 12.7 L/h for the 50-

kW model. The three generators cost $6000, $8700, and 

$12500, respectively, including transportation costs. It has an 

estimated lifetime of 15000 hours and a replacement cost of 

50% of the capital costs. Replacement cost is lower than 

capital cost as only parts of the diesel generator need 

overhauling, and the generator is not swapped with a new 

unit. The O&M cost is set at 3% of the capital cost, and the 

minimum load ratio is set at 30%. Other important 

parameters of diesel fuel for emissions calculation are carbon 

monoxide (6.5 g/L of fuel,) proportion of fuel sulfur converted 

to PM 2.2%, unburned hydrocarbons (0.72 g/L of fuel), 

particulate matter (0.49 g/L of fuel), and nitrogen oxides (58 

g/L of fuel) [12].  

4.6 Batteries 

A lead-acid battery of Surrette S480 Brand (6 V, 375Ah 

(20-hour)) is chosen for this study. The minimum state of 

charge is set at 40% and the efficiency at 80%. Four of these 

batteries are connected in series to form a DC bus voltage of 

24 V. Each battery unit has a capital and replacement cost of 

$390, while the O&M cost of $50/year is assumed [12]. 

Battery quantities of 0-20 batteries with 4 battery intervals 

are considered.  

4.7 Converter 

A bidirectional converter is needed to convert AC to DC 

and vice versa. The converter is assumed to have the 

specifications as follows [14]:  

Rectifier and inverter efficiency of 95%, capital and 
replacement cost of $900/kW, O&M cost of $1/kW/year, and 
a lifetime of 10 years. Converter sizes of 0-10kW with 1kW 
intervals are considered. A summary of the component’s 
techno-economic parameters is given in Table 3 (Appendix). 

4.8 Economic Modelling 

For each plan in the search space, the aim is to minimize 

NPC required while being susceptible to constraints such as 

whether the system can satisfy the load demand. NPC is 

defined as the cost incurred by a system over its project 

lifetime, minus its revenue. Cost includes capital cost, fuel 

cost, operation and maintenance (O&M) cost, and 

replacement cost, while revenue is the salvage value which is 

simply the value remaining in a component at the end of the 

project lifetime. The salvage value is calculated as follows 

[36]: 

𝑆 = 𝐶𝑟𝑒𝑝 ⋅
𝑅𝑐𝑜𝑚𝑝−[𝑅𝑝𝑟𝑜𝑗−(𝑅𝑐𝑜𝑚𝑝⋅𝐼𝑁𝑇(

𝑅𝑝𝑟𝑜𝑗

𝑅𝑐𝑜𝑚𝑝
))]

𝑅𝑐𝑜𝑚𝑝
  (4) 

where 𝐶𝑟𝑒𝑝 is the replacement cost ($), 𝑅𝑐𝑜𝑚𝑝 is the 

component lifetime (years), 𝑅𝑝𝑟𝑜𝑗  is the project lifetime 

(years), and INT() is a function that rounds down a real 
number to the nearest integer. Real interest rate is used in the 



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35 

 

calculation between one-time costs and annualized costs and 
is given by the formula [36]: 

𝑖 =
𝑖′−𝑓

1+𝑓
  (5) 

where 𝑖′ is the nominal interest rate (%) (the rate at which 
borrowing occurs), and f is the expected inflation rate over 
the project lifetime (%). By implementing a real interest rate, 
inflation can effectively be factored out from the economic 
analysis. Another important parameter is the COE, which is 
defined as the average cost per kWh of useful electrical 
energy produced by the system. It is calculated by dividing the 
annualized cost of electricity production by the total electric 
load supplied. In this study, a nominal interest rate of 6% and 
an inflation rate of 2% is set [37].  

5. Results and discussion 

5.1 Overview 

In this section, the optimized system, together with its 

techno-economic, environmental, and sensitivity analyses, 

are presented below. An overview of the most optimized 

systems in their respective categories is tabulated in Table 4 

(Appendix). The systems are sorted according to their NPC in 

ascending order. Out of 3,492 computer simulations, only 

1,836 were feasible, and the best five systems of each 

configuration are analyzed. The configurations are 

Hydro/DG/Battery, PV/Hydro/DG/Battery, Hydro/DG, 

PV/Hydro/DG, and standalone DG. 

The most optimal system is the Hydro/DG/Battery 

system with a 33 kW DG, 4 batteries, 2 kW Converter, and LF 

dispatch strategy. It has an NPC of $213,694.90 and a COE of 

$0.08/kWh. It has the second-lowest operating cost (OC) of 

$9,495.56/year and the third lowest initial investment 

required (IC) of $23,537.70. This system has a renewable 

fraction (RF) of 45.72% with a diesel fuel consumption (FC) 

of 14,642.17L/year. Excess electricity of 11.67% is produced 

with a 0% unmet load. Compared to the third-best performing 

system, which is a Hydro/DG system, adding the battery helps 

to bring down all the recorded parameters significantly at the 

expense of a slightly higher IC. This shows the importance of 

an ESS in a renewable energy system.  

 

 

 

 

 

By choosing the Hydro/DG/Battery system instead of the 

Hydro/DG system, a simple payback of 4.1 years can be 

achieved with a return on investment (ROI) of 25.1%, which 

is the yearly cost savings relative to the initial investment.  

The worst performing system is the standalone DG 
system with a 50 kW DG implementing a CC dispatch strategy. 
It has an NPC of $346,222.60, COE of $0.12/kWh, and OC of 
$16,664.54, which are 62%, 62.1%, and 75.5% larger than the 
Hydro/DG/Battery system. However, it has the lowest IC 
required at $12,500, which is the cost of the 50-kW diesel 
generator. FC is also the highest at 30,743.16 L of fuel 
consumed a year, contributing to 75% of the OC annually. 
Figure 8 shows a comparison between the standalone DG 
system and the best performing Hydro/DG/Battery system. 
For the first year, the standalone DG system will have a lower 
cost overall; however, after 1.4 years, the cumulative cost for 
the Hydro/DG/Battery system becomes lower than that of the 
standalone DG system. An ROI of 62.1% is also reported. 

The initially proposed system of PV/Hydro/DG/Battery 
has the lowest OC ($9,149.15/year), which is $346.41 less 
than the Hydro/DG/Battery system. However, it has a 
$10,822.90 higher NPC ($224,517.80) and a $13,510 higher 
IC ($37,037.70). The OC is not low enough to justify switching 
to the proposed system, as shown in Figure 9. Throughout the 
project lifetime simulation of 40 years, simple payback is not 
possible, and consequently, an ROI of 0% is recorded. Thus, 
economically, the proposed system is not the most optimal 
configuration in this scenario. However, this system has the 
highest RF of 52.8% and thus the lowest diesel FC of 
12,863.63 L/year, making it the most environmentally 
friendly system.  

5.2 Hybrid Hydro/DG/Battery system analysis 

The best performing system has the following 

specifications:  

8.83 kW of hydropower,  

33 kW of DG,  

4 units of batteries,  

A 2-kW converter and using LF dispatch strategy. A 
breakdown of the costs involved in this system is given in 
Figure 10.  
 
 
 

 
 
 
 

Figure 8. Hydro/DG/Batt vs DG 

 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

36 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 

 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 

Figure 9. Hydro/DG/Batt vs PV/Hydro/DG/Batt 

 

Figure 10. Cost Summary for Hydro/DG/Battery system (Cost type: Net Present) 

 

Figure 11. Electrical Analysis for Hydro/DG/Battery system 

 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

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Diesel power has the highest contribution to the NPC of the 
system at 87.4% of the total NPC, and 65.9% of diesel power’s 
total NPC is used by diesel fuel costs. Hydropower has the 
highest capital cost, but it has minimal O&M replacement cost 
due to the turbine having a lifetime of 30 years. Battery and 
converter costs are minimal due to their low quantities and 
sizes. From Figure 11, the fraction of energy generated by 
hydro is proportional to the streamflow, which is the highest 
in December and the lowest in October. Consequently, the 
energy generated by DG is inversely proportional to 
streamflow as DG compensates for the remaining load that 
hydropower is unable to supply. Overall, this system 
produced the lowest excess electricity of 11.7%, with a 0% 
unmet load. Some excess electricity is required as an 
operating reserve, which is set as 10% load in the current 
time step. Power draw is not constant and will fluctuate 
unpredictably. Without an operating reserve, the power draw 
will exceed the capacity of the power system leading to an 
increase in unmet electric load. Capacity shortage differs from 
the unmet load as capacity shortage considers both unmet 
load and unmet operating reserve. Figure 12 shows the power 
sources plot, which plots the total electrical load, DG power 
output, hydropower output, and battery input power daily for 
the whole year. DG is the main source of energy for this 
system which explains its 87.4% contribution to the NPC. In 
the months of July and October, when streamflow is the 
lowest, the DG operates continuously and does not switch off. 
Input power to the battery is approximately zero during these 
two months as the DG is operating at full capacity to supply 
the electrical load. 

5.3 Environmental Analysis 

DGs generate power from the combustion of diesel fuel, 
which releases pollutants such as carbon dioxide (CO2), 
carbon monoxide (CO), unburned hydrocarbons (UHC), 
particulate matter (PM), and sulfur dioxide (SO2) and 
nitrogen oxides (NOx). Pollutant emissions for the top 5 
performing systems are presented in Table 5 (Appendix). 
Emissions are proportional to the amount of diesel fuel 
consumed by the system annually in g/L.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The system with the least amount of pollutants is the 
proposed PV/Hydro/DG/Battery system having the least 
emissions of CO2 (33,874 kg/year), CO (83.6 kg/year), UHC 
(9.26 kg/year), PM (6.3 kg/year), SO2 (82.5 kg/year), and NOx 
(746 kg/year) due to its high renewables penetration. In 
comparison to the worst environmentally friendly system 
(DG), emissions savings of 58.2% are recorded for all 
pollutants. The optimal Hydro/DG/Battery system has an 
emission saving of 52.4% compared to the standalone DG 
system. Emissions can be reduced further by switching to a 
more efficient DG that uses a higher grade of diesel fuel, such 
as Euro5 diesel.  

5.4 Sensitivity Analysis 

In this study, sensitivity variables selected are diesel fuel 

prices, annual average streamflow, annual average solar 

radiation, and interest rate. These variables have the most 

uncertainty and are expected to change throughout the 

projects’ lifetime. The variables are modified to ±50% of their 

original values with 25% of increments, as shown in Table 6 

(Appendix). The result of the sensitivity analysis of the 

optimal system (DG/Hydro/Batt) is presented in Figure 13. 

Even with considering a 50% increase in diesel fuel price from 

$0.42 to $0.63, Hydro/DG/Battery is still the most optimal 

choice in terms of NPC. Annual average solar radiation is 

increased to give PV modules a higher energy generation 

potential, but the constant power flow from hydro is still the 

winner in this analysis. NPC increases with diesel fuel price 

changes as expected which varies from a minimum of 

$152,118.10 to a maximum of $274,592.20 with ±50% 

fluctuations in the diesel fuel price. The system configuration 

remains the same as before when the diesel fuel price reaches 

$0.525/L and above, and the power converter is increased 

from 2 kW to 3 kW. Next, the sensitivity variables are diesel 

fuel price and annual average streamflow. The results 

obtained are illustrated in Figure 14.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 12. Power Sources plot for Hydro/DG/Battery system 

 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

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Figure 14. Sensitivity analysis of diesel fuel price vs annual average streamflow with superimposed total NPC 

 

Figure 15. Graph of NPC and COE against interest rate 

Figure 13. Sensitivity analysis of diesel fuel price vs annual average solar radiation with superimposed total NPC 

 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

39 

 

The system with the lowest NPC is a DG/Hydro system 

with 33 kW DG, 8.83 kW hydro, diesel fuel price of $0.21, and 

annual average streamflow of 97.9 L/s. The most optimal 

system varies between DG/Hydro, DG/Hydro/Battery, and 

DG/PV/Hydro/Battery.  

The proposed DG/PV/Hydro/Battery system with 10 kW 

PV, 33 kW DG, 8.83 kW hydro, 8-12 batteries, and a 7-8 kW 

converter is the best choice when the scaled average of 

hydropower drops below 48.9 L/s. A minimum NPC of 

$131,882.20 is recorded with the highest streamflow and 

lowest diesel fuel price, while a maximum NPC of $363,726.70 

is recorded with the highest diesel fuel price and lowest 

streamflow. The relation between the nominal discount rate, 

that is, the interest rate, the total NPC, and COE, are plotted in 

Figure 15. The nominal discount rates are set from 3% to 9% 

to realize the effect of changes in nominal discount rates on 

COE and NPC. Total NPC decreases with an increase in the 

nominal discount rate. This is because total NPC is the present 

value of all cash flows in the system. With an increase in 

interest rate, the present value of future cash flow decreases. 

On the other hand, COE increases slightly with an increase in 

interest rate. An increase in interest rate decreases the 

salvage value of components which results in a higher COE. 

The intersection between NPC and COE occurs at ~5.75% 

representing the point at which the system is most 

economical.  

6. Conclusion 

This study presented an environmental and techno-

economic analysis of an off-grid HRES for the electrification of 

a rural village in Marudi, Sarawak, Malaysia. The initially 

proposed system was a hybrid PV/Hydro/DG/Battery 

system; however, the most economical system based on the 

computer simulation is a hybrid Hydro/DG/Battery system. 

The final Hydro/DG/Battery system consists of an 8.83 kW 

hydro generator, 33 kW DG, 1 string of 4 units of 6V batteries, 

2 kW converter with an LF dispatch strategy. This system has 

an NPC of $168,831.30, COE of $0.08/kWh, OC of 

$9223.59/year, IC of $23,537.70, and RF of 45.72%. Excess 

electricity of 11.7% is recorded, which is acceptable to meet 

the 10% operating reserve. Environmentally, this system 

produces 52.4% fewer emissions compared to the standalone 

DG system, which greatly helps in maintaining the green 

environment of the rural village. The emissions will affect the 

residents’ health and produce unwanted environmental 

impacts such as smog. Economically, the hybrid 

Hydro/DG/Battery system has a simple payback of 1.4 years 

and an ROI of 62% when compared to the standalone DG 

system. However, the proposed PV/Hydro/DG/Battery 

system is the most environmentally friendly system with the 

lowest diesel fuel consumption of 12,863.63 L/year and the 

lowest pollutant emission production. It also has the lowest 

OC at $9,149.15, but the NPC is significantly higher than the 

optimal system (Hydro/DG/Battery) at $224,517.80, such 

that a simple payback is not possible when comparing the two 

systems. Sensitivity analysis was performed to see whether 

the optimal configuration changes with ±50% changes in 

diesel fuel price, annual average streamflow, and annual 

average solar radiation. The Hydro/DG/Battery system is the 

most optimal choice in the sensitivity analysis of diesel fuel 

price and average solar radiation. For the analysis of diesel 

fuel price and average streamflow, DG/Hydro/Battery system 

is only optimal when the average streamflow is more than 

58.73 L/s. Average streamflow lower than 58.73 L/s would 

result in PV/DG/Hydro/Battery is the most optimal choice. 

NPC and COE values increase with an increase in diesel fuel 

price and a decrease in annual average streamflow. The 

interest rate is also varied to determine its economic impact 

on the system. It was observed that an increase in interest 

rate leads to an increase in NPC and a decrease in COE. 

According to the results above, a hybrid Hydro/DG/Battery 

system is the most optimal option with justifiable trade-offs 

for a slightly higher environmental impact. The initial 

investment required of $23,537.70 was reasonable 

considering that the village has a population of 308. Rural 

electrification is a priority; given the impact it brings on the 

socio-economic development of the country. 

Ethical issue 
The authors are aware of and comply with best practices 

in publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere in any language. 

Data availability statement 
Data sharing is not applicable to this article as no datasets 

were generated or analyzed during the current study. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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 This article is an open-access article 

distributed under the terms and conditions of the Creative 

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(https://creativecommons.org/licenses/by/4.0/). 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

41 

 

Appendix 

Table 1. Summary of the techno-economic studies conducted for HRESs 

Main HRES Location Daily 
Electrical 
Load 
(kWh/day) 

System Components NPC ($) Ref. 

PV/DG/Battery Harbin, China 2300 500kW PV, 1250kW DG, 
600 Surette S460 batteries 

8,162,822 [12] 

PV/DG/Battery Pulau Banggi, 
Sabah, Malaysia 

6632.86 800kW PV, 630kW DG, 
2880 kWh batteries 

9,345,510 [13] 

PV/DG/Battery Benin, Africa 679.77 150kW PV, 50kW DG, 98 
H3250 batteries 

555,492 [18] 

PV/Hydro/Wind/DG/Battery Imo, Nigeria 3853 50kW PV, 94.1kW Hydro, 
1kW Wind, 150kW DG, 
111kWh batteries 

1,007,995 [21] 

PV/Wind /DG/Battery Compound 1, 
Al-Jubail, Saudi 
Arabia 

11,160 580kW PV, 550kW Wind, 
1800kW DG, 880 Surette 
4KS25P batteries 

9,620,000 [22] 

PV/Hydro/DG/Battery Sakran, Iraq 23.29 13kW PV, 14.7kW Hydro, 
5kW DG, 8 Surette 6CS25P 
batteries 

113.201 [23] 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

42 

 

Table 2. Load Demand Assessment for Long Moh 

 

 

 

  

 

 

 

 

 

 

 

 

 

 

Load 
Categories Components 

Power 
Draw 
(W) Units 

Hours 
Active 
(hours/day) 

Daily power 
consumption 
(kWh/day) Time 

Household Ceiling Fan 75 1 6 0.45 4pm-10pm 
2 bed 1 bath Standing Fan 50 2 9 0.9 10pm-7am 

  Fluorescent Lamp 32 4 5.75 0.736 

3 units, 6am-7am, 
7pm-10pm, 1 unit 
7pm-6am 

  
LED TV + Set-Top 
Box 110 1 4 0.44 5pm-9pm 

  Washing Machine 320 1 0.3 0.096 1 hour every 3 days 

  
Water Pump 
(0.5hp) 370 1 2 0.74 2 hours a day 

  Fridge 80 1 24 1.92 24 hours 
Household 
Total      5.282   
70 
Households 
Total      369.74   

Primary 
School Ceiling Fan 75 6 8 3.6 7am-3pm 
100 students, 
4 classrooms Fluorescent Lamp 32 12 8.5 3.264 

10 units 7am-3pm, 2 
units 7pm-6am 

office, 
canteen 

Water Pump 
(1hp) 750 1 4 3 2 hours a day 

School Total      9.864   

Community 
Church Ceiling Fan 75 4 0.43 0.129 3 hours a week 

  Fluorescent Lamp 32 8 0.43 0.11008 3 hours a week 

Church Total      0.23908   

Village Store Standing Fan 50 2 12 1.2 8am-8pm 

  Fluorescent Lamp 32 5 5 0.8 
8am-10am, 5pm-
8pm 

  
Water Pump 
(0.5hp) 370 1 1 0.37 1 hour a day 

Store Total      2.37   

Village Total      382.21308   



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

43 

 

Table 3. Summary of proposed components' specifications 

 

 

 

 

 

 

 

 

 

 

 

 

Component Parameter Specification Component Parameter Specification 

PV Power Sizing 

0-40kW, 
10kW 
intervals 

Diesel 
Generator Power Sizing 12,33,50kW 

  Derating Factor 99.45%   Minimum Load Ratio 30% 

  
Temperature 
Coefficient -0.35%/℃   Lifetime 15000 hours 

  
Nominal Operating 
Cell Temperature 45℃   Capital Cost $6000,8700,12500 

  Efficiency  20.20%   Replacement Cost 50% of capital cost 

  Lifetime 25 years   O&M Cost 3% of capital cost 

  Capital Cost $1260/kW    

  Replacement Cost $1260/kW    

  O&M cost $10/kW/year    

Hydropower Design Flow Rate 60L/s Batteries Nominal Voltage 6V 

  Minimum Flow Ratio 25%   Capacity 375Ah/20hour 

  Maximum Flow Ratio 150%   Lifetime 12 years 

  Pipe Head Loss 0%   Capital Cost $390 

  Turbine Efficiency 60%   Replacement Cost $390 

  Net Head 25m   O&M Cost $50/year 

  Lifetime 30 years   Batteries per string 4 

  Capital Cost $1300/kW   String Size 
0-5, 1 string 
intervals 

  Replacement Cost $1300/kW    

  O&M cost $0/kW/year    

Converter Power Sizing 
0-10kW, 1kW 
intervals Diesel Fuel Cost $0.42 

  Efficiency 95%   Carbon Monoxide 6.5g/L 

  Lifetime 10 years   
Unburned 
Hydrocarbons 0.72g/L 

  Capital Cost $900/kW   Particulate Matter 0.49g/L 

  Replacement Cost $900/kW   

Proportion of Fuel 
Sulphur converted to 
PM 2.20% 

  O&M cost $1/kW/year   Nitrogen Oxides 58g/L 

      



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

44 

 

Table 4. Results from computer analysis 

S
y

st
e

m
 

C
o

n
fi

g
u

ra
ti

o
n

 

P
V

 (
k

W
) 

H
y

d
ro

 (
k

W
) 

D
G

 (
k

W
) 

B
a

tt
 (

U
n

it
) 

C
o

n
v

 (
k

W
) 

N
P

C
 (

$
) 

C
O

E
 (

$
/k

W
h

) 

O
C

 (
$

/
y

r)
 

IC
 (

$
) 

R
F

 (
%

) 

F
C

 (
L

/
y

r)
 

E
x

ce
ss

 (
%

) 

U
n

m
e

t 
(%

) 

H
y

d
ro

/
D

G
/B

a
tt

 
 

- 8.83 33.00 4.00 2.00 213,694.90 0.08 9,495.56 23,537.70 45.72 14,642.17 11.67 0 

P
V

/
H

y
d

ro
/

D
G

/
B

a
tt

 
 

10.00 8.83 33.00 4.00 3.00 224,517.80 0.08 9,149.15 37,037.70 52.79 12,863.63 16.24 0 

H
y

d
ro

/
D

G
 

 

- 8.83 33.00 - - 229,164.90 0.08 10,435.84 20,177.70 37.64 16,622.58 17.64 0.002 

P
V

/
H

y
d

ro
/

D
G

 
 

10.00 8.83 33.00 - 2.00 239,287.90 0.09 10,222.27 34,577.70 41.42 15,652.74 23.70 0.002 

D
G

 

- - 50.00 - - 346,222.60 0.12 16,664.54 12,500.00 - 30,743.16 12.84 0 

 

 

 

 

 

 

 

 

 

 

 

 



B.W.Z.Cheng et al. /Future Energy                                                                                              August 2022| Volume 01 | Issue 02 | Pages 28-45 

45 

 

 

Table 5. Pollutant emissions from top 5 best performing systems 

System Configuration CO2 (kg/yr) CO (kg/yr) UHC (kg/yr) PM (kg/yr) SO2 (kg/yr) NOx (kg/yr) 

Hydro/DG/Batt     38,558.00            95.20              10.50               7.17              93.90            849.00  

PV/Hydro/DG/Batt     33,874.00            83.60              9.260               6.30             82.50            746.00  

Hydro/DG     43,773.00         108.00              12.00               8.15           107.00            964.00  

PV/Hydro/DG     41,219.00         102.00              11.30               7.67           100.00            908.00  

DG     80,957.00         200.00              22.10            15.10           197.00         1,783.00  

 

 

 

 

 

Table 6. Sensitivity variables and their respective values 

Sensitivity Variables Values 

Diesel fuel price ($) 0.21, 0.315, 0.42, 0.525, 0.63 

Annual average stream flow (L/s) 32.63, 48.94, 65.25, 81.56, 97.88 

Annual average solar radiation (kWh/m2/day) 2.562, 3.843, 5.124, 6.405, 7.686 

Interest rate (%) 3, 4.5, 6, 7.5, 9