1 201200047 SEPHELANE N 30



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Off-grid hybrid renewable energy systems have proven to be the most efficient way to supply electricity to
remote areas where access to grid connection might be costly and impractical. These systems take
advantage of the available renewable energy resources that are in abundance in these locations such as
Solar, wind and water. These systems are also very environmentally friendly, and assist in improving the
lives of the people in these isolated areas. In this paper, the community of Linakeng in Thaba-Tseka,
Lesotho, with a primary domestic load of 204 kWh/ day and a peak of 83kW, will be the target for design
and simulation of a hybrid wind-solar-hydro system that has a generator and battery back-up. The system
design was based on the area’s annual average solar radiation of 5.35 kWh/m2/d, wind speed averaging
about 2.63 m/s annually and flow rates averaging about 707 L/s annually. Simulation and design of the
system was done using HOMER and the resulting optimal systems were those that had an LCOE of 0.243
$/kWh and 0.498 $/kWh and an NPC of $185,752 and $381,317 for the lowest and highest architectural
system respectively. These systems were able to meet their load demands, with a significant and efficient
use of the renewable energy systems and a low emission. Sensitivity variables were taken into consideration
to meet economic and resource fluctuations and these included the primary load, the solar radiation, wind
speed and Flow rate.


Lesotho is not an exception in the global Environment concerns that have been increasing since the 1990s.
The evident potential threats brought about by pollution, the greenhouse effect and others, prompt an urgent
solution in the energy sector, to switch to alternative energy resources in order to reduce the excessive use
of fossil fuels such as coal, natural gas and oil, which effectively reduces the rate of Global warming 1.
Lesotho’s energy heavily depends on supply from the ‘Muela Hydropower station as well as imported
electricity from South Africa and Mozambique. This landlocked country also relies on biomass (wood and
Dung) and Petroleum as other sources of energy. Majority of the country’s population resides in rural
mountainous areas (about 70% population). The country’s economic growth is viewed as steady, however,
it has declined since 2011 and as a result, unemployment and poverty levels have become high.
Imported electricity constitutes about 87% of the overall cost of electricity in the country (2017-2018 LEC
tariff determination). This can be a challenge, especially in a poverty ridden country due to the high cost of
electricity purchase, as tariffs for this imported energy ranges from about 0.77 to 1.50 per kWh as compared
to the 0.13 per kWh cost from the ‘Muela hydropower station. Moreover, the current import of energy is
still not sufficient to meet the overall load demand of the country (only about 60% of the urban region has
electricity access and in the rural areas, it is only about 18%) and therefore there is an increased reliance on
biomass and Petroleum. This leads to a rapid decline in biomass stock (the country loses, on average, about
0.5% of forest cover every year).


In addition, most people live far from the National grid connection and thus, those who reside in rural
mountainous areas do not have access to electricity, however, these areas have proven to possess sufficient
supplies of renewable energy resources such as wind, solar and water. In order to get electricity, these areas
could either extend the grid transmission lines or use stand-alone systems. The latter seems rather
inconvenient from the financial point of view because the cost of extending the national grid transmission
escalates with distance.
Renewable energy sources use environmentally friendly technologies to produce energy, they are
inexhaustible at all, and are non-polluting 2. The application of renewable energies in off-grid systems,
however, might prove to be challenging because of their dependence on the nature of the source as well as
the geographical and weather conditions of an area. In spite of this, the electrification of remote villages
would be a major step in the improvement of lives of the people in the community as well as in improving
the community’s economy. Access to electricity would be beneficial for improving sectors of agriculture,
health, food preservation as well as creating job opportunities for the community.
The main objective of this mini-project is to design an off-grid hybrid renewable energy system that can
generate and provide cost-effective electricity to rural areas, specifically and as assigned, to the Linakeng
community in the Thaba-tseka district, Lesotho. This is to be accomplished by first studying raw measured
data for wind, solar and river flow rates (of Linakeng River) of the village and estimating the load demand
for the area required for the domestic use of the electricity. This raw data will assist in simulating a hybrid
renewable energy system that consists of Hydropower, Wind power and solar power that also incorporates
a diesel generator used as a back-up, Batteries for storage and a converter. Simulation and Optimization of
this system will be done using Hybrid Optimization Model for Electric Renewables (HOMER).

There is a substantial amount of research that has already been done for off-grid hybrid renewable systems,
all in which simulation and optimization of these systems was done using HOMER.
For example, for this thesis work, Bahta 2 Simulated and optimized a wind turbine-photovoltaic-diesel
generator-battery bank-converter for the rural community of Haressaw among the sub-districts of Atsbi
district in the regional state of Tigray, Ethiopia. During optimization of this power system, a Primary load
demand of 1505kWh/day, a peak load of 284kW, a deferrable energy of about 17kWh/day and a deferrable
peak load of 3.6kWh was involved. HOMER modeling tool was used to design the off-grid system with
Wind and Solar energy being the primary sources supply electricity directly to the load and to charge the
battery bank when excess generation is happening as well as engaging the use of the generator during peak
demand times. The study suggested that the community’s energy requirements was for lighting, water
pumping, school and health clinic equipment load, television, radio, flour milling machines and local food
(enjera) baking. During power system set-up, simulation and optimization of the study was done based on
the electricity load, climatic data sources, economics of the power components and other parameters in
which the NPC has to be minimized to select an economic feasible power system as well as taking into
account capacity shortage, renewable fraction, excess electricity, COE, diesel fuel consumption to check
the technical capability so as to select a system that is sound in techno-economic aspects.


Research for rural dwellers in Nigeria was also conducted by Chiemezuo et al 3. The Unwana community
in Ebonyi state was selected for the study. The area was found to have an estimated 100 households, 8
shops, a community hall, one school, a community health center, a community water project, street lights
and miscellaneous load. With this, the average daily demand for the area was estimated to be 240.1 kWh/day
with a peak demand of 41.6 kW. HOMER pro software was used to design, simulate and optimize the best
hybrid system to satisfy the demand. This study found that the system with the lowest Net present cost
(NPC) was that which consisted of one 20kW biogas generator, one 10kW biogas generator, 4.6kW PV
panels, 31 strings of 12V, 1kWh lead acid battery and 8.65kW converter. It was also observed that 6.3% of
the total power is derived from solar and the remaining 93.7% from biomass.
A case study was conducted on an island residence by Oulis et al 4 and a hybrid AC/DC micro-grid was
designed using HOMER pro. The design was done using Photovoltaic arrays, a diesel generator, AC load
and battery energy storage for provision of energy even during long periods of low sunshine. HOMER pro
tool was used to identify a design that was the most cost-effective. A sensitivity analysis was also carried
out to evaluate the system’s sturdiness against Fluctuations in fuel costs and PV generation.
Bashiru Olalekan carried out an optimization analysis of a stand-alone hybrid system of the Senate Building
at the University of Ilorin, Nigeria 5. The energy demand for the area was averaged daily at 1520kWh
during a typical dry season, 712.5kWh during a rainy season and 212.8kWh during the weekdays. The aim
of the study was to meet the energy demand of the area at the lowest possible cost of energy and using
HOMER, it was observed that a PV-diesel-Battery system gives the most ideal results for the case study.
Sensitivity analysis was also carried out to determine to determine the condition under it would be most
feasible to include wind power in the system design, technically and financially. The results obtained in the
study showed an energy system that would cost 0.283kWh. This cost, compared with 0.087kWh currently
charged by electric facilities in Nigeria, would not compete.


To begin, the Load demand of the community was determined by estimating the use of electricity for an
individual household through assuming the type and number of appliances and electrical equipment a
family might typically possess. This was done using Excel through a matrix system were the load profile
has 2 Matrices, the 1st Matrix (nA x 1, where nA is the number of appliances) represents Appliance power
and the 2nd (24 x nA) is the time / extent of use for the appliance. This was then used to predict the electricity
Load demand for the community of about 55 households (as determined by census, 2006).
Table 1: Appliance Power Matrix for the 55 households of the Linakeng Community
Appliance Type Number Power Rating Total Power
Energy saver lights 188 11 2068
Refrigerators 30 100 3000
iron/electric jugs 35 1000 35000
DVD/Decoder 15 10 150
Amplifier 0 600 0


Radio/Speakers 12 100 1200
TV 10 60 600
Computer 0 100 0
stove plates 16 1000 16000

Table 2: Load Profile

To design this system, research on the suitable hybrid system that would be applicable to the Linakeng
community was conducted using Global Horizontal Irradiance (GHI) data and wind speed data obtained for
the European Union Meteorological data site by entering the coordinates of the area under study. The Flow
rate of the Linakeng River was estimated using annual Precipitation data for the Thaba-Tseka district.
Table 3: Hydro, Solar and Wind data
Month Hydro (L/s) Solar (kWh/m2/d) Wind (m/s)
January 1,410.3 7.1 2.1
February 1,200.1 6.8 2.2
March 1,040.4 5.2 1.9
Hour Load (kW) Hour Load (kW)
0000 – 0100 1.11 1200 – 1300 12.56
0100 – 0200 1.11 1300 – 1400 12.56
0200 – 0300 1.11 1400 – 1500 12.56
0300 – 0400 1.11 1500 – 1600 4.56
0400 – 0500 13.90 1600 – 1700 3.06
0500 – 0600 42.91 1700 – 1800 21.13
0600 – 0700 18.40 1800 – 1900 20.92
0700 – 0800 4.02 1900 – 2000 4.92
0800 – 0900 4.56 2000 – 2100 4.92
0900 – 1000 4.56 2100 – 2200 4.92
1000 – 1100 4.56 2200 – 2300 1.11
1100 – 1200 4.56 2300 – 0000 1.11


April 590.3 4.1 2.6
May 230.7 4.2 2.1
June 150.6 3.2 2.6
July 80.6 4.0 2.0
August 300.6 4.2 2.5
September 310.2 6.2 4.1
October 830.2 6.3 2.9
November 1,140.0 6.7 3.1
December 1,230.8 6.3 3.5

The cost of wind, solar and Hydro equipment was researched and for each price, a margin of Fluctuation in
cost was applied where relevant. Some of the cost estimations were done using ren21 (2018).
Table 4: Component Costs
Diesel Converter Battery PV Hydro Wind
Size (kW) 1.00 1.00 1 1.00 25 1.00
Capital ($) 110 100 400 600 13300 800
Replacement ($) 110 100 400 300 13000 600
O;M ($/yr or $/hr) 0.05 10 40 60 600 80
Lifetime (yrs) – 15 10 25 25 15
Head or Hub (m) – – – – 18 60
Fuel price ($/L) – – – – – –

Hybrid Optimization Model for Energy Renewable (HOMER) software tool was then used to design and
simulate the off-grid hydro-wind-solar system with generator and battery back-up.
After adding all the components required, the resulting schematic hybrid is shown in Fig 1.


Figure 1: Schematic of the hybrid wind-Solar-Hydro Plant
Fig 2 shows the optimization results for the simulation, arranged from lowest to highest according to the
Cost of Electricity (COE)

Figure 2: Optimization Result


Table 5 shows values for the average kWh per day, the average kW, the Peak and the Load Factor. The
average kWh/ per day shows that the daily load consumption for the Village is estimated at around 204
kWh/day. The 8.50kW average is the estimated consumption per household. The peak, indicates the amount
of consumption when most people are using electricity, which was found to be 82.7kW. The load factor of
0.10 indicates a 10% efficiency on the system. This is not ideal as it indicates that use of electricity is
inefficient, relative to what it could be if the peak demand was in control. An increased load factor is more
ideal because it will decrease the average unit cost of the kWh and thereby increasing savings 6

Table 5: Baseline and Scaled amounts for average kWh/d, average kW and load factor
Baseline Scaled
Average (kWh/d) 204 204
Average (kW) 8.51 8.50
Peak (kW) 82.8 82.7
Load factor 0.103 0.103


The daily profile shows a typical load consumption in one day for this village. In figure 3, the hours between
2200-2400hrs and 0000-0400hrs show a very low consumption of energy and this might be due to the fact
that at those hours, most people are asleep. The peak hour was found to be between 0500-0600 as indicated
in the figure and this might be an indicator that this is the hour that most people wake up to perhaps prepare
for school or go to work.

Figure 3: Daily Profile.

A seasonal profile for the scaled data monthly averages in Figure 3 below, show the average value (in kW)
for load demand for every month of the year. These do not indicate significant change in energy
consumption between seasons, however, this might be subject to change, as people become more
accustomed to the use of electricity, because, a change in energy usage is expected as lifestyle changes7.

Figure 4: Scaled data Monthly averages
Figure 5 shows the Density map. This shows the load density at every hour of the day and according to this
figure, the load density is mostly kept at minimum values between 0kW and 27kW throughout the days of
a year. Figure 6 is the Probability density Function and it shows the probability of having a particular load
demand at any given time. The graph indicates that the highest probability of about 25% is on a load demand
between 0kW and 2.5kW. This again, is proof of inefficiency of the system. Figure 7 shows a cumulative
distribution function, which accounts for the area under the PDF graph. This graph shows that the maximum
load that is able to be reached by the system is just above 80 kW. A duration curve, as shown in Figure 9,


shows how the load will vary with time. It defines the energy/power requirement of a load in terms of
maximum demand, total energy requirement and the distribution of energy demand 8. In the figure, the
load demand of the community looks to decrease with time.

Figure 5: density Map

Figure 6: Probability density distribution (PDF)

Figure 7: Cumulative distribution Function


Figure 8: Duration curve

A relationship between the amount of sunshine and its effect on Solar radiation data has long been
established 9. Specific to the area under study, an average annual global horizontal radiation of
5.35kWh/m2/d is proven to be a sufficient resource to supply solar power. Also, generally, Lesotho has an
abundant solar energy resource, with more than 300 days of sunshine. Sunshine durations range from 10.2
to 13.8 hours per day for both highlands and lowlands and this, further reassures a consistent supply of solar
During the months of April to August, the global horizontal radiation falls below average as seen in figure
9. This may be due to the fact that days are shorter in winter than they are in summer because of the
trajectory of the earth (it is tilted at an angle). This, however can be compensated for by the other renewable
energy resources, as this is a hybrid system.

Figure 9: Monthly average radiation

The wind resource was found to average around 2.63 m/s annually. This is very inefficient as the minimum
speed to start a wind turbine is 2m/s and thus the wind speed would only be used to start a wind turbine and
not to generate any power. To commence generation of power, the speed of wind was found to have to


range around 3.5m/s and the optimal generation of power was found to be at a speed of around 10-15

Figure 10: wind resource
Annual flow rates for the generation of hydro power have proven to be the most reliable renewable energy
resource among the hybrid. The average flow rate was found to be 707 L/s and this is very satisfactory, as
the minimum annual average flow rate required for a given head to generate a maximum power output of
25 kW is 150 L/s 12, which is considered the smallest economically viable hydropower system13.
Smaller amounts of flow rate are experienced during the months between May and September. This might
be due low rainfalls during winter seasons. The flow rate data was however, determined using precipitation
rates and this might be an inaccurate measure of flow rate, and thus, not a true reflection of the actual flow
rate of Linakeng River.

Figure 11: Hydro resource
The architectural system with the lowest LCOE was found to be one with 0.243 $/kWh as the cost. The net
present cost for this system is $185,752. It estimates the present value of all the costs of installing and
operating the Component over the project lifetime, minus the present value of all the revenues that it earns
over the project lifetime14 and is calculated, as per table 6. This system took into consideration all hybrid
components at the lowest overall cost. It is an established fact that a system with the lowest NPC yield
higher profit and thus the system is most ideal 5. This system, however, is more capital intensive than the
system architecture that has the highest LCOE cost due to investment in the renewable energy equipment
cost, civil work costs and other factors that contribute to setting a system in place.
The production of electricity of the system is summarized in figure 13. The PV array was found to contribute
to 3% of the electricity generated, wind turbine generate 28% of the electricity, the hydro turbine accounts


for 69% and the generator only constitutes 1%. Excess electricity produced accounted for 91.8% of the
electricity (see Figure 12). This might prove to be very inefficient, however, this excess might be used to
accommodate a growth in population, or be used to feed to other neighboring communities. The renewable
energy fraction was found to be 0.987 and this is very satisfactory, because it means that the system is
majorly dependent on the renewable resources and thus reducing the use of Diesel and only using it as a
back-up. This also implies a significant reduction in CO2 and other emissions, as seen in table 7.
In conclusion, the wind turbine and PV array contributed low energy to the proposed hybrid system when
compared to the hydro system output energy.
Table 6: Component costs for system with the lowest LCOE
Component Capital ($) Replacement ($) O ; M ($) Fuel ($) Salvage ($) Total ($)
PV 6,000 0 7,670 0 0 13,670
PGE 20/25 5,600 2,337 4,747 0 -435 11,976
Hydro 13,300 0 7,670 0 0 20,970
Generator 5,500 0 15,020 66,543 -278 55,440
Trojan L16P 16,000 13,923 20,453 0 -1,864 86,696
Converter 1,500 626 1,918 0 -116 3,927
System 47,900 16,886 57,206 66,453 -2,693 185,752

Figure 12: Production, Consumption and Quantity of electricity for system with Lowest LCOE

Table 7: Emissions
Pollutant Emissions (kg/yr)
Carbon dioxide 11,408
Carbon monoxide 28.2
Unburned hydrocarbons 3.12
Particulate matter 2.12
Sulfur dioxide 22.9
Nitrogen oxides 251


Figure 13: Monthly average Electric production

The system with the highest LCOE was found to be one with 0.498 $/kWh as the cost. The net present cost
for this system is $381,317 as seen in table 8 .This system does not take into consideration all hybrid
components and instead, it only simulates hydro and diesel resources. This system is very impractical,
despite the fact that it is less capital intensive than the system with the lowest LCOE. The high NPC in this
system is incurred through the high cost of fuel that will be used to run the generator (see table 8).
In this system, production of electricity of the system is summarized in figure 15. Hydro turbines account
for 93% of the electricity and the generator accounts for only 7%. The PV array and the wind turbines were
excluded in this simulation (Figure 14). An Excess of about 89.1% is produced and the renewable energy
fraction was found to be 0.934 and this is still very satisfactory, even though the renewable energy fraction
of the lowest LCOE system is slightly more ideal. Emissions in this system are slightly higher than those
in the lowest LCOE architecture as seen in table 9, however, they are still of an acceptable margin.
It would be ideal for a system to have a lower diesel fuel consumption because, low consumption of diesel
and a higher energy generation from renewable sources is considered a good choice, because burning of
diesel oil is the major source of environmental pollution and the emission of polluting elements. Again, the
availability of diesel might prove to be a challenge, especially in rural areas due to reasons that vary from
road access to the areas and high cost of transport to these remote areas 2
In conclusion, the cost of LCOE of this system is high due to installed PV and wind systems that are not in
use so perhaps it would be a more viable solution if PV, wind and converter systems are excluded in
simulation because as seen in both architectural systems, most energy is obtained from the hydro output.
Table 8: Component costs for system with the highest LCOE
Component Capital ($) Replacement ($) O ; M ($) Fuel ($) Salvage ($) Total ($)
Hydro 13,300 0 7,670 0 0 20,970
Generator 1 5,500 7,957 71,683 275,543 -335 360,347
System 18,800 7,957 79,353 275,543 -335 381,317


Figure 14: Production, Consumption and Quantity of electricity for system with highest LCOE

Figure 15: Monthly average electric production

Sensitivity variables assist in exploring the effects of the changes in the resources and the economy. These
variables show a range for which it would make sense to include renewable energy in the designing of the
system15. In this hybrid, the sensitivity parameters that were taken into consideration include; solar
radiation, Diesel price, Flow rate and the Primary load. The figure below (fig. 16) shows a surface plot
graph showing Diesel price ($/L) variation with flow rate (L/s) while keeping the primary load and the
Global solar radiation fixed. This plot is superimposed with the levelized cost of electricity (LCOE).
This plot shows how the LCOE changes as diesel price and Flow rate is varied and as seen in the figure, a
change in diesel price from 0.8 to 1.2 $/L leads to an increase in LCOE from 0.186 to 0.214 $/kWh. When
flow rate is varied from 650 to 750 L/s, the LCOE reduces from 0.186 to 0.185 $/kWh. Increasing both
parameters would effectively lead to an increased LCOE of 0.213 $/kWh (from a cost of 0.0186 $/kWh),
which means that the diesel price carries more weight in terms of increasing LCOE than Flow rate does in
decreasing the cost of LCOE.


Figure 16: Optimal system type

The Purpose of this mini- project was to design an off-grid hybrid system for the community of Linakeng,
Thaba- tseka, a 55 household community. Renewable energy resource data for the community was obtained
using the EU meteorological data service for wind and solar resources and precipitation data for the district
was used to estimate the flow rate of the Linakeng River. A load profile was established and with all this
data, a simulation was made using HOMER to give out a simulation that had a primary load of 204 kWh/day
with a peak of 83 kW. Using the resources, optimal system types of 0.243 $/kWh and 0.498 $/kWh as the
lowest and highest system architectural LCOE respectively, were obtained. Both systems provided enough
electricity to meet the load demand, with a very significant excess that could be used to accommodate a
population increase. Emissions in these systems were also reduced and this is a good thing, as it would lead
to a cleaner environment. The optimal system had sensitivity variables to accommodate economic changes
and resource fluctuations and in this optimal system, diesel price and flow rate were varied while keeping
the primary load and Global solar radiation fixed. This plot was superimposed with the LCOE and it was
observed that a change in diesel price from 0.8 to 1.2 $/L lead to an increase in LCOE from 0.186 to 0.214
$/kWh and when flow rate was varied from 650 to 750 L/s, the LCOE reduced from 0.186 to 0.185 $/kWh.


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