The solar photovoltaic (PV) technology has gained global importance to overcome the global warming and meet future energy needs. The performance of a solar PV plant depends on many factors such as solar irradiance, weather conditions, various types of energy losses and system degradation over time. Although the deterministic models nicely predict the PV performance at a single instant in time, however, they fail to account for the uncertainty and randomness in the input parameters. Probabilistic models, in contrast, are more useful to predict the system performance over a time span under real conditions. In this study, a probabilistic model has been developed for the performance analysis of a recently commissioned 100 MW power plant at Bahawalpur, Pakistan. The model is based on Monte-Carlo simulation method and uses the probable range of input data from the site of the power plant. The performance of the power plant is presented in terms of monthly and seasonal electricity generation. The associated energy losses are discussed in detailed. Furthermore, a comprehensive cost analysis of the power plant has been provided. According to results from the model, the power produced in the first year of operation of the plant is 136,700 MWh and the projected cumulative energy produced during a plant lifetime of 25 years is 3,108,450 MWh. The levelized cost of energy (LCOE) estimated by the model is 0.0795 $/kWh, which is quite reasonable in comparison to the average 0.1 $/kWh cost of electricity to a domestic customer in Pakistan.

1. Energy Strategy Reviews 29 (2020) 100479
Available online 30 March 2020
2211-467X/© 2020 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Performance analysis based on probabilistic modelling of Quaid-e-Azam
Solar Park (QASP) Pakistan
Azhar Abbas Khosa a,*
, Tanzeel-ur Rashid b,c
, Najam-ul-Hassan Shah c,d
, Muhammad Usman c,e
,
Muhammad Shahid Khalil b,c
a
Institute of Engineering Thermophysics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
b
Department of Energy Engineering, University of Engineering & Technology Taxila, Pakistan
c
Department of Mechanical Engineering, University of Engineering & Technology Taxila, Pakistan
d
School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, 85281, USA
e
Chair of Automation and Energy Systems, Faculty of Natural Sciences, Saarland University, Saarbrücken, Germany
A R T I C L E I N F O
Keywords:
Monte Carlo simulation
Solar PV Designing
Renewable energy power plants
LCOE
Probability analysis
A B S T R A C T
The solar photovoltaic (PV) technology has gained global importance to overcome the global warming and meet
future energy needs. The performance of a solar PV plant depends on many factors such as solar irradiance,
weather conditions, various types of energy losses and system degradation over time. Although the deterministic
models nicely predict the PV performance at a single instant in time, however, they fail to account for the un­
certainty and randomness in the input parameters. Probabilistic models, in contrast, are more useful to predict
the system performance over a time span under real conditions. In this study, a probabilistic model has been
developed for the performance analysis of a recently commissioned 100 MW power plant at Bahawalpur,
Pakistan. The model is based on Monte-Carlo simulation method and uses the probable range of input data from
the site of the power plant. The performance of the power plant is presented in terms of monthly and seasonal
electricity generation. The associated energy losses are discussed in detailed. Furthermore, a comprehensive cost
analysis of the power plant has been provided. According to results from the model, the power produced in the
first year of operation of the plant is 136,700 MWh and the projected cumulative energy produced during a plant
lifetime of 25 years is 3,108,450 MWh. The levelized cost of energy (LCOE) estimated by the model is 0.0795
$/kWh, which is quite reasonable in comparison to the average 0.1 $/kWh cost of electricity to a domestic
customer in Pakistan.
1. Introduction
Energy has a pivotal role to strengthen and develop the economy and
social norms of a country. For continuous growth, it is necessary to
balance the production and consumption of energy. According to an IEA
report [1], fossil fuels (oil, gas and coal) represent more than 80% of
global energy market, which is alarming due to many reasons [2].
Firstly, the fossil fuels have quite limited reserves, that may diminish in
less than a century [3]. Secondly, burning these fuels puts public well­
being at risk by adding pollutants to the air. Furthermore, the use of
fossil fuels releases CO2, a greenhouse gas, that stops infrared radiation
from escaping the earth atmosphere, thereby leading to global warming
[2]. In 2019, the global CO2 emissions were 33 GtCO2, 42% of which
were contributed by the electricity and heat generation sector alone [4].
Scientists believe that the earth is nearing a tipping point (a point of
irreversible change in the climate), and therefore emphasize the need for
an immediate action to save the planet. The UN Climate Action Summit
2019 set a target to limit the rise in the global average temperature
below 1.5 �
C by the end of this century. This target requires cutting the
global emissions to a net zero value by 2055 [5–7]. To mitigate these
challenges, it is necessary to develop clean and renewable technologies
like solar, wind, and hydro power plants at a faster pace. Fig. 1 shows
how during the recent years the renewables have evolved in the gen­
eration sector [8]. Among the renewable technologies, the solar
photovoltaic is most popular due to quick installation, robustness, sim­
ple operation and maintenance, and efficient conversion [9], and since a
large solar flux hits the earth every day.
Pakistan is a developing economy in the South Asia, and the 6th most
* Corresponding author.
E-mail address: abbasazhar1427@hotmail.com (A.A. Khosa).
Contents lists available at ScienceDirect
Energy Strategy Reviews
journal homepage: http://www.elsevier.com/locate/esr
https://doi.org/10.1016/j.esr.2020.100479
Received 10 May 2017; Received in revised form 23 February 2020; Accepted 17 March 2020

2. Energy Strategy Reviews 29 (2020) 100479
2
populous country in the world. Despite a high potential for solar energy
(an average insolation of 5–7 kWh/m2
/day) and other renewables [11],
the country is largely dependent on fossil fuels to feed its grid. Soaring
prices of these mostly imported fuels, together with high distribution
losses and poor management of resources have brought the country into
a state of energy crises [12]. This have hit the economic backbone of the
country over the past ten years. To overcome these challenges, the
government is now encouraging renewable development and as a
starting point, a 100 MW solar plant (the Quaid-e-Azam Solar Power,
QASP) was built and is in operation since 2015 [13]. A technological
review of solar PV in Pakistan can be found be [14]. The country has set
an ambitious target of increasing the renewable production to 20% by
2025 and 30% by 2030 (20X25 and 30X30 target) [15]. However, these
targets cannot be achieved without proper planning and feasibility
analysis. For an effective feasibility analysis, it is necessary to consider
various factors that may affect the project in any way. This research tries
to fill the gap by providing a comprehensive performance and cost
analysis of the QASP based on the key parameters determined in the
process. This work is also useful for a wider audience beyond Pakistan
since it provides an insight into the long-term performance of a solar PV
system, which is lacking in the literature.
The performance prediction of a PV system may be performed using
either a deterministic or a probabilistic approach [16,17]. The deter­
ministic models rely on the relevant physical laws to determine the ef­
ficiency of a PV system, while ignoring the uncertainty associated with
the input values. Whereas, the probabilistic models, in addition to the
physical laws, also consider the uncertainty associated with each of the
variables involved. These models try to forecast the future state of the
system under different scenarios in form of a confidence interval or
otherwise in the form of a density of states. In general, the performance
of a PV system depends on many factors including solar radiation,
ambient temperature, wind speed and material and efficiency of the PV
cells [18]. Other important factors may be shading, degradation, various
losses and weather conditions. Some of these factors (radiation, tem­
perature and wind) vary with the geographical location and climate
conditions [19], and may be recorded in the form of meteorological
data. A collection of such data for the past years is made available and
can be used for simulating the system performance. Some of the com­
mon simulation tools for PV systems are RETScreen, PVsyst, PV Watts,
PVGIS, SISSIFO, HOMER and PV Online [20]. There is a large body of
literature on the performance prediction of the solar PV systems. As a
reference, [21–23] are some of the studies that used one of these soft­
ware to assess feasibility of solar PV systems under different
circumstances. Recently, Kumar et al. [20] used PVsyst to predict per­
formance of roof-top solar PV system. In another work, Ayeng’o et al.
[24] presented a model for battery coupled PV system. A drawback of
the deterministic approach is that it doesn’t account for uncertainty in
the input factor (radiation, temperature etc.) and therefore lacks the
ability to perform predictions on the real time scale. Lindsay et al. [25],
for example, discussed the errors related to input of solar irradiance.
Probabilistic forecast over a period is therefore much useful than the
deterministic approach which is good for only one point in time. Raza
et al. [26] presented a comprehensive review of the forecast models for
the PV output power, whereas Meer et al. [27] provided a review of
probabilistic forecasting techniques for solar PV power production and
consumption. Monte-Carlo Simulation (MCS) is one such method that
predicts a range of outcome performance associated with the random­
ness of some of the important variables such as global horizontal irra­
diance. Hopkins [28] has applied MCS to predict the power generation
from wind and solar PV by accounting uncertainty in load, wind speed,
and turbine power curve. Zhou et al. [29] developed a model for
determining risks associated with the renewable energy and carried out
the sensitivity analysis on wind system. Nagaska and Youli [30] evalu­
ated reliability of MCS method on a laboratory scale micro grid. In a
recent work, Ahn et al. [31] used MCS method to perform uncertainty
analysis of a hybrid heating, cooling and PV system for energy and
economic performance evaluation. All the discussed studies are related
to the probabilistic modelling of the solar PV energy generation, how­
ever, there is little literature on performance prediction of a solar plant
for a period comparable to its entire lifespan. Consequently, this study is
focused on the development of a probabilistic model for long-term
performance prediction of the QASP.
The objective of this paper is to study the long-term performance and
cost analysis of the 100 MW QASP. This has been done by developing a
probabilistic model using Monte Carlo Simulation method. The power
generated by the PV system has been calculated by considering the solar
radiation and various losses that may affect the output of the system. The
estimated monthly output is reported to show seasonal variation over
the year. Furthermore, the performance of the QASP has been projected
to 25 years from its commissioning while accounting the system
degradation. Additionally, a cost analysis has been performed to predict
the levelized cost of electricity (LCOE). This cost analysis accounts for
the commissioning, operation and maintenance costs. The tariff of
electricity for the end users has also been determined.
This paper is divided into five sections. The first section is dedicated
to introduction. The second section provides a description of the QASP
and its site specification. This is followed by the methodology of
research in section 3. Section 4 discusses the results. Finally, section 5
provides a summary and conclusion of the work.
2. QASP introduction and site specifications
As described earlier, Pakistan has a strong potential for the renew­
able energies. It has an average solar global insolation of 5–7 kWh/m2
/
day [32]. The country also has a higher wind resource (a gross instal­
lable capacity of 346 GW [33]). In coastal areas, average wind speed
range between 6 and 8 m/s [34]. The country also have a 6.6 GW of
installed hydel plants, while an available potential projected to be 41.5
GW [35]. Fig. 2 shows an overview of the energy mix of Pakistan [36].
In view of the current economic challenges, and in accordance with
the global move on adopting renewable technologies, Pakistan has
initiated its efforts to develop renewable energy. As a first mega project,
the Quaid-e-Azam Solar Power (QASP) with a planned 1000 MW ca­
pacity has been launched in multiple phases. The First phase of this plant
with a 100 MW capacity has been completed and is in operation since
2015.
Fig. 1. Share of energy produced by using renewable, nuclear and conventional
resources all over the world [10].
A.A. Khosa et al.

3. Energy Strategy Reviews 29 (2020) 100479
3
2.1. Introduction of QASP
The QASP has been built in Bahawalpur, a district of Punjab prov­
ince. It is located at an altitude of 118 m above sea level, with the co­
ordinates of 29.394�
N (latitude) 71.664�
E (longitude). The first phase
of the power plant is a pilot project initiated by the Government of
Punjab to attract investors for the remaining 900 MW. Fig. 3 provides a
schematic of the electricity generation system of the QASP [37]. The
power plant has an on-grid system, where the DC power from the solar
panels is converted into the AC power, before injecting it into the na­
tional grid through a 132 kV grid station.
The QASP (100 MW) project contains 392,160 solar panels, covering
a land space of about 500 acres. The panels were manufactured by the
JA Solar Company. The entire area is divided into 100 sections, each
producing 1 MW of power. In each section, there are 13 rows of panels, a
1000 kVA rated power pad mounted transformer and two 500 kW ca­
pacity inverters. Out of the total 13 rows, 10 rows consist of 8 tables and
the remaining 3 rows consist of 6 tables. A table is an arrangement of 40
panels (2 strings of 20 panels). A combiner box is used to connect either
16 strings or 12 strings in parallel. Fig. 4 shows the arrangement of PV
cells, inverters and other equipment installed at the QASP.
Fig. 2. Share of energy produced by various sources to meet the energy needs
of Pakistan [36].
Fig. 3. Schematic to show the flow of electricity from generation in PV cell to national grid for QASP [37].
Fig. 4. A view of the installed PV cells and other related equipment at QASP.
A.A. Khosa et al.

4. Energy Strategy Reviews 29 (2020) 100479
4
2.2. Site specifications
Some of the factors that determine the site selection for the solar PV
project are the availability of a large unused piece of land, with high
insolation and a clear sky over most of the year. The site of the QASP, i.e.
Bahawalpur meets all these criteria because the area is in the largest
desert of Pakistan with most of its land being barren and uncultivated.
Fig. 5 shows the solar map of Pakistan with the multi-year mean
(2000–2012) of daily Global Horizontal Irradiance (GHI) in kWh/m2
.
The daily GHI for Bahawalpur is almost in the range of 6–6.3 kWh/m2
.
Fig. 6 provides the measured irradiance data, air temperature and wind
speed of the site of QASP.
3. Methodology
To predict the output of the PV system, two models were generated
using the simulation tool. The models were named as PV performance
model and Reliability model. Inputs for the PV performance model are
the design parameters of the system and the weather data containing the
irradiation data for the specified location. Array design, failure modes,
rates, and repair times are used as input parameters for the reliability
model. Each of the two models individually provide intermediate re­
sults. The PV performance model provide the ideal energy production
possible with the system, whereas the reliability model provides infor­
mation about the availability of the system for power generation. The
two models are combined to arrive at final results in terms of the esti­
mated actual power production. Fig. 7 shows the schematic of the full
model from the top to the bottom.
The developed MCS model is based on the equations discussed in this
section and all these equations are explained with detail in Ref. [23].
The model uses the hourly solar irradiance data. The irradiance data
follows the beta distribution, given in equation (1), in which α and β are
the shape parameters, where r and rmax are real and highest solar irra­
diance respectively. The units of r and rmax are (W/m2
).
FrðrÞ ¼
Гðα þ βÞ
ГðαÞГðβÞ
�
r
rmax
�αÀ 1�
1 À
r
rmax
�βÀ 1
(1)
The following equation is used to find the total active power (P*
M)
from the available modules. Power is a function of solar irradiance.
Fig. 5. Map to indicate the potential of solar energy in different regions of Pakistan [source: World Bank]. Bahawalpur is marked as a TIER 1 region.
Fig. 6. Trends of irradiance falling on the location of QASP (a), and wind speed
and air temperature in the region (b). All this data is provided by Meteonorm.
A.A. Khosa et al.

5. Energy Strategy Reviews 29 (2020) 100479
5
P*
MðrÞ ¼ rAη (2)
where A is the area (m2
) and η is the efficiency of the modules. Ex­
pressions to find the area covered by the modules and the efficiency are
given below.
A ¼
XM
m¼1
Am (3)
η ¼
PM
m¼1Amηm
A
(4)
In the above two equations M is the number of solar modules each
having an area Am and efficiency ηm. While m ¼ 1;……;M. The proba­
bility distribution function of the available power can be generated and
to obtain a mathematical expression the value of r extracted from
equation (2) is used in equation (1) which is given below.
FM
À
P*
M
�
¼
Гðα þ βÞ
ГðαÞГðβÞ
�
P*
M
RM
�αÀ 1�
1 À
P*
M
RM
�βÀ 1
(5)
RM ¼ rmaxAη (6)
The model carries out the dynamic probabilistic simulations and
predicts the probable future performance of the QASP. This is because
the future values of the inputs used in the system are unpredictable and
have a probabilistic nature such as weather, degradation rate of modules
and the inverter availability etc. As the model is probabilistic so it can be
simulated many times to provide likely future outcomes (realizations).
Dynamic simulation allows the development of a model for the reli­
ability analysis of the system under consideration. It also enables to
observe the performance of the system over a specified period. Twenty-
five years was chosen as the period of interest for the performance of the
QASP, and the simulations consisted of 5000 iterations.
The weather data obtained from Meteonorm for Bahawalpur (Fig. 6)
and the data obtained from QASP location is being used as input for the
development of the probabilistic model (Table 1). The irradiance data is
used to calculate the total available power from the sun and when
technological constraints are considered the actual generation of power
can be estimated. To predict the future production and to know the
probabilistic nature of the system the inputs are fed to the simulation
tool to perform Monte Carlo simulation. The results of the simulation
were analyzed and after the validity check probabilistic model was
developed. The model is helpful in knowing the varying nature of the
renewable sources and validates the probability distribution of the solar
irradiance.
The power generated by a PV system is a function of different vari­
ables and all these variables have different effects on it. The variables are
presented in a tabular form in Table 1 together with the range of their
values which were used in the model. The range describes the proba­
bilistic nature of the input variables and when this range is used in the
Monte Carlo Simulation it provides different results for different
realizations.
The model accounts for some losses for components of the system
that affect the power generation by the system. These losses are mostly
related to irradiance loss until the generated power has not left the
modules. After leaving the PV cell the system losses are responsible for
the waste of power. The losses are described in different categories and
are easily understandable. The effect of various losses on the power
generation is given in graphical form in Fig. 8 and reference [37]. Some
of the losses are defined below for the interest of readers.
Near Shadings leading to irradiance loss: Near shadings are due to
objects near the PV field which cover the PV modules area and prevent
solar insolation from reaching the modules. In simulations their range is
taken as 2%–4%.
Incidence Angle Modifier factor on global: This factor indicates the
Fig. 7. Steps taken to achieve the final results using the probabilistic model [38].
Table 1
The data provided by the QA Solar used as input variables in the model. The
ranges show the probability of the variables.
Input Variables Range of Values
Season wise Available Solar Hours 350–1340
Clarity Index 0.88–1
Efficiency of Panels (%) 8–15
Efficiency of Inverter (%) 80–95
Available Number of PV Modules 391,900–392,100
Availability of Transformer in days 358–365
GHI (kWh/m2
) 325–970
A.A. Khosa et al.

6. Energy Strategy Reviews 29 (2020) 100479
6
loss of irradiance falling on the PV modules due to increase in reflections
caused by incidence angle. Their values taken in model are given as a
range 2%–3.5%.
Soiling loss factor: Effect on the performance of system caused by
accumulation of dirt on the PV panels is known as soiling loss. It ranges
between 2% and 6%.
PV Module Degradation: Reduction in the power output of a module
is known as degradation. This reduction occurs gradually over time. This
can happen if a single cell in a module fails. In our model, the degra­
dation rate is considered as 0.5% per year.
3.1. Cost analysis
For cost analysis of the QASP, the tariff of the energy produced for
the customers and the levelized cost of energy (LCOE) were determined.
LCOE is the cost of installing and running a power plant to generate one
unit of energy, it is expressed in $/kWh. If Cl represents LCOE, It de­
scribes all the expenses to generate energy in a given time “t” and Et is
the total energy produced in the same time span “t”, the equation for
LCOE can be expressed as:
Cl ¼
It
Et
(7)
The tariff is the rate at which one unit of energy is sold to the
customer or the cost of one unit of energy for the end user. It includes all
the expenses to generate energy from an installed facility. If T is the total
tariff, Com is the cost of operations and maintenance, Ci the cost of in­
surance for the plant, Ce is the return on equity and Cd is the debt to be
paid that was taken, the following equation represents the calculation of
tariff for the energy produced from any power plant.
T ¼ Com þ Ci þ Ce þ Cd (8)
4. Results and discussion
The results obtained after running the MCS are of different nature
and they describe different aspects of the model. According to MCS re­
sults, the model gives an estimate about the power generation capacity
of the QASP and accounts for all the losses in the system. The model has
been used to predict the probabilistic nature of the system during its
lifetime and it also gives an estimate of the generation capacity of the
plant over its entire life. The monthly power generation of the power
plant for the first year is given in Fig. 9. This graph shows the energy
generation obtained after deducting the losses of the system i.e. inverter,
PV module, wiring, shading and soiling losses. The maximum power
generation is in the month of May, which is intuitive since the intensity
of incident light is high during this month. The model also accounts for
the loss of energy due to the temperature rise in summer. In case of April,
the level of irradiance is relatively low although the loss of energy due to
temperature rise is almost equal to that of May. This explains the lower
power output of the plant during this month. In case of June, July and
August, the loss due to temperature rise is higher which decreases the
overall energy generation in these months. The least energy generation
is obtained in the months of December and January. In case of both of
these months, the available solar hours per day are less and the irradi­
ance level is also low which causes a drop in electricity generation.
The year is divided into three seasons on the basis of temperature
variation namely winter, summer and intermediate seasons. Summer
includes those months in which temperature is usually high such as May,
June, July, August and September. The winter consists of December,
January and February, while intermediate season contains March, April,
October and November. Fig. 10 shows the electricity generation during
all the seasons. The maximum electricity generation is in the summer
season because this season has a high irradiance level, maximum solar
hours and accounts for a total of five months of the year. During the
winter, intensity of light is low, clarity index is low and solar hours are
also less that leads to lowest power generation in comparison to the
other seasons. In case of the intermediate season, the irradiance level is
normal but PV losses due to temperature rise are a bit high that causes
less electricity generation in this season.
A comprehensive overview of all the losses considered for different
components of the solar PV system are presented in Fig. 8 with their
percentage loss in graphical form. After deducting these losses, the final
annual power generation of the system was calculated. Mostly, the loss
of energy is due to loss of irradiance in the case of solar PV panels. The
loss due to system components is much less than that due to the irra­
diance. Temperature affects directly the efficiency of the PV modules,
inverters and other connected electronic components of the system,
which causes thermalization and above 25 �
C the efficiency of PV
module decreases with each �
C rise in temperature. The loss value is
much larger for the hot areas. As Bahawalpur is a warm city and the
temperature in summer is above 45 �
C so the loss factor due to tem­
perature is high. From Fig. 11, it is clear that a large amount of the
energy is lost due to temperature rise. The second largest factor
Fig. 8. Different loss factors of the system considered in the model as input
[37]. A: Near shadings loss leading to irradiance loss, B: Irradiance angle
modifier factor on global, C: Soiling loss factor, D: PV loss due to irradiance
level, E: PV loss due to temperature, F: Module array mismatch loss, G: Ohmic
wiring loss, H: Inverter loss during operation (efficiency), I: AC ohmic loss, J:
External transformer loss.
Fig. 9. Monthly energy production of the power plant.
A.A. Khosa et al.

7. Energy Strategy Reviews 29 (2020) 100479
7
contributing to the lost energy is due to the soiling effect. This effect is
high because power plant is situated in a desert and the accumulation of
sand particles on PV modules causes this effect. [ref] The two losses
other losses, which are due to the loss of irradiance are the incidence
angle modifier and irradiance loss due to shadings, which collectively
cause approximately 7000 MWh loss of energy produced in a year.
There is an age limit associated to every system because the systems
start to degrade and be less efficient with the passage of time and after a
long enough time span there is need to replace all the components or
develop the entire system afresh. In general, lifetime of PV system is
taken as 25 years because PV material deteriorates with time and the
efficiency decreases accordingly. Therefore, electricity generation of the
power plant is calculated, and the performance is analyzed for a period
of 25 years. The efficiency reduction of the system is taken as 0.5% per
year and power generation will be decreased linearly as illustrated in
Fig. 12. The irregularities can be an outcome of the unpredictable nature
of weather, because it is possible that irradiance intensity is high for one
year and low for another due to weather clarity. Other variations in
power generation may occur due to solar hour insolation difference or
major breakdown of the system.
Fig. 13 shows the statistical view of the simulations that were per­
formed on the data to achieve the results. The statistical summary of the
simulation results is for 5000 runs and tells about the probability of
occurrence of any output. It gives the distribution and number of counts
of power generation each time the simulation was run for the first year
of generation, while the second graph shows the frequency of occur­
rence of power generation each time the simulation was run for the first
year of generation.
The power produced from the plant is carbon free because solar PV
plants are environment friendly and do not emit any kind of greenhouse
gases. Furthermore, no costly fuel is required for their future operation.
Solar resource is freely available, reliable and ensures energy security.
According to the model, the power produced in the first year of opera­
tion of the plant is 136,700 MWh. The energy lost in the first year of
operation is 43,300 MWh. The cumulative energy produced within the
estimated lifetime of power plant is forecasted as 3,108,450 MWh.
4.1. Cost analysis
Installation of a power plant requires a heavy investment in terms of
the equipment, skilled labor and engineering services. An estimated
amount of money needs to be allocated for operations and maintenance
(O&M) throughout the life period of power plant. Considering all such
costs at the beginning, the cost computed against the rated power to be
obtained during the lifetime of the power plant for one unit of electricity
is termed as levelized cost of energy (LCOE). All the costs including
installation, O&M, insurance, return on equity and owed debt are
considered to set the tariff for the one unit of the produced electricity to
the end consumer. The only saving for an investor is “return on equity”.
According to the developed energy generation model, the LCOE of
QASP is 0.0795 $/kWh including all the expenses estimated for twenty-
five years to produce the energy during this period. In Table 2, power
produced during the lifetime of power plant is given for each year. O&M
cost for each year is determined for corresponding generated power for
the specified period. Capital cost, proposed in the tender for the instal­
lation of the 100 MW of PV, is $173,526,344. In all these expenses, the
share of equity is 25%, while the remaining is debt taken from local
banks. Consequently, the tariff also includes the amount of debt that is to
be paid to the banks with some interest. The debt is to be returned within
ten years so for the first ten years amount of debt is included in tariff and
after that the tariff decreases accordingly.
The calculated LCOE (0.0795 $/kWh) seems suitable. The LCOE is
Fig. 10. Season-wise power generation of the power plant.
Fig. 11. Energy loss within the whole system in a year. A: Near shadings loss
leading to irradiance loss, B: Irradiance angle modifier factor on global, C:
Soiling loss factor, D: PV loss due to irradiance level, E: PV loss due to tem­
perature, F: Module array mismatch loss, G: Ohmic wiring loss, H: Inverter loss
during operation (efficiency), I: AC ohmic loss, J: External transformer loss.
Fig. 12. Annual energy production for a lifespan of 25 years.
A.A. Khosa et al.

8. Energy Strategy Reviews 29 (2020) 100479
8
considered to be a strong metric in checking the feasibility of a project
with respect to its cost. It also helps to set the tariff for selling the
electricity to customers. The tariff is affordable by the customers and the
investor is also satisfied. QASP has proved itself as a pilot project
because after its installation the Govt. of Sindh has also initiated some
small projects ranging between 10 MW and 50 MW in areas with good
solar potential. It has also attracted the investors to invest in PV tech­
nology and remaining 900 MW is also being installed near the 100 MW
project.
The installation of the 100 MW solar PV power plant for Bahawalpur
is an example of decentralization of the power sector. Such power plants
must be installed to provide electricity to remote areas and to discourage
the centralization of power. With the development of PV technology in
power plants, its nationwide price has significantly lowered and is now
easily affordable to the individuals.
5. Conclusion
This study aims to develop a probabilistic model for the analysis of
long-term performance of a 100 MW solar PV power plant installed at
Bahawalpur, Pakistan. The probabilistic model uses Monte Carlo simu­
lation technique to determine the system performance using the input
Fig. 13. Histogram of Monte Carlo simulation results.
A.A. Khosa et al.

9. Energy Strategy Reviews 29 (2020) 100479
9
data from the power plant site. Furthermore, the cost analysis of the
power plant is performed based on the energy generation calculated
from the model. Some important conclusions are as follows:
o Solar Irradiance, weather conditions and the material degradation
are the most significant factors in determining the performance of PV
power plant. This means that a suitable geographical location, and a
module material suitable for operating range of plant is required for
the efficient plant operation.
o The output power of the PV plant peaks in May due to a higher
irradiance in this month and drops to a minimum in December when
solar irradiance is limited. Although a high temperature in May also
results in higher thermalization losses during this month, yet the net
power output stands out due to favorable solar irradiation.
o On a seasonal basis the summer season is most productive with a
power output of 69.77 GWh, followed by intermediate and winter
seasons with the power output of 47.49 and 22.34 GWh, respec­
tively. The total useable power generated by the system in a year is
139.6 GWh.
o The power loss within the system is highest due to inefficiency of
the modules at high temperatures, leading to an annual power loss of
20.5 GWh. Whereas, the annual total power loss is 45.72 GWh.
Another important loss factor is soiling due to the dry and desert
climate of the plant site.
o The model is capable of forecasting the future energy generation of
the power plant. The power generation trend of the power plant is
observed for the period of 25 years from its commissioning. The
model predicted that the cumulative power produced by the power
plant over its lifespan will be 3108.45 GWh.
o The LCOE is a key parameter for determining the tariff for selling
electricity to the consumers. The cost analysis shows that the LCOE of
the PV power plant is 0.0795 $/kWh. This means that the cost per
unit generation of energy by a PV power plant is low enough to make
it viable option for energy production.
The model established in this work is very useful as it provides the
overall trend of power generation and helps to determine the cumulative
energy generation over the life span of a power plant. Such a model is
important for PV system design projects because it is based on a prob­
abilistic approach that accounts for the probable nature of the solar
irradiance, system components and losses. From the investors’ and
regulation point of view, this model can provide the cost analysis the
LCOE.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
CRediT authorship contribution statement
Azhar Abbas Khosa: Conceptualization, Data curation, Investiga­
tion, Methodology, Writing - original draft, Writing - review & editing,
Software. Tanzeel-ur Rashid: Conceptualization, Supervision, Valida­
tion. Najam-ul-Hassan Shah: Writing - original draft, Writing - review
& editing. Muhammad Usman: Writing - review & editing. Muham­
mad Shahid Khalil: Supervision.
Acknowledgement
Authors are thankful to University of Engineering & Technology
(UET) Taxila, Pakistan for providing the opportunity and environment
to carry out this research. Authors also want to thank “Meteonorm” for
providing the weather data for the Bahawalpur, Pakistan. Furthermore,
authors acknowledge the support and information provided by Quaid-e-
Azam Solar Power (Pvt.) Limited and its staff members.
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