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Smart Grid and Renewable Energy, 2024, 15, 79-97
https://www.scirp.org/journal/sgre
ISSN Online: 2151-4844
ISSN Print: 2151-481X
DOI:
10.4236/sgre.2024.152005 Feb. 29, 2024 79
Smart Grid and Renewable Energy
Seasonal Performance of Solar Power Plants in
the Sahel Region: A Study in Senegal, West
Africa
Serigne Abdoul Aziz Niang1*, Mamadou Simina Drame1,2, Astou Sarr1, Mame Diarra Toure1,
Ahmed Gueye1, Seydina Oumar Ndiaye1, Kharouna Talla1
1FST, Département de Physique, Université Cheikh Anta Diop de Dakar, Dakar, Sénégal
2Laboratoire de Physique de l’Atmosphère et de l’Océan Siméon Fongang, Université Cheikh Anta Diop, Dakar, Sénégal
Abstract
The main objective of this study is to
evaluate the seasonal performance of 20
MW solar power plants in Senegal. The analysis revealed notable seasonal
variations in the performance of all stations. The most significant yields are
recorded in spring, autumn and winter, with values ranging from
5 to 7.51
kWh/kWp/day for the reference yield and 4.02 to 7.58 kWh/kWp/day for the
final yield. These fluctuations are associated with intense solar activity during
the dry season and clear skies, indicating peak production. Conversely, min-
imum values are
recorded during the rainy season from June to September,
with a final yield of 3.86 kWh/kW/day due to dust, clouds and high tempera-
tures. The performance ratio analysis shows seasonal dynamics throughout
the year with rates ranging from 77.40% to 95.79%, r
einforcing reliability and
optimal utilization of installed capacity. The results of the capacity factor vary
significantly, with March, April, May, and sometimes October standing out as
periods of optimal performance, with 16% for Kahone, 16% for Bokhol,
18%
for Malicounda and 23% for Sakal. Total losses from solar power plants show
similar seasonal trends standing out for high loss levels from
June to July,
reaching up to 3.35 kWh/kWp/day in June. However, using solar trackers at
Sakal has increased produ
ction by up to 25%, demonstrating the operational
stability of this innovative technology compared with the
plants fixed panel.
Finally, comparing these results with international studies confirms the out-
standing efficiency of Senegalese solar power plants, other installations aro
und
the world.
Keywords
Performance Study, Photovoltaic Power Plant, Season Variations, Senegal
How to cite this paper:
Niang, S.A.A.,
Drame, M
.S., Sarr, A., Toure, M.D.,
Gueye,
A
., Ndiaye, S.O. and Talla, K. (2024) Sea-
sonal Performance of Solar Power Plants in
the Sahel Region: A Study in Senegal, West
Africa
.
Smart Grid and Renewable Energy
,
15
, 79-97.
https://doi.org/10.4236/sgre.2024.152005
Received:
January 23, 2024
Accepted:
February 26, 2024
Published:
February 29, 2024
Copyright © 20
24 by author(s) and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
S. A. A. Niang et al.
DOI:
10.4236/sgre.2024.152005 80
Smart Grid and Renewable Energy
1. Introduction
The Intergovernmental Panel on Climate Change (IPCC) highlights the growing
global interest in renewable energy sources due to their environmentally friendly
properties compared to harmful fossil fuels [1]. It is clear that the use of these
energies is an important solution to ensure energy security, mitigate the effects
of climate change and achieve significant economic benefits [2]. From 2015 to
2016, global electricity consumption and renewable energy source production
reached 19.3% and 24.5%, respectively [3]. Global electricity increased tenfold
between 2010 and 2020, reaching 2799 GW in 2020 [4]. Solar energy, mainly
through photovoltaic installations, has become a dominant force in the global
energy landscape, both in daily life and industry [5] [6]. PV panel production
has shifted noticeably from Europe to Asia (especially China), which accounted
for 54% and 45% of global capacity added in 2017 and 2018, respectively [7] [8].
Nonetheless, the continued growth of solar energy globally faces major chal-
lenges. The ability to achieve the best conversion into cheap electricity without
losses remains a key goal for all solar power systems [9]. Solar panels must be
installed in outdoor environments, which tend to reduce the performance of PV
modules. The performance of grid-connected PV systems depends more on cell
technology, installation configuration and operating (maintenance) conditions
than on meteorological parameters [10] [11]. Knowing the performance of PV
modules at a specific location is essential to design the right system for that spe-
cific location and application. The electrical parameters provided by manufac-
turers under standard test conditions (STC) are not sufficient to accurately de-
termine the performance and reliability of PV modules under real conditions.
For an accurate assessment, it is necessary to monitor the production and opera-
tion of solar power plants throughout their entire service life [12].
Over the years, many studies have been conducted around the world analyz-
ing the performance of solar power plants. Attari
et al.
[10] evaluated the per-
formance of a 5 kW AC grid-connected PV system installed on the rooftop of a
building in Tangier, Morocco. They obtained final performance ranges from
1.96 to 6.42 kWh/kWc, efficiency ratio (PR) of 58% to 98%, and annual capacity
factor of 14.48% [10]. Wang
et al.
[13] compared the seasonal characteristics of
three different photovoltaic technologies (p-Si, a-Si and CdTe) in a climate tran-
sitioning from a typical Mediterranean climate to a semi-arid cold climate. In
summer, capture losses were reported to increase due to thermal effects and in-
verter limitations of the three PV systems. However, for the a-Si network, the ef-
fect of thermal annealing results in lower capture losses (an absolute difference
of 3%) compared to p-Si and CdTe PV networks. In Brazil, the performance of a
2.2 kWc photovoltaic system installed at the State University of Ceara, Fortaleza,
was monitored from June 2013 to May 2014. Annual energy efficiency, final
yield, system losses, system and inverter efficiencies, PR, and capacity factor
were calculated. The results they found show the good potential for electricity
production through photovoltaic solar energy in the state of Ceará, Brazil [14].
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Nouar [15] calculated the final yield, reference yield, system efficiency, perfor-
mance ratio, and total losses of a 20 MWc grid-connected PV system installed in
a challenging environment in southern Algeria over one year. The results ob-
tained show that the installation of photovoltaic stations gives good results en-
couraging investments in this region [15]. Mpholo
et al.
[16] monitored the per-
formance of a newly installed 281 kWc photovoltaic solar farm at Moshoeshoe I
International Airport in Lesotho, and the result of this study shows that the area
is suitable for grid-connected photovoltaic systems. The analysis period covers
both hot summer and cold winter [16]. In Italy, Congedo
et al.
[17] analyzed the
performance of a 960 kWc PV system consisting of monocrystalline silicon PV
modules over eight months. Final energy yield, system efficiency, performance
ratio, and PV cell temperature losses were calculated. The performance analysis
in this study is consistent with results reported in the literature for PV systems
located in the Mediterranean [17]. Four different buildings equipped with grid-
connected rooftop photovoltaic systems were analyzed in Abu Dhabi based on
two different types of PV modules: polycrystalline and monocrystalline [18]. In
Palestine, the technical performance, effects, and economic analysis of a 5 kWc
grid-connected residential PV system for three different houses were analyzed
over two years of operation [19]. The performances of the houses were also
compared by varying the tilt angle using PVsyst software. In Morocco, the per-
formances of 2 kWc of polycrystalline, monocrystalline, and amorphous PV
modules were compared using real measured data over five years, and recorded
and simulated data were also compared [20]. The simulation was carried out
using Python to predict electricity production over a week. The mean square er-
rors of the three types of PV modules were also compared. The performance pa-
rameters of the PV system were evaluated based on energy production over two
years in 2018-2019 [21]. To improve outdoor PV system performance analysis,
Hüttl
et al.
[22] proposed a Self-Referencing Algorithm (SRA) for high-precision
outdoor measurements of PV modules. Additionally, several commercial soft-
ware and models are available that use meteorological databases, PV module da-
ta, and inverter data to predict PV system performance at a specific location [23]
[24]. Furthermore, results show that the performance of grid-connected photo-
voltaic systems depends on geographical location, PV module types, and weather
conditions such as solar radiation and ambient temperature [25]. A performance
analysis of the 23 MWc photovoltaic plant in Senegal showed that performance
depends on on-site climatic conditions and technologies used [26].
This article focuses on Senegal, a Sahel country with significant solar potential
and a unique climate. Although this potential was not fully exploited until 2000,
since then the country has made significant progress in solar energy production
through the installation of numerous solar power plants and other ongoing
projects [27] [28]. The country is also characterized by the presence of desert
aerosols and clouds, which have a significant radiative impact throughout the
year [29] [30]. Careful seasonal analysis of solar power systems is therefore im-
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portant to accurately predict performance, promote better planning of the elec-
tricity system, and manage seasonal demand more efficiently [31] [32]. This
work is based on a rigorous methodology that combines field data to study sea-
sonal changes in the production of four solar power plants. This provides valua-
ble information for analyzing the performance of these facilities and highlights
the importance of conducting research to better understand the underlying
physical mechanisms and predict seasonal variability more accurately.
2. Description of Solar Power Plants
Senegal (11.5˚N, −18.5˚W), like other developing countries, is actively working
to diversify its energy sources and reduce its dependence on fossil fuels [33]. The
initiative has led to the installation of several solar power plants across the coun-
try in recent years to address energy shortages and reduce the country’s carbon
emissions.
Figure 1 shows the geographical distribution of the four solar power plants on
the map of Senegal.
Table 1 provides the characteristics of the four solar power plants considered
in this study. This includes geographical coordinates, type of panels installed,
number of panels, installed capacity, panel configuration (fixed or mobile) and
year of commissioning of each installation. In particular, with the exception of
the Sakal power plant, all of these power plants use polycrystalline solar panels
mounted on fixed poles inclined at about 15 degrees [34]. The latter is equipped
with 62,100 solar panels installed on 240 trackers to track the sun’s path. This
configuration is designed to produce 30% more power compared to fixed pa-
nels, providing a more stable power supply to the grid [35]. All of these solar
power plants are directly connected to the Senegal’s national electricity company
(SENELEC) network. Each installation is equipped with a distribution box in
front of the solar inverter and is responsible for parallel connection of the solar
circuits.
Table 1. Technical descriptions of the solar power plants.
Solar power plants Kahone Bokhol Malicounda Sakal
Latitude 14˚17 N 16˚31 N 14˚28 N 15˚85 N
Longitude −16˚02 W −15˚46 W −16˚57 W −16˚22 W
Installed capacity (MW) 20 20 20 20
Type of panel and
Configuration Polycrystalline/Fixed Polycrystalline/Fixed Polycrystalline/Fixed Polycrystalline/Tracker
Power installed 270 270 230 320
Number of panel 75,000 75,000 86,000 62,100
Area (ha) 44 40 100 40
Year of Service 2018 2016 2016 2018
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Figure 1. Location of the four solar power plants on the map of Senegal: Bokhol (20
MW), Sakal (20 MW), Malicounda (20 MW), and Kahone (20 MW).
The inverter converts the direct current generated by solar panels into alter-
nating current and synchronizes it with three parameters of the distribution net-
work: amplitude, phase and frequency. Additionally, these plants are equipped
with environmental sensors including temperature, solar radiation, humidity,
wind speed, and wind direction. This equipment continuously monitors weather
conditions near solar modules. The collected data are stored on one of the data
collection computers located in the control room.
3. Performance Evaluation Approach
3.1. Data Collection
To assess the seasonal and monthly performance of the photovoltaic system
production, production data were collected over the entire period from January
to December 2021 for the four solar power plants under study. The performance
indicators used in this study include the reference yield, final yield, performance
ratio, capacity factor, and system losses. Performance indicators are calculated
using the performance indices developed by the International Electrotechnical
Commission (IEC) standard 61,724 [36] and the International Energy Agency
(IEA)—IEA-PVPS Task 13 [37], which are the most widely used documents for
monitoring photovoltaic systems.
3.2. Key Performance Indicators Affecting Energy Production
To analysis the performance of solar energy systems, key indicators developed
by the IEA and IEC are used, such as the performance ratio (PR), the final PV
yield (
Yf
) and the reference yield (
Yr
) [16] [33] [34]. These parameters are stan-
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dardized indicators that allow for the comprehensive analysis of existing PV sys-
tems concerning the energy produced, solar irradiation, and the overall impact
of system losses.
3.2.1. Reference Yield (Yr)
The reference yield (
Yr
) is defined as the ratio of the total solar radiation
Ht
(kWh/m2) reaching the solar panel surface to the reference radiation amount
G
0
(1 kW/m2) [10] [38]. This parameter represents the time corresponding to the
reference illumination.
Yr
determines the solar energy resource of the solar
power system.
0
t
r
H
YG
=
(1)
The unit of reference yield is kWh/kWp/day or (h/day).
3.2.2. PV System Final Yield (Yf)
The final yield (
Yf
) is a significant indicator used to normalize the energy pro-
duced based on the system’s size. It is influenced by the mounting structure,
orientation, and location of the installed PV system [37] [38]. This parameter
corresponds to the total energy output (
ECA
) in kilowatt-hours (kWh) produced
by the PV system over a specified period (day, month, or year) in relation to the
installed nominal power (
P
0) in kilowatt-peak (kWp), under standard conditions
(STC: irradiation: 1000 W/m2, ambient temperature: 25˚C, and reference spec-
trum AM 1.5-G) [39].
Yf
indicates the number of daily hours during which the
photovoltaic generator operates at its nominal power.
0
CA
f
E
YP
=
(2)
The final yield unit is the kWh/kWp/jr (ou h/jr).
3.2.3. Performance Ratio
The performance ratio (PR) is determined by the ratio of the final yield (
Yf
) and
the reference yield (
Yr
) [40]. The coefficient of performance is dimensionless
and serves as a normalization factor for solar radiation incident on the aircraft.
PR depends on the total losses of the system due to the conversion tasks per-
formed by various components such as PV modules, inverters, cables, etc. [10].
The productivity coefficient is expressed by the following equation:
( )
PR % 100
f
r
Y
Y
= ×
(3)
Higher PR values indicate that the plant is operating close to rated capacity,
while lower values indicate production losses due to technical or design issues.
Typically, PR values vary between 0.6 and 0.8 due to different weather condi-
tions [41]. In cooler climates it may exceed 0.9 [42].
3.2.4. Capacity Factor (CF)
It represents the relationship between the actual amount of energy produced by
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a solar power plant over a 24-hour period throughout the year and the maxi-
mum annual energy production of that plant at its rated output. Power utiliza-
tion is usually expressed as a percentage [43].
( )
0
FC % 100
24
AC
E
P
= ×
×
(4)
The capacity factor (CF) is a site-dependent parameter. It varies based on the
received solar radiation and the number of clear sunny days experienced by the
photovoltaic plant site. It is significantly influenced by the type of module used
[28].
3.2.5. System Total Losses
The total energy losses of the photovoltaic system (
LT
), expressed in hours
(h/day) or (kWh/kWp/day), can be defined as the difference between
Yr
and
Yf
,
according to the following formula [44]:
Trf
L YY= −
(5)
These parameters include panel capture loss, module temperature loss, and
overall system loss. Performance loss during operation can be caused by various
factors such as radiation level and direction, thermal effects due to increased cell
temperature, inverter losses, and contamination [45].
4. Results and Discussion
4.1. Monthly and Seasonal Distribution of Production Form the 4
Solar Power Plants
Figure 2 shows the monthly production variation of four solar power plants in
Senegal.
Figure 2. Monthly production of the solar power plants: Kahone (green), Bokhol (black),
Malicounda (blue), and Sakal (red).
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The resulting analysis highlights significant seasonal variations in the produc-
tion of all solar power plants, which typically peaks in the spring between March
and May. During this period, power plants in Bokhol, Malicounda, and Kahone
reach maximum values of 3194 MWh, 3658.2 MWh, and 3156.2 MWh, respec-
tively. These peaks appear to be closely related to increased solar activity during
the dry season, which is characterized by clear skies [34] [46]. In particular, with
the installation of solar trackers in Sakal, production increased significantly,
reaching a peak of 4694.5 MWh in May, up to 25% more than using fixed solar
panels. Conversely, minimum production is observed during the rainy season
from June to September due to dust, clouds, and high temperatures [47]. These
results are consistent with most studies conducted in the region [48].
To summarize the above results, Figure 3 shows the seasonal power distribu-
tion of solar power plants in winter (December-February), spring (March-May),
summer (June-August), and fall (September-November).
Across all the sites, power plant production peaks in spring, ranging from
3104.12 to 4694.5 MWh. This appears to be due to increasing solar energy inten-
sity. On the other hand, minimum production is recorded in winter due to the
low altitude of the sun, and in summer due to atmospheric clouds and Saharan
dust affecting solar panels [49]. The minimum values for the three power plants
(Bokhol, Malicounda and Kahone) range from 2513.2 to 2982.2 MWh. However,
it is notable that Sakal also records high production levels during the monsoon
season, suggesting a potential positive impact from mobile solar panels.
Figure 3. Seasonal variation in solar energy production for the solar power plants: Ka-
hone (black), Bokhol (red), Malicounda (blue), and Sakal (green).
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4.2. Analysis of Performance Indicators for the Four Photovoltaic
Systems
4.2.1. System Efficiency
Based on the collected data, the profitability of the four solar power plants used
in this study was calculated. Figure 4 shows the final and baseline yield from
January to December.
The analysis of the results reveals significant seasonal variations in daily yields
for all stations. Peak performances are observed in spring (March to May), au-
tumn (October and November), as well as in winter (December and February),
with values ranging between 5 and 7.51 kWh/kWp/day for the reference yield
and from 4.02 to 7.58 kWh/kWp/day for the final yield. Conversely, minimum
yields are recorded during summer, from June to September, with a reference
yield ranging between 4.5 and 5.7 kWh/kWp/day, and a final yield varying from
3.86 to 6.20 kWh/kWp/day. These fluctuations are attributed to an increase in
sunlight during this period, emphasizing the crucial importance of the season in
energy production [38] [49]. Moreover, the Sakal station, equipped with mobile
panels, achieves a remarkable final yield of 7.58 kWh/kWp in May, thanks to the
use of solar trackers.
Table 2 summarizes the seasonal and annual averages of baseline and final
yield for each station. These figures give a general idea of seasonal changes in
performance indicators.
Figure 4. Variation in the monthly average of reference daily yields Yr (in blue) and final
yields Yf (in red) for the four solar power plants.
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Table 2. Seasonal averages of reference and final efficiencies for each station, as well as
the overall averages for each solar plant.
Seasons Reference Yield (kWh/kWp/jour) Final Yield (kWh/kWp/jour)
Kahone Bokhol Malicounda
Sakal Kahone Bokhol Malicounda
Sakal
Winter 5.73 6.14 5.66 6.26 4.56 4.70 4.63 5.18
Spring 7.23 7.36 7.17 7.16 5.01 5.03 5.60 7.29
Summer 5.33 5.49 5.62 5.85 4.07 4.01 4.48 6.21
Autumn 5.39 5.68 5.47 5.99 3.88 4.81 4.93 5.53
Annual
average 5.92 6.17 5.98 6.32 4.38 4.64 4.91 6.05
The table results confirm that seasonal variations in solar energy production
can be reliably anticipated. During spring and winter, yields reach their peak,
with seasonal averages ranging between 5.66 and 7.36 kWh/kWp/day for the
reference yield, and from 4.63 to 7.29 kWh/kWp/day for the final yield. These
high values indicate that these periods offer ideal conditions for energy produc-
tion. Conversely, during the rainy season, characterized by the summer months,
yields drop to their lowest levels, with seasonal averages fluctuating between 4.01
and 6.21 kWh/kWp/day for the final yield. These lower values during summer
confirm the negative relationship between weather conditions and solar perfor-
mance, highlighting the significant influence of seasons on solar energy produc-
tion.
4.2.2. Performance Ratio
Figure 5 represents the monthly performance ratios of the four solar power
plants over the entire examined period.
The performance analysis of these four solar power plants reveals distinct
trends and significant seasonal variations throughout the year. Kahone and
Bokhol exhibit more pronounced seasonal fluctuations, recording lower perfor-
mances during the summer months. This situation is likely related to the geo-
graphical location of these two plants. The Bokhol plant is situated in the north-
ern part of the country, in a desert area where the presence of desert dust and
cloud systems is frequent during the wet season [34].
Additionally, other meteorological factors such as extremely high tempera-
tures may also contribute to the decline in the performance of these solar sys-
tems in summer (June-September). Indeed, the Kahone plant experiences peak
performance in January (98%) and December (93%), but registers significant de-
clines in June (65%) and September (63%). On the other hand, Bokhol exhibits
high performances in November (91%) and October (90%), but undergoes nota-
ble declines in June (55%) and May (64%).
The performance ratio at Malicounda is relatively moderate and stable
throughout the year, while the Sakal plant stands out with consistently high per-
formances. Malicounda maintains relative stability, reaching its peak in November
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Figure 5. Monthly performance ratio for the four solar power plants.
(93%). The Sakal plant, equipped with mobile solar panels, demonstrates excep-
tional performances year-round, reaching peaks in April (100%), May (100%),
and December (100%). These results underscore the positive impact of the mo-
bile panel configuration, particularly at Sakal, indicating that this panel configu-
ration plays a crucial role in optimizing energy production. As highlighted by
Lee
et al.
[50], a PR exceeding 0.8 indicates performance approaching ideal con-
ditions under STC, while a PR below 0.7 may suggest defects in components, in-
stallation conditions, or extreme weather conditions.
4.2.3. Capacity Factor
Figure 6 shows the monthly variation in daily average capacity factor (CF) ob-
served at various solar power plants.
Capacity factor (CF) results for four solar power plants highlight significant
seasonal variations reflecting the impact of weather conditions on solar energy
production.
Spring, which runs from March to May and sometimes from October to fall, is
the optimal production period with a high coefficient of productivity (CF). In
fact, power factor peaks are often recorded between March and April with values
of 15.79%, 15.98%, 18.30%, and 23.48% in Kahone, Bokhol, Malicounda, and
Sakal, respectively. These results suggest higher energy efficiency over certain
periods of time.
These seasonal fluctuations are closely linked to variable sunlight throughout
the year, with higher light intensity during these months and clear skies [43].
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Figure 6. Monthly variation of the daily average Capacity Factor (CF).
Conversely, the months from June to September show relatively lower CF, which
can be attributed to less favorable weather conditions. Solar panel technology,
design choices, and site geography all play a crucial role in these variations, de-
monstrating the need for a holistic approach to understand and optimize solar
energy production in each plant [26]. The Sakal plant, equipped with mobile
panels, stands out with exceptionally high-capacity Factors, emphasizing the
crucial importance of solar panel adaptability to track the movement of the sun.
4.2.4. System Losses
Figure 7 shows the overall system losses for the four solar power plants in this
study.
The analysis of the total losses in the systems of the four solar power plants
reveals distinct seasonal variations. The months of June and July, corresponding
to the summer season, are characterized by high losses for all plants, reaching up
to 3.35 kWh/kWp/day in June. These periods are likely to pose challenges due to
high temperatures, denser cloud cover, and the presence of dust, leading to a
decrease in the operational efficiency of the solar systems [44] [45]. In contrast,
the winter and autumn seasons stand out with minimal losses, ranging between
0.38 kWh/kWp/day and 1.05 kWh/kWp/day, likely benefiting from more favor-
able weather conditions. In spring, Kahone, Bokhol, and Malicounda experience
slightly higher average losses, highlighting specific challenges related to spring
weather conditions [44], with values reaching 1.10 kWh/kWp/day, 2.42
kWh/kWp/day, and 1.75 kWh/kWp/day, respectively. Losses for the Sakal station,
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Figure 7. Seasonal variations in total losses from PV systems in Kahone (a), Bokhol (b)
and Malicounda (c) and Sakal (d).
equipped with mobile panels, remain very low throughout the year, approaching
zero values in spring, thanks to the operational stability of its innovative tech-
nology.
Table 3 summarizes the seasonal performance of the four photovoltaic plants.
The results in the table regarding the efficiency indicators of these solar power
plants clearly confirm the quality and reliability of these installations. Final Yield
ranging from 4.38 kWh/kW/day to 6.05 kWh/kW/day shows remarkable stabili-
ty, demonstrating significant energy efficiency. A stable performance ratio ranging
from 77.40% to 95.79% improves reliability, indicating optimal use of installed
capacity. These promising results, combined with relatively limited system losses,
highlight the plant’s success in converting solar energy into electricity, laying the
foundation for sustainable energy production in Senegal.
4.3. Comparison with Photovoltaic Installations on a Global Scale
To evaluate the performance of solar power plants in Senegal globally, we first
investigated the final yield (
Yf
) and the performance ratio of four solar power
plants. Compared to other global studies, the results of the Senegal power plant
were very meaningful, showing significantly superior performance. For a clearer
understanding of the performance of photovoltaic systems compared to other
studies, Table 4 summarizes the performance indicators of the selected studies.
The Sakal plant with mobile panels surpasses with an impressive, highlighting
the exceptional efficiency of Senegalese solar power plants.
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Table 3. Annual review of performance indicators for the four solar power plants.
Solar
Plants
Reference yield
(kWh/kWp/day)
Final yield
(kWh/kWp/day)
Performance
Ratio
(%)
Capacity
Factor
(%)
System losses
(kWh/kWp/day)
Kahone 5.92 4.38 78.75 13.58 1.66
Bokhol 6.17 4.64 77.40 14.11 1.85
Malicounda
5.98 4.91 83.95 14.91 1.41
Sakal 6.32 6.05 95.79 18.42 0.37
Table 4. Daily yield and performance of senegalese solar power plants vs international
comparisons.
Locations Final yield
(kWh/kWp/day)
Performance ratio
(%) References
Kahone
4.38
78.75
Present
work
Bokhol
4.64
77.40
Malicounda
4.91
83.95
Sakal
6.2
95.79
Morocco 4.45 77.4 [10]
Kuwait 4.5 77.5 [38]
Spain 3.8 64.5 [51]
India 3.99 76.97 [52]
Norway 2.55 83.03 [53]
Diass 4 78 [26]
Mauritania 4.27 63.59 [12]
Djibouti’s 4.6 85 [54]
5. Conclusion
This in-depth study aimed to evaluate the performance of four solar power
plants located in Kahone, Bokhol, Malicounda and Sakal in Senegal. Using anal-
ysis methods in accordance with IEC 61,724 standards, we examined various in-
dicators such as the average reference daily yield, final yield, performance ratio,
capacity factor, and system losses. The results show a strong seasonal trend, with
production peaks in spring, fall and winter and minimum values recorded dur-
ing the rainy season. Using solar trackers at Sakal has increased production by
up to 25%. The performance ratio of the stations ranges from 77.40% to 95.79%,
which represents optimal use of the installed capacity. The capacity factor shows
seasonal variation, indicating an increase in energy efficiency in spring and fall.
Total losses in the system show marked seasonal variation, with high levels in
the summer months. The results confirm the outstanding efficiency of Senega-
lese solar power plants compared to global studies, highlighting their exception-
S. A. A. Niang et al.
DOI:
10.4236/sgre.2024.152005 93
Smart Grid and Renewable Energy
ally good performance compared with other installations worldwide. These find-
ings provide practical recommendations for energy planners and highlight the
importance of considering seasonal conditions when planning and managing
solar power plants.
Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this pa-
per.
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