1 Introduction

The global energy demand is always on the rise along with human health and environmental challenges due to the production and consumption of energy for socio-economic activities. These issues are complicated by the rapid population growth, industrialisation, and urbanisation across the world, especially, in the global South. Humans cannot survive without these basic requirements: food, shelter, and transportation. The quest for meeting these needs has led to deforestation for charcoal and firewood for cooking; emissions of harmful gases from cement production for shelter; and fossil fuel extraction and consumption for the transportation of humans and goods. This has attracted genuine concerns and serious outcry for the need of taking certain measures to safeguard the future. The momentum of this outcry and the cutting down of fossil fuel consumption is growing. Studies have shown that the production and consumption of fossil fuels emit greenhouse gases (GHG), which exacerbate climate change. Unfortunately, the end to this global socioeconomic development trend is not yet in sight. This is expected to continue because of the heightened economic competition among countries.

The global south is pressured to stick to the use of fossil fuel by the socioeconomic standard and quality of life attained by the global north through the burning of fossil fuel. A sustained adequate energy production and supply is a panacea for national socioeconomic development. The present-day large economies, such as Germany, Russia, Britain, China, and the United States of America are products of fossil fuels. The facilitation of rapid economic growth by fossil fuel comes with high consequences, such as increased morbidity and mortality, GHG emissions, and severe climate change. The energy sector has the highest share of CO2 emissions, and the continuous extensive use of fossil fuels is increasing the concentration of CO2 in the atmosphere [1], which promotes environmental degradation and global temperature rise [2]. In the global south, especially, SSA, a substantial amount of biomass fuels are exploited as sources of energy. Consumption of fossil fuel has been accompanied by sacrificing human health and the environment for the energy needed to power and sustain socio-economic activities. In 2019, 33.4 GtCO2 was reported by the International Energy Agency (IEA), as the global energy-related CO2 emissions, which declined to 31.5 GtCO2 in 2020 due to the Covid pandemic [3]. The power sector contributes about 38.5% of the global CO2 emissions [4]; South Africa generated 1.3% of the global CO2 emissions (456 MtCO2) and ranked 1st and 14th contributor of CO2 in Africa and the world, respectively [5]; about 60.5% of the country’s emissions come from the power sector [6].

Apart from emissions of GHG, there are other uncertainties surrounding the use of fossil fuel, such as resource depletion, unstable prices and subsidies, and weaponization of it, as a tool for economic war. Despite the drawbacks associated with the usage of dirty energy, and because energy is a basic essential for human survival, fossil fuel is still being consumed heavily. Hence, the need for alternative sources of energy devoid of endangering human health and the environment, and compromising the future is imperative. Several alternative energy sources that meet modern energy requirements of adequacy, sustainability, and affordability, have been identified, and are receiving attention. The energy sources that are being developed and deployed include hydropower, solar, wind, geothermal, tidal, and biomass [7,8,9,10,11]. However, certain limitations are affecting the greater deployment of these alternative sources, which include inadequate enabling policy frameworks, inadequate potential and technical information, potential fluctuation, high cost, and cost estimate complexity. Solar energy stands out among these alternative sources because of its pattern of spread and enormous potential.

1.1 Scope of the study

The call to deploy solar energy, through the utilisation of PV technology, to meet the modern energy demands for socioeconomic growth, and as a measure to mitigate climate change is now more compelling. Over the years, PV technology has experienced tremendous improvements in terms of solar power conversion efficiency (PCE) and cost. Despite the successes recorded, PV panels PCE of commercially available crystalline silicon (c-Si) PV panels is still hovering between 10 and 17% [12]. However, recently PV PCE of over 40% has been reported by studies using concentrated multi-junction cells [13]. To use PV systems optimally, certain factors influence PV performance, that needs to be understood and addressed. These factors include solar irradiation, PV potential, season and geographical location, PV cell and panel orientation, shading effect, soiling factor, and cell temperature.

Therefore, this study estimates the solar PV potential of selected cities across SSA, using computational modelling. This will include a comparative analysis of the meteorological and insolation parameters of the selected site locations, such as direct normal radiation (DNI), global diffuse horizontal irradiation (DHI), horizontal irradiation (GHI), and global tilted irradiation (GTI). Other meteorological parameters that affect PV system performance to be considered are relative humidity (RH), speed of wind (SW), and ambient temperature. A hypothetical 10-kWp c-Si PV system, mounted on a large, tilted rooftop will be used to estimate meteorological and PV system PCE performance and PV potential of the selected sites’ locations. The study will provide information that will promote PV system sizing accuracy and offer both technical and economic guides to investors and installers. Further, it will assist policymakers in formulating the relevant framework to facilitate adequate clean electricity provision. The objectives of this study include the estimation of the yearly average GHI, DNI, DIF, GTI, and ambient temperature (Temp). Others are specific photovoltaic power output (PVOUT specific), total photovoltaic power output (PVOUT total) and Performance ratio (PR).

1.2 Significance of this study

Solar PV assessment studies in SSA bring several unique aspects and challenges compared to other regions. Overall, solar PV assessment studies in SSA offer unique opportunities and challenges due to the region's solar resource potential, energy access needs, grid integration considerations, local contexts, policy environments, and capacity-building aspects. Addressing these factors effectively is crucial for the successful deployment of solar PV systems and advancing sustainable energy development in the region.

2 Solar photovoltaic and the factors that influence electricity generation

Solar energy is harvested and transformed into electricity using photovoltaic (PV) panels. This involves the absorption of photons and converting them into electricity instead of heat by semiconductors (PV cells). The rate and amount of electricity generation are influenced by several factors, such as PV cells, light intensity influences, PV panel orientation, weather, season, and site location. Other affecting factors are the time of the day and year, length of sunshine, speed of the wind, temperature, topography, and relative humidity (RH) [14].

2.1 PV system

Solar PV involves the conversion of irradiation into electricity using a PV panel, or indirectly using concentrated solar power. The PV systems are categorised into two-stand-alone PV systems (off-grid) and grid-connected PV systems (GCPVS). The power generated by the PV modules is passed to the inverter in the stand-alone PV system, which converts it from direct current (DC) into alternate current (AC) electricity. In the off-grid system, the generated extra electricity during the day is sent to a storage device, a battery. This saved energy is used when the system is not producing adequate electricity either at night or during cloudy weather. In the case of the GCPV system, a storage unit is not used; the DC electricity generated by the PV modules is converted into AC electricity by an inverter. Many of the installed GCPV systems generate excess energy that is sent to the grid (instead of battery as in the case of SAPVS) and the owner will be compensated for that electricity. The excess energy generated by the installed GCPV system is linked to the grid.

2.2 Solar PV cells

The PV cell, which is the most significant component of solar PV systems, has evolved over the years; from first-generation to third-generation. Silicon wafers are not deployed in the second-generation PV cells. Second–generation PV cells include cadmium telluride (CdTe), amorphous silicon, and copper indium gallium selenide (CIGS), also, called copper indium selenide (CIS) [15]. The PV second generation has flexible thin-film solar cells with a performance efficiency of between 10 and 15% [16]. These PV modules are cheaper than the first-generation because of the lesser materials consumed, higher rate of production, and cheaper manufacturing technologies. Third-generation PV cell technologies are evolving technologies with less commercial value compared to PV cells of first and second generations technologies. The third-generation PV cells include quantum dot solar cells (QDSCs), copper zinc tin sulphide (CZTS), perovskite solar cells (PSCs), organic photovoltaics (OPVs), and dye-sensitized solar cells (DSSCs) [17, 18]. The PCE has been on an upwards trend, from about 10% in the 2000s to 10.8% in 2013 [19], 20.1% in 2014 [20], and 25% in 2021[21]. Despite the striking merits of third-generation cells, such as PCE, lightweight, mechanical flexibility, eco-friendliness, and free-shape exhibition, they are plagued by short lifespan, high cost, and low rate of mass production. The OPVs for instance are associated with practical application reliability, as the lifespan of most of the stable ones is not more than seven years. Some OPVs last for a few weeks to nine months compared to the present c-Si PV cells that have a service life of 20–25 years.

The first-generation PV cell technology, which is silicon-based PV cells, dominates the market [22] because of its relatively high stability and power conversion efficiency (PCE) [23]. These PV cells are categorised into two based on the type of silicon used—crystalline silicon (c-Si) and hybrid silicon PV cells. The c-Si PV cells are the oldest and commonest, and are commercially available as monocrystalline and polycrystalline solar PV panels [24]. The considered PV panel in this study is the c-Si PV panel, which is the most available PV panel for rooftop applications. Its relatively high cost is due to the cost of fabrication that is attributed to the intricate process and its energy-intensive process that is required in the growing of pure silicon large crystals.

2.3 Solar PV panel orientation: Azimuth angle (α) and tilt angle (β) °

The PCE of the commercially available PV panels is affected by their installed orientation and the PV panels work below their optimal performance if the panels are orientated wrongly. This is attributed to the rotation and spinning of the earth around the sun. This interaction between the Earth and the sun creates different temperatures, distances, and angles of sunlight that focus on different places on Earth throughout the year. The part of the earth that inclines towards the sun receives more sunlight and experiences summer while the other part farther away from the sun gets less sunlight intensity and encounters winter. These variations affect the solar radiation that reaches the surface of the PV panel and the highest energy output is obtained when the panels are directly facing the sun.

Considering the earth's relationship with the sun, many angles, such as solar altitude, solar declination, solar azimuth, tilt angle, and zenith angle have been identified [25]. Among these angles, Azimuth and tilt angles, represented by (α) and (β), respectively, are the most relevant in the discourse of solar PV optimisation. Solar panels give the highest energy output when they are directly facing the sun. The intensity of the sun as it moves across the sky will be either low or high, depending on the season and time of the day. Therefore, an ideal tilt angle is never fixed; the optimal tilt angle needs to be determined and used as the direction of the panels. Because of the sun locus differences throughout the year, several studies both empirical and theoretical have established different relations on how to orientate PV panels during installation for optimal performance [26,27,28,29]. These relations help to define the panel's inclination to the sun which ensures maximum daily energy production.

A mechanical tracking system has been developed for a more precise PV panel corresponding to varied inclinations. The PCE of a PV panel is substantially influenced by the tilt, and therefore, it is important to mount the panel correctly to generate optimal power output in a given site. The ideal tilt angle of solar panels is a function of the panel’s location from the equator and angular distance from the equator is called latitude. The deployed expressions for determining the correct tilt are defined in terms of latitude, as follows:

\(\beta = \phi - 10\) [30]; \(\beta = \phi + 10\) [31]; \(\beta = \phi + 20\) [32]; \(\beta = \phi + (10 \to 20) + 10\) [33]; \(\beta = \phi + (0 \to 30)\) [34]; and \(\phi + (10 + 30)\) [35], where the latitude (ϕ), α, and β, are measured in degrees (°).

In some other studies, these mathematical expressions to estimate the optimum tilt angle are based on seasons (winter and summer):

\(\beta = \phi \pm 20\) and \(\beta = \phi \pm 8\), where + and − were applied for winter and summer [36].

\(\beta = \phi + 10\) (winter), \(\beta = 0.69\phi + 3.7\) (summer), and \(\beta = \frac{{\left( {\left( {0.69\phi + 3.7} \right) + \left( {\phi + 10} \right)} \right)}}{2}\) (all year round) [37].

\(\beta = \phi \pm 15\), again + and − were used for winter and summer, respectively [38]. \(\beta = 0.9\phi + 29\) (winter), \(\beta = 0.9\phi - 23.5\) (summer). This was referred to as an improved method [38]. Normally, the Hemisphere determines the optimal tilt and azimuth angles; the North and South Hemispheres require southward and northward orientations for optimization, respectively [39]. In this method, collectors are considered with azimuth angles (α) of 0° and 180° for the northern and southern hemispheres, respectively.

3 Method

In this study, a hypothetical grid-connected PV system of 10-kWp installed capacity was deployed at the selected site locations, defined by latitudes and longitudes. Different tilt angle estimates relations, based on seasons all around the year were used in the PV potential assessment of the selected locations across SSA. The assessment parameters were generated using the site-bearing system coupled with meteorological data of the selected locations. A comparative study of the PV potential and performance of the 10-kWp rooftop c-Si PV system integrated into buildings in the selected locations was done based on the generated results obtained from computer modelling. Computer modelling tools, Solargis Prospect and PV*SOL applications, which use a systematic 5-stage process were utilised. The systematic 5-stage steps are site description, generation of meteorological information, PV system configuration, simulation and generation of report [40]. The technical information extracted from the reports obtained from the software applications was used to investigate and analyse the solar PV’s potential, and exploitation level, and estimate the system's performance. Solargis Prospect and PV*SOL were selected among the solar PV computational modelling applications because of their availability, accuracy, simplicity, user-friendly interface, and explicit report. The year of data of the Solargis Prospect solar resource database used in this study is about 20 years (1994–2021). The details of the deployed PV system design, simulation, and optimisation software applications are presented in Table 1.

Table 1 Software applications used for solar PV systems performance analysis [41,42,43]
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4 Computational modelling of a 10-kWp c-Si PV cell system

This section deals with every technical aspect of the PV system, from potential assessment to the configuration of the hypothetical installed capacity of 10-kWp c-Si PV system performance evaluation. This includes a detailed description of the site's locations, PV system configuration, and simulation based on the selected locations. The subsequent reports of the selected locations will be used to analyse, compare, and discuss solar PV potential in terms of irradiations, power generation output, performance ratio (PR), and capacity ratio (CR).

4.1 Site location description and system configuration

The description of the selected site locations for the 10-kWp c-Si PV systems given in terms of latitude and longitude are presented in Table 2.

Table 2 Site locations description and PV system configuration
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5 Results

The location-based results and the PV system configuration parameters, as described in Sect. 4, serve as input parameters for PV potential evaluation process. The section is categorised into two-solar insolation resources estimation and solar PV generation potential assessment.

5.1 Solar potential assessment: insolation and meteorological parameters

Solar insolation accounts for electricity production by PV systems and the magnitude of the generated electricity depends on irradiation intensity. However, there a many meteorological variables that influence PV power production, availability, and ageing of a PV system, such as air temperature (TEMP), relative humidity (RH), wind speed, precipitation, and albedo. The obtained results are based on the high-resolution meteorological database created and controlled by Solargis. The result of irradiation is based on PV panel optimisation orientation according to the location presented in Table 1. The result presented in Fig. 1, shows that—sites in Addis Ababa and Kinshasa have the highest (2019 kWh/m2) and smallest (1763.1 kWh/m2) yearly average GHI, respectively. Other irradiation observations are that sites in Pretoria and Kinshasa have the highest (21,149 kWh/m2) and smallest (1120.1 kWh/m2) yearly average DNI, respectively; and sites in Abuja and Pretoria have the highest (1049 kWh/m2) and smallest (724.8 kWh/m2) yearly average GHI, respectively. However, from May to August, a high monthly GHI, with a substantial difference compared to other locations, was observed in Tripoli, as shown in Fig. 1a.

Fig. 1
figure 1

The insolation magnitude of the selected locations a monthly GHI; b yearly average GHI, DNI, and DIF

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The amount of radiation incident on the PV panel does not only depend on the power contained in the sunlight but also on the angle between the PV panel and the sun [44]. A higher amount of solar radiation is received by the GTI at an optimum angle than the GHI, either by monthly or yearly estimate. The power density will be maximum when the PV panel is perpendicular to the sun. In this study, optimum tilt angles were considered by the Solargis Prospect’s optimisation mode. The monthly GHI and GTI and yearly average GHI and GTI profiles of the selected locations are presented in Fig. 2a, b, respectively. It was observed that the location in Pretoria possesses the highest GTI (2234.4 kWh/m2) and the lowest GTI (1766.7 kWh/m2) in Kinshasa.

Fig. 2
figure 2

The selected locations’ a GHI and GTI monthly profiles; b GHI and GTI yearly average

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Correspondingly, as presented in Fig. 3, the PV power output (PVOUT) follows the same pattern of GTI, with Pretoria having the highest (17. 292 MWh/), closely followed by Addis Ababa (16.275 MWh), Tripoli (16.228 MWh), Abuja (14.715 MWh), and Kinshasa with the least (13.678 MWh), respectively. This PV electricity generation assessment is theoretical and did not consider PV system components, such as inverters, modules, battery service life, and performance degradation.

Fig. 3
figure 3

The total PV power output (PVOUT_total) of the selected locations a monthly PVOUT_total; b yearly PVOUT_total

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5.2 Solar PV potential limiting factors

There are several other meteorological variables apart from the insolation that influences the generation of electricity by PV system. These factors include relative humidity (RH), soiling, wind speed (WS), total or partial shading of the panel, dust and dirt, and ambient and cell temperatures (TEMP). Others are systems and human-dependent, such as design flaws, power loss caused by degradation, mismatch loss, alternate current and direct current wiring ohmic drops, and faults. Among the meteorological limiting variables, TEMP, RH, WS and dust impact the performance of the PV cell’s PCE and system performance.

5.2.1 Effect of ambient temperature

The standard test condition (STC) of the PV module, according to IEC 61724-1:2021 standards, is carried out at an irradiance of 1000 kWh/m2 and TEMP of 25 °C. This TEMP represents the TEMP at which solar cells absorb maximum sunlight and perform optimally. Fundamentally, at elevated TEMP, the resistance to the flow of current increases. By implication, when the panel’s TEMP exceeds 25 °C, the open circuit voltage is affected, and the solar PV module’s PCE then drop. Only about 15–20% of the received irradiance by rooftop c-Si PV panel is converted into electricity. The remaining 80–85% is lost as heat energy, which causes the working TEMP of PV panels to increase [45], and leads to the system's voltage drop [46]. The impact of temperature on PV panel production can be calculated and the result is called temperature coefficient (TEMPcoef), used to estimate the performance of the PV system during summer. The temperature coefficient is expressed as in Eq. (1) [47]:

$$ TEMP_{coef} = \frac{Power\,loss}{{TEMP_F - TEMP_{25} }} $$
(1)

where TEMPF is TEMP greater than 25 °C and TEMP25 is the STC TEMP, 25 °C.

Ambient conditions fluctuate everywhere because of seasonal variations, and the PV panels’ performance will reflect the changes [46]. The PV panels are rapidly heated to high temperatures by the unconverted 80–85% of the solar irradiance absorbed by the c-Si PV panels, as waste heat energy. High temperature triggers the system's voltage drop, PV cell degradation and the service life reduction of both solid and thin film c-Si PV modules [45, 46]. Literature has it that the TEMPcoef of PV panels is between 0.1- 0.5% per degree Celsius. This implies for every degree rise in temperature, the PV panel power falls between 0.10 and 0.5%, otherwise the power increases by the same percentage [48, 49]. The temperature coefficient differs among the PV panel technologies and solar PV panels are expected to perform better at low temperatures (below 25 °C). The temperature coefficients of the common PV cells are as follows: [50]—crystalline silicon cells (mono or poly) have TEMPcoef between − 0.45% and 0.50%; Amorphous based thin film PV TEMPcoef panels have TEMPcoef between − 0.20% and − 0.25%; and the TEMPcoef hybrid solar cells are currently from between − 0.32%.

From the results presented in Fig. 4: the Abuja location possesses the highest annual average temperature, followed by the location in Kinshasa and the lowest was observed in Addis Ababa; and the atmospheric temperatures of both locations in Pretoria and Addis Ababa are below 25 °C throughout the year. The PV cells in Abuja and Kinshasa locations are expected to perform lesser and degrade faster.

Fig. 4
figure 4

The temperature (TEMP) profile of the selected locations a monthly TEMP; b yearly average TEMP

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5.2.2 Impact of relative humidity and wind speed

The RH information extracted from the computational modelling reports presented in Fig. 5, shows that RH is highest at the site location in Kinshasa and lowest in Pretoria. Consequently, this will have the highest and lowest power output limiting effect at the locations in Kinshasa and Pretoria, respectively. According to experimental studies [51, 52], an increase in RH produces water droplets on the surface of PV panels, which aids deflection and refraction of sunlight rays. This reduces the amount of irradiance that gets to the panel and the subsequent power generation. According to a study [53], the total power output generation efficiency is decreased by about 10–20%.

Fig. 5
figure 5

Information on RH of the selected locations a the monthly profile; b the yearly average

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Based on the information extracted from the reports, as presented in Fig. 6, shows that WS is highest at the location in Tripoli all year round and followed by Pretoria while it was least at the location in Kinshasa. This implies that the cooling effect of the wind will be felt most in the system in Tripoli and the least at the location in Kinshasa. Since elevated temperature impedes the flow of current, the wind will serve as a coolant and lowers the PV panel’s temperature. Wind with moderate speed lowers the panel’s temperature significantly [45] and then improves the PCE of the PV cells [54].

Fig. 6
figure 6

Information on WS of the selected locations a the monthly profile; b the yearly average

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The lifetime of c-Si cells and thin film modules PV panels are consistently degraded by high temperatures and humid weather while the PCE of PV panels is enhanced by lower temperatures and moderate WS. Locations in Pretoria and Addis Ababa produced the highest PV output because they are favoured by the least effects of PV power generation impeding factors, such as RH, TEMP, and WS, as shown in Fig. 7.

Fig. 7
figure 7

The three main power generation performance limiting factors are a TEMP; b RH and WS

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5.2.3 Hours of sunshine (daily)

The duration of sunshine has a significant effect on the performance of PV systems. Solar PV systems convert sunlight directly into electricity through the photovoltaic effect, and the amount of sunlight they receive directly impacts their efficiency and overall electricity generation [55]. The amount of sunlight a PV system receives varies throughout the day and across seasons. Longer daylight hours are seen in Pretoria during the winter months (June–August) compared to the shorter days of summer in Fig. 8a. The duration and intensity of sunlight vary across the months based on the location of the PV system and the highest number of sunshine hours is observed in Pretoria, as shown in Fig. 8b.

Fig. 8
figure 8

The monthly daily sunshine a the monthly sunshine profile; b the number of hours of sunshine

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Higher generally leads to higher efficiency and power output because more photons are available to be converted into electricity. The most apparent significance of sunlight duration is its direct impact on energy generation [56]. Solar PV systems convert sunlight into electricity, and the longer the system is exposed to sunlight, the more energy it can produce. More sunshine means more photons are available to be converted into electricity, resulting in higher power output, as seen in Fig. 9. Pretoria and Kinshasa have the highest and least sunlight exposures and their electricity generation, respectively. Cloud cover and atmospheric conditions affect the amount of sunlight reaching the solar panels. Overcast skies or haze can reduce the intensity of sunlight and, consequently, electricity production.

Fig. 9
figure 9

Period of sunshine and the power generated

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Understanding the duration of sunshine is crucial when sizing a PV system and determining the required energy storage capacity [57]. In areas with low sun exposure, larger PV arrays or additional energy storage may be necessary to meet electricity demand. The duration of sunshine directly impacts the economic viability of installing a PV system. In areas with ample sunlight, the return on investment for a PV installation tends to be higher due to increased electricity generation. Overall, the duration of sunshine is a critical factor in determining the performance and financial benefits of a PV system. When designing and implementing PV projects, solar resource assessments are conducted to understand the available sunlight and ensure optimal system performance and energy production.

5.3 Quality of a PV system

Details on how to evaluate the performance of PV systems have been put together by the International Electrotechnical Commission (IEC), an international standards organization IEC 61724-1:2021 [58]. The IEC 61724-1:2021, which outlines equipment, terminology, and methods, serves as a basis for performing, monitoring and analysis of PV systems.

5.3.1 Performance ratio (PR)

In a grid system, PR is used to define the net energy fraction (after own consumption and energy loss are deducted) available to be injected into the grid. It is a deviation from the standard of PV system performance in real-time situations [14, 59]. It evaluates solar PV systems' performance by considering the following meteorological factors, such as irradiation, TEMP, and RH. Other variables that are considered are potential induced degradation (PID), panel degradation, and all the system losses, which include light-induced degradation (LID), mismatch and wiring losses, inverter and transformer losses, soiling losses, and system downtime [60, 61]. There are several mathematical expressions to calculate PR [59], which include Eq. (2):

$$ PR = \frac{Measured\,PV\,system\,output}{{Estimated\,PV\,system\,output}} $$
(2)
$$ Estimated\,PV\,system\,output = GTI*Area*\% eff $$
(3)

where %eff is PV module efficiency and area is PV panel surface area (m2).

The value of PR is mostly high in the winter than in the summer and ranges between 60 and 80% due to the losses in the PV system caused by elevated temperatures in summer [62]. The performance of a PV system is measured by the magnitude of the PR of the system, the higher the PR, the better the system and a PR of 80% depicts a well-performing system. However, a PR of more than 100% is not impossible. This implies that the PV system model losses are more than that of the actual system, but it is unusual [63]. The PR was introduced internationally to estimate the degree of utilisation of the complete PV system. It signifies the total influence of losses on the rated output of the PV system caused by incomplete exploitation of the irradiation, array temperature, and inefficiencies or failures of the system’s components.

5.3.2 Capacity factor (CF)

Capacity factor (CF) is the ratio of the annual average energy generated (EAC) by a renewable energy system (solar, wind, and hydro) and the installed capacity (PPV,r). In the case of PV systems, CF varies, and it ranges from 10 to 25% and depends on the location (resource quality), inverter-sizing considerations, and tracking capabilities. The deployment of tracking equipment aids energy input into the PV system and the use of larger inverters helps to increase the total output of a system [64]. Capacity factor is a parameter used to express the effectiveness of a PV system and is stated mathematically as in Eq. (4):

$$ CF = \frac{{E_{AC} }}{{P_{PV,rated} *8760}} $$
(4)

Photovoltaic design and simulation software applications are also used to generate the PR and CF of a given PV system. In this study, the monthly PR and the annual average PR and CF profiles of PV systems sited at the selected locations are presented in Figs. 10a and 4b, respectively. Figure 10b shows that the highest PR (77.4%) was observed at the locations in Kinshasa and Pretoria while the lowest (76.4%) was in Tripoli. The average PR of the five locations is 77%, which means summarily, the performance of the PV systems in the selected locations is good. The location in Pretoria recorded the highest CF (19.7%) followed by Addis Ababa (18.6%), Tripoli (18.5%), Abuja (16.8%), and the least CF was observed in Kinshasa (15.6%), as depicted in Fig. 10b.

Fig. 10
figure 10

The PV systems’ performance at the selected locations a the monthly PR profiles; b the annual average of PR and CF

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5.4 Result summary

In summary, accurate solar irradiation estimates are of paramount importance to determine and forecast PV system performance [65,66,67,68]. They enable accurate energy yield estimation, optimal system sizing and design, performance monitoring, financial analysis, grid integration, and resource assessment. Therefore, stakeholders can leverage accurate solar radiation data, to maximise the system’s efficiency, profitability, and overall success of PV installations. The summary of solar irradiation potential, locations ambient conditions, and the expected PV power output, as obtained from the study’s reports, are presented in Table 3.

Table 3 Summary of the comparative estimates
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It is obvious from Table 3 that the power out of a PV system depends on these three categories of parameters—site location, irradiation, and environmental conditions [69]. In addition, the system's configuration (PV panel azimuth and tilt angles), and PV cell technology are key PV system performance influencing factors [70]. The interaction between the PV panel and ambient conditions during operation often leads to stress that triggers panel degradation. The ambient elements include ultraviolet (UV) irradiation, WS, RH, TEMP, and soiling due to aerosols. From Table 3, it is seen that the ambient of the Pretoria location favours the PV system’s operating conditions and this accounts for the relative high-quality parameters (PR and CF) noticed. The ambient TEMP (18.4 °C) is below 25 °C, the WS is relatively moderate and this aids cooling of the PV panels, and the RH is the lowest among the locations being examined. In the same vein, the ambient of the location in Kinshasa is the reverse of Pretoria’s as seen in the magnitude of the quality parameters—PR and CF in Table 3. The ambient TEMP (25.9 °C) is above the STC (25 °C); the WS is relatively low and has a lesser effect on the cooling of PV panels; and the RH is the highest among the locations being investigated and this increases the deflected and absorbed irradiance. The comparative estimates summary table shows that the location in Pretoria leads in both irradiance resource and PV potential closely followed by the location in Addis Ababa. The lowest PV potential and system performance were observed in Kinshasa.

6 Conclusion

The PV module PCE and PV system power output depend on several parameters, such as—solar irradiance, location, PV panel orientation (azimuth and tilt angles), and PV cell technology. Other variables include cabling losses and ambient conditions of air temperature, soiling, WS, RH and dust. Some of these parameters are location-dependent, hence, the understanding and addressing of some of these parameters are key to having a PV system that operates optimally at the present efficiency of about 17–20%. The intricate process involved motivated the development of several models and software applications, such as Homer, Solargis Prospect, pvPlanner, PVsyst, and PV*SOL to estimate solar energy potential, and design and predict PV system performance accurately. In this study, a comparative assessment of the PV potential of a hypothetical c-Si 10-kWp PV systems performance at sites located in different parts of Africa was investigated using computational modelling. The sites, which are defined by coordinate systems are located in Abuja, Addis Ababa, Kinshasa, Pretoria, and Tripoli.

This study reported some valuable insights into the feasibility of rooftop PV systems in providing clean energy to meet the energy deficit and household needs in the region. The comparative assessment study shows that:

  1. 1.

    Sites in Addis Ababa and Kinshasa have the highest yearly sum of GHI (2019 kWh/m2) and lowest (1763.1 kWh/m2/year), respectively.

  2. 2.

    The location in Pretoria possesses the highest GTI (2234.4 kWh/m2), followed by Addis Ababa’s location and the lowest GTI (1766.7 kWh/m2/year) in Kinshasa.

  3. 3.

    The PV power output (PVOUT) follows the same pattern as GTI, with Pretoria having the highest yearly sum (17.292 MWh), closely followed by Addis Ababa (16.275 MWh), Tripoli (16.228 MWh), Abuja (14.715 MWh), and Kinshasa with the least (13.678 MWh), respectively.

Based on the results, the study concludes that the deployment of c-Si PV rooftop systems is technically viable across SSA.