This paper epitomizes the analysis of the solar resource measurements and simulation of curved thin-film modules for photovoltaic applications. The analysis of the solar resource used one-year from January 1, 2018, until December 31, 2018, of data from 7 stations distributed across the eastern province (Saudi Arabia) based on measurements of Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), Direct Normal Irradiance (DNI), and related meteorological parameters. The 7 stations show the results obtained for the annual average daily GHI over the study period show a variation of almost 300 Wh/m2 among station, with mean daily total values in the eastern region 6755 Wh/m2. The results above indicate that photovoltaic would perform well at any location although extreme high temperatures in some locations may degrade the performance of some types of photovoltaic. Annual average daily DNI all above 5500 Wh/m2. The average daily total DHI over the study period among stations is 2840 Wh/m2. The second part of the study uses a two-step model, 1D, and 3D model. The 1D describes the technology and outputs device (current density, temperature, illumination), 3D model uses this data as one of its inputs and describes the size, shape within the curved flexible module.
Research Article
Modeling of Flexible Thin-Film Modules and Valuation of Solar Radiation Resources in Eastern Province – Saudi Arabia
https://doi.org/10.21203/rs.3.rs-3997007/v1
This work is licensed under a CC BY 4.0 License
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The performance of a photovoltaic panel is greatly impacted by its tilt angle with the horizontal surface because the tilt angle alterations the solar radiation reaching the surface of the collector (Despotovic & Nedic, 2015) and (Stanciu, Stanciu & Paraschiv, 2016). The better method to gather the most daily energy is to use a tracking system. A tracking system is a mechanical instrument that tracks the sun on its daily extent across the sky. However, utmost photovoltaic (PV) modules are not suited for tracking, as they gather more sunlight and, therefore, produce more electricity if they are always oriented toward the sun. Trackers are expensive, need energy for their operation. Thus, it is predominating workable to orient the solar collector at an optimum tilt angle and to correct the tilt every once in a while. Some investigators have made various recommendations for the optimum tilt, based on the latitude. Lunde (Lunde, 1980) and Garg (Garg, 1982) acquired the optimum tilt angle, βopt, 𝜑 ± 15%. Duffie and Beckman (Erbs, Klein & Duffie, 1982) obtained βopt, (𝜑 ± 15%) ± 15%. Ibrahim (Ibrahim, 1995) showed the optimum tilt angles of summer months (𝜑 − 21%) and winter months (𝜑 + 13%) for Cyprus. Lave and Kleissl (Lave & Kleissl, 2011) suggested yearly optimum tilt angles for several regions of the United States as βopt, 𝜑 − (1% − 10%), by calculating the maximum incident solar radiation on the solar collector, where 𝜑 is the latitude of the location. Jafar kazemi (Jafar kazemi & Saadabadi, 2013) concluded that the yearly optimum tilt angle (βopt = 220) is almost close to the latitude of Abu Dhabi (𝜑 = 24.4%). Some other optimization methods have also been devoted to this issue, most of which treat the problem both qualitatively and quantitatively. Abdolzadeh and Mehrabian (Abdolzadeh & Mehrabian, 2012), Bakirci (Bakirci, 2012, a, b), and Despotovic (Despotovic & Nedic, 2015) rated the total (global) solar radiation intensity to obtain the optimum slope angle in a specific period for solar collectors. Kaldellis (Kaldellis, Kavadias & Zafirakis, 2012, a, b) and Pourgharibshahi (Pourgharibshahi, Abdolzadeh & Fadaeinedjad, 2015) verified the computational optimum tilt angles of a PV module by conducting experiments. The genetic algorithm method (Talebizadeh, Mehrabian & Abdolzadeh, 2011) and (Congradac et al. 2012), particle swarm optimization (Chang, 2010) and (Tabet et al. 2014), and the artificial neural network method (Dixit, Yadav & Gupta, 2015) and (Mehleri et al. 2010) were also used to estimate the optimum tilt angle of PV modules. Thus, the present study aims to develop an analytical procedure to obtain formulae that determine βopt for any climatic type in Eastern Province – Saudi Arabia. The demand for aesthetically and flexible integrated photovoltaic (PV) materials is increasing steadily in many industries. Developing markets such as sustainable housing, temporary building structures, outdoor activities, electro- mobility, and mobile computing will drive the demand for decentralized energy solutions. Rectangular shaped modules are unmatched in achieving the highest efficiency, but the possibility of having PV module of different shapes is desirable for the applications in products from the creative industry, such as building – integrated photovoltaics (BIPV) and product integrated photovoltaics (PIPV), where besides the pure efficiency one must take care of the overall product design and visual appeal (Farkas & Horvat, 2010). Only a few papers are describing the simulation of curved PV modules. These works include triangular parametrization and optimization of curved modules (Groenewolt et al. 2016) and (Hofer, Nagy & Schlueter, 2016) and recent analytical model using geometric parameters of curvature and sun positions (Park et al. 2017). On the other hand, the most significant basis for sustainable development, as well as one of the most critical concerns of today's human societies, is energy and how it is consumed. S. Hoseinzadeh (S. Hoseinzadeh et al. 2020) concluded that when we use solar power to heat the inlet air of the combustion chamber of a gas turbine. This method decreased fuel consumption, which resulted in increased cycle efficiency. In 2017, Siamak Hoseinzadeh (Siamak Hoseinzadeh et al, 2017) studied the optimization of a solar-assisted heating and cooling system, the results are encouraging as to the potential of saving energy. Siamak Hoseinzadeh & Mohammad Esmaeil, in 2017 developed a MATLAB/SIMULINK model of a solar water heater and simulated for an aviculture building, the results showed that the collector surface area has a great impact on the absorber plate average temperature. In addition, a greater amount of auxiliary heating would cause the inlet and outlet of the heat exchanger to be equal for a much longer time and thus reduce the heat exchanger efficiency. Also, Siamak Hoseinzadeh (Siamak Hoseinzadeh et al, 2019) concluded that solar power decreased fuel consumption. In this study, we present a simulation of the performance of curved thin film modules used in BIPV and PIPV applications. Two study cases are presented: a solar street lamp and an active shading for a skylight. This is the first study of its kind in the Eastern Province (Saudi Arabia) concerned with the sustainability of the use of clean energy.
2.1 Data gathering and analysis methods
The data used in this paper is the first full year of broadband solar resource measurements (Global Horizontal Irradiance [GHI], Diffuse Horizontal Irradiance [DHI], and Direct Normal Irradiance [DNI]) (K.A. CARE, 2015).
2.2 Network design and instrumentation
Solar radiation can be transmitted, absorbed, or scattered by an intervening medium in varying amounts depending on the wavelength. Complex interactions of the Earth’s atmosphere with incoming solar radiation result in three fundamental broadband components of interest to solar energy conversion technologies (Stoffel et al. 2010);
-
Diffuse Horizontal Irradiance DHI is the amount of radiation received per unit area by a surface (not subject to any shade or shadow) that does not arrive on a direct path from the sun, but has been scattered by molecules and particles in the atmosphere and comes equally from all directions. Levels of DHI are generally lower under clear (blue) sky conditions than under partly cloudy (white) sky conditions. DHI data help estimate the Plane-of-Array (POA) irradiance, automating data quality assessments, and architectural daylighting design applications.
-
Direct normal irradiance (DNI) – Direct (beam) radiation available from a 5 field of view across the solar disk on a surface oriented normal to the sun’s position in the sky. Measurements of DNI are made with a pyrheliometer mounted in an automatic solar tracker. This solar component is of particular interest to concentrating solar technologies such as Concentrating Solar Power (CSP) and Concentrating Photovoltaic (CPV) systems.
-
Global Horizontal Irradiance is the total amount of shortwave radiation received from above by a horizontal surface. This value is of particular interest to photovoltaic installations and includes both Direct Normal Irradiance (DNI) and Diffuse Horizontal Irradiance (DHI). These basic solar components are related to the solar zenith angle (SZA) by Eq. (1). (Stoffel et al. 2010);
𝐺𝐻𝐼 = 𝐷𝑁𝐼 × 𝐶𝑂𝑆(𝑆𝐴𝑍) + 𝐷𝐻𝐼 (1)
2.3 Station operation and maintenance
K.A. CARE follows a protocol such that cleaning and maintenance are carried out twice weekly for RSRs as per best practices developed by the U.S. Department of Energy’s National Renewable Energy Laboratory (Stoffel et al. 2010). No calibration was required during the one- year study period of this paper.
2.4 Data collection
Data for this study were collected from 7 solar monitoring stations that were gathered by the K.A. CARE. Data was quality-assured daily. The automated program SERI QC was also utilized to analyze the three components of solar radiation, utilizing the secondary redundant sensors, and view long-term trends and parameter ratios. Short durations of data anomalies that occurred during verified cleaning periods were filled with values through interpolation or calculating a third solar component from two others.
2.5 Data analysis methods
The data used in this study are the GHI, DNI, DHI, and ambient air temperature data from the network’s first 7 installed RSRs covering the period from January 1, 2018, until December 31, 2018. Monthly, daily, and hourly average data were obtained primarily from the Renewable Resource Atlas of Saudi Arabia (Al Garni & Awasthi, 2017). Data from 7 of the stations were serially complete. The quality of the data from these stations was reviewed to ensure it was within acceptable limits and to check for unusually high errors, corrupted data, or excessive missing data. Figure 1 refers to the distribution of solar monitoring stations in the KSA. Table 1 lists the latitude, longitude, and elevation of each station. The data were analyzed to determine trends and patterns by station and region. The average daily total radiation (energy, in Wh/m2) was calculated for GHI, DNI, and DHI, to assess the overall magnitude of the solar resource at each site and within each region. The dates and values of maximum and minimum daily total radiation (GHI, DNI, and DHI), along with the standard deviation of average daily total GHI and DNI, were analyzed to identify resource variability and temporal patterns. Where possible, calculations and graphics were obtained directly from the Renewable Resource Atlas to leverage its utility in supporting such analysis. Additional required calculations were performed using R and Excel, and geospatial data was handled using QGIS.
2.6 Input Data
Symbols and abbreviations used in this paper are listed in Nomenclature. Hourly data including GHI, DNI, DHI and T_a for 7 station Saudi Arabia were obtained from the King Abdullah City for Atomic and Renewable Energy (K.A. CARE), which is the lead organization working to develop a renewable energy mix portfolio. For this study, data from tier-1 renewable resource monitoring and mapping RRMM weather stations is used, which is considered to be a research type station, providing the highest quality data, and is available for a complete year from January 2018 to December 2018. This class of station is maintained and cleaned on a daily basis and provides 1- minute interval data. The accuracy of these data is the main reason behind selecting such ground-measurement data rather than longer-term satellite estimates.
2.7 Solar angels’ equation
The solar declination, defined as the angle between the equator and the center of the sun, varies between + 23.45° and − 23.45° (Lunde, 1980). At any time of day, the sun’s location can be defined in terms of its altitude angle β and its azimuth angle φ_s as shown in Fig. 2 (Masters, 2004). The solar azimuth angle is considered positive before noon, when the sun is in the east, and negative in the afternoon when the sun in the west. The solar altitude angle β and solar azimuth φs can be calculated and graphed at any given latitude and Fig. 3 illustrates the sun’s path in altitude and azimuth angles for Dammam (latitude 26.40°).
2.8 Approach and techniques
In the present study, we use a combined 1D/3D model of a photovoltaic module (Fig. 4) (Bednar, Severino & Adamovic, 2015). The 1D model describes the physical processes in the solar cells. The 3D model describes the geometry and bending shape of the whole solar module. The simulations based on the two models are run sequentially, as the resulting J-V curve of the 1D model is used as input data in the 3D model. The baseline parameters of all the cell layers are taken from the literature (Krc et al. 2006) and (Ray et al. 2011).
The simulations on the module level were carried out using a finite element method (FEM) COMSOL Multiphysics software package (Werner, Mattheis & Rau, 2005) and (COMSOL Multiphysics). The simulation of curved PV cells is enabled by the introduction of a scaling factor to an equivalent flat module with the same dimensions, which is then applied on the transversal photo-current, scaling its independence of the illumination level of each point along the curved Module’s surface. The tilt and azimuth angles of the equivalent flat module are chosen to be median values of the curved module (or of the part of the module if the module model is divided into several parts). Sun positions and insolation levels were calculated using the procedure described in (Blanco et al. 2001). The incoming light intensity on a module with an arbitrary tilt and orientation can be calculated as; (Blanco et al. 2001);
𝑆MODULE = 𝑆INCIDENT 𝑐𝑜𝑠𝛾 = 𝑆INCIDENT. \(\overrightarrow{S}.\overrightarrow{N}\) (2)
where 𝛾 is the angle between the unit vector towards the Sun (S), and the unit vector normal to the surface of the module (N). Also, it can be written as; (Blanco et al. 2001).
$${S}_{M}={S}_{INCIDENT}\left[cos\alpha sin \beta cos\left(\varPsi -\theta \right)+sin\alpha cos\beta \right]$$
3
where SMODULE and SINCIDENT are the light intensities on the module and of the incoming light, α is the Sun elevation angle, 𝛽module tilt angle (𝛽= 0° for module flat on the ground, and 𝛽= 90° for the vertical module), ϴ is the Sun azimuth angle (ϴ = 0° corresponds to North) and Ψ is module azimuth angle.
|
Provence |
City |
Station Nam |
Station Abbreviation |
Station Type (Tire) |
Latitude (N) |
Longitude (E |
Elevation (m) |
|---|---|---|---|---|---|---|---|
|
Eastern Province |
Al Ahsa |
King Faisal University |
Al Ahsa - KFU |
1 C |
25.34616 |
49.5956 |
170 |
|
Al Dhahran |
King Fahd University of Petroleum and Minerals |
Al Dhahran - KFUPM |
2 |
26.30355 |
50.14412 |
75 |
|
|
Al Dammam |
Imam Abdulrahman Bin Faisal University |
Dammam - IAFU |
1 B |
26.39519 |
50.18898 |
28 |
|
|
Hafar Al Batin |
Hafar Al Batin Technical College |
Hafar Al Batin - TVTC |
2 |
28.33202 |
45.95708 |
383 |
|
|
Al Jubail |
Saline Water Conversion Corporation (Jubail) |
Al Jubail - SWCC |
2 |
26.9042 |
49.76274 |
89 |
|
|
Al Khafji |
Saline Water Conversion Corporation (Al Khafji) |
Al Khafji - SWCC |
1 C |
28.48 |
48.48 |
5 |
|
|
Salwa |
Salwa |
Salwa |
2 |
24.763 |
50.811 |
N/A |
2.9 Solar street lamp
The first module presents a highly efficient solar street light for urban areas. The lamp possesses a dome-like structure (Fig. 5), with PV modules covering the whole shape except the top. The diameter of the model is 80 cm. All components (battery, electronics, etc.) are hidden within the dome. The presented simulation approach was used to estimate the daily power yield of the prototype to design the system to be fully self-sufficient. A battery intended to be used in this application could provide 110 Wh/day. The implemented electronics include a maximum power point tracker (MPPT) and a DC/ DC converter.
The analysis includes the assessment of the daily solar irradiation data from each measurement location, evaluation of clearness, and findings from other parameters.
3.1 Summaries by station (DNI, DHI and GHI)
The first analysis was to review the average daily totals of GHI, DNI, and DHI across the 8 - month study period to understand patterns by the station, region shown in Figs. 6, 7, 8, 9 and 10.
These daily total variability statistics can be useful for understanding the impact of a variable resource on the output consistency of flat plate PV and concentrating solar systems. Comparing station statistics presented in Fig. 11, the average daily total GHI over the study period shows a variation of almost 300 Wh/m2 among station, with mean daily total values in the eastern region 6,755 Wh/m2.
The variability of average daily total DNI over the study period among stations is relatively high. This variability can be attributed to different levels of clouds, humidity, dust, and pollution (and possibly fog, sea salt particles, etc.). The stations at Hafar Al-Batin and Imam Abdulrahman Bin Faisal University have the highest average daily total DNI, all above 5500 Wh/m2. Generally, the lowest average daily total DNI values over the study period were also seen in KHAFJI and King Fahd University of Petroleum and Minerals. The average daily total DHI over the study period among stations is 2840 Wh/m2, also tied to the factors influencing DNI. As expected, the DHI generally forms a pattern opposite to the DNI pattern, such that the stations with higher DNI values have lower DHI values due to less atmospheric scattering of the radiation. The coastal station at Al Jubail high humidity levels are recorded here (average relative humidity of 58%) compared to the nearest stations, as shown in Fig. 12.
3.2 Solar street lamp “Solar Hub”
“Solar Hub” shape is a vertically oriented cut-off ellipsoid, divided into 16 vertical pieces. The shape of the modules in the system is presented in Fig. 5. To simplify the calculations, two extreme cases were simulated: one day in summer, and one day in winter. In both cases, cloudless weather conditions were presumed. The considered temperatures of the modules are shown in Table 2. Elevation and azimuth angles of the Sun for the two days were calculated using Eq. (3) and are presented in Table 3. The bending stress in this study case is not critical, since the bending radius is very large, around 750 mm.
3.2.1 Cells’ sizes and orientation
The PV device is intended to charge the 12 V battery with the charging voltage of 13.6 V. The overall voltage delivered by a PV system is determined by a number of cells connected serially, and their VMP. VMP is not much affected by the change of the illumination of the cell, and it can be considered to be constant at the constant temperature (for this iteration of the simulations the temperature was set to be 25°). The 1D simulation of the PV device has shown that the voltage of one cell is VMP = 0.4 V. Respectively, the output voltage of the system is VMPtotal = VMPn, where n is the number of serially connected cells. In order to meet the voltage requirement of VMP total = 13.6 V. Figure 13 (a, b, c, and d) presents the simulated spectra for Dammam (26.4207° N, 50.0888° E) at different times of a day in July and February for the two tilted module angle which corresponds to the median tilt value of the PV modules (2.3° and 41.1°). We simulated the illumination conditions during different hours in the afternoon since it was observed that the illumination condition in the morning is comparable to the one calculated for the afternoon. Since the “Solar Hub” device is symmetrical in all orientations, in order to simplify simulations the azimuth of the module was considered equal to the azimuth of the Sun, for all Sun positions.
|
Temperature of modules (K) |
|||||||
|---|---|---|---|---|---|---|---|
|
Time |
6 h |
8 h |
10 h |
12 h |
14 h |
16 h |
18 h |
|
Summer |
300 |
320 |
340 |
340 |
340 |
320 |
300 |
|
Winter |
- |
300 |
310 |
320 |
310 |
300 |
- |
|
Time of day |
Summer |
Winter |
||
|---|---|---|---|---|
|
\(\alpha\) – elevation (0) |
\(\theta\) – azimuth (0) |
\(\alpha\) – elevation (0) |
\(\theta\) – azimuth (0) |
|
|
6 h |
14 |
71.82 |
- |
- |
|
8 h |
36 |
90.10 |
8.15 |
119.80 |
|
10 h |
58.02 |
115.47 |
25 |
144.25 |
|
12 h |
71.58 |
176.46 |
32.90 |
176.12 |
|
14 h |
59.66 |
241.55 |
27.90 |
209.05 |
|
16 h |
37.90 |
268.25 |
12.70 |
235.25 |
|
18 h |
15.47 |
288.70 |
- |
- |
3.2.2 Simulation results: 1D device level
These illumination spectra were used in the 1D simulation (device level). The resulting I-V curves are presented in Fig. 14 (a, b, c, and d). The temperature plays a vital role in the performance of the solar cells since its changes affect some device's parameters, most notably VOC. With the temperature rise, the JSC stays relatively constant while the VOC dramatically drops; hence, the efficiency in general decreases. In the simulation of the I-V curves, temperatures from Table 3 were used.
3.2.3 Simulation results: 3D cell/module level
To simulate the curved geometry of the modules in COMSOL, modules were considered flat, and the illumination levels (IV curves) were changed to meet the angles of curvature. Based on the calculated incident angle 𝜸, I-V curves of each point of the model were scaled. In this way, the geometry of the model was not changed. For the simulation convenience, the model of the module was divided into 2 segments: lower segment (65% < 𝛽 < 87%) and higher segment (43% < 𝛽 < 65% ). Result of the simulation of one cell (distribution of voltage on the surface of the cell) in a given time of day is presented in Fig. 15.
As the “Solar Hub” shape is uniform in all azimuth angles, the azimuth angle in the simulation is relative azimuth angle between the Sun and the module. If we focus on one pair of modules connected serially, it can be seen that there are 3 characteristic cases of azimuth difference (θ - Ψ) between the Sun and modules (Fig. 16).
The following results were obtained for a typical summer and typical winter day in Dammam (Table 4 and Table 5). Power output during the day based on these data is shown in Fig. 17. It was presumed that the day is sunny, without clouds. However, the real values can vary in cloudy weather. By integration over the hours, it was obtained that total energy generated during a day is 150 Wh/day for winter and 220 Wh/day during the summer. This was proved to be enough for the intended application even considering the absence of the clear sky for several days.
3.3 Simulation techniques
In this work, we use a combined 1D/3D model of a photovoltaic module (Fig. 4) (Bednar, Severino & Adamovic, 2015). The one-dimensional material (device) level model describes the physical processes in solar cell, such as generation, transport and recombination of carriers. The three-dimensional module level model describes the geometry and bending shape of the whole solar module and enables the estimation of power generation. The simulations based on the two models are run sequentially, as the resulting J-V curve of the 1D model is used as an input data in 3D model. In this way, it is possible to simulate much larger surfaces than with one 3D model which would describe both material and module level. Simulations based on the 1D material model were carried out using SCAPS-1D simulation software. It is a widely used simulation software, which provides a good agreement between simulated and experimental data. The simulation using solar spectra calculated upon positions of the Sun obtained dark and illuminated J-V curves, as well as external quantum efficiency, fill factor, and other key cell performance attributes and module in particular times of the day. The baseline set of parameters used for the simulations was based on Cu(In, Ga)Se2 (CIGS) technology with the following stack of layers: Mo (800 nm) / CIGS (2000 nm) / Cds (70 nm) / Zn O: I (100 nm) / Zn O: Al (330 nm) on a flexible polyimide substrate. This CIGS technology with lower performance has been chosen to prove that it is satisfactory to use it in the intended applications. Also in BIPV and PIPV applications, it is often necessary to satisfy the appearance demands.
|
Temperature |
340 K |
340 K |
320 K |
300 K |
|---|---|---|---|---|
|
Time of day |
12 h |
14 h |
16 h |
18 h |
|
Sun elevation |
\(\alpha\) = 71.570 |
\(\alpha\) = 59.620 |
\(\alpha\) = 37.810 |
\(\alpha\) = 15.470 |
|
Relative azimuth |
Power output of the module (W) |
|||
|
11.25 (1,2) |
2.19 |
2.70 |
3.75 |
3.19 |
|
56.25 (3,4,15,16) |
1.76 |
2.00 |
2.51 |
1.95 |
|
101.25 (5,6,13,14) |
0.98 |
0.76 |
0.37 |
0 |
|
146.25 (7,8,11,12) |
0.47 |
0.18 |
0 |
0 |
|
168.25 (9,10) |
0.38 |
0 |
0 |
0 |
|
Total power |
17.98 |
17.16 |
19.02 |
14.18 |
|
Temperature |
340 K |
340 K |
320 K |
300 K |
|---|---|---|---|---|
|
Time of day |
12 h |
14 h |
16 h |
18 h |
|
Sun elevation |
\(\alpha\) = 32.860 |
\(\alpha\) = 27.900 |
\(\alpha\) = 12.680 |
\(\alpha\) = 00 |
|
Relative azimuth |
Power output of the module (W) |
|||
|
11.25 (1,2) |
3.99 |
4.23 |
3.27 |
|
|
56.25 (3,4,15,16) |
2.62 |
2.72 |
1.97 |
|
|
101.25 (5,6,13,14) |
0.26 |
0.2 |
0 |
|
|
146.25 (7,8,11,12) |
0 |
0 |
0 |
|
|
168.25 (9,10) |
0 |
0 |
0 |
|
|
Total power |
19.5 |
20.1 |
14.4 |
0 |
This study summarizes the data from seven stations across the eastern province (KSA), producing many findings of the solar resource characteristics and the network design and operations. GHI values are highly at all locations in the country with relatively low variability. This indicates that GHI values are well-suited for strong photovoltaic (PV) technology performance at any location with a relatively low Levelized cost of electricity. However, extremely high temperatures (over 30 0C annual average in some locations) may degrade the performance of some types of photovoltaic technologies, and dust settling requiring frequent panel cleaning is possible in some areas and seasons, so other environmental parameters should be a key consideration in siting PV plants. The full implementation of the network design will bring online more broadband and spectral irradiance data needed to further validate satellite-based models and support forecast development. Besides, the near-real-time collection of solar resource data will be very valuable for creating and validating solar resource forecasts to support utility - scale plant operation and electric grid integration of solar-based power generation. Also, the presented work describes a hybrid approach for the simulation of flexible thin - film modules. The developed model shows good flexibility and reasonable processing times of the FEM analysis for large-area models. It could be used for the detailed and precise simulation of thin-film solar modules with arbitrary orientation, shape, and bending profile.
Competing Interests
The author declare that there are no personal, organizational or financial conflicts of interest.
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The authors declare no competing interests.