Suitability of Existing Photovoltaic Degradation Models for Agrivoltaic Systems

Abstract

Agrivoltaic (AV) systems have the potential to meet the growing demand for sustainable societal development due to their ability to simultaneously enable food and energy production by using photovoltaics (PVs) on the same land used for agricultural activities. One of the major factors restricting the widespread implementation of AV systems is the lack of information regarding their operational lifetime, which is influenced by various degradation factors. This paper reviews the main degradation factors, modes, and physical mechanisms responsible for PV deterioration and performance inhibitors in conventional PV installations, including how these factors are evaluated, modeled, and potentially modified when placing PVs in the agricultural settings of typical AV systems. These degradation modes have been largely overlooked in modeling AV system designs for land use optimization. Therefore, further advancements are required to properly understand how agricultural environments play a role in modifying the thermal, irradiance, and hydrolysis degradation modes and whether such agricultural settings can lead to the onset of new degradation pathways. To enhance the adoption of AV systems in the agricultural sector, such insights are required to ensure that the maintenance costs are communicated to and well understood by the end users.

1. Introduction

Over the past 20 years, there has been a shift from non-renewable fossil fuels to the increased adoption of more environmentally friendly methods of electricity production [1,2,3,4]. Solar energy is one of these methods that, since 2020, has seen a ~74% increase in the total share of the global energy market. Moreover, it is estimated that by 2029, it will surpass both wind and hydropower in terms of the total portion of the world’s energy generation [5,6,7]. This increase has been driven by a combination of factors, such as the reduced costs of solar modules and innovations in renewable technologies, the transition toward energy independence, and the ongoing need to reduce carbon emissions and combat climate change [8,9,10]. In recent years, climate change has been at the forefront of many political and societal changes, with this issue being addressed through the Paris Climate Agreement and the UN Climate Change Conferences [11]. Consequently, government incentives for the installation of solar photovoltaic (PV) modules have been provided with the aim of increasing PV adoption in all industrial and societal environments and reducing CO₂ and other greenhouse gases (GHGs) produced from non-renewable energy sources [12,13,14,15,16,17,18,19]. This allowed individual households in urban, rural, or peri-urban areas to install PV systems and become energy producers or prosumers. In the European Union (EU), the strategy surrounding the plans and goals for solar energy is outlined in the EU Solar Energy Strategy, adopted in 2022, and is further reflected in the revised Renewable Energy Directive (EU/2023/2413) of the EU Parliament and Council [20,21]. Through these strategies, the EU seeks to accelerate the adoption of PV systems across the bloc and has identified agrivoltaic (AV) systems as an innovative approach that should be developed in member states [20,21,22,23]. Since 2023, member states such as France and Germany have modified existing national codes to define agrivoltaics, while countries such as Italy have provided significant funding for AV projects, with €1.5 billion in grants allocated for agri-solar capacity investment of up to 375 MW [22,24,25].

A significant contribution to the environmental impact of a country is attributable to the agricultural sector. For example, in the EU, it is estimated that the agricultural sector is the second largest source of greenhouse gas (GHG) emissions, as it is directly responsible for approximately 11% of all GHG emissions and the majority ($\approx$54%) of all methane produced. These numbers are related solely to the on-farm activities and do not include the GHG contributions from transportation, industrial processing, food product waste, and the food products that are imported into the region [23,26,27]. There has also been a trend of increased electrical energy consumption in the agricultural sector, partially due to a movement away from diesel and other fuel sources and the increased use of electric-based machinery and other technological advancements [28,29,30]. These factors, alongside government targets for reduced dependency on fossil fuel-based electricity production, have convinced stakeholders within the agricultural sector that there is a need to increase the adoption of renewable energy as a method to reduce both costs and carbon emissions and achieve other additional benefits [8,31,32]. However, the adoption of these technologies in the agricultural sector is still in its infancy, as various socio-economic and technological factors can act as barriers to PV adoption in agricultural regions [32,33,34].

Integrating PVs into agricultural landscapes allows for the dual use of agricultural land for crop production and electricity generation [35], leading to additional income and enhanced energy flexibility for the sector. Moreover, this can lead to reduced water usage [36,37,38,39,40], lower temperature exposure for crops and livestock [41,42,43], and improved crop yields in certain cases [37,38,41,44]. This adoption, however, comes with additional concerns that range from the effects on the environment and landscape to more practical concerns related to PV deployment options and the lifetime of PV modules in such new intensive activity settings. These settings can differ significantly from those of a classical rooftop solar installation. Consequently, placing PV modules in the intense activity environments encountered on agricultural farmlands can significantly affect PV lifetime and, in turn, the feasibility of adopting such AV solutions because the PVs could potentially be exposed to a higher number and/or more intensive degradation factors. This combination of intensified degradation factors can result in lower power conversion efficiencies from the PV system during the agricultural cycle and a shortened PV lifetime, leading to higher operating and maintenance costs for the AV system over its projected operational lifetime. This increased cost hinders the potential for further adoption and penetration of PVs into the agricultural sector by interested stakeholders. Therefore, there is a need to understand these factors and estimate their possible influence on the degradation of PV modules. Despite a significant increase in the number of publications related to PVs in agriculture (agrivoltaics) over the last five years (Figure 1), the number of publications involving degradation studies of agrivoltaics or PVs in agricultural settings or related to their exposure to these environments remains marginal. This suggests the existence of a gap in the literature related to degradation, its mechanisms and processes, and its modeling in the context of agrivoltaics. This gap needs to be explored by researchers to enhance AV adoption in rural communities.

In this paper, we will review some of the degradation factors affecting PVs and present the main models commonly used to evaluate PV degradation in conventional installations, along with suggestions for factors that will have to be introduced or revised to evaluate this degradation in new agricultural settings. Examples of possible integration configurations for PVs in agricultural settings are depicted in Figure 2, which presents both traditional configurations and novel methods. This review also intends to highlight to the research community the importance and necessity of studying the degradation processes. While the majority of research focuses on creating new and more efficient PV cells and systems, there is a limited amount of work dedicated to understanding the degradation of existing deployed systems. In particular, in agrivoltaics, the main research areas focus on developing new configurations with enhanced electrical output, but very little attention is given to evaluating PV lifetime and degradation in such environments. Such information is required in order to increase PV adoption among the population and stakeholders in rural and/or peri-urban areas.

2. Application of Agrivoltaics and Potential Requirements

Agrivoltaics is a method for intensifying the returns/usage of existing agricultural land by integrating PVs for energy generation alongside agricultural crops. The key motivation behind this concept is to maximize the total combined yield of crops and energy using the same fixed area of land [32,40,45,46]. In Figure 3, hypothetical land usage is compared for solar energy production, crop production, and the combination of these use cases. The Land Equivalent Ratio (LER), defined in Equation (1), is typically utilized to determine the improved productivity or efficiency acquired through dual-purpose land usage with AV systems as compared to their single land use counterparts [40], and is defined as follows:

$$LER=\left({\displaystyle \frac{Y_{cr,AV}}{Y_{cr,ref}}}·\left(1-LL\right)\right)+\left({\displaystyle \frac{Y_{e,AV}}{Y_{e,ref}}}\right),$$

(1)

where $Y_{cr,AV}$ is the crop yield (in units such as tonnes and bushels) under AV system conditions, and $Y_{cr,ref}$ is the crop yield without the AV system installed under the same environmental conditions; LL is the fraction of land lost or unavailable for crop use due to the footprint of the AV system; $Y_{e,AV}$ is the energy yield (measured in kWh) for the AV system, and $Y_{e,ref}$ is the energy yield for a traditional ground-mounted PV system shown in Figure 2a, which maximizes the electricity production using the land available. In the hypothetical scenario depicted in Figure 3, despite the lower individual yield for each type of land use (~30% reduction in power output and land use for crops), the LER of the same area of land can be increased to 120% through the successful integration of the PVs [32,40,45,46]. Both individual land uses are quite intensive, as large solar farms and traditional crop farms can often require several acres of land each. In addition, this form of land use is typically concentrated in rural and peri-urban areas, which are often isolated from urban infrastructure. Competition for land usage due to encroaching urban environments, population growth, and loss of land caused by the effects of climate change, especially, i.e., rising sea levels on islands, puts further pressure on finding dual or multi-use applications for existing land [40,47,48,49].

The integration of PV modules and systems into agricultural settings, such as farms, presents several challenges. The optimal method of module integration can vary depending on the type of farming present, such as orchard crops, vegetables, grains, or livestock, as well as the availability of irrigation channels and/or ponds for the installation of floating PV solutions (Figure 2) [36,40,45,46,49,50,51]. Existing structures, such as livestock sheds and storage silos, can often support the installation of building-integrated PV systems. However, some structures, such as greenhouses, require different approaches. These approaches differ based on the requirements of the crops grown inside the greenhouse, the material of the greenhouse frame, and the weight of the PV modules. The presence of automotive machinery and crop spacing is also an influential factor, as the shading produced by PV systems can alter the effective growth rate of the crops [36,41,52,53,54]. Shading effects can have a detrimental effect on crop growth, as less sunlight is made available, or a beneficial impact when it acts as a source of cooling for livestock in dry or hot regions, where shade can reduce water dependency [42,43,55]. Thus, the growth of certain plants (e.g., lettuce) that are suited to shaded conditions can be enhanced [56]. For these reasons, AVs must overcome a significant number of challenges in developing optimal methods and/or systems to be integrated into existing agricultural settings [32,40,45].

Currently, agrivoltaics research is focused on determining the optimal combination of crop and other land uses, how PVs can best be integrated into existing or planned farm infrastructure, and how the real gains of such systems can be realized. This includes research that focuses on optimizing the clearance space of PVs and ensuring maximization of total incident sunlight while reducing the shading effects of the PV modules that hinder crop growth, either between or directly below the modules [45,46,57,58,59]. Traditional ground-mounted PV systems (Figure 2a) are space-intensive and typically must be placed according to the direction of the sun’s path through the sky while minimizing shade from structures, trees, or other sources such as hillsides and vehicles. Typical AV configurations investigated have focused on the deployment of dispersed arrays of PV modules, with space between the arrays for farm equipment and crops (Figure 2b) [37,40,45,51]. This configuration allows for the shared use of land for both electricity production and crop growth. Some variations in this crop integration have been suggested, such as increasing the vertical arrangement of the (bifacial) PV modules to maximize electrical production (Figure 2c) or placing the PV arrays much higher than the height of the plants (Figure 2d). The latter solution is typically used for integration with orchards or vineyards, where clearance is required to accommodate plant growth and development while providing appropriate machinery access [51,60,61,62]. The advantages and disadvantages of these different AV configurations are provided in

Table 1. The advantages and disadvantages of the different configurations shown in Figure 2 for integrating PV modules into AV systems.

PV Configuration	Description	Advantages	Disadvantages	Ref.
Ground integration/solar plant	Traditional usage of solar modules for electricity production	Common and tested designs, maximized solar energy production	Requires significant land usage, reduced land space for crops or infrastructure, high investment cost	[32,40,45,46,63]
Crop integration	Integration of PV systems alongside existing crops	Improved land usage (LER), reduction in electrical costs	Higher installation costs, more complex harvesting techniques, potential reduced crop yield	[32,36,37,40,45,46,64]
Vertical PV module crop integration	Bifacial PV modules mounted 90° vertically alongside crops	Greater efficiency and winter energy production than tilted PV module designs, improved land usage with crop integration	Higher investment cost for bifacial PVs, installation required at greater height than tilted PVs	[40,45,64,65,66]
Orchard/tree integration	PVs integrated alongside orchard trees above the treeline	Potential reduced effect of shading on plant growth with PVs	Greater module frame heights, higher maintenance and installation costs	[40,60,61,62]
Floating PV/waterbody integration	Floating platforms with PV modules on stable bodies of water	Utilization of land unsuitable for crops, potential reduction in surface water evaporation	High-cost investment, complex maintenance and replacement, requires stable bodies of water	[40,45,46,48,66,67]
Drainage channel integration	Placement of PV modules above agricultural drainage channels	Utilization of land without interference with crop growth	Complex maintenance and replacement, higher shading on PVs due to height placement	[40,64,68]
Livestock field integration	Placement of PV modules above livestock fields/alongside livestock infrastructure	Simple design and integration, improved land usage, potential cooling/shading of livestock	Potential damage from livestock, limited to fenced grazing fields	[40,42,43,55,64]
Building/greenhouse integration	Integration of PVs onto existing farm structures/semi-transparent PVs for greenhouse	Simple design, minimal planning requirements for typical PV modules, utilization of existing space	High-cost investment, requires correct integration and placement with buildings	[40,64,69,70]

.

The more novel approaches that have been considered include the placement of PVs on floating structures (Figure 2e) or within farm irrigation and drainage systems (Figure 2f). The advantages and disadvantages of these configurations are presented in Table 1. These configurations have the advantage of not impeding the growth of crops while still utilizing space that is currently taken up and may not be fully productive. There have also been investigations into whether some shading effects caused by the overhead PV modules can be useful (e.g., in preventing water evaporation and improving aquaculture and aquatic life development). For certain crops, direct sunlight is not ideal; thus, shading that reduces incident sunlight on the plants may be preferred [52,71,72,73]. Overhead PV modules (Figure 2g) can also be utilized as shade for grazing livestock, providing cooling in open spaces where animals graze underneath such systems. This shading can help reduce water usage, as animals experience less heat stress and, thus, reduced dehydration [42,74]. Given this degree of complexity, determining the methods by which AVs can best be utilized in agriculture will require significant modeling and investigations into designing the optimal configurations that maximize both crop and energy outputs. These optimal configurations will vary depending on location, geographical landscape, cost, and other factors that come into play, such as the existing grid infrastructure. Therefore, the use of models is required to determine optimal conditions and tailor solutions for different crops, regions, and weather patterns.

In addition to determining the optimal configurations of AV systems in existing farm infrastructure, AV systems modeling should also consider PV degradation and lifetime models. The lifetime of a PV module is related to the total degradation it experiences, which can lead to reduced performance or even failure of the module. The degradation rate is the average annual reduction in the maximum power output of the PV system. Typically, a PV module is considered in need of replacement (i.e., it is a faulty module) once its maximum power output has been reduced by >20% of its original output at installation. Typically, the current commercial silicon-based PV modules have an average guaranteed operational period of >20+ years before failure occurs. Therefore, determining the degradation rate of the PV system is valuable in determining the lifespan of a PV system, as high degradation rates lead to lower lifetimes [75,76,77,78,79]. For instance, the PV modules that are currently being integrated into AV systems have not been evaluated using a standardized method of degradation testing specific to agricultural settings in which they are placed. Instead, the degradation and lifetime testing of these PV modules continue to be evaluated using standardized testing practices that were originally developed for traditional (rooftop or PV power plant) forms of PV installations [62,80,81]. For AV systems, estimating the lifetime of the system is useful for calculating the total cost of the system, including maintenance (i.e., the replacement of failed modules), and, thus, the economic feasibility of the integrated AV system. Modeling the degradation of AV systems can, therefore, be utilized to estimate the lifetime of the systems and, thus, their economic feasibility in an agricultural environment.

3. Photovoltaic Module Degradation Modeling

PV degradation modeling emerged as a direct need in the late 1990s and early 2000s, when solar energy began to gain traction and the uptake of the technology increased [82]. Although, unlike traditional methods of electricity production, PV modules can produce electricity without any moving components, wear/degradation and/or mechanical failure are still present. Therefore, there is a need to estimate the effects that various factors can have on the conversion efficiency and lifetime of a PV module, as this will allow for smooth usage and maintenance of the PV modules. It should be noted that, from this perspective and given its rapid development, PVs can still be seen as a relatively new technology. Initially, the models of degradation were primarily utilized only in the aerospace industry for the specific requirements necessary for non-terrestrial power generation [82,83]. As PV modules began to be installed and used commercially, the degradation and expected time before failure (i.e., the PV lifetime) were not well understood, as real-world testing of PV module degradation and lifetime often required several years or more to complete. Thus, the traditional method of PV degradation testing (i.e., placing the PV in relevant environments and monitoring its degradation over a period of time) quickly became unfeasible due to the rapidly evolving nature of the technology. For instance, a specific variant of a PV module could become obsolete during the period of the degradation study of the device, and newer, more innovative modules would then have to undergo the same evaluative process before they could be made commercially available [82,84,85,86]. During these early years of adoption, the PV systems had a high upfront cost due to immature technology, and the cost savings offered by PV modules were significantly tied to the overall longevity of the module’s operation.

Since PV modules require minimal maintenance and operate using sunlight, the costs of the system were frontloaded into material purchase and installation. The resulting cost of manufacturing and installing a PV system was quantified using the payback period specified for the system. This is defined as the operating time required for the PV system to offset its production and installation costs. Consequently, PV manufacturers needed to guarantee that modules could operate for long enough periods to recover these costs. As a result, methods for accelerating the lifetime testing of PVs were developed alongside PV degradation modeling to estimate lifetimes without relying solely on long-term experimental and/or analytical studies [87].

The rate of degradation of PV devices is determined using two broad groups of modeling approaches: data-driven models and physics-of-failure (POF) models. These methods are used to determine the degradation rate and lifetime of a PV system [82,87]. Data-driven models utilize statistical analysis of the performance changes in the device (in this case, the PV) measured over time, taking into account environmental factors such as temperature, irradiance, and humidity [84,88]. Typically, in the development of data-driven models, the performance of the PV device is measured periodically over a period ranging from 1000 h (usually with accelerated degradation) to 30 years (with long-term field testing). During these periods, the electrical properties of the PV module are measured and tested frequently to observe changes in its performance and, thus, to determine the rate of degradation at specific points in time under varying factors such astemperature, irradiance, and humidity. These data can then be extrapolated to create a mathematical model for the degradation of the particular PV system [89,90,91]. Data-driven models are most often utilized in accelerated degradation testing of PVs. This form of degradation testing involves subjecting a PV cell or module to significantly increased levels of irradiation and temperature to accelerate the time to failure of the PV while monitoring the degradation of its performance based on the total hours of continuous testing. Accelerated testing processes are typically carried out continuously over a period of >1000 h, which is shorter than the time required for field testing (<1 year) [86,87,92].

In comparison, physics-based modeling methods aim to identify the physical mechanisms that occur inside a PV module that can lead to degradation and performance losses in the module. These include mechanisms such as thermal damage to the crystal structure, hydrolysis, and oxidation of the electrical connections. The degradation resulting from a physical mechanism is referred to as a degradation mode. Several of these degradation modes can occur simultaneously, each arising from different environmental sources or factors. Some of these modes can also be affected by two or more different environmental factors simultaneously. Such an example is hydrolysis, which is the result of both humidity and temperature at the location where the PV module is installed. Several distinct physical mechanisms can contribute to various degradation modes, and each degradation mode can contribute to additional degradation modes, creating a complex network of degradation routes. Eventually, the combination of these factors and degradation modes contributes to several mechanisms of performance loss, such as increased resistance or reduced photocurrent in the module [82,84,85,93]. Figure 4 illustrates the main environmental factors and the predominant degradation modes attributed to each factor. The overall outcome of these degradation modes is the reduced performance of the PV module [82,84,85,94].

Although various degradation/environmental factors affect a PV module, the effectiveness of each degradation mode in causing significant degradation in the module can vary based on the type of PV material. For example, the effects of degradation arising from thermal stress, hydrolysis, and ultraviolet degradation depend on the composition and crystal structure of the PV material. As a result, the specific degradation modes responsible for the most significant degradation in the modules vary significantly between mono-crystalline silicon (mono c-Si), polycrystalline silicon (poly c-Si), amorphous silicon (a-Si), and other PV materials such as CIGS and CdTe, as presented in Figure 5 [95,96,97,98,99,100,101,102]. This range of degradation modes is further extended when considering the additional structures that are added to the PV module, such as the encapsulant, busbar material, backsheet, and frame. Therefore, modeling is often focused on a particular PV material and structure since the degradation behavior is significantly dependent on the combination of these factors in parallel [103]. Since silicon-based PVs accounted for ~97% of total PV production in 2023 [104], it is appropriate to focus on the degradation models and mechanisms most associated with these PVs, as the utilization of these degradation models is more beneficial for the vast majority of AV installations.

This study aims to review the various mechanisms through which the degradation of PVs occurs and how these mechanisms are effectively observed and modeled to enable the estimation of PV module lifetimes. Therefore, the focus will be primarily on degradation models of crystalline-Si PVs (mono c-Si and poly c-Si), which are the predominant materials used for outdoor PV modules [83,104,105]. Additionally, the use of these models and how they can be effectively utilized will be discussed, with a focus on their potential application in modeling PV modules for AV applications, the specific requirements of AV integration, and the current issues facing AVs in this context.

3.1. Comprehensive PV Degradation Modeling

The evaluation of the total degradation rate of a PV module is simultaneously challenging because the PV module is exposed to several stress/environmental factors (Figure 4). This can result in a variety of distinct and interconnected degradation modes, as shown in Figure 6 [106,107] and described later in the text.

Kaaya’s model [107], as defined by Equation (2), serves as the primary model and basis for calculating total degradation, as it defines and evaluates the cumulative effect of independent and interdependent degradation processes. Therefore, the mathematical form of the total degradation rate is as follows:

$$k_T=A_N\cdot {\prod }_{i=1}^n\left(1+k_i\right)-1,$$

(2)

where $k_T\left[\%/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\right]$ is the total degradation rate of the PV module, $k_i$ is the $i^{th}$ constant rate $\left[\mathrm{y}\mathrm{e}\mathrm{a}{\mathrm{r}}^{-1}\right]$, $n$ is the total number of degradation processes, and $A_N$ is the normalization constant $\left[\%/{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}^2\right]$ that ensures the final result is properly scaled in accordance with the physical quantities involved and the guidelines outlined in [106,107]. Thermal, photo, and hydrolysis are the three primary degradation modes. Therefore, the total degradation rate of these three modes is expressed by Kaaya in Equation (3) [107]:

$$k_T=A_N\cdot \left(1+k_{Tm}\right)\left(1+k_P\right)\left(1+k_H\right)-1,$$

(3)

where $k_{Tm}$, $k_P,$ and $k_H$, all measured in $\left[\%/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\right]$, are the rates for the thermal degradation, photo degradation, and hydrolysis degradation, presented in this study in Section 3.1, Section 3.2 and Section 3.3, respectively [106,107]. The failure time $t_f$ is the time required for the maximum power delivered by a PV module to decrease by 20% from its original value at the time of manufacture and can be calculated using Equation (4), as follows:

$$t_f={\displaystyle \frac{B}{k_T-{\left(\left|\log \left(0.2\right)\right|\right)}^{\frac{1}{\mu }}}},$$

(4)

where $B$ is a power susceptibility parameter, assumed to be a material property, while $\mu$ is a shape parameter that can be empirically determined by adjusting its value to fit a particular power degradation curve [107].

3.2. Thermal Degradation

The specific degradation rate model used to calculate the thermal degradation by Kaaya et al. [106,107] is presented in Equation (5), as follows:

$$k_{Tm}=A_t\cdot C_N\cdot {\left(273+\Delta T\right)}^{\mathsf{\Theta }}\cdot \exp \left(-{\displaystyle \frac{E_{at}}{k_B\cdot T_{max}}}\right),$$

(5)

where $E_{at} \left[\mathrm{e}\mathrm{V}\right]$ is the activation energy of power degradation due to the thermal mechanism, which represents the energy barrier that must be overcome for this degradation process to occur. $A_t$ is the pre-exponential constant of the Arrhenius equation, which is independent of the temperature, but it is process-dependent. In this case, $A_t$ is considered to be $\left[{\mathrm{K}}^{-1}\mathrm{c}\mathrm{y}\mathrm{c}\mathrm{l}{\mathrm{e}}^{-1}\right]$. $k_B$ is the Boltzmann constant $\left(8.62\cdot {10}^{-5} \mathrm{e}\mathrm{V}{\mathrm{K}}^{-1}\right)$, $T_{mod} \left[\mathrm{K}\right]$ is the module temperature, $\Delta T=T_{max}-T_{min}$ is the temperature difference, $T_{max}$ and $T_{min}$ are the module maximum and minimum temperatures, $\mathsf{\Theta }$ is Peck’s exponent, which is a material property that is determined empirically, and $C_N \left[\mathrm{c}\mathrm{y}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{s}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\right]$ is the cycling rate [106,107,108].

The impact of climate-related variables, such as PV module temperature, UV dose, and relative humidity, on the degradation mechanisms included in Kaaya’s model is estimated using other variables that are not necessarily measured directly. Thus, their values can be determined using various models. For example, commonly used models for estimating climate-related input variables for temperature can be used [105]. Two commonly used models for estimating the temperature of PV modules in the assessment of the thermal degradation are the Faiman model (Equation (6)) and the Ross model (Equation (7)) [107,109,110]. The differences between these two models are that the Faiman model includes environmental factors, such as the cooling effect of wind, while the Ross model only considers ambient temperature and solar irradiance [107], as follows:

$$T_{mod}=T_{amb}+{\displaystyle \frac{E_{POA}}{U_0+U_1\cdot w_s}},$$

(6)

$$T_{mod}=T_{amb}+k_{Ross}\cdot E_{POA},$$

(7)

where $T_{mod}$ and $T_{amb}$ $\left[\mathrm{K}\right]$ are the temperature of the PV module and the ambient temperature, respectively; $E_{POA}$ $\left[\mathrm{W}/{\mathrm{m}}^2\right]$ is the incident solar irradiance on the module; $w_s$ $\left[\mathrm{m}/\mathrm{s}\right]$ is the wind speed; $U_0\left[\mathrm{k}\mathrm{W}/{\mathrm{m}}^2/\mathrm{K}\right]$ is the coefficient which accounts for the impact of radiation on the module temperature; and $U_1\left[\mathrm{k}\mathrm{W}/\mathrm{K}\right]$ is the cooling effect of wind, both being specific to the type of photovoltaic cell and/or module used. For example, in the study by Koehl et al. [111], $U_0=30 \left[\mathrm{k}\mathrm{W}/{\mathrm{m}}^2/°\mathrm{C}\right]$, a value which is typical for a module composed of crystalline silicon cells, while $U_1=6 \left[\mathrm{k}\mathrm{W}/{\mathrm{m}}^2/\mathrm{K}\right]$ as used in the calculation of the nominal operating temperature of the module in IEC 61215-1 [107,111,112]. $k_{Ross}$ $\left[\mathrm{K}{\mathrm{m}}^2/\mathrm{W}\right]$ is a coefficient that describes the heat transfer characteristics of the PV module and has values between 0.02 and 0.04 $\left[\mathrm{K}·{\mathrm{m}}^2/\mathrm{W}\right],$ depending on the PV array types, such as well-cooled, free-standing, flat on roof, transparent PV, and on sloped roof, which are detailed in [107,113].

The ability of the Faiman [109] and Ross [110] models to accurately predict and model the temperature of a mono c-Si PV module when placed in different geographical locations, and the resulting impact of uncertainty in these predictions on the thermal degradation rate calculated using Equation (4), was evaluated by Kaaya et al. [107]. This inter-comparison of the accuracy of the two models was conducted using measured temperature data collected over the course of a year at each geographical location where the mono c-Si PV module was placed. The results of this inter-comparison showed that, depending on the model used, the uncertainty in the estimated temperature of the PV module ranged from 12.8% to 40.8% compared to the measured temperature. This uncertainty resulted in a divergence of 1.7% to 14.5% between the estimated thermal degradation rate and the experimentally measured thermal degradation rate for the PV module. Based on the results presented in [103], it was concluded that the Faiman model is more accurate than the Ross model in estimating the PV module temperature, as it exhibited a smaller error across all geographical locations. The uncertainties in temperature models seem to lead to smaller variations in degradation rates in colder regions, whereas for warmer regions, the uncertainties were larger [107].

The Faiman and Ross models offer slightly different approaches but still complement each other for PV degradation analysis, each having its own advantages and limitations. The Faiman model describes the relationship between the operating temperature of the PV module and environmental factors, such as the cooling effect of wind, which makes it easier to put into practice. It can also be used to evaluate long-term PV degradation (up to 60 years) using Equation (5). In terms of limitations, this model is not accurate enough under atypical climatic conditions, such as extreme temperatures or rapid temperature fluctuations, because it simplifies certain essential physical aspects (e.g., radiation losses, material fatigue, and heat transfer mechanisms). In comparison, the Ross model incorporates a more detailed description of the empirical relationship between the performance of a PV module and its operating temperature, making it more accurate in evaluating thermal degradation. However, this model may require adjustments (e.g., recalibration of heat transfer coefficients) when modeling the operating temperature of PVs under more complex operating conditions, such as in regions where the factors considered for the thermal degradation model have extreme values. Overall, each of these models is valuable for evaluating the thermal degradation in PV systems, and the most suitable one should be selected depending on the application requirements and the necessary level of precision. The proposal to combine these models could provide more complete results for PV evaluation and durability improvement, but this is an open discussion and requires further studies [107].

A modified version of Kaaya’s model was developed by Zhuangzhuang Li et al. [114] to consider the impacts of harsh environments on the thermal degradation rate of PVs. This includes environments with high solar irradiance (4.2 $\left[\mathrm{k}\mathrm{W}\mathrm{h}/{\mathrm{m}}^2\right]$ for land PV power plants and 3.6 $\left[\mathrm{k}\mathrm{W}\mathrm{h}/{\mathrm{m}}^2\right]$ for floating PV power plants) combined with low humidity (73.16% for land power plants, and 81.05% for floating power plants), as well as environments with high humidity, high wind speed, and corrosion from saltwater spray affecting the PV degradation rate. The improved model is presented in Equation (8), which is as follows:

$$k_t=A_t\cdot {(\Delta T)}^{\gamma }\left(1+{\left(\Delta WS\right)}^{\epsilon } \right)\cdot \exp \left(-{\displaystyle \frac{E_{at}}{k_B\cdot T_{max}}}\right),$$

(8)

where $\Delta T,$ in this case, is the ambient temperature difference $\left(T_{max}-T_{min}\right) \left[\mathrm{K}\right]\mathrm{ }$, $\Delta WS$ represents the wind speed difference $\left(Win{d}_{max}-Win{d}_{min}\right) \left[\mathrm{m}/\mathrm{s}\right]$, $\gamma$ and $\epsilon$ represent the fitting parameters obtained from solar power plants, land ($\gamma$ = 2.1, $\epsilon$ = 0.29) and floating ($\gamma$ = 3.22 and $\epsilon$ = 0.44) [114].

In this modified version of the model, Zhuangzhuang Li et al. apply a coupling weight for each of the three degradation modes presented in Figure 5. Therefore, the regional dependencies of the climatic conditions to which the PV is subjected during its operation can be accounted for in this model by adjusting these weights. In this modified version of Kaaya’s model, additional weighted couplings that account for the influence of wind stress on the temperature cycling of the PV were incorporated. This application of further real-world data from the ground-based PV power plant and floating solar power plant aids in the validation of the model by Kaaya et al. The limitation of this thermal degradation model (Equation (8)), which also includes the modifications of Kaaya’s model, is that the validation was based on data from PV power plants and studies of PV systems that have not been under study/in operation for more than five years [114,115]. In comparison, the original version of the thermal degradation rate included in Kaaya’s model (Equation (5)) has been extensively validated using data from PV systems that have been operating for 30+ years in various climatic conditions and types of installations.

For this reason, it is recommended that these thermal degradation models be validated using data that have been experimentally acquired from PV power plants and other types of PV systems over periods longer than five years. This will help increase the credibility of these studies and, consequently, determine the accuracy of these models in the long term.

3.3. Photo Degradation and Deterioration (UV Degradation)

The specific model used to determine the degradation rate for the photodegradation process by Kaaya et al. [106,107] is presented in Equation (9), which is as follows:

$$k_P=A_p\cdot U{V}^X\cdot \left(1+R{H}^n\right)\cdot \exp \left(-{\displaystyle \frac{E_{ap}}{k_B\cdot T_{mod}}}\right),$$

(9)

where $E_{ap}$ $\left[\mathrm{e}\mathrm{V}\right]$ is the activation energy of power degradation due to the photodegradation, $A_p\left[{\mathrm{m}}^2/\mathrm{k}\mathrm{W}\mathrm{h}\right]$ is the pre-exponential constant, $k_B$ is the Boltzmann constant $\left(8.62\cdot {10}^{-5}\mathrm{ }\mathrm{e}\mathrm{V}{\mathrm{K}}^{-1}\right)$,$X$ and $n$ are model parameters that indicate the impact of $UV$ dose $\left[\mathrm{k}\mathrm{W}\mathrm{h}/\mathrm{a}/{\mathrm{m}}^2\right]$ and of the relative humidity $RH$, and $T_{mod}$ $\left[\mathrm{K}\right]$ is the module temperature, which is determined using either Equation (5) or Equation (6) [106,107].

Ultraviolet (UV) radiation in the wavelength range of approximately 280–400 nm can affect the PV modules, as this radiation range is outside of the spectral window of Si-based PVs [116,117]. A degradation model often used for UV estimation is the linear model, which is defined by Equation (10), as follows:

$$U{V}_{total}=c_1\cdot E_{PoA}+c_2,$$

(10)

where $U{V}_{total}$ represents the total UV irradiance $\left[\mathrm{W}{/\mathrm{m}}^2\right]$, $E_{PoA}$ is the incident solar irradiance $\left[\mathrm{W}/{\mathrm{m}}^2\right]$, $c_1$ [%] and $c_2$ [$\mathrm{W}{/\mathrm{m}}^2$] represent the location-dependent coefficients. Typically, $c_1$ is considered to be 5%, while $c_2$ is usually regarded as negligible [92]. Equation (10), developed by Wald et al., defines a more advanced model for estimating the amount of UV irradiance [107,118]. The Wald model uses the clearness index $c_t$ and incident solar irradiance $E_{PoA}$ to estimate the total UV irradiance from its $U{V}_A$ (315–400 nm) and $U{V}_B$ (280–315 nm) components (see Equation (11)) [107]. To provide more freedom to the model, the $U{V}_A$ component can be modified in the Wald model by considering two empirical coefficients (a and b), as presented in Equation (12) as follows [107]:

$$U{V}_{total}=U{V}_A+U{V}_B$$

(11)

$$U{V}_A =\left(a-b\cdot c_t\right)\cdot 10^{-2}\cdot E_{PoA}.$$

(12)

Another modeling method commonly used to estimate and quantify the amount of UV irradiance in PV degradation testing is the method proposed by Habte et al. [107,119]. This method, defined in Equation (13), uses a polynomial function of the Air Mass (AM) by global irradiation to estimate the UV irradiance [107], as follows:

$$U{V}_m=E_{PoA}\cdot \left({\sum }_{i=0}^l{m}_i\cdot A{M}^i\right),$$

(13)

where $U{V}_m$ represents the UV degradation and $m_i$ represents different coefficients (conversion factors) that are used to correct for different wavelengths or to account for atmospheric variables (such as aerosols, water vapor, and others) and l is the number of these coefficients (for example, in reference [107], l = 4).

Each model (Equations (10)–(13)) can be calibrated using measured data to extract the location-specific values for the various empirical coefficients. For example, data from the year 2013 were used to calibrate the coefficients and calculate the normalized root mean square error of the UV models calibration presented in [107]. The calibrated models were subsequently used to predict the expected UV irradiation for the following year. The variations in degradation rates and failure times of mono c-Si PV, based on measured and modeled UV irradiance doses, range from 0.1% to 5.0%, depending on the UV model and the location. The variations in UV modeling did not have a significant impact on the calculated degradation rates. This can be explained by the parameter that models the impact of UV on the degradation rate, which can take values between 0.6 and 1 [106,107,120]. The 5% linear approximation (linear model presented in Equation (10), which considers $c_1$ to be 5%) showed under-performance in the two locations considered in [92] when compared to the performance exhibited by the other models defined in Equations (10)–(12). Therefore, it is recommended to use the Wald (Equation (10)) or Habte (Equation (12)) models, as they consistently showed better results in the locations considered in [107] when compared to the results obtained with the linear model (Equation (9)). The effects of irradiation are considered a significant factor in the degradation of PV modules, as irradiation can accelerate the aging of materials, causing deterioration of the encapsulant, cracking and delamination of materials, as well as affecting the semiconductor material. These effects can also induce defects in the crystal structure of the module and lead to a decrease in conversion efficiency [107].

Considering that encapsulation materials are directly affected by UV irradiation and temperature, an experimental study by Baloji et al. [121] analyzed the rate of progression of UV-induced degradation in two different types of commonly used PV encapsulation materials: EVA (ethylene-vinyl acetate) and POE (polyolefin elastomer). These encapsulants were exposed to desert conditions, where the encapsulant temperature reached up to 110 °C over a period of 60 days. During this period, an increase in transmittance of ~2% was observed for both materials. Overall, most likely due to its higher crystallinity, EVA showed a slightly higher transmittance (92–93%) compared to POE (89–91%). Additionally, the melting point was higher for POE (82 °C) compared to EVA (72 °C), which suggests better performance for POE in high-temperature conditions typical of desert environments. This study [121], along with its experimental data, can guide the selection of an appropriate encapsulation material for extreme temperature and UV conditions.

3.4. Hydrolysis in PVs and Moisture-Based Degradation

Hydrolysis is the third main degradation mechanism that the Kaaya model considers [106,107] with the rate constant for this degradation process defined by Equation (14), as follows:

$$k_H=A_h\cdot R{H}^n\cdot \exp \left(-{\displaystyle \frac{E_{ah}}{k_B\cdot T_{mod}}}\right),$$

(14)

where $E_{ah} \left[\mathrm{e}\mathrm{V}\right]$ is the activation energy of power degradation due to the hydrolysis, $A_h \left[\mathrm{\%}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\right]$ is the pre-exponential constant, and $n$ is the model parameter that indicates the impact of the relative humidity [106,107].

Relative humidity $RH$ can either be measured directly or estimated using the relationship between ambient temperature and dew point temperature via water vapor pressure. The estimation of water vapor pressure at different temperatures can be performed using the model proposed by Buck in 1981, as presented by Kaaya et al. [106,107]. Uncertainties in humidity modeling have been shown to significantly affect degradation rates, having a greater impact in areas with higher temperatures [107]. To conclude, humidity is a critical factor in the degradation of PV modules, as it influences hydrolysis, which in turn leads to the embrittlement and decomposition of materials [107].

Considering the climatic diversity in which c-Si modules operate, another study by Kaaya et al. [122] proposes a modification of Peck’s baseline model [86] by integrating both photodegradation and hydrolysis due to heat–humidity (Equation (15)), as follows:

$$k=A_1\cdot R{H}^n\cdot \exp \left(-{\displaystyle \frac{E_{a1}}{k_B\cdot T_{mod}}}\right) +A_2\cdot U{V}^X\cdot R{H}^n\cdot \exp \left(-{\displaystyle \frac{E_{a2}}{k_B\cdot T_{mod}}}\right),$$

(15)

where $k$ represent the degradation rate $\left[\mathrm{\%}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\right]$, $A_1\left[\mathrm{y}\mathrm{e}\mathrm{a}{\mathrm{r}}^{-1}\right]\mathrm{ }$ and $A_2\left[{\mathrm{m}}^2/\mathrm{k}\mathrm{W}\mathrm{h}\right]$ are pre-exponential constants, $E_{a1}$ and $E_{a2}$ $\left[\mathrm{e}\mathrm{V}\right]$ are the activation energies of hydrolysis due to damp-heat and photodegradation due to UV-damp-heat, respectively [122].

Thus, it is emphasized that quantifying the degradation of PV modules requires a combination of UV, humidity, and heat conditions. This modified version of Peck’s baseline has been validated through experimental measurements and has been used to correlate accelerated testing conditions with real-world environmental conditions by using climatic data from three different locations. Experimental results showed that relative humidity has a significant impact on degradation mechanisms resulting from the combination of UV and heat–humidity in different climatic zones [86,122]. Physical models help estimate the lifespan of the modules and minimize degradation effects, making understanding of the hydrolysis process essential for the development of more durable PV modules.

3.5. Corrosion of a PV Module

Another common factor that creates frequent problems in PV degradation is corrosion. Detecting the primary cause of corrosion can be difficult because it may also be related to other modes of degradation [103,107,120,123]. However, corrosion can occur from the degradation of any component of the PV module, especially in cases where damage to the protective back layer and/or encapsulation material is present [124,125]. The occurrence of corrosion is due to the presence of an electrolyte, a metal, and an oxidizing agent. The corrosion of metallization is accelerated when the EVA, used for laminating the PV module, forms acetic acid [126]. The acetic acid usually forms when EVA is exposed to UV light, heat, and relative humidity [127]. In a humid environment, moisture can penetrate the PV modules through the backsheet and the encapsulant, promoting this chemical reaction [128]. Corrosion leads to a gradual increase in series resistance and large power losses. In the study by Matheus et al., ten poly c-Si modules were subjected to 10 cycles of damp-heat conditions for 1000 h, with power losses of 4.88% recorded [124]. The onset of this degradation is especially present in metals with lower oxidation properties, such as copper, aluminum, and lead, because they act as sacrificial anodes in galvanic couples [124,125]. The first phase of the corrosion process begins on the front side of the PV, where it can start to appear at the solder joint of the edges, as shown in Figure 7. Once formed, the corrosion process evolves by slowly moving toward the center and eventually onto the back side of the PV, where it corrodes the solder joint with the aluminum contact. As it progresses and the metal is consumed, the corrosion extends to other metals, further enhancing the degradation of the PV [125].

Rabelo et al. [129] analyzed the phenomenon of galvanic corrosion and the estimated corrosion rate using Equation (16), as follows:

$$CPR=k_{Corr}\cdot \left({\displaystyle \frac{A\cdot J}{n\cdot \rho }}\right),$$

(16)

where $CPR\mathrm{ }\left[\mathrm{m}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\right]$ represents the corrosion penetration rate, $k_{Corr}=3.27\cdot {10}^{-4}[\mathrm{m}\mathrm{o}\mathrm{l}/\mathrm{C}],$ $A\mathrm{ }\left[\mathrm{g}/\mathrm{m}\mathrm{o}\mathrm{l}\right]$ is the atomic weight of the corroding metal, $J\mathrm{ }\left[\mathrm{A}/{\mathrm{m}}^2\right]$ represents the anodic current density, $n$ is the number of electrons associated with the ionization of each metal atom, and $\rho \mathrm{ }\left[\mathrm{g}/\mathrm{c}{\mathrm{m}}^3\right]$ represents the metal density [129]. This main type of degradation can be calculated separately from the model proposed by Kaaya et al. The results of the corrosion kinetics study conducted at 60% relative humidity, through finite element analysis, show that the highest penetration rates can reach up to 200 $\left[\mathsf{\mu }\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\right]$ at the front ribbons of the PV modules, which are in contact with the busbar, and at the rear aluminum side, around the Ag contact [129]. These results indicate that galvanic corrosion can significantly affect the performance of PV modules under conditions of high humidity [128] and variable temperatures. As improvements, Matheus et al. [129] propose the use of materials that are much more resistant to corrosion and some protective layers.

3.6. Discoloration of a PV Module

The discoloration effect of the PV module represents an additional degradation mechanism; however, due to the absence of a clear scientific link or established method for integration, in our analysis, this mechanism will be treated independently of the models described by Kaaya et al. (i.e., the models discussed in Section 3.1, Section 3.2 and Section 3.3 of this paper). In [130], an experimental test was carried out over a period of two years in a semi-arid location with high summer temperatures of approximately 40 °C, an annual average humidity of 60%, and an annual irradiation of 5480 $\left[\mathrm{W}\mathrm{h}/{\mathrm{m}}^2/\mathrm{d}\mathrm{a}\mathrm{y}\right]$. This allowed a comparison to be performed on the performances of different solar technologies when subjected to these operating conditions. The parameters used to quantify the degradation resulting from discoloration include the global degradation $G_d\left[\%\right]$ and the degradation rate $R_d\left[\mathrm{\%}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\right]$. The global degradation enables the calculation of the performance drop of the PV module from the initial values up to the day of the experiment using Equation (17), as follows:

$$G_d\left(x\right)=\left(1-{\displaystyle \frac{x}{x_0}}\right)\cdot 100,$$

(17)

where $x$ represents the current measured value of the module’s electrical parameters $\left(P_{max}, I_{max}, V_{max}, I_{sc}, V_{oc}, FF\right)$ [131] and $x_0$ represents the nominal value of those parameters provided by the manufacturer in the datasheet under standard test conditions (STCs) [131]. The specific degradation rate $R_d\left(x\right)$ for the discoloration process is calculated using Equation (18), which requires the global degradation $G_d\left[\mathrm{\%}\right]$ and the module’s exposure period $\Delta T \left[\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}\mathrm{s}\right]$ from the first day until the experiment day [130].

$$R_d\left(x\right)=\left({\displaystyle \frac{G_d}{\Delta T}}\right)$$

(18)

Abdellatif et al. [130] reported that after two years of continuous exposure, the mono c-Si PV module was delivering a maximum power that was 7.56% lower than its measured performance prior to exposure. Post-exposure spots of discoloration were observed on the PV module, most likely due to a combination of climatic conditions, high temperature, and UV irradiance. For in-depth testing of the discoloration effect, a comparison was made between the affected module and another new module from the same manufacturer using the same technology and characteristics. These two modules were put together for the same period, exposed to the same weather conditions, and monitored continuously for four days [130]. To highlight the difference in power loss due to discoloration, the power production deviation $\Delta P$ between the two modules (the one previously exposed for two years and the new one) was calculated using Equation (19), as follows:

$$\Delta P=P_{ref}-P_{affected},$$

(19)

where $P_{ref }\left[\mathrm{W}\right]$ is the power output of the reference module and $P_{affected }\left[\mathrm{W}\right]$ is the power output of the affected PV modules [130]. According to Abdellatif et al., due to discoloration of the affected module (exposed for two years), 13.2 W of electrical power production was lost from the PV compared to the reference module, which represents a 5.28% reduction in initial power capacity [130].

Considering the importance of accounting for the effects of discoloration and delamination, which is presented in Section 3.7, Roopmati et al. [132] show the impact of degradation caused by discoloration combined with delamination of 20 mono c-Si PV modules, exposed to the Indian climate (New Delhi) for 20 years. The samples extracted from the degraded modules showed significant losses in short-circuit current (Jsc): 36% for the dark shades of discolored EVA and 26% for the lighter shades compared to the reference samples. Regarding reflection, the discolored samples exhibited higher reflectance across the entire spectral range, contributing to additional power losses. On the other hand, FTIR (Fourier transform infrared) spectroscopic analysis revealed a decrease in the content of UV and antioxidant additives, indicating progressive chemical degradation of the discolored samples, as evidenced by the presence of carboxylic acid and the absence of the adhesive agents in the EVA. Simulations based on experimental data estimated power losses of up to 40%, depending on the extent of the EVA degradation area. These results suggest a significant impact on the performance of photovoltaic modules due to EVA discoloration [132].

3.7. Delamination of a PV Module

Delamination refers to the detachment of various layers from the PV module assembly. Thus, delamination can occur between the front glass and EVA or between the encapsulant and module cells and the back layers [133,134]. The delamination that can occur in PV modules can be classified into four types [135]: (1) delamination between EVA and superstrate glass; (2) delamination between EVA and the cells, which is concentrated around interconnect ribbons; (3) delamination between EVA and cells associated with cell cracks and snail trails; and (4) delamination of the backsheet which is typically used to protect the PV cell from humidity and air [135]. When delamination starts to occur, usually from the edges of the device toward the center, it becomes a severe problem because it can pose electrical hazards to the device and even to the entire module. These penetrations are much more common in regions with hot and humid climates and are dangerous because they can lead to an increase in the degree of UV solar irradiation, allow water to infiltrate, and cause corrosion of the metal in the equipment assembly [121,127,131,134]. Delamination usually occurs when EVA loses its adhesive resistance under conditions of high temperature and humidity [125]. For example, in the study by Yaowanee et al. [136], delamination was observed starting from the periphery of the cells, and its occurrence was more pronounced in the cells located at the edge of the PV panel. Research suggests that the delamination of PV modules could be attributed to the potential-induced degradation (PID) mechanism, which occurs when a high potential difference appears between the semiconductor material (i.e., the PV cell) and other components of the PV module (e.g., the glass, the PV mount, or the aluminum frame). Other contributing factors can include moist heat, sodium accumulation, and gaseous by-products from electrochemical processes under negative potential. Also, in the study by Yaowanee et al. [136], a method for tracking the expansion of delamination in the form of a polygon was proposed. Tracking the annual expansion was achieved by moving the center of mass of the delamination zone in a captured image using the following vector Equations (20) and (21):

$$C_x={\displaystyle \frac{1}{6A}}\sum _{i=0}^{n-1}\left(x_i+x_{i+1}\right) \left(x_i{y}_{i+1}-x_{i+1}{y}_i\right)$$

(20)

and

$$C_y={\displaystyle \frac{1}{6A}}\sum _{i=0}^{n-1}\left(y_i+y_{i+1}\right) \left(x_i{y}_{i+1}-x_{i+1}{y}_i\right),$$

(21)

where n vertices $\left(x_0,y_0\right),\left(x_1,y_1\right),\dots , \left(x_{n-1,}{y}_{n-1}\right)$ are the points $\left(C_x,C_y\right)$ and $A$ is the area of the polygons resulting from the tracing function. This area is therefore the measured area of the delamination zone and its expansion [136].

For example, after monitoring the maximum power delivered by the evaluated poly c-Si PV modules over 7 years, it was found that the maximum power decreased at a rate of 2.4% per annum, with the total reduction in power reaching 20% after 16 years of continuous exposure [136]. This period is shorter than the typical 25-year warranty provided by most manufacturers. The delamination types (1) and (2) of the modules started to appear from the 12th year, and the delamination area increased in a non-linear fashion from 8% to 30% until the 19th year [137]. The delamination extended further in the horizontal direction, affecting the cells in the middle of the module more significantly. Yaowanee’s study uses the method presented in Equations (18) and (19) to track the expansion of delamination on PV surfaces [136]. Backsheet delamination (4) initially began near the junction box and gradually spread across the entire surface of the backsheet. In the last years of observation, it covered nearly the entire area of the modules. This phenomenon was correlated with the overall deterioration of the modules, including the expansion of the corrosion area on the ribbons and the appearance of cracks. According to a study by Yaowanee et al., this phenomenon can affect up to 80% of the total surface area after 15 years of operating in climatic conditions with an average annual temperature of approximately 29 °C and an average relative humidity of 78% [138].

3.8. Reducing the Degradation Rate of PVs

Protecting PV modules means reducing or minimizing degradation rates while also increasing life expectancy. In addition to the degradation mechanisms presented in this paper, some examples of protection technologies available for mitigating degradation in PV modules are also provided [125]. However, an evaluation of these protection technologies and their success will require a separate review.

Regarding thermal degradation and degradation due to water vapor, the study published in [125] specifies several protection measures. One example is the incorporation of nanomaterials into solar panels to improve the mechanical properties of polymers and to develop greater resistance to corrosive gases and water vapor [139]. Another protective measure is surface plasma activation, which is an innovative technology that uses atmospheric pressure plasma to activate and modify the surfaces of materials such as cover glass, solar cell contact surfaces, module supports, and frames. The plasma induces chemical reactions on the treated surfaces. This process can reduce surface adhesion and enhance the cleanliness of the surface by modifying the physical–chemical properties of the treated material. Thus, the particles, dirt, or liquids falling on the treated surface do not adhere to it. The technology has, therefore, the potential to extend the lifespan of modules by protecting them from environmental factors that cause deterioration, such as oxidation, humidity, and UV exposure [140]. For UV degradation, the study by Wohlgemuth et al. [141] discusses the use of low-iron glass containing cerium oxide as a potential antidegradation solution. The cerium-containing glass absorbs UV light between 300 and 370 nm and could help protect photovoltaic modules from harmful UV radiation. However, the main disadvantage is that this type of glass is very rarely available on the market. Other similar methods have included down-conversion surface layers to absorb UV radiation and reemit it at a lower wavelength, which has the potential to improve PV efficiency while protecting the PV surface [142,143,144,145,146].

Corrosion degradation is another factor for which protective measures are considered. In [123], it is proposed to use coatings (indium tin oxide, nanocrystalline cadmium sulfide), corrosion inhibitors (zinc phosphate, thioalcohols, zinc chromate (ZnCrO₄), and sodium silicate). The use of selected materials such as stainless steel, plastics, and special alloys with a high resistance to corrosion was also proposed because utilizing these materials will improve the lifespan of structures by reducing the corrosive impact on the external and internal components of the PV system. In the study by Gabor et al. [147], solutions for mechanical degradation related to cracks and fractures are presented. Some of these proposed protection measures include the use of multiple busbars or interconnections to distribute the voltage more evenly; the use of rectangular-shaped cells or cut cells, which promote more uniform current distribution and thereby reduce the occurrence of cracks; and the use of thicker wafers, which can further reduce the risk of cracking. To reduce the risk of crack formation at the module level, several methods such as using more flexible materials for soldering, employing a greater number of bypass diodes, and constructing and using glass modules are presented as possible solutions for the manufacturing of PV modules [125].

In a report prepared by the National Renewable Energy Laboratory [148], a comprehensive guide is provided on the maintenance and upkeep of photovoltaic systems. The primary emphasis is on the concept of preventive maintenance, which recommends periodic inspections of the PV system and its associated equipment, including inverters and batteries. It also suggests regular cleaning to prevent dirt accumulation, early problem detection, and repairs, with a particular focus on identifying faults using methods such as thermography, which can detect overheating areas and help minimize the risk of major failures. Safety and regulations are also key factors; the importance of training personnel is emphasized to ensure that all operational procedures are carried out properly, both for the staff and the PV system.

3.9. Systematic Methods for Detection and Evaluation of Degradation Processes

The detection and evaluation of the degradation process of PV modules are very important aspects of PV maintenance and require a separate review to understand their usage and complementarity. However, it is worth briefly mentioning some of the main methods used, in addition to visual inspection, which is the first step in detecting defects in a PV module from different angles. The study by Mahdi et al. [149] specifies the detection techniques for degradation that occur in various environments. Infrared imaging is a detection technique used to identify defective solar cells by analyzing the spatial distribution of the heat they emit during operation. This heat emission can be tracked with the help of infrared and/or thermal cameras. This technique can be used to detect microcracks, hotspots, and PID defects, but it also has disadvantages in terms of high acquisition and operational costs. Electroluminescence imaging is another technique used to detect one or more defective cells by locating the defective area (with low contact) by passing a current through the metal contact of the cell [149]. Ultrasonic inspection is a detection technique that compares the frequency response of a defective PV cell with that of a normally functioning PV cell using an ultrasonic transducer. It is generally used to detect cracked cells in the PV module and to determine the severity of the cell crack based on the frequency bandwidth range. In the study by [149], the most common detection technique for PV module defects is presented as electrical characterization. This technique identifies changes in the electrical parameters of the PV system, such as the open-circuit voltage, short-circuit current density, fill factor, and maximum power. Monitoring and detecting faults using this technique can prevent catastrophic situations, such as the delayed response of overcurrent protection systems during a failure, which could even lead to a fire. Additionally, sensors can be installed to detect abnormal increases in I–V curves to monitor and prevent undesirable situations.

4. Application of Degradation Models in Agrivoltaics and Future Requirements

As modeling will play an extensive role in determining the ideal configurations for AV systems, it is important to consider the degradation effects resulting from regional environmental factors and those arising from additional degradation modes likely to occur for AV systems in proximity to agricultural land usage. Ideal configurations are essential for determining the most efficient and, thus, cost-effective practices for agrivoltaics. However, the lifetime of the PV modules will also be a significant factor, as a short PV lifetime can lead to significant long-term costs for the systems [57,59,150]. At present, the majority of modeling studies performed in relation to agrivoltaics focus on optimizing the placement of PV modules to maximize the power production of the AV system without impeding the growth of the crops [51,57,59,150]. However, the implementation of PV degradation models within these AV modeling frameworks could offer the additional advantage of enabling the determination of lifetime reduction and its primary contributing sources for AV systems. This, in turn, could lead to the development of new mitigation solutions that enhance the longevity of AV systems. The mitigation of PV degradation leads to an extension of the AV system lifetime, which is useful in defining the economics of such systems. These become useful indications in cases where the lifetime of PV systems has been overestimated and the actual lifetimes of working modules in real-world environments are lower than estimated [78,79,81].

Thermal degradation of AV systems is most likely to be significant in locations with high environmental temperatures, such as arid regions. Though these locations are not typically suitable for crop growth, AV systems could still be implemented for livestock, which may benefit from the shade provided by elevated PV systems to help reduce water losses [43,55]. In such scenarios, the modeling of thermal cycles would be required, especially if the day–night cycle experiences significant shifts in temperature, as this could affect PV degradation [151,152]. When examining the thermal degradation of PV modules in the agrivoltaic environment, it is essential to highlight the solutions and adaptations that can improve the performance and sustainability of these systems. The agrivoltaic environment presents unique characteristics, including the interaction between the microclimate generated by agricultural crops and PV modules, as well as the specific requirements for maximizing energy and agricultural efficiency. The use of phase change materials in the backsheet or integrated cooling systems has been proposed to improve thermal dissipation during day–night cycles and better protect the internal structure [153,154,155,156,157]. The PV modules can be mounted at a greater height above the ground to allow for more efficient ventilation, thereby reducing heat build-up. Integrating channels for air circulation or using open structures can facilitate natural cooling. Optimizing the distances between panels can allow for both cooling and the growth of plants underneath [158,159,160,161].

UV irradiation is an essential factor in the degradation of materials used in photovoltaic panels, especially in agrivoltaic environments, where prolonged exposure to the sun is unavoidable. In this context, the panels must be adapted to better withstand the effects of UV radiation and to maintain long-term performance and durability. Some potential solutions to UV degradation include the incorporation of UV stabilizers into encapsulating materials to absorb or disperse UV radiation and prevent premature degradation, the use of hindered amine light stabilizer additives to protect against photodegradation, and covering panels with a protective outer layer that can reflect or filter UV radiation before it affects sensitive materials [162]. However, care has to be taken when using polymers and plastics in agricultural environments, as the deterioration of these protective materials could lead to the formation of micro- and nano-plastics, which can later be transferred into the soil or plants.

A significant factor in the degradation of AV systems is likely the hydrolysis of the PVs. AV systems will likely experience significant water contact, either from regional climates, irrigation systems, or even increased humidity from plant-based microclimates (e.g., in greenhouses) [41,53,54]. Hydrolysis of the PVs can lead to both corrosion of the module and delamination of the cell or encapsulant. Additionally, the presence of fertilizers and animal waste leads to high amounts of ammonia, which can contribute to the corrosion of agrivoltaic modules [94]. In addition to the corrosion described in Section 3.6, the presence of corrosion can lead to damage to the supporting structures or the module frames. Eventually, this will lead to reduced power production performance and/or the need to replace the corroded sections of the system, resulting in increased overall maintenance costs for the PV system. To adapt PV systems for agrivoltaics, the likelihood and effects of these additional environmental corrosion routes must be identified. Once they are determined, such added corrosion can be managed by numerous methods, such as coatings, corrosion inhibitors, and material selection, while reducing the usage of corrosion-prone materials in the system. Therefore, the use of these resources will improve the lifetime of agrivoltaic systems [123]. Raised PV systems and solar tracking systems are at a higher risk of failure due to mechanical stress compared to fixed PV modules that are closer to the ground. These systems are likely to experience greater mechanical stress due to the weight of the modules fixed above the ground and wind stress on the panels. These stresses can increase or decrease based on the size of the panels, which is influenced by their length and breadth, the height above ground level, and the frequency of strong winds. Other factors, such as collisions with birds and agricultural machinery, should also be considered. The effect of these stresses needs to be factored into further modeling procedures for agrivoltaics modules, considering different requirements based on wind load and safety regulations across various countries [45,46].

An important aspect still to be considered is the economic cost of unmitigated PV degradation and the potential savings that improvements in operational lifetime can have on the total return generated by AV systems. This factor has significant variations, as the type of degradation, likelihood, and total reduction in lifetime due to degradation are all significantly affected by external factors, such as climate, location, and irradiance [76,94,103,107,125]. This emphasizes the need for modeling to determine the cost of degradation for a proposed AV system and the reduction in degradation that certain mitigation measures could provide. This can then also allow for the complete cost of AV systems to be accurately estimated, accounting for all these factors, as well as the costs associated with maintaining and replacing each component of the system [78,79]. A similar problem arises when determining the costs and returns of AV systems using the LER metric. The LER of an AV system can vary based on the crop grown, regional climate, and configuration of the PVs utilized. Additionally, the technology is still being developed and optimized, with ideal configurations for panels, crops, and designs currently being studied [40,51,150] through modeling and real-world studies. A full breakdown of the cost factors and real benefits of AV systems would require further results and long-term studies to provide a clear view of the economics of these systems.

5. Conclusions

The conventional approaches to installing solar PVs in the agricultural sector have demonstrated the potential of this technology to help reduce the overall carbon footprint and assist in the transition to a more energy-independent and sustainable model of food production. However, in many areas, the adoption and integration of PVs have been less than expected due to intense competition for land in peri-urban and rural areas where most of the agriculture is located. In comparison to conventional approaches, installing agrivoltaics (AV) on a farm or in other agricultural settings enables a single piece of agricultural land to serve the dual purpose of energy and crop production. The energy derived from agrivoltaic (AV) systems can be used to offset the costs associated with modern farming, while the shading conditions created by the presence of AVs can enhance agricultural yields for certain configurations of the system. Despite these advantages, the adoption of AV technology still faces several hurdles before greater proliferation can occur, as the technology requires further maturation. In addition to issues related to the design of AV systems, various techniques are needed to integrate the technology into existing agricultural infrastructure. Additionally, the effect of this integration on the growth and maintenance of crops and/or livestock needs to be evaluated. Along with these limiting factors that are contributing to the lack of progressive adoption of AVs in agricultural communities, there are also limiting factors primarily centered around how the degradation and lifetime of AV systems are considered, modeled, and evaluated. These factors are extremely important as they determine the operational lifetime of AV systems and the time required to cover the costs of their manufacture and installation. Consequently, an accurate definition of these economic constraints is required to convince the agricultural sector to increase the rate of adoption of AVs.

In the pursuit of optimizing agricultural land use for both crop production and energy generation, the modeling of AV systems has largely involved using Monte Carlo ray tracing to simulate power production from PV panels and assess the impact of the shading they provide on crop yields. The total conversion efficiency of the AV system (power + crops), predicted using this approach, is then used to estimate the number of years required to recuperate the costs of manufacture, installation, and maintenance, which may be higher than those of traditional PV installations in urban areas. Current modeling approaches typically overlook the fact that, even in traditional PV installations, the maximum power output can decrease by ~0.6% to ~1.5% per annum [95,96,99,163,164] due to a combination of thermal degradation, irradiance-induced degradation, and hydrolysis. This degradation rate is expected to increase when PV modules are installed in high-activity settings such as farmlands, where they will be exposed to higher levels of water as a result of crop irrigation, chemical reactions from fertilizers and industrial runoff, and increased wind speeds, which intensify wind-based mechanical degradation. Models and statistics on the likelihood of degradation based on these additional degradation modes will have to be developed as studies continue in parallel with the development and study of AV systems in real-world applications. However, further degradation modes may be identified through such studies, and it may take several years before the characterization of these additional modes is fully understood.

Despite the challenges involved in fully implementing AVs, the outlook for this technology continues to improve as political bodies, such as the EU, continue to invest in and promote the use of PVs in the agricultural sector. Improvements in modeling techniques benefit both lifetime determination and the identification of optimal configurations for AVs, allowing for the development of the best solution based on regional conditions and the type of agriculture involved. This is particularly useful given the changing environments as a result of recent climate change activity. At present, several ongoing studies [36,39,53,61,71,73] are investigating the real-world implementation and efficiency of crop growth under active AV systems. Similar studies [165,166,167] are being conducted across the globe, including research on the use of hydroponic systems powered by solar energy. Utilizing degradation modeling and testing to determine the lifetimes of these systems should provide a useful avenue to ensure the viability and cost-effectiveness of these technologies.