# A review of operational factors affecting photovoltaic system performance

Edgar Hernando Sepúlveda-Oviedo

► To cite this version:

Edgar Hernando Sepúlveda-Oviedo. A review of operational factors affecting photovoltaic system performance. Energy Conversion and Management: X, 2025, 26, pp.100942. 10.1016/j.ecmx.2025.100942. hal-04984853

HAL Id: hal-04984853

https://laas.hal.science/hal-04984853v1

Submitted on 10 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# A review of operational factors affecting photovoltaic system performance

Edgar Hernando Sepúlveda-Oviedo ID

LAAS-CNRS, Université Fédérale de Toulouse, CNRS, UPS, Toulouse, 31031, France

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

Performance evaluation

Degradation

Efficiency

Tilt angle

Photovoltaic (PV)

## ABSTRACT

The reduction in manufacturing costs of photovoltaic (PV) systems has driven significant growth in the PV industry. This expansion has shifted the current challenge from constructing new PV systems to maximizing the performance and longevity of installed PV modules. PV performance is influenced by two major categories of factors: environmental and operational. While environmental factors, such as dust and temperature, have been extensively studied, operational factors — critical for optimizing system efficiency — have not received the same level of attention. This study analyzes 102 articles focusing on operational factors such as PV technology, tilt and orientation angles, surface properties, height, and component aging, while also examining their interaction with environmental factors, particularly dust. In addition, the study compiles a set of standardized metrics aimed at quantifying efficiency losses and enabling consistent comparisons across studies. Finally, this review outlines a roadmap identifying key research gaps and provides recommendations for improving PV system performance. This roadmap offers valuable insights for researchers, engineers, and policymakers to better understand and address the operational factors that influence the efficiency and lifespan of PV systems.

## 1. Introduction

The global installed capacity of PV systems continues to expand rapidly, as reported by National Renewable Energy Laboratory (NREL) in January 2024 [1]. Analysts estimate that around 350 GWdc of PV was installed globally in 2023, representing a 50% increase compared to 2022. This growth could rise to 440 GWdc once the full extent of PV installations in China during 2023 is accounted for [1]. Projections for 2024 indicate continued growth, with an estimated 390 GWdc of new PV capacity, and this trend is expected to persist through 2027, reaching up to 560 GWdc per year. This rapid expansion has been driven by ongoing reductions in PV system costs, which have dropped by approximately 85% since 2010 [2]. Despite this progress, PV systems continue to face performance challenges, primarily due to both environmental factors and operational factors.

Environmental factors, including dust, temperature, rainfall, and shading from vegetation, play a crucial role in influencing the performance of PV systems. These factors obstruct sunlight, induce thermal stresses, or cause physical damage to the systems [3], leading to efficiency losses. Among these, dust has been identified as one of the most detrimental contributors in many regions, often resulting in significant performance reductions [4]. While dust has been extensively studied as a primary environmental factor, it is equally critical to understand the interplay between operational and environmental factors, as their interaction can further exacerbate or mitigate the impact on PV performance. Operational factors, such as PV technology, tilt and orientation

angles, surface properties, and component aging, directly influence how systems respond to environmental challenges. By optimizing these aspects, it is possible to minimize the adverse effects of environmental conditions and improve overall system efficiency. For example, adjusting the tilt angle or enhancing the surface properties of PV modules can reduce dust accumulation and facilitate easier cleaning. Therefore, a comprehensive understanding of operational factors, combined with environmental considerations, is essential for maximizing the performance and durability of PV systems, especially in regions with high levels of dust exposure.

This review focuses on operational factors, including PV technology, tilt angle and orientation, surface properties, height, and component aging, with a particular emphasis on their interaction with dust accumulation due to its critical impact on PV efficiency. While the interaction between operational factors and dust is the primary focus, the interplay of operational factors with other environmental conditions — such as temperature, humidity, and shading — is also examined, though to a lesser extent. By analyzing these operational factors in depth, this study aims to address a gap in the state of the art, where such factors are often considered in isolation or overshadowed by a predominant emphasis on environmental influences. Recognizing that previous studies have predominantly focused on environmental influences, this review adopts a comprehensive approach to explore how operational factors — such as PV technology, tilt angle and orientation, surface properties, height, and component aging — contribute
to the degradation of PV system performance. These operational factors are closely interconnected with environmental conditions, particularly dust, which exerts a significant influence on system efficiency. To address this, the study follows a systematic methodology, drawing inspiration from approaches outlined in publications such as [5].

The need for this study arises from a critical gap in the state of the art, where operational factors affecting PV system performance have not been collectively analyzed in relation to performance degradation. Understanding these operational factors — such as PV technology, tilt angle and orientation, surface properties, height, and component aging — is essential to complement the extensive research on environmental influences affecting PV systems. While many studies have focused on external factors such as dust [6] and other environmental conditions [7], there remains a lack of comprehensive research addressing the combined impact of operational factors within a single, unified framework. This study further contributes by compiling a set of performance metrics to assess efficiency losses, aligned with efforts by the International Energy Agency (IEA) [8], the International International Electrotechnical Commission (IEC) 61724 standard [9], and studies like that of Lindig et al. [10]. These metrics are vital for measuring performance losses and enabling consistent comparisons across studies. Although standards for PV system design, installation, and maintenance exist, additional research is needed to better integrate operational factors into these frameworks to enhance long-term system performance.

Despite extensive research on environmental influences, there remains a gap in understanding the compounded effects of operational factors and their interactions with environmental conditions. While some studies have examined factors such as tilt angle, module orientation, or surface coatings, there is a lack of comprehensive analyses that integrate these aspects within a unified framework to assess long-term PV performance degradation. Additionally, existing studies often lack standardization in performance evaluation methodologies, making cross-comparisons challenging. To bridge this knowledge gap, this study systematically analyzes key operational factors and compiles standardized performance metrics to facilitate future research. However, addressing PV performance challenges requires not only a thorough understanding of operational variables but also a forward-looking perspective on emerging technological advancements.

To complement this analysis, the Roadmap for PV Technology presented in this study contextualizes these findings within broader industry trends, including material innovations, efficiency improvements, and future PV integration strategies. By linking current challenges to anticipated technological solutions, this roadmap serves as a foundation for identifying key areas of research and industry growth, further reinforcing the need to systematically address operational factors alongside technological advancements.

While the roadmap contextualizes future technological advancements, this study primarily addresses the lack of a unified framework for evaluating operational factors that influence PV performance. To address this gap, this study compiles and analyzes key operational factors within a unified framework, emphasizing their impact on PV performance. By focusing on operational factors, it complements existing research on environmental influences, offering a more holistic understanding of the elements affecting PV efficiency and longevity. A comparison of the operational factors evaluated in this study with those discussed in the most relevant state of the art review articles is presented in Table 1.

This review makes several contributions to the field of preventive maintenance and energy management for PV systems. **First**, it consolidates operational factors — such as PV technology, tilt and orientation, and surface properties — while highlighting their interaction with environmental factors like dust. **Second**, it refines and expands existing performance metrics by incorporating both traditional and prediction-based metrics to standardize the evaluation of PV efficiency losses. **Third**, it provides focused insights into operational degradation

modes, particularly in cases of improper installation and component aging, offering practical strategies to mitigate these challenges. **Fourth**, it presents actionable recommendations for optimizing maintenance protocols, specifically regarding system configuration and operational adjustments, tailored to address the challenges posed by dust and other environmental factors.

This document is organized as follows: Section 2 presents a roadmap for PV technology, which contextualizes the rapid global expansion of PV capacity, regional deployment trends, and key technological advancements shaping the future of the industry. Section 3 describes the methodology employed in this study. Section 4 evaluates the operational variables affecting PV system efficiency. Section 5 compiles the performance evaluation metrics commonly used for PV systems. Section 6 analyzes the results obtained, discussing the primary challenges and key components for future research, as well as the limitations of this study. Finally, Section 7 summarizes the main conclusions drawn from this work.

## 2. Roadmap for photovoltaic technology

The global PV industry has experienced unprecedented growth over the past decade, with cumulative installed capacity reaching new milestones each year. This rapid expansion is expected to continue, driven by declining system costs, technological advancements, and supportive policy frameworks.

### 2.1. Global installed capacity and future projections

The global installed capacity of PV systems has seen a remarkable surge, with recent estimates indicating that in 2023, a record 447 GWdc was installed globally, reflecting an 87% growth compared to the 239 GWdc installed in the previous year [15]. This substantial increase, as illustrated in Fig. 1, exceeded earlier forecasts and underscores the accelerating rate of PV adoption.

Following this record growth, projections for the coming years suggest that the cumulative global PV capacity will surpass 2 TWdc by 2024, reaching approximately 5.1 TWdc by 2028, as presented in Fig. 2. These projections indicate a continued upward trajectory for PV deployment, with growth rates exceeding earlier expectations [15].

A key driver of this growth has been the rapid expansion of China in the PV market. In 2023, China alone accounted for 57% of the world total PV capacity additions, a sharp increase from the 40% share in 2022, as shown in Fig. 3. This surge in installations in China played a crucial role in surpassing global expectations for PV deployment.

This rapid expansion has been driven by ongoing reductions in PV system costs, which have dropped by approximately 85% since 2010 [2]. Despite this progress, PV systems continue to face performance challenges, primarily due to environmental and operational factors.

### 2.2. Regional distribution of photovoltaic expansion

The expansion of PV capacity varies significantly by region, driven by national policies, economic incentives, and energy demands. China remains the global leader in installed PV capacity, surpassing 253 GWdc by the end of 2021 and continuing its rapid growth trajectory [16]. The growth of PV capacity in China is largely attributed to strong government support, low manufacturing costs, and extensive research investments, solidifying its position as the dominant player in the PV industry [17]. In addition, the high deployment rate of PV projects in China reflects its strategic objectives for carbon neutrality and energy security [18].

In Europe, the PV industry is expanding steadily due to favorable policies and ambitious targets set by the European Union (EU). The EU aims to install 750GWdc of cumulative PV capacity by 2030 to meet its renewable energy targets [19]. Countries such as Germany, Spain, and
<table><caption>**Table 1**
Comparison of the aspects addressed in this study with the state of the art.</caption><thead><tr><th rowspan="2">Ref</th><th rowspan="2">Year</th><th>PV</th><th rowspan="2">Tilt angle</th><th rowspan="2">Tilt orientation</th><th rowspan="2">Surface properties</th><th rowspan="2">Height</th><th rowspan="2">Component aging</th><th colspan="2">Performance metrics</th></tr><tr><th>Technology</th><th>Traditional</th><th>Prediction-based</th></tr></thead><tbody><tr><td>This study</td><td>2025</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td><td>*</td></tr><tr><td>[11]</td><td>2024</td><td></td><td></td><td>*</td><td>*</td><td></td><td></td><td></td><td></td></tr><tr><td>[3]</td><td>2023</td><td>*</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[12]</td><td>2023</td><td></td><td></td><td></td><td>*</td><td></td><td>*</td><td></td><td></td></tr><tr><td>[4]</td><td>2022</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>[13]</td><td>2021</td><td></td><td></td><td></td><td>*</td><td></td><td></td><td></td><td></td></tr><tr><td>[7]</td><td>2018</td><td></td><td></td><td>*</td><td>*</td><td></td><td></td><td></td><td>*</td></tr><tr><td>[14]</td><td>2013</td><td>*</td><td>*</td><td></td><td></td><td></td><td>*</td><td></td><td></td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Annual solar photovoltaic installed capacity 2000–2023.</figcaption></figure>

*Source:* Figure adapted from [15].

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** World cumulative solar photovoltaic market scenarios 2024–2028.</figcaption></figure>

*Source:* Figure adapted from [15].

France have led this expansion, with increased PV adoption driven by declining module costs and improved grid integration strategies [20]. However, spatial variability in solar irradiance and regional differences in policy implementation continue to affect the pace of expansion in different EU member states [21].

The United States is also experiencing significant growth in PV deployment, primarily due to tax incentives, state-level renewable portfolio standards, and the Inflation Reduction Act (IRA), which provides long-term financial support for PV energy projects [22]. Utility-scale PV installations dominate the United States (US). PV market, contributing to a steady increase in renewable energy generation. However, challenges such as grid constraints and policy uncertainty at the state level continue to impact the scalability of PV energy projects [23].

India has set an ambitious goal of reaching 280 GWdc of PV capacity by 2030 under its national renewable energy initiatives [24]. The rapid deployment of PV power in India is driven by government-backed auction schemes, decreasing PV module prices, and international investments in the renewable energy infrastructure of the country [25]. Despite significant progress, challenges such as land acquisition, grid infrastructure limitations, and financial constraints still pose barriers to large-scale PV deployment in rural areas [26].

As shown in Fig. 4, the global distribution of installed renewable energy highlights the dominance of China in PV deployment, followed by significant contributions from Europe, the US, and parts of Latin America. This geographic variability reflects regional policy differences and economic incentives shaping the global PV market.
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Top 10 countries solar share in 2023.
*Source:* Figure adapted from [15].</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Global distribution of installed renewable energy capacity, highlighting photovoltaic expansion.
*Source:* Data sourced from [27].</figcaption></figure>

As the global PV market continues to grow, regional differences in policy frameworks, economic conditions, and energy demand will shape the future landscape of PV energy deployment. While China remains the dominant market, Europe, the United States, and India are also making substantial strides toward achieving their renewable energy targets. Overcoming infrastructure challenges and ensuring equitable access to PV energy will be crucial for sustaining long-term growth in the PV sector.

## 2.3. Technological roadmap and future innovations

The future of PV technology relies on advancements in multiple areas, including efficiency improvements, material innovations, and sustainable manufacturing practices. Several key challenges remain in achieving high-performance, scalable, and environmentally friendly PV solutions. Fig. 5 summarizes these challenges along with potential technological solutions that are being explored.

### 2.3.1. Efficiency improvements

One of the primary goals of PV technology development is the enhancement of energy conversion efficiency. Current silicon-based PV modules have reached efficiencies of approximately 26%, with future advancements expected to exceed 30% by 2030 through the integration of tandem Perovskite PV Cells (PSCs) [28]. Multijunction PV cells, which incorporate multiple semiconductor layers to capture a broader spectrum of sunlight, have already demonstrated efficiencies exceeding 40% under concentrated sunlight conditions [29]. Additionally, advancements in bifacial PV cells, which capture light from both sides, are further driving efficiency improvements, with recent developments demonstrating power conversion efficiencies exceeding 22% [30].

### 2.3.2. Manufacturing scalability

Advancements in PV manufacturing processes aim to reduce production costs while maintaining high efficiency. Innovations in wafer production, particularly the use of larger wafer formats such as 182 Mm Monocrystalline wafer (M10) and 210 Mm Monocrystalline wafer (M12), are increasing the cost-effectiveness of silicon PV technology [31].

Moreover, improvements in thin-film deposition techniques, particularly for Cadmium Telluride (CdTe) and PSCs, are enabling the production of ultra-thin, high-performance modules with reduced material costs [32]. Emerging fabrication processes, including roll-to-roll manufacturing for organic PVs, are also expanding the scalability of PV production, making PV energy more accessible [33].

### 2.3.3. Storage integration and grid modernization

The increasing penetration of PV into global energy markets necessitates improvements in energy storage and grid management. Battery storage systems, particularly lithium-ion and solid-state technologies, are being developed to address the intermittency of PV power [34]. Moreover, innovations in power electronics, such as smart inverters and grid-forming technologies, are improving the stability and reliability of PV-integrated power systems [35]. Advanced energy management algorithms leveraging artificial intelligence are also being implemented to optimize the interaction between PV systems, battery storage, and grid infrastructure [36].

### 2.3.4. Stability and degradation mechanisms

Understanding the stability and degradation mechanisms of PV modules is critical for extending their operational lifetime and ensuring long-term performance reliability. Various environmental factors, including exposure to UV radiation, humidity, thermal cycling, and Potential Induced Degradation (PID), contribute to the decline in PV module efficiency [37].

Long-term field studies have demonstrated that PV modules typically degrade at a rate of 0.8–1.4% per year, depending on material composition and environmental exposure conditions [38]. Degradation mechanisms such as browning, delamination, and metallization grid oxidation are commonly observed, leading to power losses in modules after prolonged outdoor operation [39]. Moreover, future climate scenarios suggest that accelerated degradation rates could occur due to rising global temperatures, necessitating the development of thermally resilient PV materials [40].

Material advancements, including encapsulants with enhanced moisture resistance and anti-reflective coatings, have shown promising improvements in mitigating degradation effects [41]. Additionally, research on failure detection methods using electroluminescence imaging and infrared thermography is improving the ability to identify early-stage degradation and predict module failures before significant efficiency losses occur [42].

### 2.3.5. Scalability and material challenges

The scalability of PV technologies is constrained by material availability, manufacturing limitations, and long-term environmental impacts. Silicon-based PV technology dominates the global market, but concerns about the sustainability of silicon wafer production and the need for alternative semiconductor materials remain key challenges [43].

Next-generation PV technologies, including PSCs and tandem silicon-perovskite architectures, offer high efficiency potential but require improvements in stability and manufacturing scalability [44]. Research has identified that material innovations such as lead-free PSCs and flexible organic PVs could contribute to achieving sustainable large-scale production [41].

Additionally, the increasing adoption of bifacial PV modules introduces new material challenges related to encapsulation durability and
<figure><img src="image_8.png" /><figcaption>Fig. 5. Roadmap of key challenges and potential solutions in photovoltaic technology development.</figcaption></figure>

rear-side light absorption efficiency [45]. Advances in transparent conductive electrodes and alternative metallization processes are critical for reducing reliance on scarce materials such as indium and silver [46].

Overall, addressing scalability and material constraints will require coordinated efforts in research, policy, and industrial implementation to ensure that PV technology continues to expand as a sustainable energy source.

### 2.3.6. Recycling and sustainability

The rapid deployment of PV technology raises concerns about end-of-life module disposal and resource sustainability. Current research focuses on developing efficient recycling processes to recover valuable materials, such as silicon, silver, and indium, reducing environmental impact [47]. Circular economy approaches, including the design of recyclable PV modules and closed-loop manufacturing, are gaining traction to minimize waste generation [48]. Additionally, sustainable material innovations, such as lead-free PSCs and bio-based encapsulants, are being explored to reduce the environmental footprint of PV energy production [49].

### 2.3.7. Future outlook

Long-term projections suggest that PV will become a dominant energy source by 2050, supplying more than 40% of the global electricity demand in high-renewable energy scenarios [29]. Continuous technological advancements, supported by research, policy frameworks, and grid modernization, will play a crucial role in achieving global decarbonization targets. Future innovations focusing on efficiency enhancements, cost reductions, and sustainability measures will drive the next generation of PV technology, ensuring its widespread adoption and long-term viability.

## 3. Methodology

The methodology comprises three main phases: First, a search protocol is developed. A list of keywords is compiled from expert consultations, existing studies, and relevant references. Using these keywords, a search equation is formulated and applied to the Scopus and Web of Science databases, chosen for their comprehensive coverage and reliability [50]. Duplicate documents are removed using Zotero software [51]. Additional relevant articles are included through expert recommendations and the "Citation Search" strategy [52]. Following the search protocol, a systematic review is conducted. Articles are filtered using predefined exclusion criteria, resulting in a refined set of documents. The detailed methodology is presented in Fig. 6.

In Fig. 6, $N_I$ represents the initial set of documents from Scopus and Web of Science, filtered through Zotero. $N_E$ are the excluded

documents, $N_A$ are documents added by experts or through the citation search strategy, $N_p$ are provisional documents, and $N_T$ are the 102 total articles used in this study.

## 4. Operational factors affecting photovoltaic system performance

The degradation of PV modules can be attributed to various operational factors, such as PV technology, tilt and orientation angles, surface properties, height, and component aging. Additionally, loss of performance of these factors may be more critical in the occurrence of human design and installation errors in PV power plants. These errors often occur during the transportation or installation of PV system components. Transportation represents the critical initial stage in the life cycle of PV modules [53], as impacts or vibrations during transit can lead to cell breakage or microcracks in PV cells [54]. Equally critical is the installation process. Improper handling during installation is one of the main causes of glass breakage [55]. Other causes include using screws and clamps that are too short or narrow, resulting in mechanical stress or glass cracks. Another common human error is incorrect connector wiring. Poorly adjusted or crimp connectors can lead to open circuits, line failures, ground faults, or energy losses [56]. In some cases, connectors are installed near flammable materials, where arc faults can cause fires.

The effect of installation errors on PV system performance has been extensively studied. In a performance assessment of a large-scale rooftop PV plant, Tajjour et al. [57] identified that inadequate planning and incorrect panel orientations significantly reduced energy output. Their study revealed that poor installation practices, including suboptimal tilt angles and azimuth positioning, resulted in performance losses of up to 30.4%. The authors further emphasized that shading effects from surrounding structures and dust accumulation accelerated module degradation, leading to a lower-than-expected system performance ratio of 68%. These results highlight the necessity of conducting comprehensive site-specific assessments prior to PV system deployment to ensure optimal energy yield.

While installation-related factors can significantly impact system efficiency, this study specifically focuses on operational factors affecting PV performance after proper installation has been carried out. Configuration aspects of PV system design, such as array topology and interconnection layouts, are also outside the scope of this review. Instead, this work addresses five key operational factors at the module level that play a crucial role in long-term PV system performance.
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Methodology used for building the database for this study. Documents were sourced from databases, citation searching, and expert recommendations, followed by multiple filtering stages.</figcaption></figure>

## 4.1. Photovoltaic technology

The performance degradation effect in PV systems caused by environmental factors such as dust, humidity, Ambient Temperature ($T_{amb}$), solar irradiance, etc., varies between different PV technologies. For instance, the thermal dissipation and absorption properties of encapsulation materials or coverings have a notable impact on PV performance [7].

Among the various types of PV cells, including monocrystalline or Single-crystalline Silicon (sc-Si), Polycrystalline Silicon (pc-Si), Amorphous Silicon (a-Si), and other thin-film technologies such as CdTe and Copper Indium Gallium Selenide (CIGS), Crystalline Silicon (c-Si) — which encompasses both Sc-Si and Pc-Si — now dominates the market, accounting for between 95% [58] and 97% [59] of the global market share. The rise in market share is largely due to ongoing advancements in Si-based technologies and the increasing deployment of larger wafer formats like M10 and M12 [60]. However, emerging PV cell technologies such as triple junctions under concentrated sunlight are expected to gain a greater market share in the near future [61].

Long-term field studies have demonstrated that degradation rates vary significantly among different PV technologies. Sharma et al. [62] analyzed the degradation behavior of A-Si, Heterojunction Intrinsic Thin-layer silicon (HIT), and Pc-Si PV technologies under real outdoor conditions. Their findings indicated that A-Si modules exhibited the highest degradation rates, with annual efficiency losses of

approximately 5.7–6.4%, whereas HIT and Pc-Si technologies displayed more stable performance, with degradation rates of 0.36–0.31% and 0.5–0.51%, respectively. The study further emphasized that high-temperature conditions accelerate degradation, particularly in thin-film technologies, highlighting the need for material advancements to enhance their long-term reliability.

Sc-Si are favored for their high efficiency, which is typically 25%–26% and is expected to reach 27% by 2025 [63]. However, their complex manufacturing processes result in higher costs. Pc-Si cells are simpler to manufacture and cost-effective, although they may have slightly lower efficiencies, reaching values of 23.3% [63]. A-Si, a popular thin-film technology, has efficiencies of 14% [63]. Technologies such as CdTe and CIGS are in development, showing efficiencies of 22.6% and 23.3–23.6%, respectively. Extensive studies are conducted to improve the efficiency of PV cells for commercial use, as seen in the Best Research-Cell Efficiency chart published by the NREL [63] and presented in Fig. 7.

The efficiency of a PV cell is a key parameter that influences the performance of a PV module and system. For this reason, some authors have focused on studying the performance of different types of PV cells. A comparative study between C-Si and CdTe crystalline PV cells under the influence of dust is presented in [64]. In these works, the authors determined that C-Si — a term used in this study when the article does not specify whether they are Pc-Si or Sc-Si cells — experiences fewer losses when exposed to different environmental conditions. Another
Best Research-Cell Efficiencies

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 7. Cell efficiency progress trend of various photovoltaic devices [63].</figcaption></figure>

study showed that at a dust concentration of 4.25 mg/cm², A-Si and CdTe showed a 33% reduction in efficiency, while C-Si and CIGS exhibited reductions of 28.6% and 28.5%, respectively, under the same amount of accumulated dust [65]. In a study by Majeed et al. [66], a 16% decrease in PV output power was found for Sc-Si PV systems and an 11% decrease for Pc-Si PV systems when dust density reached 4.6 g/m².

Multiple studies examined the relation between temperature and the efficiency of various PV technologies. The decrease in efficiency of multiple technologies due to temperature increases is studied by Al-Khazzar et al. [67]. In this study, the authors found power output decreases of -0.0114, -0.0915, and -0.0276 W/°C for A-Si, Pc-Si, and CIGS, respectively, while the maximum reported decrease is -0.1353 W/°C for Sc-Si. Similarly, in a study by Baras et al. [68], power output decreases of 16.5 W/°C at 25 °C are reported for Pc-Si and Thin-film A-Si cells installed in Saudi Arabia. Kaldellis et al. [69] reported a 0.5 W/°C efficiency decrease at 20 °C for Sc-Si cells installed in Greece. Sc-Si PV cells are observed to be more affected by temperature compared to Pc-Si and A-Si cells, with a 15% efficiency decrease in Sc-Si cells compared to a 5% decrease in A-Si cells as temperature increases [70].

In a comprehensive study by Su et al. [71], the authors studied the effect of temperature on the performance of five PV cell technologies: Sc-Si, Pc-Si, Thin-Film Heterogeneous (TFH), Thin-Film Copper Indium Gallium Selenide (TFC), and Thin-Film Amorphous Silicon (TFA) in different climatic zones in Pakistan. Data collected over a year demonstrated efficiency reductions of 3.4%, 3.1%, 2.2%, 3.7%, and 2.7% for Sc-Si, Pc-Si, TFH, TFC, and TFA technologies, respectively. Regarding the effect of temperature, the study showed that TFH had the least impact on efficiency, while TFCC is the most affected. In the same study, the authors considered the combined effect of temperature and wind speed on Sc-Si, Pc-Si, TFH, TFC, and TFA technologies installed in Pakistan. As a result, the authors found efficiency increases of 9.7%, 9.0%, 6.5%, 9.5%, and 7.0%, respectively. From the study, it is concluded that the TFH modules are the most efficient (20.76%) and the TFC modules are the least efficient (16.79%). Under the combined effect of temperature and wind speed, the Sc-Si modules are found to be the most affected, while the TFH modules are the least affected. In both scenarios, the study found that the performance ratio of TFC is the most affected, while that of TFH is the least affected.

Amorphous silicon panels demonstrate a higher sensitivity to temperature and relative humidity, whereas crystalline silicon modules tend to degrade more severely in high-humidity environments [72]. Thin-film technologies like A-Si and CIGS perform better in high-temperature environments due to their lower temperature coefficients,

making them more suitable for arid climates where temperatures fluctuate significantly [73].

### 4.1.1. Emerging photovoltaic technologies: Perovskites

In addition to silicon and CdTe-based PV technologies, PSCs represent a near-breakthrough innovation in the field of PVs. These emerging technologies have achieved power conversion efficiencies exceeding 25% under standard test conditions, positioning them as potential contenders for future commercial applications [63].

PSCs are extensively studied for their performance under various environmental conditions, including humidity. While some studies have demonstrated that exposure to moderate humidity can improve PV cell performance [74], high levels of humidity generally have a detrimental effect on these cells. For instance, exposure to high levels of humidity can lead to significant deterioration in the efficiency and stability of PSCs [75].

Testing under real-world environmental conditions, including high humidity and temperature fluctuations, has highlighted both the potential and limitations of PSCs. For instance, advancements in encapsulation techniques have shown promise in improving stability, while alloying strategies have enhanced their resistance to environmental stressors [76].

Additionally, studies such as those by Wozny et al. [77] have examined the performance of alloy metal halide PSCs in controlled humidity conditions. They observed electricity conversion rates of 15%–20%, with performance deteriorating as humidity levels increased. Yun et al. [76] further highlighted the role of relative humidity in influencing the morphology of the perovskite film. For instance, at low relative humidity (approximately 2%), a uniform and fully covered formamidinium lead iodide (FAPbI₃) film was observed. However, at high humidity levels (30%–40%), the film exhibited non-homogeneous morphology with voids or pinholes.

To address these challenges, researchers such as Zhou et al. [78] and Zhou et al. [79] have proposed using other organic halides, such as formamidinium and methylammonium, for developing moisture-resistant perovskite films. These advancements underscore the importance of operational strategies to enhance the durability of perovskite technologies, particularly in environments with high humidity and temperature fluctuations.

### 4.1.2. Impact of penetration depth on photovoltaic technologies

The penetration depth of sunlight into PV materials is a critical factor influencing their design, efficiency, and overall performance. This
determines how effectively the material absorbs different wavelengths of light and converts them into electricity.

For C-Si technologies, the penetration depth is significant, requiring thicker wafers to capture sufficient sunlight. This necessitates advanced surface engineering techniques such as texturization and anti-reflective coatings to enhance light absorption and minimize reflection losses [80]. Studies have shown that optimizing the doping depth in C-Si enhances its absorption efficiency, making it more competitive in terms of material usage [81].

Thin-film technologies, such as CdTe, benefit from higher absorption coefficients, which result in lower penetration depths. This allows the use of thinner layers while maintaining high efficiency, leading to reductions in material usage and production costs [82]. Similarly, CIGS also exhibits lower penetration depths, making it suitable for lightweight module designs while maintaining high energy conversion efficiencies [83].

PSCs stand out due to their exceptionally low penetration depths, which enable efficient light absorption and energy conversion with ultra-thin active layers. This property positions PSCs as promising candidates for lightweight and flexible PV applications [84]. However, precise control over film thickness and uniformity is necessary to prevent recombination losses and optimize device performance [85]. Recent advancements in perovskite compositions and deposition techniques have further improved their ability to harness light effectively while maintaining structural stability [84].

## 4.2. Tilt angle and orientation

The performance of PV systems is significantly influenced by their orientation and tilt angle, which affects the amount of solar irradiance, temperature, and environmental factors such as dust and humidity on the PV modules. Recent studies have provided valuable insights into optimizing PV performance under various climatic conditions, considering both tilt angle and orientation.

Mamun et al. [86] demonstrated that PV module performance deteriorates with increasing tilt angle under constant irradiation, with efficiency dropping by 0.76% for each 5° increase in tilt angle. They also found that for every 100 W/m² increase in irradiation, the temperature of the PV cells increased significantly, further affecting the overall performance of the system. Ahmed et al. [87] explored the impact of accurate PV system orientation on greenhouse gas mitigation and found that optimally orienting PV modules can significantly increase the irradiation received on their surfaces. This optimization boosts PV output and reduces the environmental impact without requiring additional operational costs. They demonstrated that adjusting the tilt and azimuth angles effectively increases solar irradiance levels received by PV modules, making orientation critical for maximizing performance.

In addition to optimizing tilt and orientation for maximizing incident solar irradiance, ensuring efficient Maximum Power Point Tracking (MPPT) is critical for mitigating shading-related losses. Under non-uniform shading conditions, conventional MPPT methods struggle to identify the global power peak, often resulting in suboptimal energy harvesting [88]. This issue is particularly significant in installations where tilt adjustments vary seasonally or where partial shading is common due to nearby structures or vegetation. Advanced MPPT techniques, such as Fuzzy Logic Control (FLC) and Artificial Neural Networks (ANNs), have demonstrated superior performance in dynamically adjusting PV system operation to account for shading-induced variations [88]. By integrating robust MPPT strategies alongside optimized tilt configurations, PV systems can enhance their overall efficiency while minimizing the adverse effects of shading and orientation mismatches.

Hanjalic et al. [89] explores the influence of various tilt angles on the energy yield, electrical characteristics, and performance of PV systems. It shows that adjusting the tilt angle can significantly improve system performance and energy yield, especially in regions with high

<figure><img src="image_12.png" /><figcaption>Fig. 8. Variation in dust density is shown as a function of tilt angle and exposure time.</figcaption></figure>

Source: Figure adapted from [94].

solar irradiance. The tilt angle affects not only the amount of irradiation received by the PV module but also the amount of dust deposited on the PV surface. When a PV module is installed parallel to the horizontal plane with a tilt angle of 0°, the dust deposition rate is at its maximum due to gravitational force. On the contrary, when the PV module is installed perpendicular to the horizontal plane (with a tilt angle of 90°), the deposition rate is at its lowest. Several studies demonstrated that the particle size of dust deposited at a tilt angle of 0° is larger than that at a tilt angle of 90° [90].

Additionally, the study [89] highlights how temperature impacts PV system efficiency, showing that the performance ratio improves with optimal tilt angle adjustments, reducing the negative effects of temperature increases on PV cell performance. Mansour et al. [91] evaluated the combined effects of tilt angle and $T_{amb}$ on PV system performance in Saudi Arabia. They demonstrated that optimizing the tilt according to the location significantly improved energy generation. Additionally, they found that $T_{amb}$ directly affected the PV output, emphasizing the need to account for local climatic conditions when configuring PV systems.

Recent studies have demonstrated that optimizing the tilt angle and orientation of PV systems significantly impacts energy production. Tajjour et al. [57] analyzed the performance of a 400 kWp grid-connected PV plant installed in a hilly terrain in India and found that subsystems installed at near-optimal tilt angles generated up to 4 kWh/day/kWp. The study highlights that an optimized PV panel tilt and orientation correction can enhance energy production by approximately 7.22%, which translates into an additional annual generation of 121,833 kWh and a reduction of 113 tons of CO₂ emissions. These findings emphasize the importance of adjusting PV installation angles based on geographical location to maximize efficiency.

A higher tilt favors gravity in the removal of excess dust [92]. According to measurements conducted in Belgium with a tilt angle of 35° and typical rainfall intervals, transmittance loss varies depending on the type of glass used, such as multilayer (0.85%), self-cleaning (1.30%), anti-reflective (1.75%), and standard glass (2.63%). In [93], it is found that when the angle of inclination is 0° relative to airflow, the efficiency of the Sc-Si modules increased from 10% to 15%, while for Pc-Si modules the increase is from 5% to 10%. When the angle of inclination is 10° with respect to the incoming airflow, both types of modules showed increases in the performance parameters of 11% to 18% and 8% to 12%, respectively, due to the wind flow being able to remove more dust particles compared to greater inclinations. Figs. 8 and 9 show two examples of dust density variation with tilt angle carried out in the studies [94,95].

Others studies have demonstrated the relationship between tilt angle and dust accumulation on the PV surface. A higher tilt angle favors
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Accumulated dust levels on glass samples placed in eight different orientations at varying tilt angles within an arid environment.
*Source:* Figure adapted from [95].</figcaption></figure>

gravity, assisting in the removal of excess dust [92]. In experiments conducted by Lu et al. [96], it was found that different tilt angles significantly affected dust deposition density and energy reduction rates. The maximum energy reduction (36.17%) was observed after 100 days of exposure to dust particles with a diameter of 200 µm at a tilt angle of 25°, while the smallest reduction occurred with dust particles of 1 µm in diameter.

In the study by Gholami et al. [97], the impact of dust on glass transmittance was analyzed through a 70-day experiment in Isfahan, Iran. The researchers observed that tilt angles had a direct effect on dust concentration, with the highest dust accumulation observed at a tilt angle of 15° and facing north, resulting in a 24.83% reduction in glass transmittance. Conversely, at a tilt angle of 90°, a lower accumulation of dust was recorded, resulting in a transmittance reduction of 15%. Although higher tilt angles favor dust removal, a balance must be struck as PV modules with very high tilt angles can reduce irradiance incident on the PV surface, thereby decreasing performance [95]. Xu et al. [98] proposed an optimal tilt angle based on local latitude ± 15°, while other researchers recommend smaller deviations (± 8° or even ±2.8°) to account for geographical limitations. These discrepancies highlight the importance of location-specific optimization of tilt angle and orientation. In [99], an annual reduction in energy production due to snow of 12%, 15%, and 18% is demonstrated for inclination angles of 39°, 24°, and 0°, respectively. One crucial point highlighted in the article is how the decrease in energy loss is influenced by both the thickness of the snow covering the surface of the PV module and the angle at which the PV assembly is inclined.

Some authors attempted to propose an optimal tilt angle. As presented in a study by Xu et al. [98], some authors suggest an optimal tilt angle based on local latitude ± 15°, while others recommend ± 8° or even ±2.8°. However, as noted in the same study, the accuracy of these proposed formulas is not always guaranteed due to geographical limitations.

Further analysis on the impact of tilt angle optimization was conducted by Yadav et al. [100], who evaluated the influence of the Optimum Tilt Angle (OPTA) on energy cost minimization and maximum power generation in diverse climatic zones of India. Their study demonstrated that the OPTA varies significantly depending on the geographical location, with annual variations ranging from 0° to 63°. The research further highlighted that seasonal tilt angle adjustments could enhance annual energy yield by up to 6.6%. Additionally, an economic evaluation revealed that optimizing the tilt angle configuration reduces the present net cost of energy, making standalone PV systems more financially viable. These findings emphasize the importance of

site-specific tilt angle adjustments to maximize PV performance while minimizing overall energy costs.

Considering the variations in optimal tilt angles across different regions, multiple models have been developed to refine location-specific estimations. Haghdadi et al. [90] proposed a model that estimates the optimal parameters for PV installations, suggesting latitude, longitude, azimuth, and tilt values of 4.08°, 0.2°, 5.85°, and 2.75°, respectively. Similarly, Kaddoura et al. [101] employed Matlab simulations to determine the ideal orientation for PV modules in Saudi Arabia. Their study concluded that adjusting modules at least six times a year could capture 99.5% of the available solar irradiance. These findings highlight the necessity of continuous monitoring and adaptive tilt adjustments to maximize energy yield under varying climatic conditions.

Further studies have investigated the impact of different tilt angle configurations on system performance. Chandel and Chandel [102] conducted an extensive performance evaluation of a 19-MWp PV plant comparing fixed-tilt, seasonally adjustable-tilt, and Horizontal Single Axis Solar Tracking (HSAT) configurations. Their results indicated that while fixed-tilt systems remain widely adopted due to their simplicity and lower maintenance requirements, adjustable-tilt and HSAT systems demonstrated performance enhancements of approximately 6% and 15.4%, respectively. However, they emphasized that improper tilt settings and inadequate maintenance could significantly reduce these performance gains, reinforcing the need for optimal tilt angle selection and periodic recalibration.

The energy yield comparison between fixed-tilt and adjustable-tilt configurations is illustrated in Fig. 10, where adjustable-tilt systems consistently exhibit higher performance across all months [102]. This improvement highlights the critical role of tilt optimization in maximizing energy production, particularly in regions with significant seasonal variations. The study [102] further suggests that while adjustable-tilt systems provide substantial benefits, their feasibility depends on installation complexity, operational costs, and regional climatic conditions.

These studies collectively underscore the importance of optimizing tilt angles to enhance PV efficiency. The selection of an appropriate tilt angle should consider factors such as geographical location, seasonal variations, and shading effects to ensure maximum PV energy capture. By integrating adaptive tilt angle strategies with real-time monitoring, PV systems can achieve improved long-term energy production and system reliability.

### 4.3. Surface properties

Modifying the surface properties of PV modules can yield significant performance benefits, such as reducing dust accumulation, lowering
<figure><img src="image_14.png" /><figcaption>**Fig. 10.** Comparison of actual energy yield between Fixed Tilt and Adjustable Tilt PV systems. The results demonstrate a higher energy yield for Adjustable Tilt systems across all months, reinforcing the benefits of tilt angle optimization.</figcaption></figure>

*Source:* Figure adapted from [102].

operating temperatures, improving transmittance, and managing irradiation more effectively. These modifications often involve changes to the surface texture, application of specialized coatings, or the use of alternative materials for the surface of the module. For example, studies by Pavan et al. [103] demonstrated that altering the surface characteristics of PV modules could enhance overall performance by mitigating environmental factors such as dust and heat.

One key area of investigation has been the use of protective coatings. Studies show that a protective glass layer on the surface of the PV module is less susceptible to dirt compared to a Tedlar layer [104]. Additionally, superhydrophobic and superhydrophilic coatings have been shown to reduce dust accumulation on the PV surface, as indicated by Toma et al. [105]. Yilbas et al. [106] further suggest that adjusting surface hydrophobicity can decrease dust accumulation while improving light transmittance, enhancing the overall efficiency of the PV modules. Moreover, slightly grooved glass surfaces perform better in high wind speeds for reducing both dust and operating temperatures, as suggested by Griffith et al. [107], while thicker surfaces are more effective under low wind conditions [108]. The impact of surface properties on dust accumulation and its correlation with tilt angle is highlighted by the study conducted by Garg et al. [109], which showed that dust deposition is lower on glass surfaces compared to plastic films. This means that the dirt correction factor for glass is higher than that for plastic, as illustrated in Fig. 11. This study remains relevant as it provides valuable insights into the interaction between surface material, tilt angle, and dust accumulation, particularly in environments prone to high dust levels.

In addition to their role in dust management, surface properties significantly influence the interaction between PV modules and solar irradiation. Arabatzis et al. [110] demonstrated that photocatalytic, self-cleaning, and antireflective coatings can enhance light transmittance, particularly under diffuse light conditions and low-angle sunlight. By increasing the amount of light absorbed by PV cells, these coatings have the potential to boost PV module efficiency by up to 5% under certain conditions, making them especially effective in regions with variable solar irradiation. This underscores the importance of surface modifications not only for dust mitigation but also for optimizing irradiation management to improve energy output.

Surface properties are also critical in managing the operating temperature of PV modules. Zhou et al. [111] demonstrated that textured glass surfaces can simultaneously enhance optical transmission and

<figure><img src="image_15.png" /><figcaption>**Fig. 11.** Dirt correction factor with various inclinations for Glass and Plastic.</figcaption></figure>

*Source:* Figure generated using data from [109].

improve heat dissipation, leading to better thermal management of PV modules. Textured surfaces were shown to increase optical transmission by 3.3%–4.6% under direct solar irradiance and boost thermal emissivity by up to 9.3%. These improvements not only increase energy yield but also reduce thermal degradation, which is particularly important in high-irradiance regions. By lowering the operating temperature, these surface modifications contribute to higher conversion efficiency and extend the lifespan of PV modules.

Furthermore, spectral transmittance measurements conducted in Said et al. [112] showed that surface roughness plays a critical role in light transmission. Their study compared coated and uncoated glass modules and concluded that the spectral transmittance of coated glass is higher due to lower surface roughness. Over a 40-day exposure period, light transmission decreased by 30% for coated glass and by 37% for uncoated glass.
<figure><img src="image_16.png" /><figcaption>**Fig. 12.** Variation in Dust Density with Height.
Source: Figure adapted from [114].</figcaption></figure>

## 4.4. Height

The height at which PV modules are installed significantly impacts their performance by influencing several key environmental factors, including dust accumulation, temperature regulation, wind speed, and shading. As PV modules are elevated away from the ground, they experience a reduction in dust concentration due to the diminishing proximity to dust sources. Additionally, stronger winds at higher altitudes facilitate the natural removal of dust from the module surfaces, enhancing their efficiency [113]. In a study by Beattie et al. [114], it was demonstrated that as the height of PV modules increased, the density of dust particles on the surface decreased. For instance, dust accumulation on the surface was reduced from 0.5 g/cm² to 0 g/cm² when the installation height increased from 0.02 m to 0.2 m. This reduction in dust accumulation is particularly important in regions with high levels of airborne particulate matter. The results of the study [114] can be clearly observed in Fig. 12, which illustrates the changes in dust density on the surface of the PV module as a function of height.

In addition to managing dust, the height of PV installations also affects their temperature regulation. Increasing the distance between the module and the ground allows for better airflow around the module, which promotes cooling and reduces the operating temperature. Wang et al. [115] found that increasing the air gap between rooftop PV modules and the surface of the roof improved heat dissipation, lowering the temperature by several degrees. This reduction in temperature enhances the energy conversion efficiency of the PV cells and extends their operational lifetime, especially in regions with high solar irradiation.

Wind speed is another factor directly influenced by the height at which PV modules are installed. Higher elevations expose the modules to stronger winds, which can significantly improve cooling and enhance the natural cleaning process. Goossens et al. [116] demonstrated that PV modules installed with larger air gaps and at greater heights experienced enhanced cooling due to increased airflow, particularly in windier environments. This resulted in improved electrical performance, with wind speeds positively affecting both cooling and energy output. Similarly, Nur and Emiroğlu [117] found that PV systems installed in free-standing configurations benefited from higher wind speeds compared to flat-mounted rooftop systems, with significant reductions in operating temperature observed in elevated installations.

Furthermore, Boudjelthia et al. [118] emphasized the critical role of wind speed in determining the operating temperature of PV modules. Their study compared different models predicting temperature reduction based on wind speed and found that higher wind speeds significantly decreased the module temperature, thereby improving system performance, particularly in hot climates. The importance of

installation height in optimizing wind exposure and cooling was highlighted, as these factors directly impact the overall efficiency and longevity of PV systems.

Wind speed at higher elevations not only aids in cooling but also contributes to the natural removal of dust from the PV module surface. Research conducted by Salami et al. [119] showed that wind speed plays a critical role in reducing the temperature of PV systems and improving their energy conversion efficiency. This effect is especially pronounced at higher altitudes, where wind speeds are typically stronger. Their study also highlighted that optimizing the installation height of PV systems can mitigate the adverse effects of high temperatures, thereby improving both economic and environmental performance. However, in low-wind environments, the benefits of elevation are diminished, and additional cleaning or cooling measures may be necessary to maintain optimal performance [119].

In a study conducted by Moraitis et al. [120], it was demonstrated that urban environments have a significant impact on the performance of PV systems. Modules installed in urban areas face challenges from increased shading caused by buildings and nearby structures, resulting in reduced solar exposure. According to the study, these shading effects can lead to performance losses of up to 15% in comparison with systems installed in rural or suburban areas. Additionally, urban setups typically have less available surface area for installation, further impacting system output. Therefore, height plays a critical role in mitigating shading losses in urban environments, as higher installations tend to avoid obstructions like buildings and trees, allowing for improved exposure to sunlight.

The height at which PV modules are installed influences several critical factors, including dust accumulation, temperature regulation, wind speed, and shading. Higher installations benefit from stronger winds that enhance both the natural cleaning process and cooling, which in turn improve energy conversion efficiency and prolong the operational lifespan of the modules. Additionally, elevated PV systems face fewer shading obstructions, allowing them to maximize sunlight exposure. Considering these aspects in the installation design is crucial for optimizing PV system performance in diverse environmental conditions.

## 4.5. Component aging

Component aging is a critical operational factor that significantly affects the long-term performance of PV systems. Aging refers to the gradual decline in the efficiency and effectiveness of PV components over time due to the stresses endured during regular operation. Proper understanding and management of the aging process are essential for optimizing system performance and ensuring the reliability of PV installations.

Studies indicate that natural degradation rates due to aging typically range between 0.6% and 1% per year, depending on the climate and operational conditions [121]. However, this process can be accelerated under specific configurations or due to inadequate maintenance. Operational decisions, such as system design, material selection, and installation, play a key role in determining how components age. For example, the choice of PV technology, such as C-Si versus thin-film modules, impacts the ability of the system to withstand environmental and mechanical stresses, resulting in differing degradation rates.

One significant challenge associated with component aging is the occurrence of cell mismatch, where cells within a module or array operate non-uniformly due to variations in aging rates. This mismatch leads to disparities in current and voltage, ultimately causing certain cells to underperform and forming localized hot spots. These hot spots arise from reverse bias conditions, where a cell operates as a load rather than a generator, resulting in localized overheating [122]. The formation of hot spots not only accelerates the aging process but also contributes to further performance losses and potential module failure.
An additional factor contributing to accelerated component aging is the improper MPPT under varying irradiation conditions. Conventional MPPT methods, such as Perturb and Observe (P&O) and Incremental Conductance (IC), often result in inefficient MPPT when dealing with rapid fluctuations in solar irradiance, leading to increased thermal stress and higher degradation rates in PV modules [88]. This inefficiency is particularly pronounced under partial shading conditions, where multiple local power peaks exist. The inability of traditional MPPT algorithms to correctly track the global Maximum Power Point (MPP) results in power losses, additional resistive heating, and potential hotspots that accelerate module degradation. Advanced MPPT approaches, including Evolutionary Algorithms (EA) and Particle Swarm Optimization (PSO), have been shown to mitigate these effects, ensuring that PV modules operate at their optimal efficiency while reducing stress on aging components [88].

Various degradation mechanisms are intrinsic to the aging process, including the delamination of encapsulants, discoloration of Ethylene-Vinyl Acetate (EVA), corrosion of solder joints, and cracking of cells. For instance, prolonged exposure to Ultraviolet (UV) light and oxygen causes EVA encapsulants to discolor, resulting in a brownish-yellow hue that reduces light transmittance and leads to performance losses [123]. Similarly, other materials in the module deteriorate over time, further contributing to the decline in power output.

Field studies indicate that environmental conditions and material defects significantly impact PV module degradation. Sharma and Chandel [124] examined early-life degradation in Pc-Si modules exposed to Himalayan conditions, revealing defects such as snail trails, browning, and junction box failures. Some modules exhibited severe performance losses of up to 50%, while others degraded at rates between 0.6% and 2.5% annually. Infrared thermal imaging detected hotspots near junction boxes, where local temperatures were 3–8 °C higher, accelerating thermal degradation. These findings underscore the need for improved manufacturing standards and early fault detection techniques to enhance long-term PV reliability.

An important characteristic of component aging is its non-uniform nature, where different parts of a PV module age at varying rates. Dust accumulation, for instance, often concentrates along the lower edges of modules, causing localized shading and accelerated degradation in those regions. Likewise, delamination or bubble formation is frequently confined to specific areas rather than occurring uniformly across the module surface. These localized degradation effects exacerbate performance mismatches within the module and accelerate the overall aging process. Strategies such as regular cleaning and maintenance are therefore critical for mitigating these effects [125].

To address aging-related mismatches, PV modules are equipped with bypass diodes that prevent the formation of reverse bias hot spots by allowing current to bypass underperforming cells. However, the failure of these diodes poses an additional challenge, as cells can still experience reverse bias conditions, leading to further degradation [126]. This highlights the importance of continuous operational monitoring and diode maintenance to minimize the impact of aging on system performance.

The degradation rates observed across various PV systems, summarized in Table 2, illustrate how operational configurations influence the aging process. The bold numbers in the table represent accelerated aging rates, often caused by factors such as poor installation, incorrect wiring, or improper module orientation. Addressing these operational issues is vital to prevent accelerated aging and extending the lifespan of PV systems.

## 5. Performance evaluation metrics for photovoltaic systems

After understanding the operational factors that reduce PV performance, it is necessary to compile a set of standard metrics to measure the level of impact on PV performance, as highlighted by Lindig et al. [10]. An accurate evaluation of PV system performance

<table><caption>**Table 2**
Degradation rates per year.</caption><thead><tr><th>Location</th><th>Year</th><th>PV Techno.</th><th>Rate (%/year)</th><th>Ref</th></tr></thead><tbody><tr><td>Trinidad, California</td><td>20</td><td>Sc-Si</td><td>0,81</td><td>[127]</td></tr><tr><td>Glendale, Arizona</td><td>12</td><td>C-Si</td><td>0,95</td><td>[128]</td></tr><tr><td>Malaga, Spain</td><td>12</td><td>Sc-Si</td><td>0,96</td><td>[129]</td></tr><tr><td>Patras, Greece</td><td>22</td><td>Sc-Si</td><td>0,96</td><td>[130]</td></tr><tr><td>Manfredonia, Italy</td><td>10</td><td>C-Si</td><td><b>1,87</b></td><td>[131]</td></tr><tr><td>Manfredonia, Italy</td><td>10</td><td>C-Si</td><td><b>1,45</b></td><td>[131]</td></tr><tr><td>Manfredonia, Italy</td><td>10</td><td>C-Si</td><td><b>2,16</b></td><td>[131]</td></tr><tr><td>Manfredonia, Italy</td><td>10</td><td>C-Si</td><td><b>1,14</b></td><td>[131]</td></tr></tbody></table>

is crucial for the continued development of the PV industry. This evaluation not only helps component manufacturers ensure the quality of their products but also assists in identifying the future needs of the industry. Several of these metrics are proposed by the IEA [8], the IEC 61724 standard [9], and studies such as that by Lindig et al. [10]. This study compiles and categorizes the most widely used metrics in the state of the art to define the overall performance of PV systems. This study groups the two metrics into two categories: Traditional Performance Metrics and Prediction-Based Performance Metrics.

### 5.1. Traditional performance metrics

Traditional performance metrics refer to those that measure and use equations based solely on measured data, not estimated.

**Energy generated:** the Monthly Production of DC Energy ($E_{DC,m}$) and the Monthly Production of AC Energy ($E_{AC,m}$) of the PV system are calculated for a specific number of days $n$ as in Eqs. (1) and (2).

$$E_{DC,m} = \sum_{d=1}^{n} E_{DC,d} \quad (1)$$

$$E_{AC,m} = \sum_{d=1}^{n} E_{AC,d} \quad (2)$$

where $E_{DC,d}$ and $E_{AC,d}$ are the daily ($d$) energy production values for Direct Current (DC) and Alternating Current (AC), respectively.

**Final yield:** the Final Yield ($Y_F$) represents the amount of electrical energy (in kWh or MWh) generated by a PV system during a specific period (annual, monthly, or daily). It is defined as the ratio of the AC energy output $E_{AC}$ of the PV system (at the output of the inverter terminal) to the Maximum Power Output ($P_{MP}$), which is the installed kilowatt peak (kWp) of the PV array under Standard Test Conditions (STC). STC refers to an irradiance of 1000 W/m$^2$, an $T_{amb}$ of 25 °C, and an air mass of 1.5 g. The expression for final yield is as in Eq. (3).

$$Y_F = E_{AC}/P_{MP} \quad (3)$$

**Reference yield:** the Reference Yield ($Y_r$) is the ratio of the total amount of Total Amount of Solar Radiation ($G_t$) (kWh/m$^2$) reaching the surface of the PV modules to the Reference Irradiation ($G_{ref}$). $Y_r$ is quantitatively expressed in units of kWh/m$^2$/day and can be calculated as in Eq. (4).

$$Y_r = G_t/G_{ref} \quad (4)$$

**Array yield:** the efficiency of the PV array, denoted Array Yield ($Y_a$), is the ratio of the Total DC Energy ($E_{DC}$), delivered by the PV arrays over a specified period of time (year, month, or day) to the $P_{MP}$. This efficiency is calculated using Eq. (5).

$$Y_a = E_{DC}/P_{MP} \quad (5)$$

where, $E_{DC}$ is defined as in Eq. (6).

$$E_{DC} = V_{DC} \cdot I_{DC} \cdot t/3600 \quad (6)$$

where $V_{DC}$ represents the DC output voltage of the PV array in volts, $I_{DC}$ represents the DC output current of the PV array in amps, and $t$ represents the time in seconds.
**Array capture losses:** Array Capture Losses ($L_C$) is the disparity between the $Y_r$ and the $Y_a$. This metric mainly serves to measure losses caused by factors such as Module Temperature in °C ($T_{mod}$), spectral losses, partial shading, dust, wiring, MPPT errors, and DC-AC conversions. This metric is calculated using Eq. (7).

$$L_C = Y_r - Y_a, \qquad (7)$$

**The photovoltaic system losses:** the PV system losses, denoted as PV System Losses ($L_S$), represent the disparity between the array yield $Y_a$ and the $Y_F$. This metric typically quantifies the losses in a PV system caused by the conversion inefficiencies of the inverter (DC to AC). It can be calculated using Eq. (8).

$$L_S = Y_a - Y_F, \qquad (8)$$

**Performance ratio:** The Performance Ratio (PR) is a widely used metric to measure the overall efficiency of a PV system. In simple terms, it is the ratio of the $Y_F$ to the $Y_r$ under STC. A higher PR indicates a more efficient PV system, while decreasing PR values over the years suggest a loss in performance. PR is a dimensionless quantity and is used to evaluate long-term changes in performance. Furthermore, this metric can also be calculated using the AC energy injected into the grid, measured in megawatt hours (MWh) or kilowatt hours (kWh) denoted as AC Energy Injected into the Grid ($E_{Grid}$), and the Global Horizontal Irradiance ($G_{GI}$) on the collector plane. PR can be calculated using Eq. (9).

$$PR = Y_F/Y_r = E_{Grid}/G_{GI}, \qquad (9)$$

In [121], an adjustment to the PR is proposed, assuming a constant annual degradation rate of 6%. However, some studies showed that degradation in PV modules does not occur uniformly across all electrical characteristics [123].

Additionally, as highlighted by Lindig et al. [10], the PR can be corrected for temperature using temperature coefficients provided by manufacturers or derived from time series data. This Temperature Corrected Performance Ratio ($PR_{Tcorr}$) allows for a more accurate representation of the actual outdoor performance of PV modules. The advantage of using temperature-corrected PR is that it accounts for a wide range of temperature variations over the lifetime of the system, improving the reliability of the power versus temperature trend. According to Lindig et al. this correction should be performed following the IEC 61724-1:2017 standard [9], and even with this correction, seasonal fluctuations due to other effects such as angle of incidence and spectrum can still be observed. Care should also be taken to avoid biases in the temperature coefficients, which could introduce seasonality due to changing temperature ranges.

**Annual degradation rate:** the degradation rate quantifies the annual decrease in the performance of a PV system during its operating life. It is usually expressed as a percentage per year and helps evaluate the long-term reliability and durability of the system. In [123], the authors conducted a study on the power output of PV modules after 10 and 30 years of field life. This study found a power output of 77.79%, which is lower than the estimated power output (90%) for the modules after 10 years of field exposure [132]. Furthermore, this study demonstrated that the performance loss of the PV system cannot be generalized, and each electrical characteristic must be analyzed separately. The percentage of degradation per year in the electrical parameters of the modules after 10 and 30 years of field exposure is shown in Table 3.

As seen in Table 3, the degradation rate per year in Short circuit current ($I_{SC}$) and Maximum power ($P_{max}$) for the 10-year exposed module is 1.26% and 2.22% per year, respectively. This degradation is very high compared to the specified power output limit after 10 years of field life. Such a high degradation not only reduces the power output of the degraded modules but also can impair the performance and integrity of the non-degraded modules. In the same Table 3, it can

<table><caption>**Table 3**
Percentage degradation per year in electrical parameters of two modules exposed to the field for 10 and 30 years.</caption><thead><tr><th>Parameter</th><th colspan="2">Degradation per year</th></tr></thead><tbody><tr><td>Module age</td><td>10 years</td><td>30 years</td></tr><tr><td>Vmax (V)</td><td>0.58%</td><td>0.07%</td></tr><tr><td>Imax (A)</td><td>1.75%</td><td>0.90%</td></tr><tr><td>Voc (V)</td><td>0.85%</td><td>0.43%</td></tr><tr><td>Isc (A)</td><td><strong>1.26%</strong></td><td>0.87%</td></tr><tr><td>Pmax (W)</td><td><strong>2.22%</strong></td><td>0.96%</td></tr><tr><td>Eff (%)</td><td>0.41%</td><td>0.10%</td></tr><tr><td>FF (%)</td><td>0.22%</td><td>0.07%</td></tr></tbody></table>

be observed that the degradation rate per year in the modules with 10 years of exposure is higher than that of the modules with 30 years of field exposure. Both types of modules studied consisted of C-Si cells with EVA as the encapsulant and Tedlar as the backsheet.

**Fill factor:** the Fill Factor (FF) evaluates the efficiency of power conversion in a PV system. It is determined by dividing the $P_{MP}$ by the product of the open circuit voltage Open circuit voltage ($V_{OC}$) and the short circuit current $I_{SC}$ [133]. This can be seen in Eq. (10).

$$FF = \frac{P_{MP}}{V_{OC}I_{SC}} = \frac{V_{MP}I_{MP}}{V_{OC}I_{SC}}, \qquad (10)$$

A higher FF indicates greater efficiency in the power conversion process.

**Capacity factor:** the Capacity Factor (CF) is defined as the ratio of the actual annual energy output Actual annual energy output ($E_{AC,a}$) of the PV system to the amount of energy the PV system would generate if it operated at Full rated power ($P_p$) for 24 h per day for the year and is given as in Eq. (11).

$$CF = \frac{E_{AC,a}}{P_p \times 8760}, \qquad (11)$$

**System efficiency:** Monthly system efficiency ($\eta_{sys,m}$) is calculated by dividing the Daily AC energy ($E_{AC,D}$) by the Irradiance in the Plane of Array ($G_{POA}$) in the Module area ($A_a$) as in Eq. (12).

$$\eta_{sys,m} = \frac{E_{AC,D}}{G_{POA} \times A_a}, \qquad (12)$$

## 5.2. Prediction-based performance metrics

These metrics are generally based on energy prediction models that estimate energy production based on weather conditions over a given period. By using these metrics, standard or representative climatic conditions are applied to all models to ensure that the prediction results are theoretically independent of actual climatic variations [8].

**XbX Model:** this model is a data-driven, multiple regression predictive model that relates the measurements of irradiance and temperature with the adjustment coefficients of the model $\beta_0, \beta_1, \beta_2$, and with $\epsilon$ which represents the residual error between the model and observed data. The relationship between all these variables is shown in Eq. (13).

$$P_{pred} = \beta_0 + \beta_1 G + \beta_2 T + \epsilon, \qquad (13)$$

The flexibility of this model allows incorporating PRs, even if these rates are not linear. Additionally, it can use different irradiance measurements, whether in the $G_{POA}$ or $G_{GI}$, and different temperature data sources (ambient or module) to calculate system performance.

The “X” in the name XbX model indicates the time interval used to group and analyze the data (day-by-day, week-by-week or month-by-month, etc.). The choice of the time interval depends on the quality and availability of the data, as well as the specific objective of the analysis. This flexibility allows adjusting the model to obtain significant insights according to the needs of the PV analysis.

**XbX + UTC:** this model improves the accuracy of temperature modeling in short periods, where temperature variations are small.
This model uses a universal Temperature Correction Coefficient (UTC) to convert temperature observations to a Representative Temperature ($T_{rep}$), thus improving the reliability of the model. Eqs. (14) and Eq. (15) illustrate two different approaches for adjusting Observed Power ($P_{obs}$) to account for variations in temperature and irradiance relative to Observed Temperature ($T_{obs}$) and Observed Irradiance ($G_{obs}$). These adjustments provide a corrected power value that more accurately represents the actual performance of the PV system under varying environmental conditions.

$$P_{\text{cor,phys}} = \frac{P_{\text{obs}}}{1 + \gamma_T(T_{\text{obs}} - T_{\text{rep}})(G_{\text{obs}}/G_{\text{rep}})} \quad (14)$$

$$P_{\text{cor,stat}} = \beta_0 + \beta_1 G + \epsilon \quad (15)$$

Eq. (14) represents a “physical correction model”, where Representative Irradiance ($G_{rep}$) is the representative irradiance, set at 900 W/m², and Irradiance Slope Over the Temperature ($\gamma_T$) denotes the irradiance slope with respect to temperature. This model adjusts $P_{obs}$ by normalizing it to reference environmental conditions.

Eq. (15), on the other hand, represents a “statistical regression model”, where $\beta_0$ and $\beta_1$ are regression coefficients that account for the influence of irradiance $G$, and $\epsilon$ represents the residual error of the model. This model provides an alternative estimation of $P_{cor}$ based on historical data and statistical relationships.

**PVUSA model:** the PVUSA (photovoltaics for utility scale applications) model is a physical model that predicts power using a linear equation that relates $G_{POA}$, $T_{amb}$ in (°C), and Wind Speed (in m/s) ($w_s$) as shown in Eq. (16).

$$P = G_{\text{POA}}(\beta_0 + \beta_1 G_{\text{POA}} + \beta_2 T_{\text{amb}} + \beta_3 w_s), \quad (16)$$

This model assumes that the current of a PV panel depends on $G_{POA}$, while the voltage is influenced by both irradiance and $T_{mod}$, which is estimated using $T_{amb}$ and $w_s$.

**6K Model:** this is a more complex model that normalizes irradiance and temperature with respect to STC. It uses a series of logarithmic and polynomial terms to adjust the power, providing greater accuracy in the prediction under different environmental conditions. The model is encapsulated by Eqs. (17), (18), and (19).

$$G' = \frac{G_{\text{POA}}}{G_{\text{STC}}} \quad (17)$$

$$T' = T_{\text{mod}} - T_{\text{STC}} \quad (18)$$

$$P = G'(P_{\text{nom}} + k_1 \ln(G') + k_2 \ln(G')^2 + k_3 T' + k_4 T' \ln(G') + k_5 T' \ln(G')^2 + k_6 T'^2) \quad (19)$$

The name “6K” reflects the six coefficients that adjust the model in (19). This model uses $G_{POA}$ and the $T_{mod}$, expressed as fractions of Irradiance under Standard Test Conditions ($G_{STC}$) and deviations from Temperature under Standard Test Conditions (25 °C) ($T_{STC}$). It also requires the Nominal Power of the Module ($P_{nom}$), so the prediction result will always be under STC.

**PVWatts Model:** the PVWatts model is a simple and efficient way to predict the power generated by a PV system. It uses $G_{POA}$ and adjusts the power based on $T_{mod}$ relative to a standard reference temperature. This model is widely used due to its simplicity and ease of implementation, especially in tools like the PVLib Python software package. The model is shown in Eq. (20).

$$P = \frac{G_{\text{POA}}}{1000} \cdot P_{\text{nom}}(1 + \gamma_T(T_{\text{mod}} - 25 \,^{\circ}\text{C})) \quad (20)$$

where $P_{nom}$ represents the nominal power of the PV system under STC. $\gamma_T$ is the temperature coefficient of power, typically given in units of %/°C, indicating the percentage change in power per degree Celsius variation in module temperature.

Another advantage of this model is that it allows users to make simple and straightforward adjustments to predict the performance of

the PV system under different environmental conditions, providing a good approximation of real field performance.

Both conventional and prediction-based metrics offer a comprehensive assessment of system performance, considering power production, solar resource availability, and overall system losses [134]. By evaluating these metrics, stakeholders can gauge PV system effectiveness, identify areas for improvement, and make informed decisions regarding maintenance, upgrades, or expansion. Following the analysis of operational factors affecting PV efficiency decline and performance evaluation metrics, this document outlines key recommendations, challenges, and essential components for future research.

## 6. Discussion

As observed in this study; to meet the new challenges of electricity generation using PV systems, it is necessary to comprehensively understand the operational factors that decrease their performance. Therefore, a quantitative analysis of the number of studies examining each operational factor, the number of articles examining multiple operational factors, and the number of operational factors analyzed in this study is presented in Fig. 13.

As shown in Fig. 13, this is the first comprehensive study that separately explains operational factors such as PV technology, tilt angle, orientation, surface properties, height, and component aging. Unlike studies that address multiple factors of performance degradation collectively, this study emphasizes soiling and mismatch. This focused approach opens opportunities for future research to integrally analyze all factors, their interactions, and their repercussions on PV systems. Such studies should be designed to be replicable and easily comparable by using the standardized metrics compiled in this study. Given that PV energy is expected to dominate the energy market in the coming years and operate sustainably within the environment, understanding these factors both in isolation and in combination is highly relevant.

The findings of this study emphasize the necessity of standardized performance metrics to ensure comparability across different PV installations. The integration of evaluation methodologies, such as those outlined in IEC 61724 and IEA-PVPS reports, is crucial for benchmarking PV system efficiency. Future studies should aim to refine these standardized approaches by incorporating AI-driven monitoring and real-time data analytics [135], to enhance predictive capabilities and long-term system reliability.

Additionally, this study highlights several critical gaps in the current research landscape. First, the interaction between operational and environmental factors remains insufficiently explored, particularly regarding their combined impact on long-term PV performance. Second, while standardized performance metrics exist, their implementation across diverse PV installations is inconsistent, necessitating further refinement to ensure broader applicability. Third, the development of intelligent monitoring systems capable of real-time performance diagnostics represents a key area for future research, as detailed in Section 2. Additionally, emerging material innovations, such as perovskite-silicon tandem cells and advanced encapsulation techniques, require further investigation to determine their long-term reliability and scalability. Future studies should also address the integration of predictive maintenance algorithms and recycling protocols to enhance PV sustainability. Addressing these gaps will be essential for optimizing PV performance and facilitating the transition to a more resilient and efficient PV energy sector.

It is worth noting that the quantitative analysis of operational factors can be strongly influenced by uncontrollable elements such as uneven ground, internal faults of mounting materials, or material incompatibility. This reinforces the need for studies that analyze multiple operational factors to develop a more comprehensive understanding using information such as that provided in this study. Improving the efficiency of PV modules is essential to minimize land
<figure><img src="image_17.png" /><figcaption>Fig. 13. On the left, the number of articles per operational factor considered in this study is shown. On the right, the number of articles addressing various operational factors, including this study, is represented.</figcaption></figure>

use and mitigate detrimental environmental consequences. In this regard, this study offers a series of challenges and key elements in research and applications.

Beyond academic research, these findings have direct implications for PV system manufacturers, installers, and operators. Manufacturers can leverage insights on material properties and surface coatings to develop more resilient PV modules, while PV plant operators can optimize maintenance schedules based on degradation patterns identified in this study. The integration of intelligent monitoring systems, as detailed in Section 2 and in [135], will be essential for implementing predictive maintenance strategies that minimize downtime and reduce operational costs. As the PV industry continues to expand, aligning operational optimizations with emerging technological innovations will be key to maximizing long-term performance and cost efficiency.

The challenges discussed in this section align with the technological advancements outlined in the roadmap for PV technology (Section 2). Many of the issues highlighted, such as performance degradation due to dust accumulation, tilt angle optimization, and material innovations, have direct implications for the future development of PV technology. The integration of advanced coatings, improved semiconductor materials, and intelligent monitoring systems, as outlined in the roadmap, represents a crucial step toward mitigating operational degradation. Future research efforts should focus on bridging the gap between these technological advancements and practical implementation in large-scale PV installations.

## 6.1. Challenges and key elements in research and applications

Significant challenges remain in addressing the detrimental impacts on PV systems, particularly from the interaction of operational factors with environmental conditions such as dust and temperature. Tackling these challenges is essential to ensure long-term performance and broader adoption of PV systems.

**Seasonal variations in solar irradiance:** Seasonal changes in solar irradiance significantly affect the *PR* and *Y*<sub>*F*</sub> of PV systems. During spring and summer, higher irradiance levels typically improve *Y*<sub>*F*</sub> and *PR* values, while autumn and winter reduce them due to lower solar angles, shorter days, and adverse weather conditions [136]. These fluctuations, combined with factors such as *T*<sub>amb</sub> and tilt angle, can lead to up to 15% variations in *Y*<sub>*F*</sub> depending on the PV technology and location [137]. To address this variability, system designs often optimize tilt angles or employ tracking systems to maximize irradiance capture year-round. Additionally, seasonal maintenance, such as snow removal or module cleaning, is essential to minimize performance losses in regions with pronounced seasonal shifts [138].

**Standardized performance metrics:** A consistent framework for evaluating performance degradation in PV systems is critical. As highlighted in the IEA-PVPS report, the *PR* has become a fundamental

metric to assess both irreversible degradation and reversible operational events, which affect power output over time [8]. This study refines these performance metrics by integrating both traditional and prediction-based metrics, allowing for a more comprehensive evaluation of PV efficiency losses. This approach is especially relevant when considering operational factors, such as tilt angle, orientation, and surface properties, which require standardized performance evaluations to ensure comparability across diverse PV installations.

**Nanomaterial coatings and surface innovations:** The development of advanced nanomaterial coatings, such as self-cleaning materials, can significantly reduce the need for water-intensive cleaning processes, particularly in dusty regions. However, the challenge lies in selecting materials that minimize dust accumulation without reducing the transmittance of the PV module [4]. For example, low dust-adhesion glass covers could be complemented by these coatings to improve performance in dry environments. Research on new semiconductor materials that maximize energy production in smaller areas should also be prioritized to reduce the overall environmental footprint of PV systems.

**Photovoltaic system degradation and risk management:** The operational degradation of PV systems, particularly due to component aging and improper installation, presents significant risks to system performance. According to the extensive review of degradation rates by NREL, the average degradation rate is approximately 0.8% per year, with higher rates observed in thin-film technologies [139]. This underscores the need for more resilient operational strategies, such as improved mounting and encapsulation techniques that can reduce long-term performance losses. Additionally, the inclusion of risk management methodologies, such as the Cost Priority Number (CPN), provides a new lens for evaluating the economic impact of technical failures across the entire PV value chain as explained by Moser et al. [140]. These methodologies allow stakeholders to prioritize operational risks based on both downtime and repair costs, enabling better resource allocation for maintenance and repair.

**Degradation mechanisms and lifetime analysis:** The impact of degradation on PV systems continues to be a central challenge. While significant progress has been made in reducing the degradation rates of thin-film technologies, the average degradation rate remains close to 0.8% per year, which still limits the operational lifetime of these systems [139]. Addressing this issue requires a better understanding of the linearity of degradation and the impact of climate and operational decisions on performance. As proposed in [141], more extensive datasets and the use of statistical models to predict degradation trends could help extend the operational life of PV systems.

**Operational and environmental factor interactions:** While environmental factors such as dust, temperature, and rainfall are well documented, their interaction with operational factors like tilt angle, surface properties, and module height is less understood [4]. Future research should explore how operational adjustments can mitigate the
effects of environmental stressors. For instance, optimizing the tilt angle of PV modules has been shown to significantly reduce dust accumulation, but further studies are needed to quantify these effects across different geographic regions and climates [139].

**Advanced photovoltaic mounting and encapsulation structures:** New designs for PV mounting and encapsulation structures are necessary to withstand extreme environmental conditions. Mounting structures should be able to handle high winds, snow loads, and temperature fluctuations, while allowing for optimal tilt and orientation adjustments to capture maximum PV energy. Encapsulation materials must also offer superior protection against moisture and thermal cycling, preventing issues such as delamination and corrosion [141]. Future research should explore the use of sustainable materials for these structures, minimizing the environmental impact of PV installations and extending the operational life of the modules.

**Photovoltaic recycling protocols:** As the global PV market grows, so does the issue of PV waste. Efficient recycling processes must be developed to recover valuable materials and reduce the environmental impact of discarded PV modules. Studies such as those from the International Technology Roadmap for PV 2024 report emphasize the need for research into efficient recycling methods and the environmental impacts of PV module disposal [58]. This area requires further investigation to create protocols that reduce waste and support the circular economy in the PV sector.

**Intelligent monitoring systems and Predictive Maintenance:** The integration of intelligent monitoring systems capable of real-time diagnosis is essential for the proactive maintenance of PV systems [142]. As noted by the IEA-PVPS report, the accurate determination of the Performance Loss Rate (PLR) heavily depends on effective data filtering and the selection of appropriate performance metrics [8]. Artificial intelligence and machine learning techniques can enhance predictive maintenance by identifying potential issues, such as dust accumulation or shading, before they result in significant performance losses [135]. The development of energy-autonomous, wireless monitoring systems that can operate in remote or challenging environments would further improve the reliability and efficiency of PV plants.

**Certification of photovoltaic systems:** A comprehensive evaluation of the certification process of PV systems is essential to ensure their long-term reliability and performance. This includes rigorous testing protocols under various environmental conditions, as outlined in standards such as IEC 61215 and IEC 61730. These protocols help identify potential failures and improve product quality, which is critical for the continued expansion of PV systems [8].

**Mitigation of mismatch losses:** Addressing mismatch losses, caused by shading, dirt accumulation, or module degradation, remains critical for optimizing system performance. Advanced mitigation strategies, such as bypass diodes and module-level power electronics, offer promising solutions. Additionally, adaptive algorithms that dynamically reconfigure PV arrays in response to shading or soiling can improve power output [140]. Further research should focus on the integration of these technologies with real-time monitoring systems to rapidly identify and address mismatch issues.

**Intercellular interconnections:** The stability and durability of PV systems are greatly influenced by the configuration of intercellular interconnections, which play a critical role in reducing performance losses caused by shading and variations in irradiance. Optimized interconnection designs, such as multi-busbar and shingled cell configurations, can enhance electrical performance by minimizing resistive losses and improving the structural integrity of the cells [8]. Further research should focus on advanced materials and techniques to improve the electrical conductivity and thermal stability of these interconnections, ensuring that they maintain performance under varying operational conditions.

**Advanced inverter technologies:** Inverters and power converters are critical components that can significantly impact the overall

performance of PV systems. Research into advanced inverter technologies with improved fault tolerance, higher efficiency, and better thermal management is essential to ensure the long-term reliability of PV systems [8]. Innovations in wide-bandgap semiconductors also have the potential to enhance the efficiency of power conversion, reducing system downtime and maintenance costs. Smart inverters capable of supporting grid functionalities, such as voltage regulation and frequency control, will be crucial as PV penetration into the grid increases.

**Plane of Array (POA) Irradiance Models:** $G_{POA}$ models are essential for accurately estimating the solar irradiance incident on PV arrays by considering the tilt and orientation of the modules. Advanced models like the Perez model improve upon isotropic transposition models, providing greater accuracy in diverse atmospheric conditions. For example, it has been demonstrated that convolutional neural networks can optimize $G_{POA}$ estimation, improving the prediction of PV system performance under variable conditions [143]. Another study highlights the application of Monte Carlo methods in modeling spectral $G_{POA}$, which provides detailed insights into the impact of spectral variations on energy generation [144]. These methods are particularly beneficial for locations with highly variable weather conditions. Furthermore, transposition models like those evaluated by Xie et al. have shown that incorporating radiative transfer methods significantly enhances the reliability of $G_{POA}$ estimations [145]. Studies also emphasize that directly using measured $G_{POA}$. A data in performance modeling, instead of relying solely on transposition models, can substantially improve accuracy [146].

**Photovoltaic Cell Models:** PV cell models are indispensable tools for simulating the electrical behavior of PV systems under varying environmental and operational conditions. The single-diode equivalent circuit model is one of the most widely used approaches due to its simplicity and accuracy in predicting PV performance. For instance, Jena and Ramana reviewed its applications and highlighted its robustness in simulating uniform irradiance scenarios [147]. Furthermore, this model has been adapted to account for non-uniform irradiance conditions, which are often encountered in large-scale PV systems, enhancing its applicability to real-world scenarios [147].

## 6.2. Limitations and future work

Although this study provides a comprehensive evaluation of operational factors affecting PV performance, certain limitations must be acknowledged. The analysis is constrained by case-specific data, which complicates direct numerical comparisons between different PV systems. Future research should prioritize the development of generable models that integrate structural and environmental variables to enhance comparability. Additionally, as this review is based on 102 studies, relevant insights from other sources may not have been fully captured. Expanding the state of the art review using advanced computational tools, such as natural language processing, topic modeling, and generative artificial intelligence [135], could help mitigate this limitation by broadening the range of analyzed studies. These techniques, combined with expert analysis, have already shown promising results in the PV domain [5].

Beyond methodological improvements, future studies should focus on addressing the research gaps identified in Section 6, particularly regarding the integration of intelligent monitoring systems, predictive maintenance strategies, and emerging material innovations. The roadmap in Section 2 provides a strategic framework for guiding these advancements, emphasizing interdisciplinary collaboration between engineering, data science, and policy-making. By aligning research efforts with technological advancements, future work can enhance PV system efficiency, reliability, and long-term sustainability.
## 7. Conclusions

This study provides a comprehensive analysis of the impact of operational factors on the performance of PV systems, complementing the extensive research on environmental influences. By reviewing 102 articles, key operational factors — such as PV technology, tilt angle, orientation, surface properties, height, and component aging — were evaluated for their role in influencing PV performance, with a particular focus on dust accumulation.

The findings emphasize the critical role of operational factors in shaping PV system efficiency. Factors like PV technology, tilt angle, orientation, and surface properties are highlighted as fundamental in mitigating environmental effects, especially dust. A refined set of standardized performance metrics, incorporating both traditional and prediction-based methods, is compiled to facilitate transparent and consistent evaluation of efficiency losses. These metrics not only enhance comparability across studies but also provide a framework for developing targeted preventive maintenance strategies. Additionally, the study underscores the importance of addressing operational degradation modes, such as improper installation and aging-related issues, to improve system reliability and longevity. Practical recommendations are offered for optimizing system configurations and implementing operational adjustments to mitigate performance losses caused by environmental factors.

The study also presents quantitative insights that reinforce the importance of these operational factors. For instance, the efficiency of Sc-Si photovoltaic cells ranges from 15% to 25%–26%, while A-Si cells reach up to 14%, as shown in Fig. 7. Among different photovoltaic technologies, TFH cells are the least sensitive to temperature variations, whereas TFC cells are the most affected.

Dust accumulation is highly dependent on the tilt angle of PV modules. Reducing the tilt angle from 90° to 15° significantly decreases transmittance, from 15% to 24.83%. Furthermore, for every 100 W/m² increase in irradiation, the temperature of PV cells rises substantially, affecting overall performance.

Grooved glass covers have been shown to reduce operating temperatures by up to 3.5 °C at wind speeds of 10 m/s. Similarly, coated glass surfaces exhibit a 30% reduction in transmittance after 40 days of exposure, compared to a 37% reduction for uncoated glass. Natural degradation due to aging remains inevitable, with rates ranging from 0.6% to 1% per year depending on operational conditions.

These findings provide a solid foundation for understanding the effects of operational factors on PV performance. They highlight the need for standardized protocols to evaluate these impacts systematically and encourage further research to optimize operational strategies.

The broader implications of this study extend beyond current operational challenges, linking directly to the technological advancements and strategic developments outlined in the Roadmap for PV Technology (Section 2). Future research must focus on integrating novel materials, intelligent monitoring, and grid modernization efforts to enhance PV efficiency and reliability. The continued expansion of PV capacity worldwide will require interdisciplinary efforts, combining engineering, data science, and policymaking to ensure the scalability and sustainability of PV energy systems. By addressing both immediate operational concerns and long-term technological innovations, this study provides a foundation for optimizing PV system performance and advancing global PV energy adoption.

## Acronyms

* η<sub>sys,m</sub> Monthly system efficiency

* γ<sub>T</sub> Irradiance Slope Over the Temperature

* A<sub>a</sub> Module area

* CF Capacity Factor

* E<sub>AC,a</sub> Actual annual energy output

* E<sub>AC,d</sub> Daily AC energy

* E<sub>AC,m</sub> Monthly Production of AC Energy

* E<sub>DC,m</sub> Monthly Production of DC Energy

* E<sub>DC</sub> Total DC Energy

* E<sub>Grid</sub> AC Energy Injected into the Grid

* G<sub>t</sub> Total Amount of Solar Radiation

* G<sub>GI</sub> Global Horizontal Irradiance

* G<sub>obs</sub> Observed Irradiance

* G<sub>POA</sub> Irradiance in the Plane of Array

* G<sub>rep</sub> Representative Irradiance

* G<sub>STC</sub> Irradiance under Standard Test Conditions

* G<sub>ref</sub> Reference Irradiation

* I<sub>SC</sub> Short circuit current

* L<sub>C</sub> Array Capture Losses

* L<sub>S</sub> PV System Losses

* P<sub>nom</sub> Nominal Power of the Module

* P<sub>obs</sub> Observed Power

* P<sub>max</sub> Maximum power

* P<sub>MP</sub> Maximum Power Output

* P<sub>p</sub> Full rated power

* PR Performance Ratio

* PR<sub>Tcorr</sub> Temperature Corrected Performance Ratio

* T<sub>amb</sub> Ambient Temperature

* T<sub>mod</sub> Module Temperature in °C

* T<sub>obs</sub> Observed Temperature

* T<sub>rep</sub> Representative Temperature

* T<sub>STC</sub> Temperature under Standard Test Conditions (25 °C)

* V<sub>OC</sub> Open circuit voltage

* w<sub>s</sub> Wind Speed (in m/s)

* Y<sub>a</sub> Array Yield

* Y<sub>F</sub> Final Yield

* Y<sub>r</sub> Reference Yield

* a-Si Amorphous Silicon

* AC Alternating Current

* ANNs Artificial Neural Networks

* c-Si Crystalline Silicon

* CdTe Cadmium Telluride

* CIGS Copper Indium Gallium Selenide

* CPN Cost Priority Number

* DC Direct Current

* EA Evolutionary Algorithms

* EU European Union

* EVA Ethylene-Vinyl Acetate

* FF Fill Factor

* FLC Fuzzy Logic Control

* HIT Heterojunction Intrinsic Thin-layer silicon

* HSAT Horizontal Single Axis Solar Tracking

* IC Incremental Conductance

* IEA International Energy Agency

* IEC International Electrotechnical Commission

* IRA Inflation Reduction Act

* M10 182 mm Monocrystalline wafer

* M12 210 mm Monocrystalline wafer

* MPP Maximum Power Point

* MPPT Maximum Power Point Tracking

* NREL National Renewable Energy Laboratory

* OPTA Optimum Tilt Angle

* P&O Perturb and Observe

* pc-Si Polycrystalline Silicon

* PID Potential Induced Degradation

* PLR Performance Loss Rate

* PSCs Perovskite PV Cells

* PSO Particle Swarm Optimization

* PV Photovoltaic

* sc-Si Single-crystalline Silicon

* STC Standard Test Conditions

* TFA Thin-Film Amorphous Silicon
TFC Thin-Film Copper Indium Gallium Selenide

TFH Thin-Film Heterogeneous

US United States

UTC Temperature Correction Coefficient

UV Ultraviolet

## Additional information

No additional information is available for this study.

## Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This project acknowledges the National Renewable Energy Laboratory (NREL) for permitting the use and dissemination of its Best Research-Cell Efficiency Chart.

## Data availability

No private datasets were generated or analyzed during the current study. All data can be retrieved from the mentioned search engines using the search protocol described in this study.

## References

[1] Feldman D, Dummit K, Zuboy J, Heeter K, Xu J, Margolis R. Winter 2024 solar industry update. National Renewable Energy Lab.(NREL), Golden, CO, USA; 2022, Available from: https://www.nrel.gov/docs/fy24osti/88780.pdf.

[2] David Feldman, Vignesh Ramasamy, Robert Margolis. Documenting a decade of cost declines for PV systems. National Renewable Energy Lab.(NREL), Golden, CO, USA; 2021, Available from: https://www.nrel.gov/news/program/2021/documentation-a-decade-of-cost-declines-for-pv-systems.html.

[3] Shaik F, Lingala S, Veeraboina P. Effect of various parameters on the performance of solar PV power plant: a review and the experimental study. J Sustain Energy 2023;10:1-23.

[4] Salamah T, Ramahi A, Alamara K, Juaidi A, Abdallah R, Abdelkareem MA, Amer E-C, Olabi AG. Effect of dust and methods of cleaning on the performance of solar PV module for different climate regions: Comprehensive review. Sci Total Environ 2022;827:154050.

[5] Sepúlveda-Oviedo EH, Travé-Massuyès L, Subias A, Pavlov M, Alonso C. Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach. Heliyon 2023;9(11):e21491.

[6] Gupta V, Sharma M, Pachauri RK, Dinesh Babu KN. Comprehensive review on effect of dust on solar photovoltaic system and mitigation techniques. Sol Energy 2019;191:596-622.

[7] Said SA, Hassan G, Walwil HM, Al-Aqeeli N. The effect of environmental factors and dust accumulation on photovoltaic modules and dust-accumulation mitigation strategies. Renew Sustain Energy Rev 2018;82:743-60.

[8] IEA. Assessment of performance loss rate of PV power systems. IEA; 2021, Available from: https://iea-pvps.org/wp-content/uploads/2021/04/IEA-PVPS-T13-22_2021-Assessment-of-Performance-Loss-Rate-of-PV-Power-Systems-report.pdf. [Accessed 26 June 2024],

[9] IEC. Photovoltaic system performance monitoring—guidelines for measurement, data exchange and analysis. International standard IEC 61724, 1998.

[10] Lindig S, Moser D, Curran AJ, Rath K, Khalilnejad A, French RH, Herz M, Müller B, Makrides G, Georghiou G, Livera A, Richter M, Ascencio-Vásquez J, van Iseghem M, Meftah M, Jordan D, Deline C, van Sark W, Stein JS, Theristis M, Meyers B, Baumgartner F, Luo W. International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology). Prog Photovolt: Res Appl 2021;29(6):573-602.

[11] Wan L, Zhao L, Xu W, Guo F, Jiang X. Dust deposition on the photovoltaic panel: A comprehensive survey on mechanisms, effects, mathematical modeling, cleaning methods, and monitoring systems. Sol Energy 2024;268:112300.

[12] Jathar LD, Ganesan S, Awasarmol U, Nikam K, Shahapurkar K, Soudagar MEM, Fayaz H, El-Shafay A, Kalam M, Bouadila S, Baddadi S, Tirth V, Nizami AS, Lam SS, Rehan M. Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle. Environ Pollut 2023;326:121474.

[13] Hasan K, Yousuf SB, Tushar MSHK, Das BK, Das P, Islam MS. Effects of different environmental and operational factors on the pv performance: A comprehensive review. Energy Sci Eng 2022;10(2):656-75.

[14] Sharma V, Chandel SS. Performance and degradation analysis for long term reliability of solar photovoltaic systems: A review. Renew Sustain Energy Rev 2013;27:753-67.

[15] SPE. Global market outlook for solar power 2024 - 2028. SolarPower Europe; 2024.

[16] Lazaroiu AC, Gmal Osman M, Strejoiu C-V, Lazaroiu G. A comprehensive overview of photovoltaic technologies and their efficiency for climate neutrality. Sustainability 2023;15(23).

[17] Han Y, Teng Y, Xiao Z. Comparison and analysis of the photovoltaic industry between China and the US. Highlights Bus Econ Manag 2023;23:423-30.

[18] Fu P, Yang H. Advancing sustainable energy development: A study of the factors influencing distributed photovoltaic industry in Heilongjiang province. Strat Plan Energy Env 2024;1-20.

[19] Barchi G, Prina MG, Moser D. A PV hosting capacity assessment in distribution grids: towards the 2030 and 2050 energy scenarios. In: 2023 AEIT international annual conference. AEIT, 2023, p. 1-6.

[20] Aleksandra A, Sara BP, Malgorzata J, Brian B, Davide P, Miguel C. Role of solar PV in net-zero growth: An analysis of international manufacturers and policies. Prog Photovolt: Res Appl 2024;32(9):607-22.

[21] Bosmans J, Schipper A, Mielke KP, Čengić M, Gnaat D, van Vuuren DV, Huijbregts M. Determinants of the distribution of utility-scale photovoltaic power facilities across the globe. Env Res Lett 2022;17.

[22] Yang S-X, Zhang Y, Cheng X-Y. Economic modeling of distributed photovoltaic penetration considering subsidies and countywide promotion policy: An empirical study in Beijing. J Renew Sustain Energy 2022;14(5):055301.

[23] Ge F, Wang G, He G, Zhou D, Yin R, Tong L. A hierarchical information extraction method for large-scale centralized photovoltaic power plants based on multi-source remote sensing images. Remote Sens 2022;14(17).

[24] Qin X, Tong D, Liu F, wu R, Zheng B, Zheng Y, Liu J, Xu R, Chen C, Yan L, Zhang Q. Global and regional drivers of power plant CO2 emissions over the last three decades revealed from unit-based database. Earth's Futur 2022;10:1-18.

[25] Li J, Wang C, Guo J, Xin Y, Zhang N, Liu X, Feng K. Promoting sustainable development goals by optimizing city-level solar photovoltaic deployment in China. Env Sci Technol 2024;58:5196-209.

[26] Meng X, Baofeng L, Yu Q, Bin X. Research on security protection strategies for household distributed photovoltaic clusters. J Phys Conf Ser 2024;2823:012004.

[27] Saeed SS, Siraj MT. Global renewable energy infrastructure: Pathways to carbon neutrality and sustainability. Sol Energy Sustain Dev J 2024;13:183-203.

[28] Blakesley JC, Bonilla RS, Freitag M, Ganose AM, Gasparini N, Kaienburg P, Koutsourakis G, Major JD, Nelson J, Noel NK, Moose B, Yun JS, Aliwell S, Altermatt PP, Ameri T, Andrei V, Armin A, Bagnis D, Baker J, Beath H, Bellanger M, Berrouard P, Blumberger J, Boden SA, Bronstein H, Carnie MJ, Case C, Castro FA, Chang Y-M, Chao E, Clarke TM, Cooke G, Docampo P, Durose K, Durrant JR, Filip MR, Friend RH, Frost JM, Gibson EA, Gillett AJ, Goddard P, Habisreutinger SN, Heeney M, Hendsbee AD, Hirst LC, Islam MS, Jayawardena KDGI, Johnston MB, Kauer M, Kettle J, Kim J-S, Lamb D, Lidzey D, Lim J, MacKenzie R, Mason N, McCulloch I, McKenna KP, Meier SB, Meredith P, Morse G, Murphy JD, Nicklin C, Ortega-Arriaga P, Osterberg T, Patel JB, Peaker A, Riede M, Rush M, Ryan JW, Scanlon DO, Skabara PJ, So F, Snaith HJ, Steier L, Thiesbrummel J, Troisi A, Underwood C, Walzer K, Watson T, Walls JM, Walsh A, Whalley LD, Winchester B, Stranks SD, Hoye RLZ. Roadmap on established and emerging photovoltaics for sustainable energy conversion. J Phys Energy 2024;6(4):041501.

[29] Benjamin O, Ogundipe OB, Okwandu AC, Abdulwaheed SA. Recent advances in solar photovoltaic technologies: Efficiency, materials, and applications. GSCARR 2024;1-17.

[30] Jiang Q, Song Z, Yan Y, Zhu K. Highly efficient bifacial single junction perovskite solar cells. In: 2023 IEEE 50th photovoltaic specialists conference. PVSC, 2023, p. 1-3.

[31] Baliozian P, Tepner S, Fischer M, Trube J, Herritsch Roadmap for photovoltaics and the significance of its decade-long projections. In: Conference: 37th European photovoltaic solar energy conference and exhibition. 2020, p. 420-6.

[32] Qian C, Sun K, Cong J, Cai H, Huang J, Li C, Cao R, Liu Z, Green M, Hoex B, Chen T, Hao X. Bifacial and semitransparent Sb 2 (S,Se) 3 solar cells for single-junction and tandem photovoltaic applications. Adv Mater 2023;35.

[33] Bulavko G. Organic photovoltaics: A journey through time, advancements, and future opportunities. Hist Sci Technol 2024;14:10-32.

[34] Kenny R. Photovoltaics in 2021. Sol RRL 2022;6.
[35] Guo Q, Ingram A. Powering the future: Advancements and applications of photovoltaic systems with political, economic, and environmental considerations. *Eng Sci Technol Int J* 2024;109-20.

[36] Ghosh S, Yadav R. Future of photovoltaic technologies: A comprehensive review. *Sustain Energy Technol Assess* 2021;47:101410.

[37] Al Mahdi H, Leahy PG, Alghoul M, Morrison AP. A review of photovoltaic module failure and degradation mechanisms: Causes and detection techniques. *Solar* 2024;4(1):43-82.

[38] Adar M, Najih Y, Chebak A, Mabrouki M, Bennouna A. Performance degradation assessment of the three silicon PV technologies. *Prog Photovolt: Res Appl* 2022;30(10):1149-65.

[39] Meena R, Niyaz HM, Gupta R. Investigation and differentiation of degradation modes affecting series resistance in photovoltaic cells and modules. *IEEE J Photovolt* 2023;13(2):283-90.

[40] Poddar S, Rougieux F, Evans JP, Kay M, Prasad AA, Bremner SP. Accelerated degradation of photovoltaic modules under a future warmer climate. *Prog Photovolt: Res Appl* 2024;32(7):456-67.

[41] Segbefia OK, Imenes AG, Smet TO. Moisture ingress in photovoltaic modules: A review. *Sol Energy* 2021;224:889-906.

[42] Diallo S, Melhaoui F, Mohamed R, Elassoudi A. Understanding photovoltaic module degradation: An overview of critical factors, models, and reliability enhancement methods. *E3S Web Conf* 2023;469:1-27.

[43] Kettle J, Aghaei M, Ahmad S, Fairbrother A, Irvine S, Jacobsson JJ, Kazim S, Kazukauskas V, Lamb D, Lobato K, Mousidis GA, Oreski G, Reinders A, Schmitz J, Yilmaz P, Theelen MJ. Review of technology specific degradation in crystalline silicon, cadmium telluride, copper indium gallium selenide, dye sensitised, organic and perovskite solar cells in photovoltaic modules: Understanding how reliability improvements in mature technologies can enhance emerging technologies. *Prog Photovolt: Res Appl* 2022;30(12):1365-92.

[44] Hossain K, Nayak S, Kabra D. Challenges and opportunities in high efficiency scalable and stable perovskite solar cells. *Appl Phys Lett* 2024;125(17):170501.

[45] Vimala M, Ramadas G, Perarasi M, Manokar AM, Sathyamurthy R. A review of different types of solar cell materials employed in bifacial solar photovoltaic panel. *Energies* 2023;16(8).

[46] Deceglie MG, Gaulding EA, Mangum JS, Silverman TJ, Johnston SW, Rand JA, Reed MJ, Flottemesch R, Repins IL. Correspondence: Bill of materials variation and module degradation in utility-scale PV systems. *IEEE J Photovolt* 2022;12(6):1349-53.

[47] Gervais E, Shammugam S, Friedrich I, Schlegl T. Raw material needs for the large-scale deployment of photovoltaics - effects of innovation-driven roadmaps on material constraints until 2050. *Renew Sustain Energy Rev* 2021;137:110589.

[48] Duda J, Kusa R, Pietruszko S, Smol M, Suder M, Teneta J, Wójtowicz T, Żdanowicz T. Development of roadmap for photovoltaic solar technologies and market in Poland. *Energies* 2022;15(1).

[49] Jafarzadeh F, Castriotta LA, Legrand M, Ory D, Cacovich S, Skafi Z, Barichello J, Rossi FD, Di Giacomo F, Di Carlo A, Brown TM, Brunetti F, Matteocci F. Flexible, transparent, and bifacial perovskite solar cells and modules using the wide-band gap FAPbBr3 perovskite absorber. *ACS Appl Mater Interfaces* 2024;1-16.

[50] Falagas M, Pitsouni E, Malietzis G, Pappas G. Comparison of PubMed, scopus, web of science, and google scholar: strengths and weaknesses. *FASEB J* 2008;22(2):338-42.

[51] Rakshikar N. Zotero: an ultimate citation management tool for researchers and academicians. In: UGC sponsored two day's national seminar on emerging trends in library technology. 2015. p. 1-6.

[52] Wright K, Golder S, Rodriguez-Lopez R. Citation searching: a systematic review case study of multiple risk behaviour interventions. *BMC Med Res Methodol* 2014;14(73):1-8.

[53] Strohkendl K, Herrmann W, Vaassen W, Althaus J, Reil F. The effect of transportation impacts and dynamic load tests on the mechanical and electrical behaviour of crystalline PV modules. In: 25th European photovoltaic solar energy conference and exhibition / 5th world conference on photovoltaic energy conversion. 2010. p. 3989-92.

[54] Sepúlveda-Oviedo EH, Travé-Massuyès L, Subias A, Alonso C, Pavlov M. Feature extraction and health status prediction in PV systems. *Adv Eng Inf* 2022;53:101696.

[55] Dietrich S, Pander M, Ebert M, Bagdahn J. Mechanical assessment of large photovoltaic modules by test and finite element analysis. In: 23rd European photovoltaic solar energy conference and exhibition. 2008. p. 2889-92.

[56] Gallardo-Saavedra S, Hernández-Callejo L, Duque-Pérez O. Quantitative failure rates and modes analysis in photovoltaic plants. *J Energy* 2019;183:825-36.

[57] Tajjour S, Chandel SS, Chandel R, Thakur N. Power generation enhancement analysis of a 400 kWp grid-connected rooftop photovoltaic power plant in a hilly terrain of India. *Energy Sustain Dev* 2023;77:101333.

[58] **VDMA. In: Photovoltaics E, editor. ETIP Photovoltaics; 2024, Web page. Available from: https://etip-pv.eu/news/other-news/international-technology-roadmap-for-photovoltaic-itrp-v-r-d-findings-from-the-13th-edition/**.

[59] **Philips S. In: Philips S, editor. Photovoltaics report. Fraunhofer-ISE; 2024, Available from: https://www.ise.fraunhofer.de/en/publications/studies/photovoltaics-report.html. [Accessed April 2024].**

[60] **ReGlobal. Technology roadmap: anticipated trends in the c-Si PV industry. REGlobal; 2021, Available from: https://reglobal.co/technology-roadmap-anticipated-trends-in-the-c-si-pv-industry/**.

[61] Morales-Acevedo A, Casados-Cruz G, et al. Forecasting the development of different solar cell technologies. *Int J Photoenergy* 2013;2013.

[62] Sharma V, Sastry OS, Kumar A, Bora B, Chandel SS. Degradation analysis of a-Si, (HIT) hetro-junction intrinsic thin layer silicon and m-C-Si solar photovoltaic technologies under outdoor conditions. *Energy* 2014;72:536-46.

[63] **NREL. In: NREL, editor. NERL best research-cell efficiency char. NREL; 2024, https://www.nrel.gov/pv/assets/pdfs/best-research-cell-efficiencies.pdf. [Accessed 30 July 2024].**

[64] Costa SCS, Kazmerski LL, Diniz ASA. Impact of soiling on Si and CdTe PV modules: Case study in different Brazil climate zones. *Energy Convers Manag: X* 2021;10:100084.

[65] Barkhouse D, Gunawan O, Gokmen T, Todorov T, Mitzi D. Device characteristics of a 10.1% hydrazine-processed CuZnSn(Se,S)4 solar cell. *Prog Photovolt: Res Appl* 2012;20(1):6-11.

[66] Majeed R, Waqas A, Sami H, Ali M, Shahzad N. Experimental investigation of soiling losses and a novel cost-effective cleaning system for PV modules. *Sol Energy* 2020;201:298-306.

[67] Al-Khazzar A, Hashim E. Temperature effect on power drop of different photovoltaic modules. *J Eng* 2016;22:129-43.

[68] Baras A, Bamhair W, Alkhoshi Y, Alodan M, Engel-Cox J. Opportunities and challenges of solar energy in Saudi Arabia. In: World renewable energy forum. *Vol. 1, Denver, 2012, p. 4721.*

[69] Kaldellis JK, Kapsali M, Kavadias KA. Temperature and wind speed impact on the efficiency of pv installations. Experience obtained from outdoor measurements in Greece. *J Renew Energy* 2014;66:612-24.

[70] Kumar R, Rosen MA. A critical review of photovoltaic-thermal solar collectors for air heating. *Appl Energy* 2011;88(11):3603-14.

[71] Tahir ZR, Kanwal A, Asim M, Bilal M, Abdullah M, Saleem S, Mujtaba MA, Veza I, Mousa M, Kalam MA. Effect of temperature and wind speed on efficiency of five photovoltaic module technologies for different climatic zones. *Sustainability* 2022;14(23):1-32.

[72] do Nascimento LR, Braga M, Campos RA, Naspolini HF, Rüther R. Performance assessment of solar photovoltaic technologies under different climatic conditions in Brazil. *Renew Energy* 2020;146:1070-82.

[73] Shahriar A, Hasnath S, Islam MA. Effects of operating temperature on the performance of c-Si, a-Si:H, CIGS, and CdTe/CdS based solar cells. *EJCE* 2021;1:31-7.

[74] Noh MFM, Arzaee NA, Mumthas INN, Mohamed NA, Nasir SNFM, Safaei J, Nazeeruddin MK, Teridi MAM, et al. High-humidity processed perovskite solar cells. *J Mater Chem* 2020;8(21):10481-518.

[75] Ouedraogo NAN, Chen Y, Xiao YY, Meng Q, Han CB, Yan H, Zhang Y. Stability of all-inorganic perovskite solar cells. *Nano Energy* 2020;67:104249.

[76] Yun JS, Kim J, Young T, Patterson RJ, Kim D, Seidel J, Lim S, Green MA, Huang S, Ho-Baillie A. Humidity-induced degradation via grain boundaries of HC (NH2) 2PbI3 planar perovskite solar cells. *Adv Funct Mater* 2018;28(11):1705363.

[77] Wozny S, Yang M, Nardes AM, Mercado CC, Ferrere S, Reese MO, Zhou W, Zhu K. Controlled humidity study on the formation of higher efficiency formamidinium lead triiodide-based solar cells. *J Mater Chem* 2015;27(13):4814-20.

[78] Zhou H, Chen Q, Li G, Luo S, Song T-b, Duan H-S, Hong Z, You J, Liu Y, Yang Y. Interface engineering of highly efficient perovskite solar cells. *J Sci* 2014;345(6196):542-6.

[79] Zhou X, Xin C, Hou F, Shi B, Pan S, Hou S, Zhang J, Wang P, Ren H, Zhao Y, et al. Role of moisture in the preparation of efficient planar perovskite solar cells. *ACS Sustain Chem Eng* 2019;7(21):17691-6.

[80] Kabalan A. Controlling the doping depth in silicon micropillars. *Appl Sci* 2020;10(13).

[81] Korovin AV, Alvarez J, Kleider J-P. Influence of the dopant penetration depth on the solar cell performance of n-type interdigitated back contact silicon solar cells. *Energy Procedia* 2016;92:103-8, *Proceedings of the 6th International Conference on Crystalline Silicon Photovoltaics (SiliconPV 2016)*.

[82] Cheng Q, Peng H, Hu S, Liu Y, Chen Z, Raghtothamachar B, Dudley M. Effective penetration depths investigation on synchrotron X-Ray topographic images of basal plane dislocations in 4H-SiC crystals through ray-tracing simulation. *ECS Meet Abstr* 2022;MA2022-02(37):1355.

[83] Ghobadi A, Ulusoy Ghobadi TG, Karadas F, Ozbay E. Semiconductor thin film based metasurfaces and metamaterials for photovoltaic and photoelectrochemical water splitting applications. *Adv Opt Mater* 2019;7(14):1900028.

[84] Machín A, Márquez F. Advancements in photovoltaic cell materials: Silicon, organic, and perovskite solar cells. *Materials* 2024;17(5).

[85] Sarkar S, Nefzaoui E, Hamaoui G, Marty F, Basset P, Bourouina T. Wideband mid infrared absorber using surface doped black silicon. *Appl Phys Lett* 2022;121(23):1-11.

[86] Maman MAA, Islam MM, Hasanuzzaman M, Selvaraj J. Effect of tilt angle on the performance and electrical parameters of a PV module: Comparative indoor and outdoor experimental investigation. *Energy Built Env* 2022;3(3):278-90.
[87] Ahmed W, Sheikh JA, Ahmad S, Farjana SH, Mahmud MAP. Impact of PV system orientation angle accuracy on greenhouse gases mitigation. Case Stud Therm Eng 2021;23:100815.

[88] Ishaque K, Salam Z. A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition. Renew Sustain Energy Rev 2013;19:475–88.

[89] Hanjalic M, Melic E. Impact of the tilt angle on the electrical output, characteristics and performance of a grid-connected photovoltaic system. In: 2023 22nd international symposium INFOTEH-JAHORINA. 2023. p. 1–6.

[90] Haghdadi N, Copper J, Bruce A, MacGill I. A method to estimate the location and orientation of distributed photovoltaic systems from their generation output data. J Renew Energy 2017;108:390–400.

[91] Ben Mansour R, Khan MAM, Alsulaiman F, Mansour R. Optimizing the solar PV tilt angle to maximize the power output: A case study for Saudi Arabia. IEEE Access 2021;9:15914–28.

[92] Mekhilef S, Saidur R, Kamalisarvestani M. Effect of dust, humidity and air velocity on efficiency of photovoltaic cells. Renew Sustain Energy Rev 2012;16(5):2920–5.

[93] Ali M, Iqbal M, Sheikh N, Ali H, Manzoor S, Khan M, Tamrin K. Performance investigation of air velocity effects on PV modules under controlled conditions. Int J Photoenergy 2017;2017:1–10.

[94] Hegazy AA. Effect of dust accumulation on solar transmittance through glass covers of plate-type collectors. J Renew Energy 2001;22(4):525–40.

[95] Elminir HK, Ghitas AE, Hamid R, El-Hussainy F, Beheary M, Abdel-Moneim KM. Effect of dust on the transparent cover of solar collectors. Energy Convers Manag 2006;47(18–19):3192–203.

[96] Lu H, Zhao W. Effects of particle sizes and tilt angles on dust deposition characteristics of a ground-mounted solar photovoltaic system. Appl Energy 2018;220:514–26.

[97] Gholami A, Saboonchi A, Alemrajabi AA. Experimental study of factors affecting dust accumulation and their effects on the transmission coefficient of glass for solar applications. J Renew Energy 2017;112:466–73.

[98] Xu R, Ni K, Hu Y, Si J, Wen H, Yu D. Analysis of the optimum tilt angle for a soiled PV panel. Energy Convers Manag 2017;148:100–9.

[99] Powers L, Newmiller J, Townsend T. Measuring and modeling the effect of snow on photovoltaic system performance. In: 2010 35th IEEE photovoltaic specialists conference. 2010. p. 000973–8.

[100] Yadav AK, Malik H, Chandel SS, Khan IA, Otaibi SA, Alkhammash HI. Novel approach to investigate the influence of optimum tilt angle on minimum cost of energy-based maximum power generation and sizing of PV systems: A case study of diverse climatic zones in India. IEEE Access 2021;9:110103–15.

[101] Kaddoura TO, Ramli MA, Al-Turki YA. On the estimation of the optimum tilt angle of PV panel in Saudi Arabia. Renew Sustain Energy Rev 2016;65:626–34.

[102] Chandel R, Chandel SS. Performance analysis outcome of a 19-MWp commercial solar photovoltaic plant with fixed-tilt, adjustable-tilt, and solar tracking configurations. Prog Photovolt: Res Appl 2022;30(1):27–48.

[103] Pavan AM, Mellit A, De Pieri D. The effect of soiling on energy production for large-scale photovoltaic plants. Sol Energy 2011;85(5):1128–36.

[104] Kalogirou SA, Agathokleous R, Panayiotou G. On-site PV characterization and the effect of soiling on their performance. J Energy 2013;51:439–46.

[105] Toma M, Loget G, Corn R. Flexible teflon nanocone array surfaces with tunable superhydrophobicity for self-cleaning and aqueous droplet patterning. ACS Appl Mater Interfaces 2014;6.

[106] Yilbas B, Ali H, Khaled M, Al-Aqeli N, Al-Sulaiman F. Laser assisted texturing of alumina surfaces and effects of environmental dry mud solution on surface characteristics. Ceram Int 2016;42(1):396–404.

[107] Griffith JS, Rathod MS, Paslaski J. Some tests of flat plate photovoltaic module cell temperatures in simulated field conditions. In: 15th photovoltaic specialists conference. 1981. p. 822–30.

[108] Duell M, Ebert M, Muller M, Li B, Koch M, Christian T, Perdichizzi RF, Marion B, Kurtz S, Doble DM. Impact of structured glass on light transmission, temperature and power of PV modules. In: Proc. 25th European photovoltaic solar energy conf. and exhibition, Valencia, Spain. 2010. p. 3867–72.

[109] Garg HP. Effect of dirt on transparent covers in flat-plate solar energy collectors. Sol Energy 1974;15(4):299–302.

[110] Arabatzis I, Todorova N, Fasaki I, Tsesmeli C, Peppas A, Li WX, Zhao Z. Photocatalytic, self-cleaning, antireflective coating for photovoltaic panels: Characterization and monitoring in real conditions. Sol Energy 2018;159:251–9.

[111] Zhou Z, Jiang Y, Ekins-Daukes N, Keevers M, Green MA. Optical and thermal emission benefits of differently textured glass for photovoltaic modules. IEEE J Photovolt 2021;11(1):131–7.

[112] Said SAM, Walwil HM. Fundamental studies on dust fouling effects on PV module performance. Sol Energy 2014;107:328–37.

[113] Cano J. Photovoltaic modules: effect of tilt angle on soiling (diploma thesis), Arizona: Arizona State University; 2011.

[114] Beattie NS, Moir RS, Chacko C, Buffoni G, Roberts SH, Pearsall NM. Understanding the effects of sand and dust accumulation on photovoltaic modules. J Renew Energy 2012;48:448–52.

[115] Wang Q, Yu Y, Xu T. Study on air gap effects on photovoltaic modules operating temperature on typical metal rooftop application. In: 2023 IEEE 50th photovoltaic specialists conference. PVSC, 2023, p. 1–3.

[116] Goossens D, Goverde H, Catthoor F. Effect of wind on temperature patterns, electrical characteristics, and performance of building-integrated and building-applied inclined photovoltaic modules. Sol Energy 2018;170:64–75.

[117] NUR S, Emiroglu S. Effects of wind speed and mounting type on pv module in unbalanced distribution systems. J Sci Rep 2023.

[118] Kouadri Boudjelthia EA, Abbas M, Semaoui S, Kerkouche K, Zeraïa H, Yaïche R. Role of the wind speed in the evolution of the temperature of the PV module: Comparison of prediction models. Rev Energ Renouv 2016;19:1–119.

[119] Alhassan Salami T, Salbiah A. Effects of wind speed and temperature on economics and environmental impact assessment of different solar PV systems in Malaysia. Rev Ci Tec 2023;6(3):e144.

[120] Moraitis P, Kausika BB, van Sark WGJHM. Effects of urban environment on solar PV performance. In: 2017 IEEE 44th photovoltaic specialist conference. PVSC, 2017, p. 3167–73.

[121] Walker A, Desai J, heimiller D. Performance of photovoltaic systems recorded by open solar performance and reliability clearinghouse (OSPARC). National Renewable Energy Lab.(NREL), Golden, CO, USA; 2020.

[122] Kim KA, Xu C, Jin L, Krein PT. A dynamic photovoltaic model incorporating capacitive and reverse-bias characteristics. IEEE J Photovolt 2013;3(4):1334–41.

[123] Noman M, Tu S, Ahmad S, Zafar FU, Khan HA, Rehman SU, Waqas M, Khan AD, Rehman Ou. Assessing the reliability and degradation of 10–35 years field-aged PV modules. PLoS ONE 2022;17(1):1–16.

[124] Sharma V, Chandel SS. A novel study for determining early life degradation of multi-crystalline-silicon photovoltaic modules observed in western himalayan Indian climatic conditions. Sol Energy 2016;134:32–44.

[125] Manganiello P, Balato M, Vitelli M. A survey on mismatching and aging of PV modules: The closed loop. IEEE Trans Ind Electron 2015;62(11):7276–86.

[126] Kaplanis S, Kaplanli E. Energy performance and degradation over 20 years performance of BP c-Si PV modules. Simul Model Pr Theory 2011;19(4):1201–11.

[127] Chamberlin CE, Rocheleau MA, Marshall MW, Reis AM, Coleman NT, Lehman PA. Comparison of PV module performance before and after 11 and 20 years of field exposure. In: 2011 37th IEEE photovoltaic specialists conference. 2011. p. 000101–5.

[128] Mallineni J, Knisely B, Yedidi K, Tatapudi S, Kuitche J, TamizhMani G. Evaluation of 12-year-old PV power plant in hot-dry desert climate: Potential use of field failure metrics for financial risk calculation. In: 2014 IEEE 40th photovoltaic specialist conference. PVSC, 2014, p. 3366–71.

[129] Sánchez-Friera P, Piliougine M, Peláez J, Carretero J, Sidrach de Cardona M. Analysis of degradation mechanisms of crystalline silicon PV modules after 12 years of operation in southern europe. Prog Photovolt 2011;19(6):658–66.

[130] Kaplanli E. Degradation effects in sc-si PV modules subjected to natural and induced ageing after several years of field operation. JESTR 2012;5:18–23.

[131] Parretta A, Bombace M, Graditi G, Schioppo R. Optical degradation of long-term, field-aged c-Si photovoltaic modules. Sol Energy Mater Sol Cells 2005;86(3):349–64.

[132] Park NC, Jeong JS, Kang BJ, Kim DH. The effect of encapsulant discoloration and delamination on the electrical characteristics of photovoltaic module. Microelectron Reliab 2013;53(9):1818–22, European Symposium on Reliability of Electron Devices, Failure Physics and Analysis.

[133] Sharma D, Purohit G. Analysis of the effect of fill factor on the efficiency of solar PV system for improved design of MPPT. In: The 6th world conference on photovoltaic energy conversion. 2014. p. 1281–2.

[134] Marion B, Adelstein J, Boyle Ke, Hayden H, Hammond B, Fletcher T, Canada B, Narang D, Kimber A, Mitchell L, et al. Performance parameters for grid-connected PV systems. In: Conference record of the thirty-first IEEE photovoltaic specialists conference. 2005. IEEE; 2005. p. 1601–6.

[135] Sepúlveda-Oviedo EH. Detection and diagnosis of faults and performance losses in high-power photovoltaic power plants (Theses), UT3 : Université Toulouse 3 Paul Sabatier; 2023, Available from: https://laas.hal.science/tel-04888632.

[136] Dhimish M. Thermal impact on the performance ratio of photovoltaic systems: A case study of 8000 photovoltaic installations. Case Stud Therm Eng 2020;21:100693.

[137] Eke R, Betts TR, Gottschalg R. Spectral irradiance effects on the outdoor performance of photovoltaic modules. Renew Sustain Energy Rev 2017;69:429–34.

[138] Correa-Betanzo C, Calleja H, De León-Aldaco S. Module temperature models assessment of photovoltaic seasonal energy yield. Sustain Energy Technol Assess 2018;27:9–16.

[139] Jordan DC, Kurtz SR. Photovoltaic degradation rates – an analytical review. National Renewable Energy Lab.(NREL), Golden, CO, USA; 2012.

[140] Moser D, Del Buono M, Jahn U, Herz M, Richter M, De Brabandere K. Identification of technical risks in the photovoltaic value chain and quantification of the economic impact. Prog Photovolt: Res Appl 2017;25(7):592–604.

[141] Anctil A, Beattie MN, Case C, Chaudhary A, Chrysler BD, Debije MG, Essig S, Ferry DK, Ferry VE, Freitag M, Gould I, Hincer K, Hoppe H, Inganäs O, Jagadamma LK, Jee MH, Kostuk RK, Kirk D, Kube S, Lim M, Luther JM, Mansfield L, McGehee MD, Minh DN, Nain P, Reese MO, Reinders A, Samuel IDW, van Sark W, Savin H, Sellers IR, Shaheen SE, Tang Z, Toor F, Vähänissi V, Wassweiler E, Warren EL, Whiteside VR, Woo HY, Xiong G, Zhu X. Status report on emerging photovoltaics. J Photonics Energy 2023;13(4):042301.
[142] Sepúlveda-Oviedo EH, Travé-Massuyès L, Subias A, Pavlov M, Alonso C. An ensemble learning framework for snail trail fault detection and diagnosis in photovoltaic modules. Eng Appl Artif Intell 2024;137:109068.

[143] Pierce BG, Braid JL, Stein JS, Augustyn J, Riley D. Solar transposition modeling via deep neural networks with sky images. IEEE J Photovolt 2022;12(1):145-51.

[144] Li M, Liao Z, Coimbra CFM. Spectral solar irradiance on inclined surfaces: A fast Monte Carlo approach. J Renew Sustain Energy 2020;12:053705.

[145] Xie Y, Sengupta M, Dooraghi M. Assessment of uncertainty in the numerical simulation of solar irradiance over inclined PV panels: New algorithms using measurements and modeling tools. Sol Energy 2018;165:55-64.

[146] Freeman J, Freestate D, Hobbs W, Riley C. Using measured plane-of-array data directly in photovoltaic modeling: Methodology and validation. In: 2016 IEEE 43rd photovoltaic specialists conference. PVSC, 2016, p. 2653-6.

[147] Jena D, Ramana VV. Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review. Renew Sustain Energy Rev 2015;52:400-17.