# Optimizing PV maintenance: Methods, cleaning frequency, and a selection protocol

Edgar Hernando Sepúlveda-Oviedo

► To cite this version:

Edgar Hernando Sepúlveda-Oviedo. Optimizing PV maintenance: Methods, cleaning frequency, and a selection protocol. Energy Reports, 2025, 14, pp.1578-1605. 10.1016/j.egyr.2025.07.008. hal-05251828

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## Review article

# Optimizing PV maintenance: Methods, cleaning frequency, and a selection protocol

Edgar Hernando Sepúlveda-Oviedo ID

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

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## ARTICLE INFO

**Keywords:**

Photovoltaic degradation

Cleaning methods

Mitigation framework

Optimal maintenance

Environmental impact

## ABSTRACT

Dust accumulation significantly reduces the efficiency of Photovoltaic (PV) systems, with energy losses reaching up to 50% in arid and semi-arid regions. This study presents a comprehensive review of PV cleaning methods, analyzing both passive (natural mitigation, coated surfaces, architectural solutions) and active methods (manual cleaning, water-based systems, electromechanical techniques, robotic cleaning, and piezoelectric approaches). A systematic evaluation of their operational principles, effectiveness, and economic implications is conducted, considering environmental constraints and site-specific conditions. A key contribution of this study is the assessment of optimal cleaning frequency, identifying how climatic and geographical factors influence maintenance schedules. Additionally, a novel Strategic PV Cleaning Optimization Method (SPV-COM) is introduced, offering a structured, multi-criteria decision-making framework to compare and rank cleaning methods based on technical performance, economic feasibility, and long-term sustainability. This methodology integrates operational costs and maintenance requirements to ensure an adaptive selection process that aligns with real-world PV installations. The proposed framework is scalable and applicable to diverse PV cleaning technologies, supporting decision-making in both small-scale and large-scale installations. The findings highlight, for example, the economic trade-offs between water-intensive methods and emerging autonomous solutions, as well as the need for region-specific strategies. By addressing critical gaps in prior studies, this work provides a structured approach to optimizing PV maintenance, contributing to improved efficiency, cost reduction, and sustainable energy production.

## 1. Introduction

The increasing global demand for renewable energy is driven by the declining availability of fossil fuels, rising energy consumption, and heightened concerns regarding climate change (Das et al., 2023). Among the diverse sources of renewable energy, Photovoltaic (PV) technology has been recognized as a pivotal solution due to its scalability, versatility, and economic feasibility. Between 2010 and 2019, PV production costs decreased by 82%, significantly contributing to an increase in global PV capacity from 1.3 GW in 2000 (Europe, 2018) to 627 GW by 2019 (Salamah et al., 2022). This rapid growth underscores the transformative potential of PV technology in meeting global energy needs. The International Energy Agency (IEA) projects an exponential rise in PV adoption, positioning it as a cornerstone of global energy systems by 2030 (IEA, 2021).

Despite recent advancements, the performance of PV systems remains highly sensitive to environmental conditions. Key factors such as dust accumulation, humidity, and temperature fluctuations are among the primary causes of efficiency degradation. Beyond the amount of dust, recent studies highlight that its composition plays a critical role

in performance losses. For example, case studies from diverse regions report efficiency reductions of up to 64% due to coal dust, 32% due to fine sand, and approximately 30% due to gypsum particles, under-scoring the need for localized dust characterization (Yakubu et al., 2025). Moreover, environmental stressors such as hailstorms, sand-storms, and prolonged humidity exposure further accelerate PV module degradation, reinforcing the necessity of tailored mitigation strategies (Chanchangi et al., 2021).

The economic implications of soiling are significant and globally widespread. According to recent estimates, soiling accounted for a 3%–4% reduction in annual PV energy production in 2018, resulting in economic losses ranging from €3 to €5 billion (Schill et al., 2022). These losses are projected to rise to €4–7 billion by 2023, driven by the expansion of PV installations in high-insolation, dust-prone regions, the decreasing value of recovered energy, and the increased sensitivity of high-efficiency modules to soiling. This highlights the significant financial burden of inadequate maintenance practices, including both under-maintenance, which leads to efficiency losses, and

<footnote>E-mail address: ehsepulved@laas.fr</footnote>
over-maintenance, which increase operational costs unnecessarily. Ad-
dressing this issue requires cost-effective, region-specific cleaning and
mitigation strategies tailored to local environmental conditions.

Over the past decade, numerous cleaning and mitigation methods
have been developed, ranging from anti-soiling coatings to robotic
cleaning systems (Hassan et al., 2024). However, existing reviews
of PV cleaning and dust mitigation methods often focus narrowly
on individual techniques or lack comprehensive comparisons across
diverse operational scenarios (Said et al., 2024). Additionally, while
prior studies have examined cleaning frequency optimization, there
is a need to address this aspect in conjunction with economic feasi-
bility, regional adaptability, and selection methodologies to provide
more comprehensive and practical guidance for practitioners (Simpson
et al., 2022). These gaps underscore the need for a systematic evalu-
ation that not only reviews existing methods but also integrates fre-
quency optimization and decision-making frameworks into a cohesive
approach.

Recent advancements in PV maintenance have introduced fron-
tier techniques — such as self-cleaning coatings (Said et al., 2024),
Artificial Intelligence (AI)-based soiling detection and cleaning opti-
mization (Rangelov et al., 2023), and Multi-Criteria Decision-Making
(MCDM) frameworks tailored for regional adaptation (Abuzaid et al.,
2024) — that underscore the growing relevance of smart, self-sustained
maintenance systems. These innovations offer promising improvements
in dust mitigation and decision support; however, they often remain
limited in their adaptability to site-specific operational and economic
constraints.

In parallel, emerging cleaning technologies such as electrostatic
systems and vibration-based mechanisms further expand the landscape
of solutions. For instance, electrostatic cleaning methods like im-
proved Electrodynamic Screen (EDS) have proven effective in removing
strongly adhered dust from PV panels without water or moving parts,
making them ideal for desert environments (Younis and Onsa, 2022).
These systems employ high-voltage alternating currents to generate
electrodynamic forces, with recent developments enhancing perfor-
mance through optimized electrode designs and airflow assistance.
Similarly, triboelectric and piezoelectric vibration-based systems offer
energy-efficient, waterless solutions for harsh conditions (Shenouda
et al., 2022a). Additionally, Internet of Things (IoT)-based platforms
that integrate real-time monitoring with autonomous control are be-
ing adopted to optimize maintenance schedules and increase system
reliability (Mostofa and Islam, 2023).

These novel approaches highlight the shift toward intelligent, re-
gionally adaptive maintenance strategies and emphasize the need for
flexible frameworks capable of evaluating and integrating such tech-
nologies. Addressing this need, the present study contributes a com-
prehensive and practical decision-making framework tailored to these
emerging challenges. The contributions of this study are significant and
extend beyond existing studies by offering a comprehensive and system-
atic evaluation of PV cleaning methods, integrating both technical and
economic considerations into a structured selection framework.

First, this work provides an in-depth review of PV cleaning and dust mitigation strategies, categorizing them into passive and active approaches. While previous reviews have discussed cleaning methods at a general level, this study advances the field by systematically analyzing their *operational principles, cost implications, energy recovery efficiency, scalability, and environmental impact*. The classification of methods is complemented by a comparative evaluation that considers site-specific constraints, ensuring that the findings are applicable across diverse climatic and geographic conditions.

Second, a critical gap in existing studies is addressed by incorpo-
rating *cleaning frequency optimization* as a fundamental factor in the
evaluation of PV maintenance strategies. Instead of treating cleaning
frequency as a fixed parameter, this study examines its *dynamic rela-
tionship with soiling rates, environmental variables, and economic feasibility*.

The analysis includes cost-benefit assessments that highlight how clean-
ing intervals influence both energy yield and long-term operational
costs, offering data-driven insights for site-specific decision-making.

Third, this study introduces the Strategic PV Cleaning Optimization Method (SPV-COM), a novel decision-making framework that enables the systematic selection of cleaning methods based on multi-criteria evaluation. Unlike previous works that provide qualitative discussions, this framework integrates quantitative weight assignments for Customer Requirements (CRs) and Technical Requirements (TRs), facilitating an objective ranking of PV cleaning alternatives. The structured methodology ensures that decision-making is adaptable, scalable, and aligned with sustainability goals.

By combining a detailed technical review with an original selection
methodology, this study not only synthesizes existing knowledge but
also enhances its practical applicability. The findings contribute to
improved PV maintenance strategies by bridging the gap between
theoretical evaluations and real-world implementation, supporting the
long-term efficiency and economic viability of PV systems. This study
provides actionable insights for researchers, practitioners, and policy-
makers, advancing both the theoretical understanding and practical
application of PV dust mitigation strategies.

The remainder of this study is organized as follows: Section 2 reviews existing studies on PV cleaning and mitigation methods, identifying key gaps and challenges. Section 3 details the methodology adopted for this study. Section 4 presents a framework for optimizing PV cleaning and maintenance, offering a detailed review and comparison of mitigation methods, insights on optimal cleaning frequencies, and a proposed methodology for selecting cleaning methods. Section 6 explores broader implications, highlighting critical aspects of PV maintenance and actionable recommendations. Finally, Section 7 summarizes the main findings and their practical implications for enhancing PV performance and sustainability.

## 2. Related work

The accumulation of dust on PV modules has been identified as a significant challenge for energy generation, particularly in arid and semi-arid regions. Numerous studies have examined various cleaning technologies, material treatments, and environmental interactions that influence dust behavior. However, many of these works fall short in providing structured methodologies to guide the selection of cleaning strategies adapted to site-specific constraints. This section first reviews studies focused primarily on technical aspects, followed by an overview of contributions that attempt to introduce decision-making frameworks.

Jamil et al. (2017) presented a detailed review of dust accumulation
mechanisms and mitigation strategies, including manual, mechanical,
electrostatic, and surface-modification-based methods. Environmental
influences such as tilt angle, wind speed, and humidity were also
explored. While comparative tables were provided, the review did
not establish a formal framework for selecting or evaluating strategies
based on performance or economic trade-offs.

Gupta et al. (2019) conducted a two-part analysis covering environ-
mental, material, and installation factors influencing dust deposition,
and reviewed techniques such as natural cleaning, manual washing,
and self-cleaning coatings. Economic aspects were discussed qualita-
tively, but no comparative methodology was applied to assess strategy
selection or adaptability.

Kazem et al. (2020) proposed a schematic classification for clean-
ing strategies based on dust morphology and chemical composition.
Although useful conceptually, the review lacked actionable steps for
prioritization or contextual deployment of the techniques examined.

Quan et al. (2023) focused on self-cleaning coatings and super-
hydrophobic surfaces, demonstrating improvements under lab condi-
tions. However, issues of integration with active cleaning systems and
real-world feasibility were not considered.
<table><caption>**Table 1**
Comparison of related studies on dust mitigation strategies for PV systems.</caption><thead><tr><th>Study</th><th>Cleaning methods</th><th>Economic analysis</th><th>Cleaning frequency</th><th>Selection methodology</th><th>Regional adaptability</th><th>MCDM approach</th></tr></thead><tbody><tr><td>Jamil et al. (2017)</td><td>✓</td><td>x</td><td>x</td><td>x</td><td>✓</td><td>x</td></tr><tr><td>Gupta et al. (2019)</td><td>✓</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Kazem et al. (2020)</td><td>✓</td><td>x</td><td>x</td><td>x</td><td>✓</td><td>x</td></tr><tr><td>Aljaghoub et al. (2022)</td><td>✓</td><td>x</td><td>x</td><td>x</td><td>✓</td><td>✓</td></tr><tr><td>Conceição et al. (2022)</td><td>✓</td><td>✓</td><td>x</td><td>x</td><td>✓</td><td>x</td></tr><tr><td>Quan et al. (2023)</td><td>✓</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Najmi and Rachid (2023)</td><td>✓</td><td>✓</td><td>x</td><td>x</td><td>✓</td><td>x</td></tr><tr><td>He et al. (2023)</td><td>✓</td><td>x</td><td>x</td><td>x</td><td>✓</td><td>x</td></tr><tr><td>Hassan et al. (2024)</td><td>✓</td><td>✓</td><td>x</td><td>x</td><td>✓</td><td>x</td></tr><tr><td>Chadly et al. (2024)</td><td>✓</td><td>x</td><td>x</td><td>x</td><td>✓</td><td>✓</td></tr><tr><td>Gandomzadeh et al. (2025)</td><td>✓</td><td>✓</td><td>✓</td><td>x</td><td>✓</td><td>✓</td></tr><tr><td>This Study (2025)</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Conceição et al. (2022) provided a historical overview of soil- ing in PV, Concentrated Photovoltaics (CPV), and Concentrated Solar Power (CSP) systems, covering measurement techniques, modeling ap- proaches, and mitigation efforts. While standardization and global monitoring efforts were discussed, the study did not propose compar- ative tools to evaluate cleaning methods under practical economic or environmental constraints.

He et al. (2023) delivered a structured summary of dust physics and cleaning options, including assessments of cost and energy require- ments. However, it did not present a decision-support mechanism for selecting strategies according to site-specific conditions.

Hassan et al. (2024) classified mitigation techniques as passive or active and applied criteria such as efficiency, water use, and scalab- ility. While hybrid models and predictive tools were addressed, the study lacked a systematic framework for selecting context-appropriate methods.

Najmi and Rachid (2023) employed a MCDM approach to compare manual, semi-automated, and robotic cleaning solutions. Despite iden- tifying promising results for autonomous systems, the work did not explore dynamic adaptation or integration with monitoring platforms.

Aljaghoub et al. (2022) applied Technique for Order of Prefer- ence by Similarity to Ideal Solution (TOPSIS) to evaluate cleaning technologies using 18 criteria aligned with Sustainable Development Goals (SDGs), and offered regional recommendations. However, the selection process remained static, without mechanisms to adapt to specific operational constraints.

In contrast, more recent studies have begun to incorporate for- malized decision frameworks. Chadly et al. (2024) proposed a hybrid MCDM model combining Analytic Hierarchy Process (AHP), Quality Function Deployment (QFD), Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS), and Preference Selection Index (PSI) to evaluate eight cleaning strategies. Although environmental and operational factors were considered, the study lacked detailed technical categorization and economic modeling.

Gandomzadeh et al. (2025) offered a comprehensive four-decade review, grouping mitigation strategies into preventive coatings, active cleaning, and intelligent scheduling. Emphasis was placed on AI-based optimization and sensor integration, with reported reductions in Lev- elized Cost of Energy (LCOE). Nonetheless, the work did not develop a unified evaluation methodology applicable across diverse sites and constraints.

To address these gaps, this study proposes a comprehensive and adaptable decision-making framework that integrates economic feasi- bility, regional constraints, and operational variables. Unlike previous works that consider technical or decision aspects in isolation, this approach combines them within a structured multi-criteria evalua- tion. Additionally, dynamic selection methods are introduced to opti- mize strategy deployment under varying environmental conditions. A comparative synthesis of the reviewed studies is presented in Table 1.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Systematic review process for article selection, following the stages of Identifcation, Screening, Eligibility, and Inclusion.</figcaption></figure>

## 3. Methodology

The methodology of this study follows a structured four-stage approach, adapted from the systematic review process described in Sepúlveda-Oviedo et al. (2023c), which in turn builds upon a modified version of the PRISMA framework (Page et al., 2021). These stages include Identification, Screening, Eligibility, and Inclusion, as shown in Fig. 1.

### 3.1. Identification

The first stage focused on identifying relevant studies using a struc- tured search protocol. The search equation, presented in Eq. (1), was designed to capture research related to dust mitigation methods for PV systems.

$$ (\text{mitigation OR cleaning}) \text{ AND } (\text{techniques OR methods}) \text{ AND } (\text{photovoltaic systems OR PV systems}) \tag{1} $$

Searches were conducted across Scopus, Web of Science, and Google Scholar, ensuring extensive coverage of peer-reviewed studies (Falagas et al., 2008). This process led to the initial identification of 280
<table><caption>**Table 2**
Inclusion criteria for document selection.</caption><tr><td>Inclusion criteria</td></tr><tr><td>Peer-reviewed journal articles</td></tr><tr><td>English language</td></tr><tr><td>Published between 2010 and 2024</td></tr><tr><td>Conference articles</td></tr></table>

documents. To maintain consistency and relevance in the selection process, specific inclusion criteria were applied, as detailed in Table 2. After applying these criteria, 90 documents were excluded, leaving a total of 190 documents for the next stage. Duplicate entries across databases were identified and removed using Zotero, an open-source reference management tool, prior to the screening stage to ensure accurate document counts.

The time frame from 2010 to 2024 (see Table 2) was selected because it captures the evolution of key dust mitigation technologies in photovoltaics, including the adoption of robotic cleaners (Syafiq et al., 2018), advanced surface coatings (Tzikas et al., 2018), and predictive cleaning algorithms (Micheli et al., 2020). This period also reflects increased availability of experimental data resulting from the global expansion of PV deployment in arid and semi-arid regions (Abdallah et al., 2022).

## 3.2. Screening

In this phase, documents were filtered based on their titles, abstracts, and keywords. The selection prioritized studies published in high-impact journals and those with significant citation counts. Documents that were not directly relevant to dust mitigation strategies for PV systems were excluded. This step resulted in the removal of 10 additional documents, reducing the dataset to 180 documents. To enhance the reliability of this phase, an iterative discussion process was conducted to resolve any ambiguities. Documents where uncertainty arose about their relevance were marked for further discussion. This approach is similar to screening strategies used in systematic reviews, where researchers engage in collaborative decision-making to refine the selection of documents (Stefanidi et al., 2023). The screening process was carried out independently by two reviewers. Disagreements regarding the inclusion of specific documents were resolved through iterative discussions. Documents were excluded if they lacked methodological detail, focused on unrelated degradation phenomena, or did not evaluate cleaning techniques or performance impacts.

## 3.3. Eligibility

A second, more rigorous screening process was conducted, involving a full-text review of the remaining documents. Studies were assessed based on their methodological robustness, experimental validation, and direct relevance to dust mitigation techniques. At this stage, additional exclusion criteria were refined to ensure the final selection accurately represented studies that provided substantial contributions to dust mitigation in photovoltaics: (i) studies that only mentioned dust accumulation but did not propose mitigation techniques were excluded, (ii) articles that presented experimental setups without validation or comparative analysis were removed; and (iii) review articles that only summarized previous findings without new insights were discarded. After applying these criteria, 16 documents were excluded, reducing the dataset to 164 documents.

## 3.4. Inclusion

To ensure the most comprehensive coverage of relevant studies, a snowballing technique (Wohlin et al., 2022) was applied. This included: Forward snowballing, which involved examining citations in the selected documents to identify additional relevant studies. Backward snowballing, which reviewed references within the selected documents.

This process led to the inclusion of 7 additional studies, bringing the final corpus to a total of 171 documents. Each selected document was categorized according to its focus, covering both passive and active dust mitigation methods. The evaluation prioritized key factors such as effectiveness, economic feasibility, and adaptability to environmental conditions. This structured approach ensures a comprehensive comparison of existing methods and highlights critical gaps related to cleaning frequency, operational costs, and long-term efficiency.

To further enhance the quality of this systematic review, the full texts of the final corpus were analyzed not only for their methodological contributions but also for recurring research trends and identified challenges in real-world PV applications. This follows similar practices in systematic reviews of other research domains, where studies are examined beyond inclusion/exclusion criteria and assessed for overarching themes (Stefanidi et al., 2023).

# 4. Framework for optimizing PV cleaning and maintenance

This section presents a comprehensive framework for improving the cleaning and maintenance of PV systems, with a particular focus on mitigating the impact of dust accumulation. The proposed framework aims to address the gaps identified in the existing studies by systematically evaluating and comparing cleaning and mitigation methods, exploring the optimal frequency of cleaning, and proposing a novel methodology for selecting appropriate strategies. By integrating technical, economic, and environmental considerations, this framework enhances the reliability, efficiency, and cost-effectiveness of PV systems in diverse operational contexts.

## 4.1. Review of mitigation methods

This section examines the predominant methods for mitigating dust accumulation on PV modules, categorized into passive and active approaches. The proposed classification, depicted in Fig. 2, organizes these methods based on their operational principles, energy requirements, and applicability to diverse environmental conditions. This study focuses exclusively on dust mitigation strategies, providing a detailed comparison of these methods in terms of effectiveness, cost-efficiency, and suitability for specific scenarios. Other mitigation techniques, such as cooling systems and lightning protection, are acknowledged as important for overall system reliability but are beyond the scope of this analysis.

### 4.1.1. Passive mitigation methods

Passive mitigation methods are strategies designed to minimize the accumulation of dust on PV panels without requiring external energy or active control systems. These methods leverage natural elements such as rain, wind, and gravity or utilize material-based solutions like hydrophobic and hydrophilic coatings to reduce dust adhesion. Their primary advantages are cost-effectiveness and low environmental impact, making them especially appealing in regions with consistent natural cleaning agents like rainfall or wind. However, their efficiency can be significantly constrained in arid or semi-arid climates, where natural phenomena are less frequent. This study categorizes passive methods into three primary groups: natural mitigation methods, coated surfaces, and architectural and design solutions. Each category includes detailed assessments of specific approaches, their operational mechanisms, and their advantages and limitations.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Classification of PV System Mitigation Methods: Passive vs Active.</figcaption></figure>

**Natural mitigation methods.** Natural mitigation methods leverage environmental factors such as rain, wind, and gravity to reduce dust accumulation on PV panels without requiring external energy inputs or mechanical intervention. These methods are inherently cost-effective and environmentally sustainable, relying on the natural forces present in the installation environment. However, their effectiveness is highly dependent on local climatic conditions and specific site characteristics.

**Rain:** Rain serves as one of the most accessible natural cleaning methods for PV panels, utilizing natural precipitation to remove accumulated dust and debris from panel surfaces. Rainfall has been shown to restore PV panel efficiency significantly, with studies demonstrating that a single rainfall event can clean panels to a level within 1% of their performance after manual cleaning (Smith et al., 2013). In a comparative study, natural cleaning by rainfall resulted in a power recovery of approximately 22% after heavy rains over a three-week period, underlining its potential effectiveness in maintaining PV panel efficiency (Islam et al., 2017).

However, the effectiveness of rain as a cleaning method depends on factors such as dust particle size, rainfall intensity, and the duration of dry periods. For instance, fine dust particles, which adhere strongly to panel surfaces, may not be effectively removed by light rain (Chen et al., 2020). Moreover, in regions with limited or infrequent rainfall, natural cleaning may be insufficient to maintain optimal panel performance, necessitating supplementary cleaning methods (Chanchangi et al., 2021). Another limitation of natural rain cleaning is its tendency to redistribute dust rather than remove it completely, particularly in cases where panels are installed at suboptimal tilt angles. These factors can lead to uneven soiling and reduced efficiency in certain areas of the panels (Wang et al., 2022).

**Wind:** Wind acts as a natural cleaning method for PV panels by dislodging loosely adhered dust particles. Its effectiveness is influenced by factors such as wind speed, direction, and the adhesive properties of the dust. Moderate wind speeds effectively remove larger dust particles,

typically those exceeding 1 µm in diameter. However, smaller particles are more challenging to dislodge, as they require significantly higher shear velocities to overcome adhesive forces (Salimi et al., 2023). In experimental simulations, wind speeds ranging from 0.23 to 57.56 m/s were effective for removing larger particles but had limited success with smaller particles due to increased adhesion forces (Jiang et al., 2018).

Seasonal variations in wind patterns significantly affect natural cleaning efficiency (Shenouda et al., 2022b). For instance, in regions like Perth, Australia, higher wind speeds during the winter season improved cleaning effectiveness, while lower speeds during the summer allowed significant dust accumulation (Roslizar et al., 2022). Similarly, experiments in controlled environments have shown that wind-based cleaning can achieve up to 90% removal of dust particles under optimal conditions, particularly for large particles (Zheng et al., 2022).

Innovative methods to enhance wind-based cleaning efficiency have been explored. One study proposed the use of airflow amplification mechanisms, such as passive slits on inclined panels, which increased cleaning efficiency fourfold by concentrating natural wind onto the PV surface (Kawamoto et al., 2021). Another approach examined the use of compressed airflows to simulate high-speed wind, effectively removing dust while simultaneously cooling PV modules (Li et al., 2021). Despite these advantages, wind cleaning faces limitations in areas with low average wind speeds or high dust adhesion. Additionally, strong winds, while effective in cleaning, may cause dust redeposition if surrounding surfaces are heavily contaminated (Jiang et al., 2018).

**Gravity:** Gravity influences the self-cleaning properties of PV panels by facilitating the sliding of dust particles. This effect is primarily determined by the tilt angle of the panels, which directly affects the efficiency of dust removal. Studies emphasize the importance of selecting optimal tilt angles to maximize both cleaning efficiency and energy production. Studies have shown that steeper tilt angles generally reduce dust accumulation by allowing particles to slide off more easily (Cano
<figure><img src="image_5.png" /><figcaption>Fig. 3. Comparison of water drops on hydrophilic and hydrophobic surfaces (License Number 6055270611557) (Gupta et al., 2019).</figcaption></figure>

et al., 2014). In contrast, flatter angles tend to accumulate more dust, significantly reducing energy output (Chanchangi et al., 2021).

In arid regions like Mesa, Arizona, experiments demonstrated that a tilt angle of 23° resulted in minimal soiling losses of around 1%, compared to over 2% for a flat 0° tilt (Cano et al., 2014). Similarly, in Nigeria, vertically positioned panels experienced significantly less transmittance reduction (1%) compared to horizontally positioned ones (88%), highlighting the crucial role of tilt angle in mitigating soiling losses (Chanchangi et al., 2021). Furthermore, in tropical climates, where rainfall is more frequent, higher tilt angles not only aid in natural cleaning but also help optimize energy yield by maximizing solar radiation capture (Kacira et al., 2004). However, in some environments, excessive tilt may compromise energy efficiency due to reduced radiation incidence, necessitating a balance between cleaning and energy production (Xu et al., 2017).

Optimization strategies, such as using region-specific tilt angles, have been proposed to enhance the self-cleaning properties of panels and maintain energy performance. Adjusting the tilt angle periodically based on seasonal variations can further improve system efficiency (Chanchangi et al., 2021).

**Coated surfaces.** Coated surfaces represent a critical advancement in the maintenance and performance optimization of PV panels. By utilizing specialized coatings such as hydrophobic, hydrophilic, and emerging advanced technologies, these solutions aim to prevent dust adhesion, enhance natural cleaning processes, and mitigate soiling-related efficiency losses, as shown in Fig. 3.

Coatings are engineered to interact with environmental elements, leveraging water and light to maintain clean and high-performing panel surfaces.

**Hydrophobic coatings:** Hydrophobic coatings, characterized by their ability to repel water, play a vital role in reducing dust adhesion and enhancing the self-cleaning properties of PV panels. These coatings achieve their functionality through micro- or nano-scale surface structures, which increase the water contact angle to over 150°. This feature allows water droplets to roll off effortlessly, carrying dust particles away and mitigating soiling losses effectively. Studies have highlighted the significant benefits of hydrophobic coatings in improving PV performance in dusty environments. For example, nanocoated PV panels have demonstrated efficiency gains of up to 8.7% under real-world conditions where soiling is a critical concern (Aljdaeh et al., 2021).

The integration of materials such as SiO₂ and TiO₂ nanoparticles in these coatings not only enhances their hydrophobic properties but also ensures high optical clarity, minimizing light transmission losses (Ekren et al., 2022). Furthermore, hydrophobic coatings have

shown remarkable durability, retaining their functionality over extended periods and reducing the need for frequent reapplications. The economic advantages of hydrophobic coatings are noteworthy. By minimizing the frequency of manual cleaning, these coatings significantly reduce operational and maintenance costs. A comparative analysis revealed that hydrophobic coatings lowered cleaning-related water consumption and operational costs by approximately 16% annually compared to uncoated PV systems (Fathi et al., 2017).

Additionally, they contribute to maintaining stable energy outputs, particularly in arid and semi-arid regions where rainfall is infrequent, further underscoring their importance in ensuring the long-term reliability of PV installations. Despite their advantages, hydrophobic coatings are not without limitations. Challenges such as mechanical wear, Ultraviolet (UV) degradation, and exposure to harsh environmental conditions can impact their longevity. Recent advancements in multi-layered nanocomposites aim to address these issues by improving the durability and resistance of hydrophobic coatings to environmental stressors. These innovations enhance water repellency and extend the operational lifespan of the coatings, making them a more viable solution for PV maintenance in challenging environments.

**Hydrophilic coatings:** Hydrophilic coatings play a significant role in enhancing the self-cleaning efficiency of PV panels by utilizing water-attracting properties. These coatings allow water to spread uniformly across the panel surface, effectively loosening and removing dust particles. The mechanisms and applications of such coatings, particularly those incorporating titanium dioxide (TiO₂) and other nanomaterials, have been widely studied. Studies indicate that superhydrophilic coatings significantly reduce dust accumulation by promoting water film formation, which facilitates the removal of adhered particles during rainfall or cleaning processes (Cao et al., 2022).

A notable study demonstrated that panels treated with super-hydrophilic coatings showed an efficiency improvement of up to 92% compared to untreated panels under simulated cleaning conditions (Lu and Zheng, 2022). Furthermore, coatings utilizing morphologically varied ZnO microstructures have exhibited excellent wettability, with water contact angles as low as 2.8°, making them particularly effective in self-cleaning applications (Nundy et al., 2020).

The durability and stability of hydrophilic coatings under environmental stressors are critical factors for their long-term efficacy. TiO₂-based coatings have been highlighted for their photocatalytic properties, enabling them to maintain high transparency and self-cleaning capabilities even in harsh conditions (Zhong et al., 2017). Additionally, novel formulations combining TiO₂ with silane coupling agents have shown improved corrosion resistance and water repellency, further extending their applicability in PV systems (Hong et al., 2017).

Economic evaluations suggest that while initial application costs for hydrophilic coatings may be higher than traditional methods, the long-term benefits in terms of reduced maintenance and enhanced energy output justify their adoption. Studies have reported energy yield increases of 5%–6% for panels equipped with hydrophilic coatings compared to uncoated ones over several months of real-world testing (Arabatzis et al., 2018).

**Emerging coating technologies:** Emerging coating technologies such as photoactive, self-repairing, and anti-reflective coatings have the potential to significantly enhance PV performance by addressing multiple challenges simultaneously. These coatings are designed to integrate functionalities such as increased light absorption, reduced reflection, and self-cleaning capabilities, offering promising solutions for sustainable PV operation.

Photoactive coatings utilize materials like TiO₂ to leverage photocatalytic properties. These coatings not only increase light transmittance but also aid in degrading organic contaminants, thereby enhancing the self-cleaning functionality of PV panels. A study demonstrated that coatings incorporating TiO₂ achieved a transmittance improvement of approximately 6.35% and enhanced anti-dust properties,
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Tilt angle $\beta$ and precautions to avoid shadowing other panels (License Number 6055270894898) (Brecl and Topić, 2011).</figcaption></figure>

making them particularly effective in environments with high dust deposition (Wang et al., 2021).

Self-repairing coatings represent another innovative approach, using materials that can heal minor surface scratches or degradation caused by environmental factors. These coatings typically incorporate polymer-based microcapsules or dynamic covalent bonds that respond to mechanical stress. Such features enhance the durability and lifespan of PV systems, especially in regions prone to harsh climatic conditions.

**Architectural and design solutions.** Architectural and design solutions offer innovative approaches to enhancing the natural cleaning efficiency of PV panels. By optimizing physical attributes such as tilt angles and incorporating advanced surface microstructures, these strategies aim to mitigate dust accumulation and improve energy performance. Unlike external cleaning methods or applied coatings, these solutions are integrated into the structural design of PV systems, providing long-lasting and maintenance-reducing benefits.

**Tilt optimization:** The optimization of the tilt angle for PV panels significantly enhances both energy efficiency and natural cleaning performance. This optimization is particularly critical in regions with high dust accumulation or varying seasonal solar radiation. It is important that the tilt angle $\beta$ is selected without casting shadows on the panels located behind the current panel. Therefore, it is essential to consider not only $\beta$, but also the distances *d* and *b* shown in Fig. 4.

Studies have shown that the optimal tilt angle $\beta$, as shown in Fig. 4, depends on geographic location and climatic conditions. For instance, research in desert environments demonstrates that steeper angles reduce the impact of soiling by allowing dust to slide off more effectively. A study conducted on PV panels in Egypt identified a 45° tilt angle as optimal for minimizing energy losses from dust accumulation over a ten-month period (Abdeen et al., 2017). In regions with moderate climates, such as Turkey, seasonal adjustments to the tilt angle have been found to maximize energy yield. Monthly optimization of tilt angles in Şanlıurfa demonstrated that angles varied from 13° in summer to 61° in winter to capture the maximum solar radiation (Kacira et al., 2004).

Furthermore, research integrating the effects of soiling and cleaning schedules has shown that adjusting tilt angles can significantly reduce the maintenance burden. A model developed in Portugal revealed that optimal tilt configurations incorporating soiling considerations increased annual energy production while reducing cleaning costs (Conceição et al., 2019). Experimental analyses in Arizona highlighted that tilt angles of 23° to 33° minimized soiling losses, while steeper angles offered diminishing returns due to reduced solar incidence (Cano et al., 2014). The study underlines the trade-off between tilt angle optimization for cleaning efficiency and maximum energy capture.

In addition to static optimizations, the use of dynamic tilt adjustment mechanisms has been explored. Systems capable of seasonal

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Super hydrophobic coating (License Number 6055281027751) (Jamil et al., 2017).</figcaption></figure>

or monthly adjustments achieved up to 6.6% higher energy output compared to fixed-tilt systems (Ullah et al., 2019).

**Surface microstructures:** Surface microstructures are designed to reduce dust adhesion and enhance natural cleaning. These represent an innovative approach rooted in architectural and design solutions rather than externally applied coatings. Such structures are inherently part of the physical configuration of PV panels, achieved through the fabrication of hierarchical micro- and nanostructures on the panel surface. Unlike coated surfaces that depend on external chemical layers, these microstructures are engineered during the manufacturing process to provide durable, long-lasting self-cleaning properties.

The design of these structures is inspired by biomimetic principles, such as the lotus leaf effect, where hierarchical micro- and nanoscale roughness minimizes contact between water droplets and the surface. This property allows water droplets to roll off easily, carrying dirt particles with them (Koch et al., 2009). Artificial replication of similar structures has been achieved through advanced techniques like molding and self-assembly, enabling the creation of surfaces with enhanced water repellency and dust mitigation (Bhushan et al., 2009).

Microstructured surfaces, such as those mimicking rice leaves and butterfly wings, exhibit combined effects of antifouling and self-cleaning. These are fabricated using photolithography and hot embossing techniques, which enhance resistance to fouling and improve cleaning efficiency under natural conditions (Bixler et al., 2014). Significant advancements have been made, including the fabrication of microstructured polypropylene/graphene composites. These surfaces display an extremely small roll-off angle, enabling effective removal of water and contaminants while maintaining durability under mechanical stress (Chen et al., 2019).

The integration of nanostructured surfaces with hierarchical microstructures, such as artificial lotus leaves, has demonstrated improvements in superhydrophobicity and reduced adhesion. This effect is illustrated in Fig. 5, which compares the self-cleaning behavior of flat surfaces with that of microstructured hydrophobic surfaces by depicting the difference in water droplet contact angle, denoted as $\theta$.

The self-cleaning enhancement illustrated in Fig. 5 is critical for maintaining the performance of PV systems in environments prone to soiling (Koch et al., 2009). The comparison highlights how microstructured surfaces achieve larger contact angles (typically $\theta$ > 150°), leading to reduced adhesion and enabling water droplets to roll off easily, removing contaminants in the process. Challenges persist in scaling these solutions for large-scale applications and ensuring their long-term durability. The architectural nature of these microstructures provides intrinsic resilience compared to coatings that may degrade over time due to environmental exposure. Research efforts should
<figure><img src="image_8.png" /><figcaption>Fig. 6. Manual Cleaning (License Number 6055331021088) (Kazem et al., 2020).</figcaption></figure>

prioritize optimizing fabrication methods to achieve a balance among cost, mechanical robustness, and cleaning efficiency, facilitating their widespread adoption (Ebert and Bhushan, 2012).

### 4.1.2. Active mitigation methods

Active mitigation methods involve mechanical, electrical, or automated systems that actively remove dust from PV panels. These approaches are energy-dependent and often employ advanced technologies, including robotic systems, water-based cleaning, and electromechanical or piezoelectric devices, to ensure consistent and thorough cleaning. Active methods are particularly effective in regions with high dust accumulation rates or where passive strategies prove insufficient due to environmental limitations.

While these methods demonstrate significant efficiency in restoring panel performance, they are often associated with higher operational costs and energy demands. Moreover, their implementation may require careful consideration of regional climatic conditions, water availability, and cost constraints. This study categorizes active methods into five primary groups: manual cleaning, water-based systems, electromechanical systems, robotic cleaning systems, and piezoelectric systems.

**Manual cleaning.** Manual cleaning, as presented in Fig. 6, remains one of the most traditional methods for removing dust from PV panels. This process relies on labor-intensive techniques such as wiping, scrubbing, or using specialized tools, often requiring regular interventions to maintain optimal panel performance. The effectiveness, cost implications, and potential drawbacks of this method are evaluated. Studies have shown that manual cleaning is effective for removing stubborn dirt and achieving significant performance recovery. For instance, experiments conducted in Lahore, Pakistan, demonstrated that optimal cleaning schedules for panels tilted at 30° were necessary approximately once per week during high soiling periods to mitigate daily power losses of up to 0.8% (Ullah et al., 2020). However, the frequency and intensity of cleaning required are heavily dependent on environmental conditions, such as the local dust deposition rate and meteorological factors (Kazem et al., 2020).

Manual cleaning can be enhanced by employing detergents or surfactants to improve dust removal efficacy. Research conducted in Egypt revealed that the use of surfactant-based cleaning solutions maintained PV panel efficiency more effectively over a 45-day period compared to water-only cleaning methods (Moharram et al., 2013). This highlights the importance of adopting targeted cleaning methods, especially in regions with heavy soiling. Despite its benefits, manual cleaning presents significant limitations for large-scale installations. The labor and logistical challenges increase proportionally with the size of the PV plant, often making it economically unfeasible (Nezamisavojbolaghi et al., 2023). Moreover, improper cleaning methods, such as using abrasive

<figure><img src="image_9.png" /><figcaption>Fig. 7. Automatic PV panel cleaning system from Heliotex (License Number 6055340488803) (Farrokhi Derakhshandeh et al., 2021).</figcaption></figure>

brushes, can lead to physical damage, including scratches on the PV glass surface, which may permanently reduce panel efficiency (Sayyah et al., 2013).

In arid and semi-arid regions, where soiling rates are particularly high, manual cleaning often requires frequent intervention. However, the associated water usage and labor costs pose environmental and economic concerns. As a result, this method is recommended primarily for small-scale installations or as a complementary approach when automated cleaning systems are unavailable (Nezamisavojbolaghi et al., 2023).

**Water-based systems.** Water-based cleaning systems are integral to maintaining the efficiency of PV panels, leveraging both pressurized water jets and recycled water technologies to address soiling challenges. These systems provide effective solutions for removing accumulated dust, debris, and stubborn contaminants, enhancing panel performance in various environmental contexts. The integration of surfactants further optimizes cleaning by reducing water usage and improving efficacy, making these methods versatile and impactful for diverse PV installations.

**Pressurized water systems:** Pressurized water systems are employed in PV cleaning to ensure efficient dirt and dust removal. These systems utilize high-pressure water jets to dislodge persistent soiling, such as bird droppings and adhered particulate matter, which are difficult to remove with natural cleaning methods. Studies indicate that the use of surfactant-enhanced water significantly improves the cleaning process by reducing the amount of water and energy needed (Moharram et al., 2013). An analysis conducted at the German University in Cairo evaluated a cleaning system comprising PV modules and pressurized water. The study revealed that increasing water pressure minimized energy losses from soiling, reducing efficiency degradation from 16% to 8% over an experimental period (Moharram et al., 2013).

Additional findings demonstrated that systems like the Heliotex, presented in Fig. 7, cleaning setup, employing high-pressure water sprays, can recover up to 95% of reflectance and achieve notable improvements in PV efficiency (Farrokhi Derakhshandeh et al., 2021). Despite their effectiveness, these systems require substantial water usage and energy inputs, posing challenges in regions where resources are scarce. However, optimization efforts have shown promise in reducing operational costs by employing automated and precisely controlled water jet systems (Cattani et al., 2021).

Concerns persist regarding potential thermal shock and mechanical stress caused by high-pressure sprays. Best practices recommend using demineralized water to mitigate mineral deposits, while morning application leverages natural dew to reduce cleaning time and water demand Aljdaeh et al. (2021). Nevertheless, the advantages of pressurized water systems, particularly in maintaining high PV efficiency in urban and tropical environments, underline their significance in large-scale PV installations.

**Recycled water systems:** Recycled water systems have emerged as a sustainable solution to address the dual challenges of water scarcity and the need for effective PV panel cleaning. These systems utilize
<figure><img src="image_10.png" /><figcaption>Fig. 8. Conceptual 3D model of the Automated Water Recycle (AWR) method. Image reproduced from Islam et al. (2022) under CC BY 4.0.</figcaption></figure>

innovative methods to reclaim and reuse water, significantly reducing overall water consumption while maintaining cleaning efficiency. Recycling mechanisms are increasingly being integrated into PV cleaning technologies. For instance, a novel self-sustainable hybrid cleaning system coupled with a water distillation unit demonstrated promising results. The system recycled water through a single-slope solar still, achieving a 67.94% distillation efficiency and producing 5.3 liters of purified water daily. This recycled water was effectively used for cleaning PV panels, achieving a 1.34% increase in panel efficiency (Guptaa et al., 2019). In Islam et al. (2022), the authors presented the prototype of an Automated Water Recycle (AWR) system shown in Fig. 8, demonstrating a water loss per cleaning cycle of 0.32%, which was normalized to a net loss of 28.8% with a frequency of 1 cycle/day over 90 days of operation.

Air-to-Water Generator (AWG) have also been applied to extract water from ambient air for cleaning PV panels. These systems optimize water extraction processes to minimize water wastage while ensuring adequate cleaning. Studies have highlighted the ability of AWGs to prevent 83% of energy loss associated with soiling when integrated into PV cleaning schedules (Cattani et al., 2021). In agrivoltaic systems, recycled water not only facilitates PV panel cleaning but also supports sustainable agriculture. In one study, recycled water was used to grow vegetation and rear fish, showcasing its potential for multipurpose applications. The integration of these systems into PV farms ensures efficient resource utilization, with minimal wastage (Shams et al., 2023).

The environmental benefits of recycled water systems are notable. These systems reduce dependency on freshwater sources and help mitigate water scarcity issues in arid regions. Furthermore, they align with sustainability goals by minimizing the ecological footprint of PV installations.

**Surfactant-assisted cleaning:** Surfactant-assisted cleaning represents a promising solution for enhancing the cleaning efficiency of PV panels, particularly in regions with persistent dust accumulation or stubborn pollutants. By reducing the surface tension of water, surfactants enable better penetration into dust and particulate layers, allowing for more effective dirt removal while potentially reducing water consumption.

Studies conducted by Moharram et al. (2013) emphasized the significant benefits of surfactant-enhanced cleaning over traditional water-only methods, as shown in Fig. 9. Their findings demonstrated that using surfactants not only improved the cleaning process but also helped maintain PV efficiency over extended periods. Panels cleaned with surfactant solutions exhibited higher energy output and reduced performance degradation, particularly in areas with heavy soiling caused

by bird droppings, industrial emissions, or desert dust. The integration of surfactants in pressurized water systems has been shown to further optimize the cleaning process. Research by Zahedi et al. (2021) highlighted the synergistic effect of combining surfactants with high-pressure water jets, which resulted in a nearly 15% improvement in cleaning efficiency compared to water-only systems. This approach is especially advantageous for large-scale PV installations where manual cleaning is impractical.

Despite these advantages, the environmental implications of surfactants must be carefully addressed. Non-biodegradable surfactants can lead to soil and water contamination, posing risks to local ecosystems. However, the use of biodegradable surfactants has been identified as a viable solution to mitigate these risks. Research by Venhuis and Mehrvar (2004) highlighted the development of eco-friendly surfactants that degrade more readily in natural environments, minimizing their long-term impact. Innovative surfactant formulations are also being explored to enhance the performance and sustainability of this cleaning method. For example, the inclusion of nanomaterials in surfactant-based solutions has been shown to increase the longevity and effectiveness of cleaning agents, as reported by Mustafa et al. (2020). These advancements are expected to make surfactant-assisted cleaning more cost-effective and environmentally friendly in the coming years.

Surfactant-assisted cleaning is particularly relevant in water-scarce regions, where optimizing water usage is critical. By reducing the amount of water required for effective cleaning, surfactant-enhanced methods can significantly lower operational costs while maintaining high cleaning efficiency. Furthermore, the adaptability of this approach makes it suitable for both ground-mounted and rooftop PV installations, further broadening its application.

**Electromechanical systems.** Electromechanical systems have become a pivotal technology in the maintenance of PV panels, employing mechanized methods such as brushes, wipers, Surface Acoustic Waves (SAW), ultrasonic cleaning, and EDS. These systems cater to diverse environmental and operational conditions, offering both water-based and waterless solutions to mitigate soiling-related energy losses. Their mechanized approach reduces reliance on manual intervention, making them a scalable and efficient alternative for modern PV installations.

**Brush cleaning systems:** Brush-based cleaning systems are widely employed as electromechanical solutions for maintaining the cleanliness and efficiency of PV panels. These systems utilize mechanical brushes to remove dust, debris, and other contaminants that accumulate on panel surfaces, thereby mitigating performance losses due to soiling. The effectiveness of brush cleaning systems depends on factors such as the material and design of the bristles, the mechanical force applied during cleaning, and the frequency of operation.
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Recycled Water Systems combined with surfactants for cleaning PV systems. (1) PV module, (2) tank, (3) pump, (4) filter, (5) manifold with 120 nozzles, and (6) drain pipe (License Number 6055350025959) (Moharram et al., 2013).</figcaption></figure>

A typical brush-based system integrates various electromechanical components, including cleaning and driving devices, which are often controlled by an electronic controller. The cleaning device moves horizontally or vertically across the PV surface, guided by sensors that detect dirt accumulation (Mustafa Al-attar et al., 2015). This method is particularly effective in regions with frequent dust storms or environments where water-based cleaning is impractical due to resource scarcity. However, it must be noted that brushes in direct contact with PV surfaces may pose a risk of scratches, especially if combined with water or abrasive particles.

Innovative designs, such as the system developed by Tejwani and Solanki (2010), incorporate rotating brushes powered by stepper motors and microcontrollers. These systems are often combined with solar tracking mechanisms, ensuring panels are cleaned multiple times a day. Similarly, Singh and Ravi (2014) reported a waterless sliding brush system that cleans panels automatically when they are vertically oriented. In terms of energy savings and efficiency, Deb and Brahmbhatt (2018) proposed a water-free brush-based cleaning device capable of increasing panel efficiency by approximately 9.2% per month. Another study by Sweezey et al. (2009) demonstrated that coupling a brush cleaning system with a water injection mechanism enhanced efficiency by up to 15%. Additional advancements include low-mass mechanical mops and portable brush systems, as shown by Moreno et al. (2006) and Manju et al. (2018), respectively. The system installed in Manju et al. (2018), and presented in Fig. 10, proved to be particularly cost-effective and suitable for smaller PV installations but may struggle with adhesive dust and dirt in hard-to-reach corners.

Despite their operational benefits, brush cleaning systems face several challenges. These include reliance on external power sources, the need for periodic reconfiguration based on environmental conditions, and the potential for surface degradation from repeated dry cleaning. Furthermore, brushes may have limited efficacy against sticky or cemented dust, necessitating additional cleaning measures in certain scenarios (Mariprasath and Reddy, 2019). Efforts to overcome these limitations include the development of automated brush systems, such as the design by Deb and Brahmbhatt (2018), which minimizes maintenance costs and improves cleaning precision. Moreover, Halbhavi et al. (2015) demonstrated the effectiveness of a microcontroller-based system, achieving a 30% improvement in energy output for cleaned panels compared to dirty ones.

**Wiper systems:** Wiper systems, which function similarly to traditional windshield wipers, are designed to clean PV panels by employing a squeegee-like mechanism. These systems are particularly effective for environments where dust and debris form a uniform layer on panel surfaces, requiring a sweeping motion for efficient cleaning. Compared to brush systems, wiper mechanisms are gentler on the panel surface, reducing the risk of scratches and damage. Their performance depends on factors such as wiper material, the range of motion, and the applied pressure during operation.

Syafaruddin et al. (2017) developed a wiper-controlled cleaning method for PV panels, controlled by an ATmega16 microcontroller. The system included a water spray feature activated alongside the wiper. The study reported a power consumption of 0.79% in standby mode and 8.94% during operation, demonstrating energy efficiency. In another study, Gogi et al. (2017) designed an advanced PV cleaning system integrated with a sun tracker. The system utilizes sensors, a microcontroller, Direct Current (DC) motor drivers, and gear arrangements, enabling the wiper to clean the panel effectively without recalibration. The combination of tracking and cleaning enhances energy output by ensuring optimal sunlight exposure and cleanliness.

Yunus and Kumar (2017) proposed a Programmable Logic Controller (PLC)-based automatic cleaning system for PV panels. Their prototype, equipped with a wiper, sprinkler, and motor, improved panel energy output by approximately 30%, highlighting the effectiveness of combining wiper mechanisms with water-based cleaning methods. Further innovation was demonstrated by Mohammed et al. (2018), who developed an Arduino-controlled wiper system. This system activated cleaning when panel output dropped to 50% of its rated value. The combination of water spraying and wiper motion restored panel performance, emphasizing the importance of automated activation based on real-time performance metrics.

Sugiartha et al. (2020) introduced a semi-automatic wiper control system where the rotation of the wiper was manually activated. The system combined water spraying with wiper motion, and experiments showed efficiency recovery to 86.7% after 30 wiper cycles, demonstrating the potential for restoring significant energy output. Jaiganesh et al. (2022) advanced the concept with an automated water wiper for rooftop PV cleaning, controlled by an Arduino microcontroller. Their system achieved a 15% increase in panel efficiency, highlighting the benefits of automation for rooftop applications. Hudedmani et al.
<figure><img src="image_12.png" /><figcaption>Fig. 10. PV cleaning using automated brushes. Image reproduced from Manju et al. (2018) under CC BY 4.0.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 11. PV cleaning using car wipers and sprinklers (Figure adapted with License Number 6055350374258) (Nahar Myyas et al., 2022).</figcaption></figure>

(2017) described a system that combined a water spray feature with a DC motor-powered wiper. The wiper could be programmed for multiple daily cleaning cycles or manually operated using a remote control, showcasing flexibility in usage.

Recently, in Nahar Myyas et al. (2022), the authors implemented a PV panel cleaning method using car wipers and sprinklers, as shown in Fig. 11. The authors demonstrated that this approach achieves high cleaning efficiency for surface dust removal, although certain limitations were identified. For example, the wipers do not cover the entire PV panel area, and maintenance, such as replacing wiper blades annually, increases operational costs. Despite these drawbacks, the system proved effective for periodic cleaning in small-scale PV installations.

**Surface acoustic wave systems:** SAWs systems represent an innovative and sustainable approach to cleaning PV panels by utilizing mechanical vibrations to dislodge dust particles. This technology harnesses acoustic energy to generate waves on the panel surface, which effectively lift and remove debris without requiring water or detergents.

SAWs are particularly advantageous in arid regions where water is scarce, making them a promising waterless cleaning alternative.

Research by Alagoz and Apak (2020) has highlighted the potential of SAWs for cleaning PV panels. The study provided a comprehensive theoretical analysis of particle detachment mechanisms, emphasizing the role of factors such as panel inclination angle, gravitational forces, and dust adhesion properties. Experimental findings demonstrated the effectiveness of SAWs in removing medium-to-large-sized particles, although their efficiency decreases when dealing with nanoscale particles due to stronger adhesive forces. The work of Mutluer and Erat (2019) explored integrating SAW systems with intelligent cleaning frameworks. By dynamically adjusting wave intensity and frequency based on environmental conditions and soiling levels, such systems can enhance operational efficiency while minimizing energy consumption. This adaptability is critical for deploying SAW systems in diverse climates and soiling scenarios.

Further studies, such as Syafiq et al. (2018), have examined the long-term effects of SAWs on panel surfaces, focusing on potential issues like material fatigue and wear. The findings suggest that optimizing wave frequencies and amplitudes can mitigate these effects, extending the lifespan of panels subjected to regular SAW cleaning. Additionally, hybrid systems combining SAWs with complementary cleaning technologies, such as automated wipers or brush mechanisms, have shown promise in addressing the limitations of standalone SAWs. In terms of environmental benefits, SAW systems eliminate the need for chemical detergents and significantly reduce water consumption. This aligns with global efforts to develop sustainable cleaning technologies for renewable energy systems. However, challenges remain, including the high initial cost of SAW infrastructure and the need for further research to improve their efficiency in removing stubborn or adhesive dust layers.

The study conducted in Alagoz and Apak (2020) investigates the effectiveness of SAWs for cleaning heavily soiled PV panels. The authors demonstrated that SAWs can effectively remove large dust particles by generating vibrations that dislodge contaminants from the panel surface. However, the cleaning efficiency decreases for finer particles due to stronger adhesion forces.

Fig. 12 illustrates the experimental setup and results. Subfigure (a) shows the location of the vibration generators used to produce SAWs. Subfigure (b) depicts a heavily soiled PV panel surface before the application of SAWs, while subfigure (c) shows the same panel partially cleaned after the application of SAWs. The experimental findings revealed that SAWs are particularly effective for removing larger particles, while finer particles may require complementary cleaning methods or increased wave intensity for efficient removal.

**Ultrasonic cleaning:** Ultrasonic cleaning systems represent an innovative and efficient approach to maintaining PV panel cleanliness.
<figure><img src="image_14.png" /><figcaption>**Fig. 12.** (a) Location of vibration generators for producing SAWs. (b) Heavily soiled PV panel before SAW application. (c) Partially cleaned PV panel after SAW application (Figure adapted with License Number 6055350705730) (Alagoz and Apak, 2020).</figcaption></figure>

These systems utilize high-frequency sound waves to generate mi-
croscopic cavitation bubbles within a liquid medium, such as water.
The implosion of these bubbles near the panel surface produces local-
ized high pressure, effectively dislodging dust and contaminants from
the panel without requiring mechanical abrasion or significant water
consumption (Vasiljev et al., 2013).

One notable advantage of ultrasonic cleaning is its ability to access hard-to-reach areas and crevices on PV panels. This capability ensures a thorough cleaning process, even in regions with intricate panel configurations or areas prone to stubborn debris. In Vasiljev et al. (2013), the authors demonstrated the effectiveness of ultrasonic systems in removing dust and other deposits from PV panels in experimental setups. Environmental benefits are also associated with this method. Ultrasonic systems significantly reduce water usage compared to traditional water-based cleaning methods, aligning with sustainability goals for solar energy maintenance. Furthermore, the integration of ultrasonic systems with renewable energy sources, such as solar-powered mechanisms, has been explored. For instance, Satyan and Sutar (2020) developed an ultrasonic cleaning prototype powered by PV arrays, showcasing the potential for energy-efficient operation.

Despite these advantages, several challenges remain. The initial
cost of implementing ultrasonic systems can be high, and their long-
term effects on PV panel materials require further investigation. The
study by Vasiljev et al. (2013) noted the importance of optimizing
frequency and amplitude settings to balance cleaning efficiency with
material preservation. Recent advancements include the combination
of ultrasonic waves with thin water films to enhance cleaning effi-
ciency. This approach minimizes water consumption while leveraging
the acoustic energy for effective dust removal (Satyan and Sutar, 2020).
Additionally, hybrid systems incorporating ultrasonic cleaning with
other methods, such as hydrophilic coatings or robotic mechanisms, are
being explored to address diverse soiling challenges in PV installations.

The findings presented in Vasiljev et al. (2013) demonstrate the
development and evaluation of an ultrasonic cleaning system for PV
panels. This system employs a bidirectional ultrasonic vibrator (indi-
cated as 1 in Fig. 13) and two long plates (indicated as 2 in Fig.
13), which are joined to each side of the vibrator. These components
generate ultrasonic waves that create cavitation effects in a thin water
layer, effectively removing contaminants from the panel surface.

Fig. 13 illustrates the proposed system. In subfigure (a), a detailed view of the ultrasonic cleaning system is shown, highlighting its main components. Subfigures (b) and (c) depict an artificially polluted panel and the same panel after undergoing ultrasonic cleaning for 15 s using 32 W of power, respectively. The experimental results demonstrated significant cleaning efficiency, with ultrasonic cavitation effectively dislodging dirt and dust particles.

<figure><img src="image_15.png" /><figcaption>**Fig. 13.** (a) View of the proposed ultrasonic cleaning system with bidirectional ultrasonic vibrator (1) and two plates (2). (b) Artificially polluted PV panel. (c) PV panel after ultrasonic cleaning with water for 15 s at 32 W power (Figure adapted with License Number 6055350980575) (Vasiljev et al., 2013).</figcaption></figure>

**Electrodynamic screens:** EDS represent a cutting-edge technology for PV panel maintenance, utilizing electrostatic forces to remove dust and debris. These systems generate traveling waves across the PV surface by applying high-voltage three-phase alternating current to a series of alternating electrodes embedded in a transparent substrate. The traveling waves propel dust particles off the panel, offering a waterless
<figure><img src="image_16.png" /><figcaption>**Fig. 14.** (a) Block diagram of the electrodynamic screen system; (b) Three-phase electrode configuration for self-cleaning PV panels (License Number 6055360142022) (Servar et al., 2013).</figcaption></figure>

and chemical-free solution. This makes EDS particularly advantageous in arid and water-scarce regions, positioning it as a promising option for sustainable PV cleaning.

Recent advancements in EDS technology have been reported by Bock et al. (2008). Their design integrates a series of alternating electrodes into the PV panel, creating an efficient self-cleaning method. The EDS operates without external power sources by harnessing energy directly from the PV panels themselves. This innovative feature enhances its practicality and sustainability for widespread adoption. The configuration and wiring diagram of this system are shown in Fig. 14.

Research by Mazumder et al. (2007) demonstrated that EDS could achieve dust removal efficiencies exceeding 90% under dry conditions. The system operates by inducing electrostatic charges on dust particles, creating repulsive forces that dislodge them from the panel. This method is effective for both positively and negatively charged particles and even neutral particles, which can acquire charges through dielectrophoresis (Calle et al., 2006). Advanced implementations of EDS have been developed to enhance dust removal and energy efficiency. For instance, Mazumder (2016) introduced transparent electrodynamic screens that integrate seamlessly with PV modules, ensuring minimal impact on light transmittance. These systems use optimized waveforms and frequencies to maximize cleaning performance while reducing power consumption.

Despite their efficiency, EDS systems face limitations in high-humidity environments, where adhesive forces between dust and panel surfaces increase significantly. Studies have shown that under such conditions, higher voltages are required to maintain cleaning efficiency, which can lead to increased power consumption (Sharma et al., 2009). Another critical challenge is the potential degradation of the electrodynamic screen material due to prolonged exposure to UV radiation. However, innovations in durable coatings and advanced material designs are being explored to address this issue (Mazumder et al., 2013). The study conducted in Mazumder et al. (2013) highlights the effectiveness of EDS in removing dust from PV panels. EDS panels operate by generating electrostatic forces that lift and repel dust particles without the need for water or mechanical components, achieving a cleaning efficiency of over 90% within 2 min. This method

is particularly advantageous in arid and semi-arid regions, where water resources are scarce.

**Fig. 15** illustrates the cleaning process of an EDS panel. Subfigure (a) shows the initial state of the panel covered by a layer of dust collected from a desert environment. Subfigures (b) and (c) present the progressive cleaning of the panel, with images captured every 3.3 s, culminating in a fully cleaned surface after a total elapsed time of 20 s. This demonstration underscores the rapid and efficient cleaning capability of EDS technology, which uses less than 0.1% of the energy produced by a standard PV panel.

In terms of energy consumption, EDS systems require minimal power, typically ranging between 1.0 and 2.9 mW per square centimeter for dust loads of 0.4 to 0.6 mg/cm² (Biris et al., 2004). Their operational costs are considerably lower compared to traditional cleaning methods, making them a viable solution for large-scale PV farms. Emerging hybrid systems that combine EDS with other cleaning technologies, such as IoT-based monitoring, offer enhanced dust mitigation while maintaining energy efficiency. These systems can dynamically adjust cleaning schedules based on real-time environmental data, ensuring optimal performance.

**Robotic cleaning systems.** Robotic cleaning systems, encompassing both autonomous and semi-autonomous robots, represent a transformative approach to PV panel maintenance. These systems are designed to address the challenges of soiling while minimizing labor requirements and optimizing cleaning efficiency. Their adaptability to diverse environmental conditions and panel configurations makes them a pivotal technology in modern PV energy systems.

**Autonomous robots:** Autonomous robotic systems for PV panel cleaning represent a significant technological advancement in renewable energy maintenance. These robots are equipped with intelligent navigation systems, advanced sensors, and energy-efficient mechanisms, enabling them to operate with minimal human intervention and adapt to diverse panel configurations and environmental conditions.

In Antonelli et al. (2020), a robotic system designed for desert zones was presented, addressing the high soiling rates caused by sandstorms. The robot operates without rails or water, using helical brushes and ultrasonic sensors for navigation and dust removal. This system demonstrated effectiveness in reducing dust-related efficiency losses with low power consumption, making it suitable for arid environments. The robot proposed in Antonelli et al. (2020), is presented in Fig. 16.

The work of Antonelli et al. (2018) introduced a battery-powered autonomous robot designed to clean dusty PV panels in extreme conditions. The robot employs forward and backward helical brushes to dislodge dust efficiently, with a control system based on the Arduino DUE platform. Experimental results showed significant improvements in panel efficiency without the use of water.

Another innovative approach is the IoT-enabled robotic system discussed in Ghadban and Sharif (2023), which integrates real-time monitoring and control capabilities. This system uses AI-based algorithms to detect soiling and optimize cleaning schedules (Sepúlveda-Oviedo et al., 2024). Equipped with a high-pressure water jet and onboard tank, the robot achieves consistent cleaning results while minimizing labor and resource costs. The study by Khadka et al. (2020) presented a prototype that combines autonomous navigation with a cloud interface for remote monitoring and control. The robot features a rotary cleaning brush and regression-based algorithms to determine optimal cleaning schedules, effectively maintaining PV efficiency in various environmental conditions.

Several commercial solutions for automated PV cleaning have been developed in recent years. Among the most well-known systems are the hyCLEANER SolarROBOT, designed for large-scale installations with inclined surfaces (hyCLEANER, 2023); the GEKKO Solar Hightec Robot by Serbot, which uses suction technology to operate on vertical facades (SERBOT, 2023); the F1 SolarCleano robot, notable for its modularity and remote control (SolarCleano, 2023); and the SUN-X
<figure><img src="image_17.png" /><figcaption>**Fig. 15.** (a) EDS panel initially covered with a dust layer. (b) Progressively cleaned panel after EDS activation, with images captured at 3.3-second intervals. (c) Fully cleaned panel after 20 s (License Number 6055360479445) (Jamil et al., 2017).</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>**Fig. 16.** Autonomous robot for cleaning PV panels (Figure adapted with License Number 6055370083188) (Antonelli et al., 2020).</figcaption></figure>

system by Sunbotics, which combines lightweight construction with autonomous navigation capabilities (SUN-X, 2023). These solutions illustrate the diversity of design strategies employed to address specific cleaning challenges in different PV deployment contexts.

In a complementary way, Unmanned Aerial Vehicles (UAV)-based cleaning represents a cutting-edge approach to maintaining PV panel efficiency. This technology employs UAVs equipped with cleaning liquids, pneumatic pressure systems, and active sensors with illumination sources and mapping cameras to optimize positioning. Autonomous features allow UAVs to perform continuous cleaning operations efficiently.

Studies have shown various innovative uses of UAVs in PV cleaning. For example, AlHousani et al. (2019) compared different cleaning

methods using drones, highlighting microfiber-based cloth attachments as the most cost-effective and high-performing method (Al-Housani et al., 2019b). Additionally, Mohandes et al. (2021) demonstrated the effectiveness of drones in harsh desert environments, utilizing their downward thrust to remove dust, with horizontal flight proving to be the most energy-efficient method (Mohandes et al., 2021). Furthermore, Sarkis et al. (2022) introduced a hybrid drone system, integrating automated brush cleaning methods for small solar power units, such as street solar lights, water pumping systems, and residential applications (Sarkis et al., 2022). The ability of UAVs to integrate thermographic and thermal detector technologies enhances cleaning prognosis methods, significantly improving panel efficiency and accessibility.

A basic schematic of UAV-based cleaning for PV panels is presented in Fig. 17.

**Semi-autonomous robots:** Semi-autonomous robots bridge the gap between manual intervention and fully autonomous cleaning systems, offering an economical alternative for PV panel maintenance. These robots often require some level of human input, such as initialization or periodic monitoring, but are capable of executing cleaning tasks independently once activated.

Research highlights that semi-autonomous systems are particularly effective in scenarios where resource constraints or cost considerations limit the use of fully autonomous robots. For example, a study by Sallem et al. (2023) introduced a rail-based semi-autonomous cleaning robot designed for inclined PV panels, emphasizing adaptability to the topographical conditions of North Africa. This system, equipped with a rotating brush mechanism as shown in Fig. 18, demonstrated notable improvements in panel efficiency while maintaining low operational costs.

Li and Li (2021) proposed a following robot designed to supply power to cleaning systems, addressing challenges such as short battery life and high-power demands in semi-autonomous solutions. The innovative design of the system ensured stable operation on inclined panels, enhancing the cleaning process while reducing the labor intensity associated with traditional methods.

In another development, Nguyen et al. (2022) investigated the use of rubber wheel crawlers in semi-autonomous cleaning robots. These robots were optimized for wet and tilted PV panels, showcasing effective dust removal capabilities while minimizing slippage. The theoretical and experimental results validated the anti-slip properties of the system, making it a viable option for rooftop PV installations. Semi-autonomous robots excel in their ability to balance cost and performance, making them suitable for medium scale PV farms. However,
<figure><img src="image_19.png" /><figcaption>**Fig. 17.** Basic schematic of UAV-based cleaning for PV panels, highlighting autonomous robotic operations and cleaning methods (License Number 6055370412427) (Khalid et al., 2023).</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>**Fig. 18.** Configuration of a semi-autonomous PV cleaning robot. Image reproduced from Sallem et al. (2023) under CC BY 4.0. (1) Handling drum. (2) Rotatable brush. (3) Trolley. (4) Rails. (5) Lateral transmission caster. (6) Box of the electric part.</figcaption></figure>

their reliance on periodic human intervention and the need for terrain-
specific adjustments highlight the importance of continued innovation
in this field.

***Piezoelectric systems.:*** Piezoelectric systems utilize the properties of piezoelectric materials to generate mechanical vibrations or acoustic waves that dislodge dust and debris from PV panels. Piezoelectric systems are generally attached to the back of the PV panel, as shown in Fig. 19.

This innovative method, presented in Fig. 19, is particularly ad-
vantageous in water-scarce regions, as it does not rely on water or
detergents, making it an environmentally sustainable alternative.

Research by Williams et al. (2007) demonstrated that mechanical vibration systems can recover up to 95% of the power generation capacity of a PV panel. These systems employ external energy sources, such as wind or piezoelectric actuators, to generate the required vibrations without reducing the overall efficiency of the PV system (Attia et al., 2019). A notable application of this approach involves the integration of piezoelectric materials with ultrasonic cavitation techniques. This method creates a thin liquid layer (0.1 to 1 mm) during the compression wave rarefaction cycle, forming ultrasonic cavities that effectively remove dust by suctioning it away from the surface (Lu et al., 2013). Another study developed a lightweight and compact piezoelectric actuator that applied optimal pressure to the PV surface, enhancing cleaning efficiency without causing physical damage to the panel (Sweezey et al., 2009).

Recent innovations include piezoelectric devices installed at the edges of PV panels, generating rotating air currents across the surface to facilitate cleaning (Lu et al., 2013). These systems are lightweight and require minimal energy, making them particularly suitable for remote or off-grid PV installations. Despite their promise, piezoelectric systems face challenges, such as limited effectiveness against adhesive or cemented dust particles and potential material fatigue over extended use.

## 4.2. Comparison of mitigation methods

This section critically compares the various dust mitigation meth-
ods for PV panels, categorized into passive and active approaches.
The evaluation highlights their operational principles, advantages, and
limitations while considering their effectiveness under different cli-
matic conditions. As shown in prior studies, no universally optimal
method exists because the performance of each method strongly de-
pends on specific environmental and climatic conditions. For example,
anti-soiling coatings have demonstrated significant potential under lab-
oratory conditions, with hydrophilic coatings increasing energy produc-
tion by 2.5% to 5% during peak soiling months (Brown et al., 2012).
Similarly, nanoparticle-based coatings have reduced soiling effects by
0.55% in arid climates (Piliougine et al., 2013). However, labora-
tory findings often fail to fully reflect the complexity of real-world
conditions.

Passive approaches, including natural cleaning methods (rain, wind, and gravity), and surface treatments (coatings and microstructures), are widely used due to their low maintenance and energy requirements. However, their efficiency often depends on environmental factors such as precipitation and wind availability. Rain and wind effectively remove loosely adhered dust, but their inconsistency in arid regions limits their applicability. Gravity, through panel tilt, improves natural cleaning but requires careful optimization of tilt angles to balance cleaning efficiency and energy capture (Cano et al., 2014). Surface treatments, such as hydrophobic, hydrophilic coatings, and biomimetic microstructures, have demonstrated superior dust mitigation. Superhydrophobic coatings effectively repel water and mitigate dust accumulation, while hydrophilic coatings enhance rain-based cleaning. These coatings are cost-effective but require periodic maintenance due to wear and environmental degradation (Ekren et al., 2022). Biomimetic microstructures, inspired by the lotus leaf, reduce adhesion forces and promote self-cleaning but face challenges in scalability and durability (Koch et al., 2009).

Active methods, encompassing water-based cleaning systems,
robotic systems, and emerging technologies, provide highly effective
cleaning solutions, especially in high-soiling regions. High-pressure
water systems and recycled water setups have shown excellent clean-
ing results in arid climates, with notable sustainability improvements
<figure><img src="image_21.png" /><figcaption>Fig. 19. Placement of piezoelectric actuators at the back side of PV panels (Public Use Permitted) (Dawson et al., 2011).</figcaption></figure>

through water reuse (Gupta et al., 2019). Surfactant-assisted cleaning further enhances efficiency but raises environmental concerns related to chemical disposal (Zahedi et al., 2021). Electromechanical systems, such as brushes and wipers, are effective in removing stubborn dirt but involve risks of panel scratches and require significant maintenance (Deb and Brahmbhatt, 2018). Robotic cleaning systems, both autonomous and semi-autonomous, minimize human intervention and provide adaptable cleaning solutions for large-scale PV installations. However, their high initial cost and maintenance requirements pose barriers to widespread adoption (Antonelli et al., 2020).

Emerging innovative technologies, including piezoelectric systems, EDS, ultrasonic cleaning, and SAW systems, demonstrate promising advancements. Piezoelectric systems and SAWs offer waterless cleaning but are constrained by cost and material fatigue (Mutluer and Erat, 2019). EDS provides high efficiency in arid climates but is less effective in humid conditions due to increased adhesive forces (Mazumder et al., 2007). Ultrasonic cleaning is highly effective in removing particles from complex surfaces with minimal water usage but involves high implementation costs (Vasiljev et al., 2013). Manual cleaning, although labor-intensive, remains a reliable solution for small-scale applications and regions with moderate soiling. It is, however, less feasible for large installations due to high operational costs and risks of panel damage through improper handling (Moharram et al., 2013). Table 3 summarizes these findings, providing a detailed comparison of each method based on its principles, advantages, and limitations.

### 4.3. Cleaning costs and operation & maintenance

Operation and Maintenance (O&M) of PV systems is critical to sustain both performance and economic viability. Among all maintenance activities, cleaning accounts for a significant share of expenses due to the detrimental effects of soiling on energy output. The accumulation of dust and debris reduces the efficiency of PV panels, and the methods and frequency of cleaning are influenced by environmental conditions, system size, and cleaning materials. The Soiling Ratio (SR) is commonly used as a performance indicator to quantify the impact of soiling. This metric is defined as the ratio of the power output of a soiled PV panel to that of a clean panel under identical conditions and is expressed mathematically in Eq. (2).

$$SR = \frac{P_{\text{dirty}}}{P_{\text{clean}}} \qquad (2)$$

where $P_{\text{dirty}}$ represents the power output of the soiled panel (in kWh) and $P_{\text{clean}}$ denotes the power output of the clean panel (in kWh) (Abdulla et al., 2024). A lower SR indicates higher soiling losses, which necessitate more frequent cleaning to restore efficiency. Abdulla et al. (2024) provide a comprehensive review of PV O&M practices, highlighting that cleaning remains one of the most significant and recurring maintenance activities in terms of operational costs. Their analysis underscores the importance of accounting for cleaning frequency, environmental conditions, and leveraging emerging technologies — such

as UAV, IoT, and AI — to optimize inspection and cleaning processes. These insights reinforce the relevance of developing structured methodologies to evaluate and select cost-effective cleaning strategies, which constitutes the focus of the present study. As reported by Hammad et al. (2018), energy losses due to soiling averaged 0.6875% over a seven-month period, corresponding to approximately 9211 kWh/m² of energy loss. The associated O&M costs were estimated at 3.37 USD/m².

Cleaning costs are significantly influenced by factors such as environmental conditions, cleaning technologies, and cleaning frequency. In arid regions with high dust deposition, bi-weekly cleaning is often required to maintain performance, leading to increased operational expenses (Shah et al., 2020). In contrast, temperate climates with lower soiling rates typically require cleaning every six weeks, resulting in reduced costs (Abdallah et al., 2022). Advanced cleaning technologies, including robotic systems and EDS, have been proposed as effective solutions for reducing water consumption and labor requirements. However, these technologies often require higher initial investments, which must be weighed against long-term savings in O&M costs (Syafiq et al., 2018).

Predictive models and machine learning algorithms have demonstrated significant potential in optimizing cleaning schedules. These approaches leverage meteorological data and soiling predictions to minimize unnecessary cleaning cycles while maintaining system efficiency. For example, Micheli et al. (2020) show that predictive cleaning algorithms could reduce costs by targeting cleaning interventions based on environmental conditions and system performance.

The economic impact of cleaning costs and O&M strategies is summarized in Table 4, which highlights the key factors influencing costs and their associated studies. Additionally, Table 5 presents an estimation of cleaning costs for various methods applied to PV systems. These tables illustrate the variability of cleaning methods and their associated costs across diverse climatic regions and technological configurations.

### 4.4. Optimal cleaning frequency for PV modules

Determining the optimal cleaning frequency for PV modules is essential for maximizing their efficiency and ensuring the sustainability of energy generation. Cleaning schedules must account for diverse environmental conditions, varying soiling rates, and economic constraints. Research has shown that the accumulation of dust and particulate matter can cause significant energy losses if left unmanaged, necessitating tailored cleaning methods for different regions and climates. In arid and desert climates, where dust deposition is severe, frequent cleaning is often required. Studies in the United Arab Emirates (UAE) demonstrate that bi-weekly cleaning can prevent up to 13% efficiency loss over three months, with optimal results observed when cleaning schedules are adjusted to high-dust periods (Shah et al., 2020). In Mediterranean climates, such as Palestine, seasonal variations influence cleaning schedules, with weekly cleanings recommended during dusty summers and less frequent maintenance during wetter seasons (Abdallah et al., 2022).
<table><caption>**Table 3**
Comparison of dust mitigation methods for PV systems.</caption><thead><tr><th>Cat.</th><th>Method</th><th>Principle</th><th>Advantages</th><th>Limitations</th></tr></thead><tbody><tr><td rowspan="6">Passive</td><td>Rain</td><td>Precipitation</td><td>Cost-free, restores up to 95% efficiency; effectiveness increases in regions with consistent rainfall (Dahlioui et al., 2023)</td><td>Limited in arid climates; cannot clean all soiling types (Prasad et al., 2022)</td></tr><tr><td>Wind</td><td>Air Currents</td><td>Cost-effective in windy regions; particularly effective for loose dust particles (Guo et al., 2019)</td><td>Ineffective in low wind speeds; seasonal variability impacts performance (Lasfar et al., 2021)</td></tr><tr><td>Gravity</td><td>Gravitational Force</td><td>Enhances natural cleaning, reduces soiling when panels are optimally tilted (Li et al., 2021)</td><td>May reduce energy yield when panels are excessively tilted; less effective for heavy particles (Jawale et al., 2016)</td></tr><tr><td>Hydrophobic Coatings</td><td>Water Repellency</td><td>Reduces maintenance, effective in rainy regions; improves transmission of light rays up to 98.7% (Wu et al., 2022)</td><td>Mechanical wear, UV degradation; limited durability in extreme climates (Aljdaeh et al., 2021)</td></tr><tr><td>Hydrophilic Coatings</td><td>Water Spreading</td><td>Improves rain cleaning, cost-effective; can remove dirt uniformly with water films (Adak et al., 2020)</td><td>Reliant on regular rainfall; effectiveness decreases in arid regions (Prasad et al., 2022)</td></tr><tr><td>Surface Microstructures</td><td>Nano/Micro Structures</td><td>Minimizes adhesion, enhances natural cleaning; reduces surface reflectance and increases energy output by up to 28% (Omar et al., 2023)</td><td>Scalability and durability concerns; wear reduces long-term effectiveness (Aljdaeh et al., 2021)</td></tr><tr><td rowspan="10">Active</td><td>Pressurized Water Systems</td><td>High-Pressure Jets</td><td>Effective for stubborn dirt, efficient cleaning; restores efficiency by 17.55% in average conditions (Syafaruddin et al., 2017)</td><td>High water usage, risk of thermal shock; waste generation requires disposal systems (Dahlioui et al., 2023)</td></tr><tr><td>Recycled Water Systems</td><td>Water Reclamation</td><td>Sustainable in arid regions, minimizes wastage; reduces water use by up to 54% (Alagoz and Apak, 2020)</td><td>Requires initial setup investment; dependency on proper recycling systems (Alagoz and Apak, 2020)</td></tr><tr><td>Surfactant-Assisted Cleaning</td><td>Chemical Reduction</td><td>Improves cleaning efficiency, reduces water use (Dahlioui et al., 2023)</td><td>Environmental concerns with surfactants; potential surface damage over time (Syafaruddin et al., 2017)</td></tr><tr><td>Brush Cleaning Systems</td><td>Mechanical Brushing</td><td>Effective for heavy soiling; reduces dirt adhesion over repeated cycles (Lasfar et al., 2021)</td><td>Risk of surface scratches; requires periodic maintenance of brushes (Sun and Böhringer, 2020)</td></tr><tr><td>Wiper Systems</td><td>Mechanical Sweeping</td><td>Gentle on panels, effective for uniform dust; efficiency recovery up to 86.7% after multiple cycles (Syafaruddin et al., 2017)</td><td>Limited for adhesive dirt, maintenance needed; can be less effective in regions with high particle density (Lasfar et al., 2021)</td></tr><tr><td>Piezoelectric System</td><td>Acoustic Vibrations</td><td>Waterless, effective for medium-sized particles; reduces cleaning frequency in arid regions (Alagoz and Apak, 2020)</td><td>High cost, limited for nanoscale particles; cavitation effects impact durability (Alagoz and Apak, 2020)</td></tr><tr><td>Piezoelectric System</td><td>Vibrations</td><td>Waterless, energy-efficient; effective for reducing adhesion forces (Omar et al., 2023)</td><td>Limited for adhesive particles, material fatigue observed over long-term use (Omar et al., 2023)</td></tr><tr><td>Electrodynamic Screens</td><td>Electrostatic Forces</td><td>Waterless, suitable for arid regions; efficiency improvement by up to 33% (Guo et al., 2019)</td><td>Ineffective in humid conditions; high energy consumption (Guo et al., 2019)</td></tr><tr><td>Ultrasonic Cleaning</td><td>Cavitation</td><td>Water-efficient, effective in complex surfaces (Syafaruddin et al., 2017)</td><td>High cost, material compatibility concerns; dependent on external energy sources (Syafaruddin et al., 2017)</td></tr><tr><td>Autonomous Robots</td><td>Mechanized Navigation</td><td>Minimal human intervention, adaptable designs; improves efficiency by 30-33% (Jawale et al., 2016)</td><td>High initial costs; requires skilled operation for maintenance (Jawale et al., 2016)</td></tr><tr><td>Semi-Autonomous Robots</td><td>Assisted Navigation</td><td>Cost-effective for medium-scale applications; integrates adaptive cleaning features (Jawale et al., 2016)</td><td>Periodic human intervention required; operational challenges in uneven terrains (Jawale et al., 2016)</td></tr><tr><td>Manual Cleaning</td><td>Manual Effort</td><td>Reliable, effective for stubborn dirt; suitable for both wet and dry conditions (Lasfar et al., 2021)</td><td>Labor-intensive, costly for large installations (Lasfar et al., 2021)</td></tr></tbody></table>

<table><caption>**Table 4**
Key factors influencing cleaning costs and O&M strategies for PV systems.</caption><thead><tr><th>Factor</th><th>Impact on Costs</th><th>Examples/Studies</th><th>Ref</th></tr></thead><tbody><tr><td>Soiling Ratio (SR)</td><td>Higher SR increases costs due to frequent cleaning</td><td>0.6875% loss over 7 months in Jordan</td><td>Hammad et al. (2018)</td></tr><tr><td>Cleaning Technology</td><td>Reduces labor/water use but increases capital costs</td><td>Robotic systems and EDS</td><td>Syafiq et al. (2018)</td></tr><tr><td>Environmental Conditions</td><td>Arid climates require frequent cleaning, increasing costs</td><td>13% efficiency loss in UAE over 3 months</td><td>Shah et al. (2020)</td></tr><tr><td>Predictive Models</td><td>Optimize cleaning schedules to reduce costs</td><td>Machine learning for soiling prediction</td><td>Micheli et al. (2020)</td></tr></tbody></table>

<table><caption>**Table 5**
Cost estimation of cleaning methods for PV systems.</caption><thead><tr><th>Country</th><th>Method</th><th>Cost ($/m²)</th><th>Year & Ref</th></tr></thead><tbody><tr><td>Jordan</td><td>Hydrophobic Coatings</td><td>2.34</td><td>2023 (Al-Salaymeh et al., 2023)</td></tr><tr><td>Jaipur, India</td><td>Surface Microstructures</td><td>0.0011</td><td>2022 (Gupta et al., 2022)</td></tr><tr><td>Jaipur, India</td><td>Autonomous Robots</td><td>0.0578</td><td>2022 (Gupta et al., 2022)</td></tr><tr><td>Saudi Arabia</td><td>Robotic Dry Cleaner</td><td>0.11-0.13</td><td>2021 (Salman et al., 2021)</td></tr><tr><td>Pakistan</td><td>Pressurized Water Systems</td><td>22.43</td><td>2020 (Majeed et al., 2020)</td></tr><tr><td>Japan</td><td>Electrodynamic Screens</td><td>0.09</td><td>2020 (Antonelli et al., 2020)</td></tr><tr><td>Egypt</td><td>Autonomous Robots</td><td>0.15-0.20</td><td>2020 (Hassan et al., 2017)</td></tr><tr><td>Saudi Arabia</td><td>Pressurized Water Systems</td><td>2.67 (SAR10)</td><td>2019 (Qdah et al., 2019)</td></tr><tr><td>Qatar</td><td>Wiper Systems</td><td>0.41</td><td>2019 (Al-Housani et al., 2019b)</td></tr><tr><td>Northeast China</td><td>Manual Cleaning</td><td>0.075</td><td>2019 (Zhao et al., 2019)</td></tr><tr><td>Zarqa, Jordan</td><td>Electrodynamic Screens</td><td>3.76</td><td>2018 (Hammad et al., 2018)</td></tr><tr><td>Netherlands</td><td>Hydrophilic Coatings</td><td>0.457-0.915</td><td>2018 (Tzikas et al., 2018)</td></tr><tr><td>Australia</td><td>Manual Cleaning</td><td>0.10-0.46</td><td>2018 (Tanesab et al., 2018)</td></tr><tr><td>Turkey</td><td>Recycled Water Systems</td><td>0.069-0.09</td><td>2016 (Erduman et al., 2016)</td></tr><tr><td>Central Saudi Arabia</td><td>Manual Cleaning</td><td>0.2-0.5</td><td>2016 (Jones et al., 2016)</td></tr><tr><td>India</td><td>Robotic Cleaning System</td><td>0.14</td><td>2016 (Jawale et al., 2016)</td></tr></tbody></table>
<table><caption>**Table 6**
Recommendations for optimal cleaning frequency of PV modules based on environmental factors.</caption><thead><tr><th>Environment/Region</th><th>Recommended frequency</th><th>Key factors considered</th><th>Ref</th></tr></thead><tbody><tr><td>Global (adaptive strategies)</td><td>Variable based on predictive modeling</td><td>Integration of meteorological data and image processing</td><td>Wang et al. (2022)</td></tr><tr><td>Spanish Dehesa (semi-arid)</td><td>Every three weeks in summer; quarterly in other seasons</td><td>Seasonal dust deposition and environmental conditions</td><td>Mazumder et al. (2007)</td></tr><tr><td>Arid climates (UAE)</td><td>Bi-weekly</td><td>High dust deposition; 13% efficiency loss over three months without cleaning</td><td>Shah et al. (2020)</td></tr><tr><td>Mediterranean climates (Palestine)</td><td>Weekly in summer; monthly in winter</td><td>Seasonal dust variation; high soiling during dry periods</td><td>Abdallah et al. (2022)</td></tr><tr><td>Doha, Qatar</td><td>Weekly</td><td>High soiling rates for CdTe-type modules in dusty environments</td><td>Al-Housani et al. (2019a)</td></tr><tr><td>Phoenix, Arizona</td><td>Weekly in high-dust months</td><td>Economic viability and dust accumulation rates</td><td>Naeem and TamizhMani (2015)</td></tr><tr><td>Desert regions (EDS technology)</td><td>Every 45 days</td><td>Reduced dust adhesion through electrodynamic cleaning systems</td><td>Mazumder et al. (2007)</td></tr><tr><td>Mauritania (desert)</td><td>Monthly</td><td>Dust deposition reduces efficiency by 21.6%; cleaning restores reliability</td><td>Lasfar et al. (2021)</td></tr><tr><td>China (northeast)</td><td>10 to 23 days</td><td>Optimized cycles for dust load and efficiency losses of 4.5%–10%</td><td>Zhao et al. (2019)</td></tr></tbody></table>

<table><caption>**Table 7**
Comparison of cleaning frequency and estimated monthly OPEX in selected regions.</caption><thead><tr><th>Region</th><th>Recommended frequency</th><th>Cost per cleaning ($/m²)</th><th>Monthly OPEX estimate ($/m²)</th><th>Ref</th></tr></thead><tbody><tr><td>Qatar (Doha)</td><td>Weekly</td><td>2.67</td><td>10.68</td><td>Qdah et al. (2019)</td></tr><tr><td>Northeast China</td><td>Every 15 days</td><td>0.075</td><td>0.15</td><td>Zhao et al. (2019)</td></tr><tr><td>UAE (Arid)</td><td>Bi-weekly</td><td>0.12</td><td>0.24</td><td>Salman et al. (2021)</td></tr><tr><td>Palestine (Summer)</td><td>Weekly</td><td>0.10</td><td>0.40</td><td>Abdallah et al. (2022)</td></tr></tbody></table>

Dynamic approaches using predictive algorithms and meteorological data have shown promise in optimizing cleaning schedules. Hybrid frameworks combining periodic planning with dynamic adjustments enhance both efficiency and cost-effectiveness (Wang et al., 2022). For instance, methods integrating stochastic weather generation algorithms have been tested to optimize cleaning schedules by predicting soiling losses based on environmental parameters, such as rainfall and particulate matter, without relying on long-term soiling data (Micheli et al., 2020). Financial considerations are also critical in determining cleaning schedules. In Cairo, Egypt, integrating energy production data with cleaning costs resulted in bi-weekly cleaning schedules being identified as the most economically viable option (Said et al., 2023). Similarly, high-soiling areas like Doha, Qatar, require weekly cleanings to maintain module efficiency, particularly for Cadmium Telluride (CdTe)-type modules that are sensitive to soiling (Al-Housani et al., 2019a).

In temperate climates, less frequent cleaning is often sufficient. For instance, in regions with lower dust accumulation, cleaning every six weeks can effectively balance energy yield and operational costs. However, regions prone to seasonal dust storms, such as Arizona, require more frequent maintenance during high-soiling periods (Naeem and TamizhMani, 2015). Advanced cleaning technologies, such as EDS and surface coatings, have further refined cleaning schedules. These methods reduce the required frequency of manual cleaning by mitigating soiling accumulation. EDS, in particular, has proven effective in arid regions, significantly extending cleaning intervals by preventing the adhesion of dust particles (Mazumder et al., 2007). A comprehensive summary of recommendations based on environmental conditions and research findings is provided in Table 6.

To better integrate cost and frequency insights, a comparative analysis of cleaning intervals and their estimated monthly Operational Expenditure (OPEX) is summarized in Table 7. This evaluation highlights the economic impact of cleaning frequency decisions across different environmental conditions.

Table 7 provides monthly operational expenditure estimates that reflect how cleaning frequency impacts ongoing costs. These figures also influence the Return On Investment (ROI), as they relate directly to both operational expenses and energy yield improvements. However, calculating ROI requires additional site-specific parameters — such as electricity tariffs, total energy gains from cleaning, and initial system investment — that extend beyond the scope of this study. Nevertheless, the OPEX estimates presented here offer a valuable foundation for future evaluations of cost-effectiveness and long-term economic performance. This comparative approach provides practical insights for system operators aiming to balance performance and cost-efficiency in varying climatic conditions.

## 5. Proposed PV cleaning methodology

The efficient maintenance of PV systems is essential for ensuring long-term performance and economic viability. Dust accumulation significantly reduces the efficiency of PV modules, requiring the implementation of optimized cleaning methods. However, selecting the most suitable cleaning method remains a complex challenge due to diverse environmental conditions, cost constraints, and technological limitations. This section introduces a structured and iterative methodology for evaluating, selecting, and implementing PV cleaning methods, integrating technical, economic, and environmental considerations.

To address these challenges, a novel framework, the SPV-COM, is introduced. This methodology is designed to systematically evaluate and prioritize PV cleaning methods by integrating both technical performance indicators and implementation feasibility factors. A structured, data-driven selection process is ensured, balancing operational performance, environmental constraints, and economic viability.

The methodology follows an *iterative decision-making process*, beginning with data collection, followed by the definition of evaluation criteria, construction of the Strategic Cleaning Evaluation Matrix (SCEM), and structured ranking of alternative cleaning methods. This structured approach ensures that the final decision is based on a data-driven framework that balances operational performance with environmental and economic constraints.

The overall process is illustrated in Fig. 20, where blue boxes represent the primary methodological steps, red boxes indicate performance evaluation processes, and yellow boxes highlight key evaluation criteria. This structured framework enables a systematic comparison of cleaning methods, ensuring that the selected strategy aligns with site-specific constraints and operational requirements.

By integrating SCEM within a multi-criteria decision-making process, this methodology provides a robust framework for optimizing investment — that extend beyond the scope of this study. Nevertheless, the OPEX estimates presented here offer a valuable foundation for future evaluations of cost-effectiveness and long-term economic performance. This comparative approach provides practical insights for system operators aiming to balance performance and cost-efficiency in varying climatic conditions.
<figure><img src="image_22.png" /><figcaption>Fig. 20. Proposed methodology flowchart for PV cleaning.</figcaption></figure>

PV maintenance strategies, ultimately contributing to enhanced energy output, cost reduction, and system longevity. To illustrate its application, three cleaning methods are analyzed in this study: Manual Cleaning, Robotic Cleaning, and EDS. These serve as representative examples; however, the proposed framework is adaptable and scalable to accommodate additional cleaning methods as needed.

## 5.1. Step A: Data collection and initial assessment

The first step in the proposed methodology consists of systematically collecting relevant data to characterize the operational conditions of the PV system. This data serves as the foundation for evaluating existing cleaning methods and selecting optimal solutions tailored to specific environmental and economic constraints. The collected information is classified into three primary categories: system performance data, current cleaning method information, and environmental and operational conditions.

The system performance data includes key metrics that quantify the efficiency and degradation of PV modules over time. These metrics encompass power output trends, SR variations, and energy yield fluctuations under different environmental conditions. Monitoring the degradation rate of PV modules is crucial for assessing the impact of dust accumulation and optimizing maintenance schedules. Prior studies have demonstrated that detailed performance analysis significantly improves the reliability and economic viability of PV installations (Cierlicki et al., 2024). Additionally, advanced monitoring frameworks that integrate real-time data acquisition systems enhance the accuracy of performance evaluations, enabling predictive decision-making in O&M planning (Nowak, 2021).

The current cleaning method information provides insights into the effectiveness and limitations of existing cleaning protocols. This includes details on cleaning frequency, the type of cleaning methods employed (manual, robotic, or electrodynamic screens), and any constraints related to water consumption, energy requirements, or labor availability. Understanding the cost-benefit trade-offs of different cleaning methods is essential for optimizing O&M expenses while maintaining high system efficiency. Recent research highlights the importance of technical due diligence in cleaning evaluations to prevent efficiency losses while ensuring economic feasibility (Pangarso et al., 2022). Additionally, risk assessments of cleaning methods are necessary to identify potential

mechanical damage or long-term performance degradation caused by abrasive cleaning methods (Moser et al., 2017).

The environmental and operational conditions provide context for the performance of the PV system within its specific geographic and climatic characteristics. Important parameters include dust deposition rates, humidity levels, wind speed, and precipitation frequency, all of which affect the rate of soiling and, as a result, the necessity for cleaning interventions. Previous studies have demonstrated that real-time environmental monitoring enhances predictive maintenance models, enabling adaptive scheduling of cleaning operations (Adamov et al., 2023). Additionally, site-specific factors such as panel tilt angles, module spacing, and exposure to nearby vegetation or industrial pollutants play a significant role in dust accumulation patterns (Shang et al., 2024). Integrating environmental data into the decision-making framework ensures that cleaning methods remain both effective and sustainable.

## 5.2. Step B: Definition of evaluation criteria

A structured and objective comparison of PV cleaning methods requires a well-defined set of evaluation criteria. These criteria are categorized into two primary groups: CRs and TRs. The selection of these criteria follows established MCDM methodologies, which have been widely applied in the evaluation of PV maintenance strategies, integrating technical, economic, and sustainability aspects (Miraj and Berawi, 2021). The collected data in Step A regarding system performance, cleaning method effectiveness, and environmental conditions directly influence the selection of CRs and TRs. These criteria ensure that the assessment of cleaning methods is data-driven and aligned with real-world operational constraints.

The evaluation criteria serve as the foundation for the SCEM, ensuring that the performance and feasibility of each cleaning method are systematically assessed. SCEM quantifies the relationships between CRs and TRs, allowing for a structured ranking of cleaning methods based on their effectiveness, sustainability, and operational constraints.

The selected CRs are presented in Table 8. These indicators measure the direct impact of a cleaning method on system performance and maintenance efficiency. Similarly, the TRs, which define feasibility and sustainability constraints, are summarized in Table 9. These factors
<table><caption>**Table 8**
Customer requirements for PV cleaning evaluation.</caption><thead><tr><td>Customer Requirement</td><td>Description</td></tr></thead><tbody><tr><td>Cleaning Efficiency (CR1)</td><td>Measures the effectiveness of dust removal to ensure optimal PV performance (Hübl et al., 2024).</td></tr><tr><td>Cleaning Speed (CR2)</td><td>Evaluates the time required to complete a full cleaning cycle (Abualkishik and Al-majed, 2022).</td></tr><tr><td>Cost-Effectiveness (CR3)</td><td>Assesses the trade-off between cleaning costs and efficiency (Wei, 2021).</td></tr><tr><td>Environmental Impact (CR4)</td><td>Considers water, energy, and chemical use to minimize ecological harm (Thebault et al., 2020).</td></tr><tr><td>Safety (CR5)</td><td>Examines risks related to personnel safety and potential PV module damage (Almunem et al., 2024).</td></tr></tbody></table>

<table><caption>**Table 9**
Technical requirements for PV cleaning feasibility assessment.</caption><thead><tr><td>Technical requirement</td><td>Description</td></tr></thead><tbody><tr><td>Implementation Feasibility (TR1)</td><td>Evaluates ease of integration into existing PV systems (Zhou et al., 2020).</td></tr><tr><td>Maintenance Requirements (TR2)</td><td>Assesses upkeep frequency and complexity (Miraj and Berawi, 2021).</td></tr><tr><td>Energy Consumption (TR3)</td><td>Measures operational power demand (Abualkishik and Al-majed, 2022).</td></tr><tr><td>Water Usage (TR4)</td><td>Determines water consumption per cleaning cycle (Wei, 2021).</td></tr><tr><td>Durability and Reliability (TR5)</td><td>Examines expected lifespan and resistance to environmental conditions (Almunem et al., 2024).</td></tr></tbody></table>

determine whether a cleaning method can be successfully integrated into real-world PV operations.

The selected CRs and TRs serve as the basis for the SCEM, a structured assessment tool designed to quantify the relationships between performance indicators and feasibility factors. Unlike conventional QFD-based approaches, SCEM eliminates unnecessary components while preserving a clear prioritization of criteria, ensuring that cleaning methods are evaluated holistically.

By assigning relative weights to CRs and TRs, SCEM provides a data-driven framework that balances operational effectiveness, economic feasibility, and sustainability. The next step in the methodology involves the construction of SCEM, where the relationships between CRs and TRs are systematically assigned, allowing for a structured ranking of cleaning alternatives.

### 5.3. Step C: Construction of the strategic cleaning evaluation matrix

Once the evaluation criteria are defined in Step B, the next step is to construct the SCEM. This matrix systematically quantifies the relationships between CRs and TRs, allowing for a structured prioritization of PV cleaning methods.

As part of the SPV-COM, the SCEM serves as a core assessment tool for evaluating PV cleaning methods. This matrix is specifically designed to quantify the relationships between key performance indicators, referred to as CRs, and implementation feasibility factors, referred to as TRs. Unlike conventional decision-making matrices, SCEM eliminates unnecessary complexity while maintaining a structured prioritization process that ranks cleaning methods based on their overall effectiveness, sustainability, and feasibility.

#### 5.3.1. Justification for using SCEM over existing MCDM methods

Several MCDM techniques can be used to evaluate cleaning methods. The AHP is widely applied for assigning relative importance to criteria through pairwise comparisons (Kamaludin et al., 2021); however, it does not provide a structured means to quantify direct relationships between performance and feasibility factors (alzahmi and Ndiaye, 2023). The Fuzzy TOPSIS effectively handles uncertainty by ranking alternatives based on their proximity to an ideal solution (Benítez et al., 2020), but it lacks a structured visualization of the interplay between operational efficiency and implementation feasibility.

The QFD approach has been widely used in engineering applications, as it explicitly maps relationships between performance requirements and technical feasibility within a structured House of Quality Matrix (HoQ) (Sianturi et al., 2020). However, when applied to PV cleaning, QFD introduces unnecessary complexity by incorporating a Correlation Roof to assess interdependencies among TRs—an aspect that is not relevant for evaluating independent cleaning methods.

To address these limitations, SPV-COM incorporates the SCEM as an evaluation framework specifically tailored for PV cleaning method selection.

Unlike QFD, SCEM eliminates redundant elements while preserving a structured prioritization of criteria. This ensures that decision-making is both quantitative and scalable. Within the SPV-COM methodology, SCEM is utilized to systematically compare and rank cleaning methods based on operational effectiveness, feasibility, and sustainability.

#### 5.3.2. Structure of the strategic cleaning evaluation matrix

The SCEM is structured with a total of *M* CRs as rows and *N* TRs as columns, forming an evaluation framework that links performance indicators with feasibility and sustainability factors. Each element $S_{ij}$ in the matrix represents the correlation score between a given $CR_j$ and a $TR_i$. The general form of SCEM is expressed as in Eq. (3).

$$ \text{SCEM} = \begin{bmatrix} S_{11} & S_{12} & \dots & S_{1N} \\ S_{21} & S_{22} & \dots & S_{2N} \\ \vdots & \vdots & \ddots & \vdots \\ S_{M1} & S_{M2} & \dots & S_{MN} \end{bmatrix} \quad (3) $$

where $S_{ij}$ represents the assigned correlation score between the *j*th Customer Requirement ($CR_j$) and the *i*th Technical Requirement ($TR_i$). The parameter *M* denotes the total number of CRs, while *N* represents the total number of TRs. To quantify the strength of each relationship between $CR_j$ and $TR_i$, a structured scoring system is implemented using discrete values:

* **Strong correlation (9):** Indicates that $CR_j$ has a major influence on $TR_i$, meaning that improvements in the customer requirement strongly depend on the corresponding technical requirement.

* **Moderate correlation (3):** Suggests a significant but not dominant relationship, where $CR_j$ is influenced by $TR_i$ but to a lesser extent.

* **Weak correlation (1):** Represents a minor connection between $CR_j$ and $TR_i$, meaning that changes in the technical requirement have a negligible impact on performance.

* **No correlation (0):** Indicates that $CR_j$ and $TR_i$ are independent and that modifying the technical requirement does not affect the customer requirement.

The selection of these discrete values — 0, 1, 3, and 9 — follows the principles established in QFD methodologies to ensure a structured, simplified, and reproducible evaluation. These values are preferred over continuous scores to reduce subjectivity and enhance comparability across different strategies. By using a discrete scale, decision-makers can focus on identifying significant dependencies rather than making marginal distinctions that may introduce bias. Additionally, the logarithmic progression of the values (1, 3, 9) ensures that stronger relationships are weighted more significantly, while weaker correlations remain less influential in the final ranking.
Unlike QFD, SCEM does not incorporate a *Correlation Roof*, as the interdependencies between TRs are not analyzed in this methodology. Instead, the focus remains solely on evaluating each cleaning method independently, ensuring a direct and methodical prioritization of strategies based solely on their technical feasibility and performance outcomes.

### 5.3.3. SCEM matrices for different cleaning methods

To illustrate the application of SCEM, separate matrices are constructed for each of the three cleaning methods used as examples in this section: Manual Cleaning, Robotic Cleaning, and EDS. These matrices systematically quantify the relationships between the CRs and the TRs for each cleaning method.

For Manual Cleaning, the SCEM is given by:

$$ \mathbf{SCEM}_{\text{Manual}} = \begin{bmatrix} 9 & 3 & 1 & 3 & 1 \\ 3 & 9 & 1 & 1 & 1 \\ 1 & 3 & 9 & 3 & 1 \\ 1 & 1 & 3 & 9 & 3 \\ 3 & 1 & 1 & 3 & 9 \end{bmatrix}. \quad (4) $$

This matrix reflects the relative impact of technical feasibility factors on the performance criteria of Manual Cleaning, highlighting strong correlations in key aspects such as cleaning efficiency and implementation feasibility.

For Robotic Cleaning, the corresponding SCEM is:

$$ \mathbf{SCEM}_{\text{Robotic}} = \begin{bmatrix} 9 & 9 & 3 & 3 & 1 \\ 3 & 9 & 3 & 3 & 1 \\ 1 & 3 & 9 & 3 & 3 \\ 3 & 3 & 3 & 9 & 9 \\ 3 & 1 & 1 & 3 & 9 \end{bmatrix}. \quad (5) $$

This matrix shows an overall higher interaction between CRs and TRs compared to Manual Cleaning, particularly in factors such as operational efficiency and long-term sustainability.

For EDS, the SCEM is structured as:

$$ \mathbf{SCEM}_{\text{EDS}} = \begin{bmatrix} 9 & 3 & 1 & 3 & 9 \\ 3 & 9 & 3 & 3 & 3 \\ 1 & 3 & 9 & 3 & 3 \\ 3 & 3 & 3 & 9 & 9 \\ 9 & 1 & 1 & 3 & 9 \end{bmatrix}. \quad (6) $$

This configuration highlights a stronger emphasis on environmental impact and durability, factors that are particularly relevant in autonomous cleaning systems.

By examining these matrices, a direct comparison can be made between the three cleaning methods in terms of how each strategy aligns with key evaluation criteria. The structured approach of SCEM ensures that the most effective and feasible cleaning method is selected based on a balanced assessment of performance and technical feasibility.

### 5.4. Step D: Analysis and comparison of cleaning methods

After constructing the SCEM for each cleaning method, the next step is to compute a final weighted score for each method. This score quantifies the overall effectiveness of a strategy by integrating the weighted importance of CRs and TRs with the performance scores assigned to each method.

The weight vector of the CRs, denoted as $w_{CR}$, contains the computed importance weights of each CR as obtained from the SCEM:

$$ w_{CR} = \begin{bmatrix} w_{CR1} \\ w_{CR2} \\ \vdots \\ w_{CRM} \end{bmatrix}. \quad (7) $$

<table><caption>**Table 10**
Final weighted scores of PV cleaning methods.</caption><thead><tr><th>Method</th><th>Final score (S<sub>final,t</sub>)</th></tr></thead><tbody><tr><td>Robotic Cleaning</td><td><b>8.42</b></td></tr><tr><td>Electrodynamic Screens</td><td><b>7.88</b></td></tr><tr><td>Manual Cleaning</td><td><b>7.14</b></td></tr></tbody></table>

Similarly, the weight vector of the TRs, denoted as $w_{TR}$, represents the computed weights of the technical feasibility factors:

$$ \mathbf{w}_{TR} = \begin{bmatrix} w_{TR1} \\ w_{TR2} \\ \vdots \\ w_{TRN} \end{bmatrix}. \quad (8) $$

To ensure that both performance and feasibility aspects are considered in the final ranking, the total weighted score $S_{final,t}$ for each cleaning method $t$ is computed as follows:

$$ S_{\text{final},t} = \sum_{j=1}^{M} (w_{CR_j} \times S_{t,j}) + \sum_{i=1}^{N} (w_{TR_i} \times S_{t,i}), \quad (9) $$

where $S_{final,t}$ represents the total weighted score of the cleaning method $t$. The term $w_{CR_j}$ corresponds to the weight of each Customer Requirement $j$, as derived from the SCEM. The value $S_{t,j}$ denotes the performance score assigned to method $t$ for Customer Requirement $j$, based on prior evaluations. Similarly, $w_{TR_i}$ represents the weight of each Technical Requirement $i$, also obtained from the SCEM, while $S_{t,i}$ corresponds to the feasibility score of method $t$ for Technical Requirement $i$.

By applying Equation (9), each cleaning method receives a final score that balances technical efficiency, economic feasibility, and sustainability. In this study, as an example and without loss of generality, the same three cleaning methods are considered ($T = 3$): Manual Cleaning, Robotic Cleaning, and EDS. The computed final scores are presented in Table 10, where the methods are systematically ranked in descending order, from the most to the least effective strategy.

The final scores in Table 10 provide an objective ranking of the evaluated cleaning methods, facilitating an informed selection process. The methodology proceeds to Step E: Selection of the Top Strategy, where the highest-ranked method is selected for real-world implementation. If the top-ranked strategy fails to meet performance expectations, the next-ranked alternative is evaluated following the structured selection process.

### 5.5. Step E: Selection and implementation of the top-ranked cleaning method

Following the prioritization process, the highest-ranked PV cleaning method is selected for real-world implementation and performance evaluation. The method with the highest total weighted score from Table 10 is prioritized, ensuring that the selection is based on a structured and quantitative framework derived from the SCEM. If multiple strategies achieve similar rankings, additional considerations such as operational feasibility, cost-effectiveness, and long-term sustainability are incorporated to finalize the selection.

The implementation phase begins with a baseline performance assessment, in which key system parameters — including energy yield, soiling accumulation, and operational costs — are recorded before applying the selected cleaning method. This step establishes a benchmark against which improvements in efficiency and resource utilization can be measured. The cleaning method is then deployed following standardized operational guidelines and industry best practices to ensure effectiveness and reliability.

Throughout the implementation period, continuous monitoring is performed to track energy recovery, cleaning efficiency, and resource
consumption. The effectiveness of the method is evaluated by comparing its results with previously tested cleaning methods, assessing whether the selected strategy demonstrates superior performance. If the method meets or exceeds the expected performance benchmarks, it is formally integrated into the PV maintenance framework as the preferred cleaning solution.

However, if the selected strategy fails to achieve significant advantages or does not perform as anticipated, the methodology transitions to the removal phase. In this phase, underperforming strategies are systematically discarded, allowing alternative solutions to be evaluated in an iterative selection process. This ensures that only the most effective and sustainable cleaning methods remain under consideration. If no further strategies are available in the ranking list, the methodology advances to the reassessment phase, where new cleaning technologies are explored to identify alternative approaches.

## 5.6. Step F: Removal of underperforming strategies

If the PV cleaning method selected in Step E fails to meet performance expectations, it is systematically removed from the prioritized list. This removal process ensures that ineffective solutions do not remain under consideration, preventing unnecessary operational and financial burdens. A strategy is deemed underperforming if it fails to achieve measurable improvements in PV energy output, if its implementation costs exceed projected benefits, or if it presents sustainability concerns such as excessive water or energy consumption. Additionally, methods that contribute to mechanical wear or degradation of PV modules are considered unsuitable for long-term adoption.

Once a method is removed, the next-ranked strategy from Table 10 is selected for evaluation. This iterative selection process continues until an effective solution is identified, ensuring that alternative approaches are tested systematically. The transition to a new strategy maintains the integrity of the evaluation framework, allowing for continuous refinement based on empirical performance data.

However, if all cleaning methods listed in Table 10 have been tested and none meet the required performance benchmarks, the methodology advances directly to Step H. In this phase, the evaluation framework is expanded to incorporate newly identified cleaning technologies, ensuring that innovative solutions are considered. This adaptive approach guarantees that the selection process remains responsive to evolving operational constraints, emerging technologies, and environmental requirements. technological advancements.

## 5.7. Step G: Iterative selection of the next best strategy

If a PV cleaning method is removed due to underperformance, the next highest-ranked method from Table 10 is selected for evaluation. This ensures that viable alternatives are systematically tested until an optimal solution is identified. The method with the highest remaining total score is prioritized for implementation. If multiple strategies achieve similar rankings, additional feasibility assessments are conducted to determine the most suitable alternative based on operational constraints.

Before full-scale deployment, the newly selected strategy undergoes a re-evaluation phase to ensure that environmental and site-specific conditions are considered. Following validation, the method is implemented using the same structured procedure as outlined in Step E. Baseline performance metrics, including energy recovery, cleaning efficiency, and sustainability indicators, are recorded before deployment. Throughout the testing phase, continuous monitoring is performed to assess the effectiveness of the method compared to previously evaluated strategies.

If the newly selected method meets or exceeds the predefined performance benchmarks, it is formally adopted as the preferred PV cleaning solution. However, if its effectiveness remains suboptimal,

the iterative selection process continues, ensuring that the next best-ranked alternative from Table 10 is tested under the same evaluation framework.

By maintaining a structured, iterative evaluation process, the SCEM methodology ensures that PV maintenance strategies remain adaptable to changing operational needs, environmental conditions, and technological advancements. If no viable strategies remain in the ranked list, the methodology advances to Step H, where alternative solutions are explored, and the evaluation framework is expanded to incorporate emerging cleaning technologies.

## 5.8. Step H: Exploration of new cleaning technologies

If all previously ranked PV cleaning methods fail to meet the expected performance benchmarks, the methodology transitions to a broader reassessment phase. This step ensures that alternative approaches are systematically explored to identify more effective solutions that align with operational, economic, and environmental constraints.

The search for alternative strategies involves reviewing recent advancements in PV cleaning technologies, including self-cleaning coatings, electrostatic dust removal, and hybrid cleaning methods that integrate robotic automation with environmental monitoring systems. Additionally, emerging solutions such as AI-driven predictive maintenance, nanotechnology-based dust repellents, and adaptive cleaning schedules are examined to determine their feasibility in PV maintenance applications (Sepúlveda-Oviedo, 2023). This search is informed by scientific studies, industry reports, case studies, and expert consultations, ensuring that innovative strategies addressing the limitations of previously tested methods are identified.

Once new strategies are identified, the evaluation framework is updated to incorporate relevant technological advancements and site-specific constraints. Adjustments may include redefining performance benchmarks, recalibrating weight coefficients within the SCEM, or introducing new TRs that better reflect the feasibility of emerging cleaning technologies. The updated framework is then applied to reassess and rank the newly identified methods, integrating them back into the iterative selection process, which restarts from Step C.

By maintaining an adaptive and continuously evolving approach, the SPV-COM methodology ensures that PV cleaning methods remain aligned with technological innovations, changing environmental conditions, and evolving economic requirements. Through this iterative refinement process, the long-term efficiency and sustainability of PV maintenance operations are enhanced, ensuring that only the most effective and feasible cleaning solutions are ultimately implemented.

# 6. Discussion

The maintenance of PV systems requires a multifaceted approach that integrates technical, economic, and environmental considerations. This study provides an in-depth analysis of existing PV cleaning methods, highlighting the need for adaptable, cost-effective, and scalable solutions. The findings reaffirm that no single method is universally optimal, as performance depends on site-specific conditions, including climate, dust composition, and system scale. The SPV-COM introduced in this study offers a structured decision-making framework, allowing for an objective selection of cleaning methods based on predefined criteria.

## 6.1. Key insights from the comparative analysis

The results demonstrate that passive strategies, such as hydrophobic coatings, provide long-term benefits in reducing soiling rates but face durability challenges in harsh environments (Mouterde et al., 2018). Active strategies, including robotic and electrodynamic cleaning systems, deliver higher efficiency but require substantial investment and
<table><caption>**Table 11**
Recommended PV cleaning strategies by environmental region.</caption><thead><tr><td>Region type</td><td>Recommended strategy</td><td>Rationale</td></tr></thead><tbody><tr><td>Arid/Desert Regions</td><td>Hybrid approach: Hydrophobic coatings + Robotic dry cleaning or Electrodynamic Screens</td><td>Minimizes water use; effective for high dust deposition; coatings reduce cleaning frequency.</td></tr><tr><td>Tropical/Humid Regions</td><td>Manual or robotic wet cleaning</td><td>Frequent rainfall assists natural cleaning; wet methods remove organic residues and biofilms.</td></tr><tr><td>Coastal Areas</td><td>Anti-corrosion coatings + Manual cleaning with treated water</td><td>Mitigates salt accumulation and prevents material degradation.</td></tr><tr><td>Industrial/Urban Environments</td><td>Robotic cleaning + IoT sensors for predictive maintenance</td><td>Soot and pollutants require more frequent and precise cleaning schedules.</td></tr><tr><td>Agricultural/High-Pollen Zones</td><td>Hydrophilic coatings + Periodic wet cleaning</td><td>Promotes efficient removal of pollen and organic matter during seasonal peaks.</td></tr></tbody></table>

ongoing maintenance (Salamah et al., 2022). The trade-offs between efficiency, cost, and sustainability are critical factors that must be considered when selecting an optimal strategy. The SPV-COM methodology enables a systematic evaluation of these trade-offs, ensuring that cleaning methods align with regional and economic constraints.

A key finding of this study is that hybrid strategies combining passive and active approaches may provide the most effective solution in regions with severe soiling conditions. While passive coatings reduce the frequency of cleaning interventions, they cannot fully eliminate dust accumulation, necessitating periodic cleaning with active systems. The methodology proposed in this study allows for a flexible combination of approaches, ensuring that maintenance efforts remain cost-effective and sustainable.

## 6.2. Challenges in PV cleaning and maintenance

Despite technological advancements, several challenges persist in PV cleaning and maintenance. The economic feasibility of robotic systems remains a major limitation, particularly for small- and medium-scale installations. Although these systems reduce labor dependency and enhance efficiency, their high initial costs and maintenance requirements limit their adoption (Parrott et al., 2018). Adaptive financing models, such as subscription-based cleaning services, may facilitate broader implementation, particularly in regions where water scarcity restricts conventional cleaning methods (Salamah et al., 2022).

Another critical challenge is the optimization of cleaning frequency. Many maintenance schedules rely on fixed intervals rather than real-time soiling data, leading to unnecessary cleaning operations and increased operational costs. The integration of IoT sensors and machine learning algorithms can significantly improve cleaning efficiency by dynamically adjusting cleaning schedules based on dust accumulation patterns, environmental conditions, and system performance data (Micheli et al., 2020).

## 6.3. Regional recommendations for PV cleaning strategies

Given the significant influence of environmental and geographic factors on the effectiveness of PV cleaning methods, this study proposes a set of region-specific recommendations to guide practical implementation. While the SPV-COM methodology enables site-specific optimization, Table 11 summarizes general recommendations based on predominant regional characteristics.

These general recommendations should be refined using the SCEM matrix within the SPV-COM framework to account for local cost structures, policy constraints, and operational scale.

## 6.4. Practical implications and future perspectives

The SPV-COM framework provides a structured approach for selecting cleaning methods, ensuring that PV maintenance strategies are

optimized for specific operational conditions. By integrating technical feasibility, economic constraints, and environmental sustainability, this methodology enables decision-makers, operations and maintenance contractors, and researchers to make informed choices about PV cleaning methods.

Emerging trends in automation, predictive maintenance, and material science are expected to further enhance PV cleaning methods. The development of self-repairing coatings, AI-driven maintenance schedules, and hybrid cleaning systems will likely improve the efficiency and longevity of PV installations (Sepúlveda-Oviedo et al., 2021). Additionally, regional policies and incentives for sustainable PV maintenance could accelerate the adoption of innovative cleaning technologies (Guo et al., 2023).

In summary, this study provides a comprehensive evaluation of PV cleaning methods and introduces a novel selection framework that enables site-specific optimization. Future research should focus on validating this methodology in real-world installations, expanding economic feasibility assessments, and incorporating advanced automation techniques to further enhance PV maintenance strategies.

## 6.5. Future research directions in PV cleaning

While this study provides a structured framework for selecting and optimizing PV cleaning methods, several research gaps remain, requiring further investigation to enhance system efficiency, adaptability, and cost-effectiveness. Future studies should explore advanced methodologies that refine cleaning schedules, incorporate automation, and integrate predictive maintenance techniques (Guo et al., 2023).

### 6.5.1. Optimization of cleaning frequency

A critical aspect of PV maintenance is the determination of the optimal cleaning frequency for different environmental conditions. Cleaning interventions are often scheduled at fixed intervals, which may not accurately reflect real-time soiling accumulation (Salamah et al., 2022). Future work should focus on developing adaptive cleaning models that integrate real-time monitoring data, utilizing dust sensors, weather forecasts, and machine learning algorithms to dynamically adjust cleaning schedules. This approach would ensure that maintenance efforts are both cost-effective and energy-efficient (Micheli et al., 2020).

### 6.5.2. Integration of IoT and AI-based cleaning systems

The integration of IoT sensors and AI into PV maintenance workflows represents a promising avenue for improving efficiency and reducing operational costs (Sepúlveda-Oviedo et al., 2022). Future research should explore autonomous cleaning systems that trigger cleaning cycles based on real-time environmental data. AI-driven predictive models could optimize cleaning schedules by analyzing historical soiling patterns, weather conditions, and operational constraints (Sepúlveda-Oviedo et al., 2023a). The implementation of such technologies would enhance the adaptability of PV maintenance strategies while reducing unnecessary operational costs (Sepúlveda-Oviedo et al., 2023b).
### 6.5.3. *Hybrid cleaning methods for enhanced efficiency*

Existing PV cleaning methods present trade-offs in terms of efficiency, cost, and sustainability. Future studies should investigate hybrid cleaning methods that combine multiple technologies to maximize performance while minimizing resource consumption (Datta et al., 2020). For instance, self-cleaning coatings could be integrated with robotic dry-cleaning methods to reduce reliance on water-based methods, particularly in arid regions (Zhang et al., 2019). Similarly, airflow-assisted cleaning could be paired with electrodynamic screens to improve dust removal without increasing energy demand.

### 6.5.4. *Cost-effectiveness and lifecycle analysis*

Economic feasibility is a fundamental factor in the selection of PV cleaning methods. However, long-term cost-benefit analyses remain limited in current research (Salamah et al., 2022). Future work should incorporate comprehensive lifecycle cost assessments, evaluating not only initial investment and operational expenses but also maintenance requirements and long-term financial impacts (Jaradat et al., 2015). Additionally, region-specific studies should assess the viability of subscription-based or on-demand cleaning services, particularly for large-scale PV farms (Parrott et al., 2018).

### 6.5.5. *Impact of extreme weather conditions on cleaning methods*

Extreme weather events, such as sandstorms, heavy rainfall, and prolonged dry periods, directly influence the effectiveness and durability of cleaning methods (Bošnjaković et al., 2023). Future research should examine the resilience of different cleaning technologies under extreme conditions, assessing their ability to maintain efficiency despite environmental stressors. For instance, robotic cleaning systems may require structural reinforcements to withstand wind-driven sand, while water-based solutions must optimize water use in regions with high evaporation rates (Wang et al., 2023).

### 6.5.6. *Standardization and best practices for PV cleaning*

Despite the increasing adoption of cleaning technologies, standardized protocols for PV maintenance remain inconsistent across different regions and installations (Madrigal et al., 2024). Future work should focus on developing universal best practices for PV cleaning, defining optimal techniques that ensure efficiency, durability, and environmental sustainability. Additionally, regulatory frameworks should be investigated to encourage the widespread adoption of low-water and energy-efficient cleaning solutions (Mirletz et al., 2022).

## 6.6. *Limitations and future work*

Although this study provides a structured review of PV cleaning methods and introduces a selection framework, certain limitations must be acknowledged. These constraints should be addressed in future research to improve the robustness and applicability of the proposed methodology.

One key limitation is the reliance on a predefined selection of articles obtained through systematic review methodologies. Although efforts were made to ensure comprehensive coverage, the inclusion criteria may have excluded relevant studies, particularly those published in less accessible journals or languages other than English. Future research should broaden the scope of studies inclusion to incorporate a more diverse dataset (Sepúlveda-Oviedo, 2023).

Another limitation relates to the experimental validation of the selection methodology. The SPV-COM was designed to provide a structured framework for evaluating and ranking PV cleaning methods; however, its implementation in real-world settings remains untested. Future studies should focus on applying the methodology across various operational environments, testing its performance under different climatic and economic conditions. Conducting experimental case studies would allow for a more precise assessment of its effectiveness and feasibility (Salamah et al., 2022).

Finally, while economic feasibility was analyzed within this study, future research should incorporate detailed financial assessments considering location-specific variables such as labor costs, water availability, and energy prices (Sica et al., 2018). These additional analyses would refine the financial models used for decision-making in PV maintenance planning.

## 7. *Conclusions*

This study presents a structured and comparative evaluation of dust mitigation strategies for PV systems, addressing a critical challenge in maintaining long-term performance and economic sustainability. By categorizing cleaning methods into passive and active approaches and analyzing their effectiveness under varying environmental conditions, the study highlights the need for context-specific solutions.

A major innovation of this work is the development of the SPV-COM, a novel decision-making methodology that enables the systematic selection of PV cleaning strategies based on technical, environmental, and economic criteria. Unlike previous reviews that emphasize individual technologies or general trends, this framework offers a quantitative, multi-criteria evaluation process that supports more objective and tailored maintenance decisions.

The proposed methodology enhances practical applicability by enabling decision-makers, operations and maintenance contractors, and researchers to design adaptive cleaning schedules that account for cleaning frequency, dust accumulation rates, economic trade-offs, and environmental limitations. The inclusion of emerging technologies — such as IoT sensors, machine learning algorithms, and hybrid cleaning systems — reinforces the relevance of this study for real-world implementation.

To further improve the applicability of the proposed framework, several specific areas are identified for future research. These include experimental validation in real-world PV installations, integration of real-time soiling data, development of hybrid cleaning methods, refinement of economic assessments through regional cost modeling, and the creation of standardized cleaning protocols. Additionally, the resilience of cleaning systems under extreme weather conditions and their long-term environmental impact remain critical areas for investigation.

In summary, this study advances both the theoretical and practical understanding of PV cleaning by introducing an innovative and scalable methodology. By addressing technical, economic, and environmental factors in a unified framework, the work contributes to the development of sustainable and cost-effective maintenance strategies for improving the long-term efficiency of PV systems.

### Acronyms

*AHP* Analytic Hierarchy Process

*AI* Artificial Intelligence

*AWG* Air-to-Water Generator

*AWR* Automated Water Recycle

*CdTe* Cadmium Telluride

*CPV* Concentrated Photovoltaics

*CRs* Customer Requirements

*CSP* Concentrated Solar Power

*DC* Direct Current

*EDS* Electrodynamic Screen

*F-TOPSIS* Fuzzy Technique for Order Preference by Similarity to Ideal Solution
**HoQ** House of Quality Matrix

**IEA** International Energy Agency

**IoT** Internet of Things

**LCOE** Levelized Cost of Energy

**MCDM** Multi-Criteria Decision-Making

**O&M** Operation and Maintenance

**OPEX** Operational Expenditure

**PLC** Programmable Logic Controller

**PSI** Preference Selection Index

**PV** Photovoltaic

**QFD** Quality Function Deployment

**ROI** Return On Investment

**SAW** Surface Acoustic Waves

**SCEM** Strategic Cleaning Evaluation Matrix

**SDG** Sustainable Development Goal

**SPV-COM** Strategic PV Cleaning Optimization Method

**SR** Soiling Ratio

**TOPSIS** Fuzzy Technique for Order Preference by Similarity to Ideal Solution

**TRs** Technical Requirements

**UAE** United Arab Emirates

**UAV** Unmanned Aerial Vehicles

**UV** Ultraviolet

## 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.

## Data availability

Data will be made available on request.

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