Advances and Optimization Trends in Photovoltaic Systems: A Systematic Review

Abstract

This article presents a systematic review of optimization methods applied to enhance the performance of photovoltaic (PV) systems, with a focus on critical challenges such as system design and spatial layout, maximum power point tracking (MPPT), energy forecasting, fault diagnosis, and energy management. The emphasis is on the integration of classical and algorithmic approaches. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA) methodology, 314 relevant publications from 2020 to 2025 were analyzed to identify current trends, methodological advances, and practical applications in the optimization of PV performance. The principal novelty of this review lies in its integrative critical analysis, which systematically contrasts the applicability, performance, and limitations of deterministic classical methods with emerging stochastic metaheuristic and data-driven artificial intelligence (AI) techniques, highlighting the growing dominance of hybrid models that synergize their strengths. Traditional techniques such as analytical modeling, numerical simulation, linear and dynamic programming, and gradient-based methods are examined in terms of their efficiency and scope. In parallel, the study evaluates the growing adoption of metaheuristic algorithms, including particle swarm optimization, genetic algorithms, and ant colony optimization, as well as machine learning (ML) and deep learning (DL) models applied to tasks such as MPPT, spatial layout optimization, energy forecasting, and fault diagnosis. A key contribution of this review is the identification of hybrid methodologies that combine metaheuristics with ML/DL models, demonstrating superior results in energy yield, robustness, and adaptability under dynamic conditions. The analysis highlights both the strengths and limitations of each paradigm, emphasizing challenges related to data availability, computational cost, and model interpretability. Finally, the study proposes future research directions focused on explainable AI, real-time control via edge computing, and the development of standardized benchmarks for performance evaluation. The findings contribute to a deeper understanding of current capabilities and opportunities in PV system optimization, offering a strategic framework for advancing intelligent and sustainable solar energy technologies.

1. Introduction

In the current context of environmental crisis, fossil fuel depletion, and the urgent need for energy transition, the development of clean and sustainable technologies has become a central focus in public policy, technological innovation, and scientific research [1,2]. Within this framework, photovoltaic solar energy (PV) has emerged as one of the most promising alternatives due to its renewable nature, low environmental impact during operation, and scalability for both urban and rural applications [3,4]. This review encompasses optimization studies applied to the full spectrum of PV system configurations, including standalone (off-grid), grid-connected (on-grid), and hybrid systems (e.g., integrated with storage or other renewable sources), with a focus on methodologies that enhance their structural, operational, and economic performance. Distributed generation, local energy storage, and the decreasing cost of photovoltaic modules have further fueled the sustained growth of this technology in recent years [5,6].

However, the efficient utilization of PV systems depends not only on material quality or technological advancements, but also on the development of accurate mathematical and computational tools capable of simulating, analyzing, and optimizing their behavior under real-world conditions [7,8]. In this regard, mathematical models play a critical role by providing a formal representation of the physical and electrical performance of key system components such as PV modules, inverters, batteries, and charge controllers [9,10,11].

Classical models, such as the single-diode model used to describe the current–voltage (I–V) curve of a solar cell or the Thevenin equivalent circuit for lead-acid batteries, have been widely adopted due to their analytical simplicity and their ability to capture the system’s dominant behaviors [12,13]. Nevertheless, these models exhibit limitations when applied to highly nonlinear phenomena, abrupt environmental fluctuations, or transient operating conditions that require greater temporal and spatial resolution. To address these challenges, researchers have proposed more accurate formulations, such as the two-diode model that accounts for internal recombination processes [14,15], and hybrid models that integrate physical descriptions with empirical adjustments based on experimental data [16,17,18].

Moreover, the rise of artificial intelligence and algorithmic approaches has enabled a paradigm shift in the modeling and control of PV systems [19,20]. Techniques such as neural networks, fuzzy logic, genetic algorithms, and metaheuristic optimization have been applied not only to parameter identification but also to the design of maximum power point tracking (MPPT) schemes, often outperforming classical methods like Perturb and Observe in terms of adaptability, noise tolerance, and real-time responsiveness [21,22]. While these algorithmic models typically demand greater computational resources, they offer substantial advantages in terms of flexibility and dynamic decision-making [23].

This work offers a comprehensive and integrative perspective on the optimization of photovoltaic systems by combining classical methodologies with state-of-the-art algorithmic approaches. Despite the abundance of reviews on PV systems, a significant gap exists in works that offer a holistic, methodologically rigorous synthesis which simultaneously (a) encompasses the entire optimization lifecycle from design to diagnosis, (b) integrates a critical analysis of both classical and AI-based paradigms, and (c) employs a transparent, reproducible systematic methodology (PRISMA) to minimize selection bias. This review aims to fill this gap by providing a unified framework for evaluating and selecting optimization strategies based on their accuracy, complexity, and implementability, thereby offering a strategic roadmap for researchers and practitioners. The following are the main contributions derived from this study:

• A structured taxonomy and critical comparison of traditional optimization techniques, including analytical models, linear programming, dynamic programming, and gradient-based methods emphasizing their applicability, strengths, and limitations across PV system design and control stages.
• The application of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA)-compliant systematic review methodology, enabling the identification, filtration, and synthesis of 314 high-quality studies relevant to PV optimization, with a strong emphasis on structural, operational, and topological improvements.
• The integration of bibliometric analysis using VOSviewer 1.6.20 and a multicriteria evaluation framework to highlight key research trends, knowledge clusters, and emerging areas in PV modeling and optimization strategies.
• A detailed examination of metaheuristic and machine learning techniques, including hybrid models applied to MPPT enhancement, PV layout optimization, energy forecasting, fault detection, and real-time energy management, demonstrating their superiority over conventional approaches.
• The formulation of a future research agenda that identifies current limitations such as data quality, model interpretability, and computational cost, proposing the adoption of explainable artificial intelligence (XAI), lightweight algorithms, and edge-computing solutions to enhance practical implementation in intelligent PV systems.

In this context, the objective of the present study is to identify, classify, and evaluate the mathematical models used to represent photovoltaic systems, comparing their performance and applicability through both traditional physics-based methods and modern algorithmic approaches. The aim is to establish selection criteria for modeling strategies based on their accuracy, computational complexity, and feasibility of implementation in simulation environments or real-time embedded systems.

This paper is organized as follows: Section 1, Introduction, outlines the motivations driving the optimization of PV systems, emphasizing the need for integrated, high-efficiency modeling approaches in response to growing global energy demands and environmental constraints. Section 2, Materials and Methods, details the systematic review protocol based on PRISMA guidelines, including the inclusion/exclusion criteria and the classification of traditional and algorithmic optimization methods applied to PV systems. Section 3, Results, presents a comparative analysis of optimization paradigms, supported by bibliometric mapping and critical evaluation of five traditional methods: analytical, numerical, linear programming, dynamic programming, and gradient-based approaches. Section 5, Discussion, interprets the observed research trends, highlighting the shift from classical to intelligent algorithmic approaches, and analyzes future directions in hybrid optimization frameworks. Finally, Section 6, Conclusions, synthesizes the main contributions of this work, including the comprehensive evaluation of optimization strategies, the demonstrated relevance of hybrid approaches for energy-efficiency maximization, and the identification of current limitations and research gaps in the practical deployment of AI-enhanced PV systems.

2. Materials and Methods

A systematic review is a form of scientific research where the unit of analysis comprises original primary studies addressing a common topic. In this case, the review focuses on identifying the main factors influencing the efficiency of photovoltaic solar systems (PSS), the relevant variables involved, and the percentage ranges associated with their performance under different environmental and structural conditions. The objective of this systematic review is to answer a clearly defined research question regarding the optimization of energy use in PSS, thereby providing a comprehensive and reliable synthesis of existing evidence while minimizing potential research biases [24,25].

The PRISMA methodology was adopted to guide the review process. PRISMA offers a standardized 27-item checklist and a flow diagram to promote clarity, transparency, and replicability in systematic reviews, especially in interdisciplinary fields like engineering, energy systems, and sustainability. Although PRISMA was originally developed for healthcare, its structured approach has proven valuable in technical and environmental research as well [26,27].

This systematic review was retrospectively registered in the Open Science Framework (OSF) registries under the registration DOI https://doi.org/10.17605/OSF.IO/3S87F, accessed on 1 July 2025. This registration ensures transparency in the research process, providing public access to the review protocol and facilitating reproducibility of the methodology employed.

To provide a comprehensive conceptual framework for the optimization methodologies analyzed in this systematic review, Figure 1 illustrates the interconnected relationships between photovoltaic system challenges, different optimization approaches (traditional, AI-based, and hybrid), their specific applications, and the resulting performance improvements. This framework synthesizes the key themes examined across the selected studies, showing how various optimization strategies address PV system challenges to ultimately enhance energy yield, robustness, and cost-effectiveness.

2.1. Photovoltaic Solar System

Photovoltaic (PV) systems are technologies that convert solar radiation directly into electricity through the photovoltaic effect. These systems typically comprise solar panels (modules), inverters, mounting structures, charge controllers, batteries (in standalone or hybrid systems), and auxiliary equipment for monitoring and protection. PV systems can be configured for a variety of scales and purposes, ranging from small residential rooftop systems to large-scale solar farms integrated into national power grids [28,29].

In the current global context of energy transition, PV systems are essential for reducing greenhouse gas emissions and enhancing energy access in remote and underserved regions. Their scalability, declining costs, and environmental benefits make them a cornerstone of sustainable energy policies. However, challenges such as fluctuating solar irradiance, energy losses due to suboptimal configurations, and storage limitations require strategic technical solutions to ensure reliable and efficient operation.

Need for Optimization in PV Systems

Optimization plays a pivotal role in overcoming the limitations of PV systems and ensuring their technical and economic sustainability. Without proper optimization, systems may be underperforming due to mismatches between generation and demand, inefficient use of components, or poor adaptation to local environmental conditions [30,31]. Optimization aims to improve system behavior in both design and operational phases.

Key objectives of optimization include the following:

• Ensuring that systems capture and convert the maximum possible solar energy by optimizing orientation, tilt angles, and spacing of panels.
• Minimizing energy losses due to resistive wiring, shading, mismatch effects, and thermal inefficiencies.
• Selecting appropriate sizes for PV arrays, inverters, and storage units based on load profiles and solar availability.
• Implementing intelligent algorithms for power flow management, load prioritization, and battery scheduling.
• Increasing overall cost-effectiveness through lifecycle-based financial and technical optimization.

Improving efficiency in PV systems is multi-faceted and can be approached from several angles:

• Module level enhancements: Using high-efficiency solar cells, anti-reflective coatings, and bifacial modules to capture more sunlight.
• System-level improvements: Implementing MPPT algorithms, reducing shading effects through layout optimization, and using tracking systems.
• Energy management: Integrating smart controllers and forecasting tools to align energy production with load demands.
• Hybridization and storage integration: Combining PV with other renewable sources (e.g., wind and biomass) and advanced battery systems to enhance reliability.

In this context, the application of traditional optimization methods provides robust, interpretable, and often computationally efficient solutions. These methods can significantly contribute to tailoring PV system configurations to specific technical, environmental, and economic constraints, thereby enhancing their attractiveness, especially in cost-sensitive or remote deployment scenarios. This review focuses on such methods and their practical relevance to real-world PV system challenges.

2.2. Mathematical Models to Analyze and Optimize Photovoltaic Systems

Mathematical models are essential for analyzing and optimizing photovoltaic systems, as they represent the behavior of components such as modules, inverters, batteries, and controllers [32]. Modules are typically modeled using nonlinear one- or two-diode equations, inverters with efficiency curves, batteries with equivalent electrical models, like Thevenin, and MPPT controllers with discrete algorithms, such as Perturb and Observe. The choice of model depends on the purpose of the study, making it crucial to understand its nature (linear/nonlinear; continuous/discrete) and level of accuracy [33].

A reliable simulation and analysis of PV systems requires mathematical models that accurately represent the physical and electrical behavior of each component [34]. Below are the five main models commonly used in PV system modeling.

2.3. Single-Diode Model for PV Modules

This model describes the current voltage (I–V) relationship of a solar cell based on the Shockley equation. It includes a photocurrent source, a diode, series resistance $R_s$, and shunt resistance $R_{sh}$ [7].

$$I=I_{\mathrm{ph}}-I_0\left(e^{\frac{q(V+I{R}_s)}{nkT}}-1\right)-{\displaystyle \frac{V+I{R}_s}{R_{\mathrm{sh}}}},$$

(1)

where

• I is the output current of the solar cell (A);
• $I_{\mathrm{ph}}$ is the photogenerated current (A);
• $I_0$ is the reverse saturation current of the diode (A);
• q is the elementary charge (C);
• V is the voltage across the cell (V);
• $R_s$ is the series resistance ($\mathsf{\Omega }$);
• n is the diode ideality factor;
• k is the Boltzmann constant (J/K);
• T is the temperature (K);
• $R_{\mathrm{sh}}$ is the shunt resistance ($\mathsf{\Omega }$).

Model characteristics: Nonlinear, continuous, deterministic, and approximated, offering a good balance between computational simplicity and physical accuracy.

2.4. Two-Diode Model for PV Modules

An extension of the single-diode model, this version adds a second diode to account for recombination losses in the depletion region of the cell [35].

$$I=I_{\mathrm{ph}}-I_{01}\left(e^{\frac{q(V+I{R}_s)}{n_{1}kT}}-1\right)-I_{02}\left(e^{\frac{q(V+I{R}_s)}{n_{2}kT}}-1\right)-{\displaystyle \frac{V+I{R}_s}{R_{\mathrm{sh}}}},$$

(2)

where

• I is the output current of the solar cell (A);
• $I_{\mathrm{ph}}$ is the photogenerated current (A);
• $I_{01},I_{02}$ are the reverse saturation currents of the two diodes (A);
• q is the elementary charge (C);
• V is the voltage across the cell (V);
• $R_s$ is the series resistance ($\mathsf{\Omega }$);
• $n_1,n_2$ are the ideality factors of the diodes;
• k is the Boltzmann constant (J/K);
• T is the temperature (K);
• $R_{\mathrm{sh}}$ is the shunt resistance ($\mathsf{\Omega }$).

Model characteristics: Highly nonlinear, continuous, and more accurate than the single-diode model but computationally more intensive.

2.5. Inverter Efficiency Model

This model represents the inverter’s efficiency as a function of the input direct current (DC) power, typically using a quadratic or polynomial fit derived from manufacturer data [36].

$$\eta \left(P_{\mathrm{dc}}\right)={\eta }_{\max }\left(1-k_1{\left({\displaystyle \frac{P_{\mathrm{dc}}}{P_{\mathrm{rated}}}}-1\right)}^2\right),$$

(3)

where

• $\eta \left(P_{\mathrm{dc}}\right)$ is the inverter efficiency (dimensionless);
• ${\eta }_{\max }$ is the maximum inverter efficiency (dimensionless);
• $P_{\mathrm{dc}}$ is the DC input power (W);
• $P_{\mathrm{rated}}$ is the rated power of the inverter (W);
• $k_1$ is the efficiency adjustment coefficient (dimensionless).

Model characteristics: Nonlinear, continuous, empirical, and approximated, making it useful in estimating conversion losses in energy yield simulations.

2.6. Battery-Equivalent Circuit Model (Thevenin)

The dynamic behavior of a battery is represented through a voltage source in series with an internal resistance and an RC network that models transient response [12].

$$\begin{array}{cc}\hfill V_{\mathrm{bat}}& =E_{\mathrm{oc}}-I·R_{\mathrm{int}}-V_{\mathrm{RC}},\hfill \end{array}$$

(4)

$$\begin{array}{cc}\hfill {\displaystyle \frac{d{V}_{\mathrm{RC}}}{dt}}& ={\displaystyle \frac{1}{RC}}(I-V_{\mathrm{RC}}),\hfill \end{array}$$

(5)

where

• $V_{\mathrm{bat}}$ is the battery voltage (V);
• $E_{\mathrm{oc}}$ is the battery open-circuit voltage (V);
• I is the battery charge/discharge current (A);
• $R_{\mathrm{int}}$ is the battery internal resistance ($\mathsf{\Omega }$);
• $V_{\mathrm{RC}}$ is the voltage across the RC network (V);
• R is the resistance of the RC network ($\mathsf{\Omega }$);
• C is the capacitance of the RC network (F);
• t is the time (s).

Model characteristics: Linear (under constant parameters), continuous, and suitable for capturing charge/discharge dynamics.

2.7. MPPT Algorithm: Perturb and Observe (P&O)

This algorithm determines the maximum power point by iteratively perturbing the voltage and observing changes in the power output [21].

$$\mathsf{\Delta }P=P\left(k\right)-P(k-1),$$

(6)

where

• $\mathsf{\Delta }P$ is the power change (W);
• $P\left(k\right)$ is the power measured at time step k (W);
• $P(k-1)$ is the power measured at time step k-1 (W);
• $V\left(k\right)$ is the voltage at time step k (V);
• $\mathsf{\Delta }V$ is the voltage change (V).

Control logic:

• If $\mathsf{\Delta }P>0$, increase $V(k+1)=V\left(k\right)+\mathsf{\Delta }V$;
• If $\mathsf{\Delta }P<0$, decrease $V(k+1)=V\left(k\right)-\mathsf{\Delta }V$.

Algorithm characteristics: Discrete, nonlinear, heuristic, and widely used due to its simplicity and robustness in embedded systems.

Systematic Review Procedure

The methodological steps followed in this review, in alignment with PRISMA, are as follows:

1.

Define the objective: This step is used to analyze which factors (e.g., tilt angle, orientation, structural arrangement, and material selection) most significantly impact the efficiency and energy production of PSS installations.

2.

Develop a protocol: A protocol is designed, including the main research questions, inclusion and exclusion criteria, relevant databases, and methods, for synthesis and analysis.

3.

Conduct a comprehensive search: Databases such as Scopus, Web of Science and ScienceDirect are queried using advanced search strategies combining terms like “photovoltaic solar system”, “optimization”, “structure”, “efficiency”, and metaheuristic algorithms.

4.

Select studies: Studies are filtered based on scope, publication year, relevance to the engineering and energy fields, and technical alignment with the objectives. A PRISMA flow diagram is used to document the selection process.

5.

Extract data: Key data are collected from each selected article, including optimization objectives, system configurations, methodologies used, and quantitative results on energy output or system performance.

6.

Evaluate the quality of the studies: Methodological quality and risk of bias are assessed through predefined criteria, prioritizing peer-reviewed articles and excluding those with insufficient technical rigor or unrelated focuses.

7.

Analyze the data: The studies are categorized and analyzed based on their optimization approach (traditional vs. AI-based), the energy gains reported, and applicability to structural design improvements in PSS.

8.

Interpret the results: The findings are synthesized to identify trends, common strategies, and gaps in the literature, with a focus on improving the practical implementation of optimization in real-world PSS installations.

2.8. Optimization and Its Application in Photovoltaic Solar Energy

This study focuses on optimizing energy utilization in photovoltaic solar systems, specifically through the arrangement of panels, efficient use of the available area, and maximization of energy production. To address this objective, two methodological approaches are considered: traditional optimization methods and those based on artificial intelligence (AI). Both are discussed and critically evaluated below.

2.8.1. Traditional Optimization Methods

Traditional methods for photovoltaic system optimization rely on analytical and numerical techniques based on deterministic algorithms. These include linear and nonlinear programming for determining optimal configurations under geometric and electrical constraints, local search methods, such as gradient descent or Newton–Raphson, for adjusting tilt and orientation angles, and parametric modeling approaches, including sensitivity analysis and static simulations using specialized software like PVsyst [37] or Helioscope [38]. Although these methods offer structured and reproducible solutions, they are often limited when addressing non-convex problems or systems with multiple interdependent variables, as is common in real-world photovoltaic design. Their dependence on idealized assumptions can also reduce accuracy in complex environments.

2.8.2. Artificial Intelligence-Based Methods

AI-based methods, particularly metaheuristic algorithms and machine learning techniques, have gained popularity for their effectiveness in solving highly nonlinear, multivariable problems without relying on derivatives or continuity. Common approaches include genetic algorithms for optimizing panel orientation, quantity, and spatial distribution under environmental constraints, particle swarm optimization (PSO) and simulated annealing for jointly refining electrical, spatial, and energy production parameters, and artificial neural networks (ANNs) for predicting energy output across varying configurations and weather conditions. These techniques are well-suited to handling uncertainty, exploring large solution spaces, and adapting to real-world operational scenarios. Nevertheless, they demand careful parameter tuning, are computationally intensive, and may converge to suboptimal solutions if not appropriately implemented.

2.9. Critical Considerations

The choice between methods depends on the desired trade-off between complexity, accuracy, and available resources. While traditional methods offer speed and clarity in well-defined problems, AI-based approaches are more effective in dynamic contexts where simultaneous consideration of multiple factors (geometric, electrical, environmental, and economic) is necessary.

In this research, a hybrid approach is adopted, combining physical modeling and geometric principles from traditional methods with AI-based optimization algorithms. This integration aims to maximize the energy efficiency of the photovoltaic system under real-world conditions and site constraints.

2.10. Criteria for the Selection and Exclusion of Research

To ensure the transparency, rigor, and replicability of this systematic review, a clearly defined protocol was adopted. Systematic reviews require both methodological precision and consistent criteria to evaluate the relevance and quality of included studies. Despite their growing popularity, many reviews suffer from a lack of rigor, often due to the absence of standardized methodologies. This can lead to unreliable conclusions and diminished academic impact. Therefore, a strict set of inclusion and exclusion criteria was developed to maintain the focus and relevance of this research.

The current study aims to explore optimization techniques applied to photovoltaic solar energy systems, specifically in the context of structural or topological improvement. Based on this focus, the following exclusion criteria were applied:

1.

Keyword Focus: Studies must include terms related to solar photovoltaic systems and optimization techniques, such as genetic algorithms, particle swarm optimization, multi-objective or topology optimization.

2.

Publication Date Range: Only documents published between 2020 and 2025 were considered.

3.

Subject Area Filtering: Disciplines unrelated to engineering or computational optimization, such as neuroscience, dentistry, biochemistry, agriculture, materials science, chemistry, sociology, business, arts, economics, medicine, earth sciences, and physics, were excluded.

4.

Document Type: Review articles were excluded to prioritize original research. Patents were excluded as the focus of this review is on peer-reviewed scientific methodologies and results rather than proprietary implementations. However, selected document types, including conference papers, data papers, book chapters, books, discussion papers, editorials, and technical briefs, were allowed.

5.

Thematic Relevance: Articles focusing primarily on unrelated technical topics, such as MPPT, DC-DC converters, electric vehicles, battery systems, emissions, hybrid systems, cost analysis, fuel cells, or hydrogen production, were excluded.

6.

Infrastructure Relevance: Studies that did not explicitly address structural or topological aspects in PV systems, or instead emphasized control systems, inverters, or software development, were also excluded.

7.

Final Search Code: TITLE-ABS-KEY ((“solar photovoltaic system” OR “photovoltaic solar energy” OR “PV system”) AND (“optimization” OR “optimisation” OR “metaheuristic” OR “genetic algorithm” OR “particle swarm” OR “simulated annealing” OR “multi-objective optimization” OR “structural optimization” OR “topology optimization”)) AND PUBYEAR > 2020 AND PUBYEAR < 2025 AND (EXCLUDE (SUBJAREA, “NEUR”) OR EXCLUDE (SUBJAREA, “DENT”) OR EXCLUDE (SUBJAREA, “BIOC”) OR EXCLUDE (SUBJAREA, “AGRI”) OR EXCLUDE (SUBJAREA, “MATE”) OR EXCLUDE (SUBJAREA, “CENG”) OR EXCLUDE (SUBJAREA, “CHEM”) OR EXCLUDE (SUBJAREA, “SOCI”) OR EXCLUDE (SUBJAREA, “BUSI”) OR EXCLUDE (SUBJAREA, “ARTS”) OR EXCLUDE (SUBJAREA, “ECON”) OR EXCLUDE (SUBJAREA, “MEDI”) OR EXCLUDE (SUBJAREA, “EART”) OR EXCLUDE (SUBJAREA, “PHYS”)) AND (EXCLUDE (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “cr”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “bk”) OR LIMIT-TO (DOCTYPE, “dp”) OR LIMIT-TO (DOCTYPE, “er”) OR LIMIT-TO (DOCTYPE, “tb”) OR LIMIT-TO (DOCTYPE, “ed”)) AND (EXCLUDE (EXACTKEYWORD, “MPPT”) OR EXCLUDE (EXACTKEYWORD, “DC-DC Converter”) OR EXCLUDE (EXACTKEYWORD, “Electric Vehicle”) OR EXCLUDE (EXACTKEYWORD, “Fuzzy Logic”) OR EXCLUDE (EXACTKEYWORD, “Vehicle-to-grid”) OR EXCLUDE (EXACTKEYWORD, “Maximum Power Point Tracking (MPPT)”) OR EXCLUDE (EXACTKEYWORD, “Electric Load Flow”) OR EXCLUDE (EXACTKEYWORD, “Gas Emissions”) OR EXCLUDE (EXACTKEYWORD, “Maximum Power Point”) OR EXCLUDE (EXACTKEYWORD, “Battery Storage”) OR EXCLUDE (EXACTKEYWORD, “Battery Energy Storage Systems”) OR EXCLUDE (EXACTKEYWORD, “Electric Power Distribution”) OR EXCLUDE (EXACTKEYWORD, “Electric Power Generation”) OR EXCLUDE (EXACTKEYWORD, “Secondary Batteries”) OR EXCLUDE (EXACTKEYWORD, “Electric Loads”) OR EXCLUDE (EXACTKEYWORD, “Electric Vehicles”) OR EXCLUDE (EXACTKEYWORD, “Microgrids”) OR EXCLUDE (EXACTKEYWORD, “Greenhouse Gases”) OR EXCLUDE (EXACTKEYWORD, “DC-DC Converters”) OR EXCLUDE (EXACTKEYWORD, “Electric Power System Control”) OR EXCLUDE (EXACTKEYWORD, “Electric Inverters”) OR EXCLUDE (EXACTKEYWORD, “Electric Power Transmission Networks”) OR EXCLUDE (EXACTKEYWORD, “Photovoltaic Cells”) OR EXCLUDE (EXACTKEYWORD, “Maximum Power Point Tracking”) OR EXCLUDE (EXACTKEYWORD, “Fossil Fuels”) OR EXCLUDE (EXACTKEYWORD, “Carbon”) OR EXCLUDE (EXACTKEYWORD, “Electric Batteries”) OR EXCLUDE (EXACTKEYWORD, “Electric Power System Protection”) OR EXCLUDE (EXACTKEYWORD, “Microgrid”) OR EXCLUDE (EXACTKEYWORD, “Fossil Fuel Power Plants”) OR EXCLUDE (EXACTKEYWORD, “Wind Power”) OR EXCLUDE (EXACTKEYWORD, “Extraction”) OR EXCLUDE (EXACTKEYWORD, “Diodes”) OR EXCLUDE (EXACTKEYWORD, “Single-diode Models”) OR EXCLUDE (EXACTKEYWORD, “Single Diode Model”) OR EXCLUDE (EXACTKEYWORD, “Hybrid Systems”) OR EXCLUDE (EXACTKEYWORD, “Renewable Energy Resources”) OR EXCLUDE (EXACTKEYWORD, “Fault Detection”) OR EXCLUDE (EXACTKEYWORD, “Economic Analysis”) OR EXCLUDE (EXACTKEYWORD, “Cost Benefit Analysis”) OR EXCLUDE (EXACTKEYWORD, “Timing Circuits”) OR EXCLUDE (EXACTKEYWORD, “Intelligent Buildings”) OR EXCLUDE (EXACTKEYWORD, “Hydrogen Production”) OR EXCLUDE (EXACTKEYWORD, “Hybrid Energy System”) OR EXCLUDE (EXACTKEYWORD, “Fuel Cells”) OR EXCLUDE (EXACTKEYWORD, “Cost Reduction”) OR EXCLUDE (EXACTKEYWORD, “Cost Of Energies”) OR EXCLUDE (EXACTKEYWORD, “Hybrid System”) OR EXCLUDE (EXACTKEYWORD, “CO 2 Emission”) OR EXCLUDE (EXACTKEYWORD, “Cost Effectiveness”) OR EXCLUDE (EXACTKEYWORD, “OR EXCLUDE (EXACTKEYWORD, “Anomaly Detection”) OR EXCLUDE (EXACTKEYWORD, “Architectural Design”))

2.11. Analysis Guide for Systemic Review

The analysis followed a structured framework comprising three main phases: initial search, deepening of the search, and application of inclusion criteria. As shown in the diagram, the initial search retrieved 7183 articles from Scopus. After refining the search to the period 2020–2025 and applying thematic filters, 4122 articles remained. Subsequently, based on specific inclusion and exclusion criteria focused on engineering and energy-related studies, 314 articles were selected for detailed review.

This selection process ensures that the analyzed studies are closely aligned with the technological, structural, and operational factors influencing the efficiency and performance of photovoltaic solar systems (PSSs).

Specifically, technological innovations, such as improvements in solar cell materials (e.g., monocrystalline, polycrystalline silicon, and perovskites), advances in bifacial and concentrated photovoltaics, optimization in design and installation parameters (orientation, tilt, and tracking systems), operational strategies (predictive maintenance and material monitoring), and emerging trends (energy storage, smart grids, and advanced manufacturing), were considered critical for the final selection (see Figure 2).

Review articles were excluded due to their summarized nature, while other document types, such as conference papers and data papers, were included for their potential to provide specific primary evidence not found in previously reviewed literature. These documents were considered in the discussion stage to highlight the contributions of this article and its relevance to the state-of-the-art.

3. Results

In order to identify the main research trends in the field of photovoltaic systems, a bibliometric analysis was conducted using VOSviewer version 1.6.20. Based on a curated database of recent publications, co-occurrence maps of key terms were generated from article titles, abstracts, and keywords. Figure 3a,b display the resulting network visualization and density map, respectively.

The results reveal a distinctly multidisciplinary approach, where electrical engineering, computational optimization, and energy sustainability intersect. Among the most frequently occurring and relevant terms are “photovoltaic system”, ”solar energy”, ”optimization algorithms”, ”parameter extraction”, ”smart grid”, and ”efficiency”. Additionally, there is a strong representation of computational intelligence techniques, with prominent concepts including the “genetic algorithm”, ”particle swarm optimization”, ”artificial intelligence”, and ”forecasting”.

These findings suggest that optimization strategies in photovoltaic systems are focused both on precise electrical modeling using tools, such as the single-diode model and parameter estimation techniques, and on the integration of heuristic algorithms aimed at enhancing energy performance under dynamic conditions, such as partial shading or variable solar irradiation.

Furthermore, the analysis highlights a growing interest in coupling photovoltaic systems with smart grids and emerging predictive technologies, thereby expanding the scope toward more resilient, sustainable, and adaptable energy solutions.

3.1. Traditional Optimization Methods in Photovoltaic Solar Energy

This systematic review identified five traditional optimization methods with significant applications in PV energy systems: analytical methods, numerical methods, linear programming (LP), dynamic programming (DP), and gradient-based methods. Each technique presents distinct strengths and limitations, making them suitable for different stages in the PV system lifecycle, from preliminary design to real-time operation [39].

Figure 4 presents a flowchart summarizing traditional optimization methods applied to photovoltaic systems. Three main approaches are identified: linear and nonlinear programming, used to determine optimal configurations under geometric and electrical constraints; local search methods, such as gradient descent and Newton–Raphson, commonly employed for adjusting tilt and orientation angles; parametric modeling, which involves simulations using tools like PVsyst and Helioscope. While these techniques provide structured and reproducible solutions, they exhibit limitations when addressing non-convex problems or systems with multiple interdependent variable conditions frequently encountered in real-world applications.

Analytical Methods

Analytical methods rely on closed-form equations and functional relationships between system variables. Due to their mathematical simplicity, they are especially useful in the early design stage of PV installations. For instance, they are applied to calculate the optimal tilt angle of PV panels based on latitude and seasonal solar paths. Moreover, these methods allow theoretical estimation of a system’s energy potential under ideal conditions. In rural electrification or prefeasibility studies, where computational resources or detailed data may be limited, analytical methods are valuable for initial performance assessments [40,41].

Application example: The optimal tilt angle for photovoltaic panels in mountainous regions was calculated to maximize annual solar irradiance, taking into account the complex topography and seasonal variations in solar incidence. Additionally, the annual energy output of a rooftop photovoltaic system with fixed orientation was estimated to evaluate its long-term performance under site-specific climatic conditions. The practical applicability is high during conceptual design and limited under real operational conditions due to simplifying assumptions.

3.2. Numerical Methods

Numerical techniques solve differential or nonlinear equations representing PV system behavior under real and variable conditions. These methods are widely used for hourly or sub-hourly energy simulations, including temperature effects, partial shading, inverter efficiency, and dynamic environmental variations. They are particularly applicable when high-resolution meteorological data are available, enabling accurate modeling of generation-demand profiles [42,43].

Application example: Simulating the energy yield of a grid-tied PV system based on hourly irradiance and temperature profiles enables accurate performance assessment under dynamic environmental conditions. Additionally, modeling the thermal behavior of bifacial PV panels in urban settings provides insights into heat dissipation, ambient interactions, and their influence on overall system efficiency.

The practical applicability is high for detailed design and performance evaluation and computationally intensive and data-dependent.

3.3. Linear Programming

Linear programming is widely adopted for strategic and operational planning in PV systems. It addresses resource allocation, energy management, and optimal sizing under technical and economic constraints. Its most prominent use is in optimal system sizing, minimizing the levelized cost of energy (LCOE) by considering demand profiles, solar generation, and electricity tariffs. In systems with storage, LP optimizes battery charging and discharging schedules to maximize self-consumption or reduce grid dependency [44,45].

Application example: Mixed integer linear programming (MILP)-based optimization has been widely applied for the optimal sizing of photovoltaic panels, inverters, and battery storage in rural microgrids, ensuring cost-effective and reliable energy supply under resource constraints. Additionally, MILP models are used to coordinate the operation of household PV systems and electric vehicles within smart grids, allowing for efficient energy dispatch, demand response, and grid stability while integrating renewable generation.

The practical applicability is very high in energy planning and integration and limited for highly nonlinear or uncertain systems.

3.4. Dynamic Programming

Dynamic programming is effective for multi-stage decision-making problems, especially in systems with energy storage or dynamic pricing. It determines optimal operation policies across time horizons, accounting for future states such as battery charge or solar availability. Its recursive structure suits hybrid PV systems where current decisions impact future outcomes, like minimizing diesel usage in PV–diesel–battery systems [46].

Application example: Hourly battery charge and discharge control strategies are implemented to maximize economic benefits under time-of-use (TOU) electricity tariffs by shifting energy consumption to periods with lower costs. Additionally, energy management in hybrid systems is designed to prioritize the utilization of renewable sources, ensuring efficient resource allocation while minimizing reliance on non-renewable energy inputs.

The practical applicability is high for systems with temporal variability and storage and computationally intensive with scalability challenges.

3.5. Gradient-Based Methods

Gradient methods solve continuous optimization problems in PV systems, particularly for parameter tuning, model calibration, or maximum power point tracking (MPPT). They are ideal for smooth, differentiable functions and are widely used in adaptive control and power electronics [47].

Application example:

• A gradient descent MPPT algorithm in solar inverters.
• Calibrating thermal models of PV modules with stochastic gradient descent.

The practical applicability is high in embedded systems and controller design and limited for non-differentiable or non-convex problems.

3.6. Critical Comparison of Methods

Given the diversity of PV system configurations, operational contexts, and optimization objectives, it is crucial to compare traditional optimization methods not only based on their mathematical foundations but also on their practical relevance. Each method brings unique advantages and constraints that determine its suitability for a particular application domain, ranging from preliminary system design to real-time control in hybrid microgrids [48].

This subsection presents a structured comparison among five traditional optimization techniques, highlighting their principal applications, real-world examples, key operational strengths, and major limitations. By analyzing these dimensions, researchers and engineers can make informed decisions about the appropriate method for specific PV system challenges.

Table 1. Comparison of traditional optimization methods for photovoltaic systems.

Method	Main Application	Use Case	Advantages	Limitations	Typical Use Stage
Analytical Methods	Pre-design and estimation	Optimal tilt angle calculation	Low computational cost, easy to interpret	Oversimplified assumptions, limited accuracy	High (conceptual design)
Numerical Methods	System modeling and simulation	Hourly PV energy yield estimation	High accuracy, good fit to real conditions	Requires detailed data, not always optimal	High (performance analysis)
Linear Programming	Component sizing and energy dispatch	MILP-based microgrid optimization	Scalable, guarantees optimality in linear problems	Only suitable for linear systems	Very high (planning and design)
Dynamic Programming	Energy storage scheduling	Battery control under TOU tariffs	Handles multi-stage decisions and uncertainties	High memory and computational demand	High (temporal control and storage)
Gradient-Based Methods	Parameter tuning and control	MPPT using P&O or Incremental Conductance	Fast convergence, suitable for embedded systems	Sensitive to local optima, requires gradient information	High (real-time control)

summarizes this critical comparison and serves as a reference framework for selecting the most adequate optimization approach based on technical requirements, data availability, and computational resources.

4. Optimization Methods for Solar Photovoltaic Systems

4.1. Context: The Imperative for Solar PV Optimization

The global energy landscape is undergoing a profound transformation, driven by the urgent need to mitigate climate change and transition towards sustainable energy sources. Solar PV technology stands at the forefront of this transition, experiencing exponential growth in installed capacity and becoming an increasingly vital component of the world’s electricity mix [49]. Global PV capacity reached nearly 1200 Gigawatt by the end of 2022, with continued rapid expansion anticipated as nations pursue decarbonization goals [50]. However, harnessing solar energy effectively presents significant challenges (Figure 5). The inherent intermittency of solar irradiance, heavily influenced by unpredictable weather patterns, geographical location, and time of day, complicates the reliable integration of PV power into the grid [51]. Furthermore, various factors, including partial shading, component degradation, operational faults, and suboptimal system design, can lead to substantial energy losses, thereby reducing overall system efficiency and impacting economic viability [52]. Thus, a key challenge is to maximize the use of available area and energy yield without compromising efficiency (for instance, by avoiding excessive shading or losses). Consequently, optimizing PV systems across their entire lifecycle, from initial design and layout to real-time operation and predictive maintenance, is crucial for maximizing energy harvest, ensuring grid stability, enhancing reliability, and ultimately improving the return on investment for solar energy projects.

4.2. Metaheuristic Approaches for PV System Optimization

Metaheuristic optimization algorithms represent a class of stochastic, iterative search strategies inspired by natural phenomena, social behaviors, or physical processes [53]. Unlike traditional deterministic methods, which often rely on gradient information and can be susceptible to getting trapped in local optima, metaheuristics employ probabilistic rules to explore the search space more broadly, making them particularly well-suited for complex, nonlinear, multi-modal optimization problems frequently encountered in PV systems. These algorithms are generally gradient-free, meaning they do not require derivative information of the objective function, enhancing their applicability to problems where such information is unavailable or computationally expensive to obtain [54].

Common classifications of metaheuristics include the following:

• Evolutionary Algorithms (EAs): Inspired by biological evolution, such as genetic algorithms (GA) [55].
• Swarm Intelligence (SI) Algorithms: Mimicking the collective behavior of social insects or animal groups, such as particle swarm optimization (PSO) [56,57], ant colony optimization (ACO) [58], artificial bee colony (ABC) [59], cuckoo search (CS) [60], gray wolf optimization (GWO) [61], and the firefly algorithm (FA) [62].
• Physics-Based Algorithms: Drawing analogies from physical laws, like simulated annealing (SA) [63].
• Human-Based Algorithms: Modeling human interactions or learning processes, such as teaching–learning-based optimization (TLBO) [64].

The field has witnessed a rapid proliferation of new metaheuristic algorithms, with over 500 proposed in recent decades and a significant number emerging within the 2019–2024 timeframe [65]. This abundance presents a challenge in selecting the most appropriate algorithm for a specific PV optimization task. Reviews focusing on PV applications often highlight the frequent use of algorithms like PSO, GA, ACO, GWO, CS, and ABC, particularly for MPPT challenges.

4.3. PV Layout and Installation Area Optimization

One major application of metaheuristics is optimizing the placement and layout of PV modules on available land or rooftops. By intelligently arranging panels (and choosing tilt angles, row spacing, etc.), one can maximize the total installed capacity and energy yield from a given area while minimizing shading and losses. For instance, Barbón et al. (2022) developed a comprehensive algorithm to optimally deploy PV modules on irregularly shaped flat rooftops with obstacles. Their method accounted for roof shape, shading between rows, required maintenance clearances, and multiple module sizes. The optimized layout achieved 35% greater PV module area and 25–28% higher annual energy output compared to conventional layout guidelines [66]. This improvement underscores how metaheuristic or heuristic optimization can extract significantly more value from the same rooftop area (and even lower the levelized cost of energy).

At the utility scale, Ren et al. (2023) applied a genetic algorithm to pack PV panels on numerous city rooftops, followed by an integer programming step for planning, in a high-density urban case (Hong Kong). The GA optimized panel placement on each irregular roof to maximize area utilization, then an optimization selected which roofs to prioritize, aiming to minimize the overall levelized cost of electricity. This two-step metaheuristic approach yielded a 48% LCOE reduction in the case study, and about 15% lower cost than a rule-based deployment, by better utilizing high-yield roof areas [67]. Such work demonstrates the global planning of distributed PV using metaheuristics, enabling large-scale PV adoption even in space-constrained cities.

Other studies have combined computational geometry with GAs; for example, Aslani and Seipel (2023) used light detection and ranging (LiDAR) data to identify viable roof surfaces and then a GA to determine optimal panel layouts on each segment, effectively solving a complex packing problem for rooftop PV [68]. These metaheuristic layout optimizations often consider multiple objectives (maximize capacity, maximize energy, and minimize shading or cost), yielding Pareto-optimal designs that balance area use and efficiency. Optimization for energy conservation in buildings could implicitly involve PV placement decisions [69], and the use of ACO for a “Technology Packaging Problem” that involves selecting and combining technologies (potentially including PV placement) to minimize cost and maximize CO2 reduction [70], are other use cases. The use of geospatial data and optimization for placing other infrastructure [71] also suggests analogous applicability for PV layout.

4.4. Partial Shading Mitigation and MPPT Optimization

Another critical domain is maximizing energy production under partial shading conditions, which can drastically reduce PV efficiency if not managed. Metaheuristics have been extensively used to improve maximum power point tracking (MPPT) algorithms for PV arrays that exhibit multiple power peaks under shading. Traditional MPPT methods (like perturb-and-observe or incremental conductance) can get stuck in local maxima, whereas algorithms like PSO, GA, ACO, etc., are better at global optimization of the PV power output surface. A host of recent works propose metaheuristic MPPT controllers to find the true global peak quickly [72]. For example, particle swarm optimization has been adapted to reliably track the global MPP under any shading pattern (outperforming basic algorithms, albeit sometimes with a slightly slower convergence) [73]. Similarly, ACO has been tailored for MPPT: one study presented an ACO-based controller that could dynamically adjust for shading and achieved fast convergence to the optimal power point [74]. These approaches ensure the PV system always operates at its highest possible power output, thereby maximizing energy yield without hardware changes.

Beyond tracking, metaheuristics have enabled reconfiguring PV array connections in real time to mitigate shading losses. Reconfiguration involves changing the series/parallel wiring of panels to redistribute the shade impact, which is an inherently combinatorial optimization problem. Durango-Flórez et al. (2022) used a GA to search for the best interconnection pattern of modules (e.g., total-cross-tied, bridge-linked, or irregular custom topologies) for whatever shading pattern is present [72]. Their GA-based reconfiguration finds the wiring that yields the highest power output under the given conditions, significantly outperforming fixed configurations. In one example, an irregular GA-optimized configuration delivered 646 W, versus 641 W for a standard total-cross-tied (TCT) layout and only 559 W for a simple series-parallel layout under the same partial shading. The GA could find this optimal setup much faster than the brute-force search of all wiring options. Other researchers have explored alternative metaheuristics (e.g., gray wolf optimizer, marine predators’ algorithm, and honeybee mating) for the reconfiguration problem [72]. The common outcome is that smart reconfiguration can recoup a large fraction of energy lost to shading, ensuring high efficiency is maintained even as more panels are packed into an area. By using metaheuristics, the reconfiguration can be solved in near-real time whenever shading patterns change, thus maximizing energy yield continuously without manual intervention.

One review classified and analyzed 40 different metaheuristic MPPT algorithms, categorizing them based on their inspiration (physics, biology, sociology, and human behavior) [75]. Performance evaluation typically focuses on the following key metrics:

• Tracking Speed/Convergence Time: How quickly the algorithm locates the global MPP (GMPP), especially after a change in conditions [73].
• Tracking Accuracy/Efficiency: How close the algorithm operates to the true GMPP [73].
• Steady-State Oscillations: The magnitude of power fluctuations around the MPP once convergence is achieved [76].
• Robustness: The ability to consistently find the GMPP under various dynamic shading patterns and changing environmental conditions [75].

Extensive research in this area underscores that overcoming environmental variability, particularly under partial shading conditions, is crucial for maintaining high energy yield and operational efficiency, making metaheuristic MPPT a critical technology for modern PV systems.

4.5. Tilt and Orientation Optimization for Maximum Yield

The tilt angle and azimuth orientation of PV panels strongly affect energy production. Many recent studies use optimization algorithms to find the optimal tilt/orientation that maximizes solar exposure for a given location or season. While one can calculate optimal fixed tilt from latitude or use brute-force search, metaheuristics and machine learning have been applied for more complex scenarios (e.g., considering reflectors, bifacial panels, or time-varying tilt). Abdelaal et al. (2024) introduced the gorilla troop algorithm (GTA), a novel swarm metaheuristic, to identify the optimal tilt angle (OTA) for PV panels. Using 181 days of experimental data (panels at different tilts), GTA converged on an optimal tilt of 28.45°, which matched the best experimental angle and was 59.3% higher in captured solar radiation than a suboptimal 50° tilt. Notably, the GTA’s result agreed with classic algorithms: when they compared GTA with nine other state-of-the-art metaheuristics (GA, PSO, harmony search, ACO, etc.), all algorithms found the same optimal tilt (28.45°), validating the solution quality [77]. This indicates that for such single-variable optimizations, many metaheuristics can reliably find the global optimum, where the differences lie in convergence speed and robustness. Nonetheless, the exercise shows metaheuristics can handle experimental, non-ideal data to tune PV tilt for maximum yield.

In practice, multi-objective formulations are sometimes used, for example, optimizing tilt angles to maximize energy while also considering aesthetic or wind load constraints. Metaheuristic approaches (like GA) have been used to schedule seasonal tilt adjustments or to design orientations for multi-panel arrays such that each panel’s output is optimized without excessively shading others. By using these algorithms, systems can achieve near-optimal irradiation capture year-round, improving overall efficiency. In summary, metaheuristics have proven effective in fine-tuning installation geometry (layout, tilt, and orientation) to squeeze out the most energy from each PV module given the space and environment, maximizing production without adding more panels.

4.6. Other Metaheuristic Applications

Beyond layout and MPPT, metaheuristic optimization has been applied to various other PV system aspects. A notable area is component sizing and electrical configuration. For instance, genetic algorithms have been used to optimize the placement of inverters and the routing of cables in large PV plants to minimize resistive losses [78]. One such GA-based internal network optimization achieved a 75% reduction in power losses in the DC cabling and a 25% improvement in net present value of the project, by finding an optimal wiring layout versus the initial design. Metaheuristics have also been employed in sizing hybrid PV systems (PV + storage or PV + wind), where multiple objectives (cost, autonomy, and fuel savings) must be balanced; hybrid algorithms (e.g., PSO combined with simulated annealing) often perform well in these multi-dimensional searches [79,80]. The objective is typically multi-faceted: determining the optimal capacities of various components (PV array size, battery bank capacity, and converter ratings) to reliably meet a given load demand while minimizing economic costs and potentially environmental impact [4]. Additionally, some researchers use modern bio-inspired algorithms (firefly, cuckoo search, dragonfly, etc.) to solve PV model parameter identification problems [73,81], which helps accurately predict performance and thus optimize system settings. The trend in recent years has been to develop hybrid metaheuristic techniques that combine the strengths of multiple algorithms, for example, PSO-assisted GA or fuzzy-augmented PSO for faster MPPT [73]. These hybrid methods often yield faster convergence or more reliable performance under diverse conditions.

Studies have utilized algorithms like genetic algorithms (GAs) and the non-dominated sorting genetic algorithm III (NSGA-III) for multi-objective optimization in energy system design. Software tools like HOMER (Hybrid Optimization of Multiple Energy Resources) Pro, which often incorporate underlying optimization algorithms (including metaheuristics), are widely used for simulating and optimizing the design of hybrid renewable energy systems, including those with significant PV components. These tools facilitate techno-economic analysis by evaluating numerous configurations based on resource availability, load profiles, component costs, and economic parameters [82].

Beyond design, metaheuristics play a role in optimizing the real-time operation and control of PV systems, particularly when integrated with energy storage, controllable loads, or the wider power grid (microgrids or utility grids) [52]. Application reviewed include the following:

• Optimizing Power Flow: Managing the flow of energy in transmission and distribution networks incorporating PV [83].
• Reactive Power Dispatch and Volt/Var Control: Optimizing voltage profiles and minimizing losses in distribution systems with PV penetration [83].
• Economic and Emission Dispatch: Scheduling the output of generation units (including PV and storage) to meet demand at minimum cost and/or minimum emissions [83].
• Optimal Placement and Sizing of Distributed Generation (DG): Determining the best locations and capacities for PV systems within a distribution network [83].
• Energy Management Systems (EMS): Developing optimal operating strategies for hybrid systems or microgrids, coordinating PV generation, battery charging/discharging, and load management to achieve specific goals (e.g., cost minimization and self-sufficiency maximization). Advanced optimization algorithms, such as particle swarm optimization (PSO) and genetic algorithms, have been employed to stabilize output power and enhance system reliability in PV systems with battery storage [52].

Overall, metaheuristic optimization has penetrated almost all aspects of PV system engineering, providing flexible tools to enhance energy output and efficiency in ways classical methods cannot easily achieve (

Table 2. Compact summary of metaheuristic algorithms in PV optimization (2020–2025).

Algorithm	Application(s)	Objective(s)	Strengths	Limitations
PSO	MPPT, Sizing, ML tuning	GMPP, LCOE, hyperparams	Fast, nonlinear capable, widely used	Premature conv., param. sens., slow end-phase
GA	Sizing, MPPT, ML tuning	LCOE, reliability, ML opt.	Robust, flexible, cross-domain use	Slow MPPT, complex ops
ACO	MPPT, Mgmt., Packaging	GMPP, CO2/Cost min.	Fast conv., simple logic	Needs complex est. for MPPT
GWO	MPPT	GMPP	Stable, no oscill., fast conv.	Cost ↑ with search space size
ABC	MPPT, ML tuning	GMPP, ML opt.	Few params, robust, init-indep.	May miss GMPP, slow conv.
CS	MPPT	GMPP	Few params, solid conv.	Long calc., perf. loss risk
FA	MPPT	GMPP	PSC-capable tracking	Param. tuning (attractiveness) critical
SA	General opt.	Cost/Perf. opt.	Simple, well-studied	Sensitive to cooling sched.
TLBO	MPPT	GMPP	Easy impl., no control params	Few PV-specific validations
Hybrid (e.g., GWO-P&O)	MPPT	GMPP	High perf., speed, synergy	High complexity, comp. load

).

4.7. Performance Analysis: Effectiveness, Complexity, Advantages, and Limitations

Advantages: The primary advantage of these optimization methods lies in their gradient-free nature, which makes them applicable to problems where derivative information is unavailable or impractical to obtain. This characteristic is particularly beneficial for the complex, nonlinear dynamics inherent in PV systems. Their capacity for global search allows them to more effectively escape local optima and identify global or near-global solutions, a significant benefit for multi-modal problems, such as perturb and observe (P&O) maximum power point tracking (MPPT). Furthermore, their inherent flexibility enables adaptation to diverse problem formulations and constraints.

Limitations and Challenges: Despite these advantages, several limitations and challenges must be acknowledged. As stochastic methods, they offer no guarantee of finding the absolute global optimum within a finite timeframe; performance is typically validated empirically through comparative analysis rather than by mathematical proof of optimality. This stochasticity also means that results can vary between runs, necessitating multiple trials for a robust assessment. A significant challenge is their potential for high computational complexity. Population-based algorithms, for instance, often require numerous function evaluations per iteration, which can impede their real-time applicability in certain control tasks. Convergence behavior can also be inconsistent, with some algorithms susceptible to premature convergence to suboptimal solutions or exhibiting slow convergence rates. Moreover, their performance is often highly sensitive to parameter tuning; the selection of algorithm-specific parameters like population size or mutation rate requires careful, problem-dependent calibration, which can be a time-consuming process. Finally, the lack of standardized benchmarks and validation protocols complicates rigorous and objective comparisons among the vast array of available algorithms.

4.8. Machine Learning and Deep Learning Techniques in PV Optimization

ML and DL are catalyzing a paradigm shift toward data-driven optimization and analysis in PV systems. These techniques employ algorithms that learn complex patterns, relationships, and dynamics directly from historical and real-time operational data, circumventing the need for explicit mathematical models of system behavior. DL, a specialized subset of ML, utilizes artificial neural networks with deep architectures to learn hierarchical data representations, proving particularly effective for tasks involving large datasets and intricate patterns, such as time-series analysis or image recognition [49,84]. Within the PV domain, ML/DL techniques are broadly applied to predict or select optimal system configurations, forecast power output for improved grid planning, and control PV systems in real-time to enhance efficiency and energy yield. Recent studies highlight that integrating these data-driven models with physics-based optimization can yield significant performance gains.

The application of ML/DL in PV optimization spans a diverse range of techniques. Supervised Learning, which relies on labeled input–output data pairs, is fundamental to many applications. Its regression branch is used for predicting continuous values, such as energy yield or power output, employing algorithms, like linear regression, and advanced ensemble methods, including random forest (RF), gradient boosting (GB), XGBoost, LightGBM, and CatBoost, as well as Gaussian process regression (GPR) [51,85,86]. The classification branch, conversely, assigns data points to predefined categories, such as identifying specific fault types, using algorithms like support vector machines (SVM), K-nearest neighbors (KNN), and decision trees (DT) [49,51,87].

In scenarios where labeled data are unavailable, unsupervised Learning is employed to uncover hidden patterns. Clustering algorithms, such as K-means, can group similar data points to identify distinct atmospheric condition clusters relevant to MPPT control [88]. Furthermore, techniques for dimensionality reduction and feature extraction, including autoencoders like stacked sparse autoencoders (SSAE), are used to distill relevant features from high-dimensional data, which is a critical step in applications like fault diagnosis [89].

Deep Learning models, characterized by their deep neural network architectures, represent the state of the art for many complex tasks. ANNs and multi-layer perceptrons (MLPs) are widely used for forecasting, MPPT, parameter extraction, and classification [49,90]. For sequential data like time series, recurrent neural networks (RNNs) and their advanced variants, such as long short-term memory (LSTM), gated recurrent units (GRU), and their bidirectional versions (e.g., Bi GRU), are extensively applied in forecasting and fault diagnosis [49]. Convolutional neural networks (CNNs), which excel at processing grid-like data structures like images, are utilized in fault diagnosis and are being explored for forecasting based on I–V curve analysis [84]. Emerging techniques include graph neural networks (GNNs) for modeling spatiotemporal characteristics in networked PV systems and reinforcement learning (RL), which learns optimal control policies through environmental interaction and shows promise for PV energy management [84,91].

Reflecting a trend towards synergistic designs, hybrid ML/DL models are increasingly common, combining different architectures to leverage their respective strengths. Examples of such hybrids include CNN-BiGRU, CNN-ResGRU, CNN-LSTM, SSAE-optimized MLPs, and fusions of traditional ML with metaheuristics like ANN-GA and RF-PSO [89,92,93]. The pervasiveness of these advanced techniques across the PV system lifecycle, from design and manufacturing to operation and maintenance, signals a fundamental shift towards integrating data-driven intelligence into modern solar energy engineering practices, offering powerful tools to manage the inherent complexity and variability of PV systems.

4.9. Application in Spatial Optimization and Energy Prediction for Design

One emerging use of ML is in the assessment of solar potential and layout planning using data-driven analysis of sites. For example, in urban deployment, deep learning can process complex geospatial data to identify where PV panels can be installed. Aslani et al. (2022) developed a pipeline where PointNet++ (a deep neural network for 3D point clouds) was trained to extract building rooftops from LiDAR scans. The algorithm then segmented each roof into planar surfaces and identified “utilizable areas” that are large enough and sufficiently unshaded for PV modules [94]. This automated ML-driven analysis can rapidly evaluate a city’s rooftops and produce candidate PV installation areas, far faster than manual surveying. In their results, the method accurately detected available roof area for PV, accounting for tilt, orientation, and obstructions, which is essential for scaling up rooftop PV deployment.

Machine learning has also been applied to building-integrated PV (BIPV) design. Data-driven optimization frameworks use ML to evaluate many design variants (e.g., different PV facade configurations) against performance criteria. A novel approach termed ”BIM-AITIZATION” integrates photogrammetry (to capture building geometry as point clouds), building information modeling (BIM) data, and DL techniques (using assembled meteorological and BIM datasets) to automate and enhance the accuracy of BIPV energy forecasts [95]. Comparative studies suggest this integrated approach can surpass the accuracy and precision of existing BIPV software tools.

Machine Learning can enhance the design and optimization of innovative systems like ”all-PV blended systems”, which combine conventional rooftop panels with BIPV and transparent solar windows. While HOMER Pro might perform the core sizing optimization, ML-based predictions of component performance and load profiles can refine the inputs and potentially guide the optimization strategy [96]. ML integration via IoT for real-time monitoring and control is also proposed to further boost the efficiency of such systems.

4.10. Application in Intelligent MPPT Strategies

ML/DL techniques offer intelligent alternatives or enhancements to conventional and metaheuristic MPPT algorithms, aiming for faster, more accurate tracking, especially under dynamic conditions and partial shading.

In Learning Control Policies, supervised learning models like ANNs and intelligent controllers like fuzzy logic controllers (FLCs) can be trained to learn the complex, nonlinear mapping between PV operating conditions (irradiance, temperature, voltage, and current) and the optimal operating point (MPP), often represented by the required duty cycle for the DC–DC converter. ANNs can potentially achieve high accuracy but require extensive, system-specific training data and may need retraining over time. FLCs can handle imprecise inputs without a precise system model but rely heavily on expert knowledge for designing rules and membership functions [76,97].

For operating zone identification, unsupervised learning, specifically K-means clustering, has been used to analyze historical atmospheric data (irradiance and temperature) and identify distinct operating clusters or zones. By determining which cluster current conditions belong to, the search space for subsequent MPPT algorithms (like P&O or PSO applied locally) can be significantly reduced, potentially improving convergence speed and efficiency. Guessoum et al. reported a DC/DC converter efficiency of 97.5% and faster settling times for this K-means-based approach compared to local P&O and PSO under tested conditions [88].

Recognizing the limitations of individual methods, hybrid techniques combining ML with conventional or metaheuristic algorithms are gaining traction. Examples include adaptive neuro-fuzzy inference systems (ANFIS), Fuzzy-PSO (FPSO), hill climbing-ANFIS (HC-ANFIS), ANN-GA, and RF-PSO. These aim to leverage the learning capabilities of ML with the robustness or search capabilities of other methods, offering improved performance, particularly under challenging PSC scenarios [93].

Figure 6 shows an intelligent MPPT system for partial shading, including a Block diagram of an intelligent maximum power point tracking (MPPT) system for partial shading. The system utilizes inputs such as solar irradiance, temperature, and PV array voltage–current (V–I) measurements, which are processed by an advanced MPPT controller. This controller, which can employ algorithms like particle swarm optimization (PSO), genetic algorithms (GAs), or be artificial neural network (ANN)-based, generates a duty cycle signal for the DC–DC converter to optimize power transfer from the PV array to the load or grid, especially under partial shading conditions.

4.11. Predictive Optimization and Control

ML and DL have significantly enhanced PV system optimization by enabling accurate performance prediction and proactive operational adjustments. For instance, Khan et al. (2022) used a stacking ensemble ML model combining CatBoost, XGBoost, and random forest to predict power output for various panel tilts and orientations, achieving high accuracy ($R^2\approx 0.86$, MAPE 2.5% on test data) [98]. This approach enables dynamic recommendations for optimal panel angles, improving energy capture beyond traditional static formulas by capturing complex environmental effects.

Deep learning also advances real-time control in PV systems. Umasankar et al. demonstrated an ANN-based MPPT controller enhanced with a deep learning layer that adaptively adjusts tracking strategies, achieving about 86.4% MPPT efficiency under rapid irradiance changes and reducing inverter harmonic distortion by approximately 40% [99]. This integrated approach boosts both power extraction and overall power quality compared to conventional methods.

ML-assisted PV array reconfiguration is another promising field. Kamal et al. implemented an ANN-based topology selector that predicts the best wiring layout for varying shading, allowing near-instantaneous reconfiguration with high accuracy [100]. Moreover, deep learning models, such as CNN-LSTM hybrids, have significantly improved solar power forecasting, facilitating better grid integration and storage management, as reviewed by Hosseini et al. [101]. These advances collectively support maximizing PV system efficiency and scalability.

4.12. Application in Fault Detection and Diagnosis for Efficiency Maintenance

Faults within PV systems, such as open circuits, short circuits, module degradation, bypass diode failures, partial shading, and soiling, can significantly degrade performance, reduce energy yield, compromise safety, and shorten system lifespan. Consequently, the early and accurate detection and diagnosis of these faults are crucial for maintaining high system efficiency and reliability. ML and DL techniques have emerged as powerful tools for automated, real-time fault detection and diagnosis (FDD), often outperforming traditional methods that rely on visual inspection, infrared thermography (IRT), or simple thresholding of electrical parameters [92]. These advanced algorithms are trained to identify subtle deviations from normal operating behavior that are indicative of specific fault types.

The application of ML/DL in PV FDD leverages a wide array of algorithms and data sources. The models are typically trained using electrical measurements (e.g., voltage, current, power, and I–V curves) combined with environmental data (e.g., irradiance and temperature) to effectively distinguish genuine faults from normal variations caused by changing weather conditions. Some advanced approaches also integrate IRT data with DL, though deployment challenges persist [92]. The algorithms employed range from supervised and unsupervised methods, including KNN, SVM, and RF, to more complex neural network architectures. Artificial neural networks (ANNs/MLP), probabilistic neural networks (PNNs), and autoencoders are commonly used for anomaly detection and classification. Furthermore, CNNs are highly effective for analyzing I–V curve shapes or thermal images, while RNNs and their variants, like LSTM and GRU, excel at processing time-series data [92].

A prominent trend in the field is the development of sophisticated hybrid models that combine different architectures to achieve state-of-the-art performance. These models, such as a CNN combined with an RNN variant (e.g., CNN-BiGRU and CNN-ResGRU) or an autoencoder paired with a classifier (e.g., SSAE-OMLP), leverage the complementary strengths of each component for enhanced feature extraction and classification. The performance of these models is notable; for instance, a CNN-BiGRU model has demonstrated a 99.46% detection accuracy, while an SSAE-OMLP model reported 99.82% accuracy, 99.7% precision, 99.4% sensitivity, and 100% specificity [89,92]. Research confirms that these advanced ML/DL models can successfully detect and classify a variety of critical faults, including open circuits, short circuits at both module and string levels, partial shading, degradation, and faulty bypass diodes [79]. This trend towards complex, often hybrid, deep learning architectures for FDD underscores the intricacy of accurately identifying diverse fault signatures under varying operating conditions and continues to push the boundaries of diagnostic performance. Figure 7 shows an ML-based PV fault detection and spatial optimization system.

4.13. Performance Analysis: Effectiveness, Complexity, Data Needs, Advantages, and Limitations

ML and DL techniques have demonstrated significant effectiveness across the spectrum of PV optimization tasks, often achieving higher accuracy and superior performance compared to traditional methods, particularly for forecasting and fault diagnosis. A primary advantage is their intrinsic ability to model the complex and nonlinear relationships inherent in PV systems without relying on explicit physical equations. This capability directly contributes to their high accuracy, enabling state-of-the-art performance in prediction and classification tasks. Furthermore, these methods facilitate a high degree of automation, empowering systems with capabilities for automated feature extraction (a notable strength of DL) and real-time, data-driven decision-making.

Despite these strengths, several significant limitations and challenges must be addressed. The most critical of these is data dependency, as the performance of ML/DL models is fundamentally reliant on the availability of large volumes of high-quality, representative training data. Practical hurdles such as data scarcity, inconsistencies, missing values, and noise can severely impede model efficacy, making data preprocessing and feature engineering crucial and demanding steps. This reliance on data quality and quantity represents a major practical bottleneck for the effective deployment of sophisticated models. Concurrently, computational complexity is a significant concern. Training deep learning models can be an expensive and time-consuming process, requiring substantial hardware resources, while model inference time can also pose a constraint for real-time control applications.

Furthermore, ensuring that models can generalize well to unseen data and maintain robustness across diverse real-world operating conditions, including different geographical sites, weather patterns, and equipment aging, is a formidable challenge. This often necessitates periodic retraining or the implementation of adaptive learning capabilities. Another major barrier, particularly for complex models like deep neural networks, is the issue of interpretability, often referred to as the “black box” problem. The opaque nature of their internal decision-making processes can make it difficult to ascertain why a specific prediction or classification was made, which can hinder trust and adoption in critical applications. Finally, model performance is highly sensitive to hyperparameter tuning. The process of identifying the optimal set of hyperparameters, such as learning rate or network architecture, typically requires extensive experimentation or the use of sophisticated optimization techniques.

Addressing these challenges, particularly regarding data availability, model robustness, computational efficiency, and interpretability, is essential for translating the theoretical potential of ML/DL into widespread, reliable applications in the PV industry (

Table 3. Compact summary of ML/DL techniques in PV optimization (2020–2025).

Technique	Application(s)	Objective(s)	Strengths	Limitations
ANN/MLP	MPPT, Forecasting, FDD	Yield/load pred., MPP, fault classif.	Nonlinear modeling, broad use	Needs large data, retraining, opaque logic
SVM	Forecasting, FDD	Classification, regression	Effective in high dimensions	Kernel choice critical, less scalable
RF	Forecasting, FDD	Yield/load/fault prediction	Robust, nonlinear, ensemble power	Slower, less transparent
KNN	Forecasting, FDD	Instance-based prediction	Simple, fast inference	Scaling sensitive, low in high dims
Boosting (XGB/LGBM)	Forecasting	Accurate yield/load pred.	High accuracy, scalable	Prone to overfit, needs tuning
CNN	FDD, Forecasting	Image/I–V curve analysis	Top image feature extractor	Needs large labeled data, GPU cost
RNN/LSTM/GRU	Forecasting, FDD	Time-series pred., temporal faults	Good temporal dynamics	Training complexity, vanishing grads
Autoencoders	FDD	Feature extraction, anomaly detect.	Unsupervised, reduces dims	Network-dependent performance
K-Means	MPPT	Cluster env. states	Simplifies MPPT search	Sensitive to init., assumes shape
Hybrid DL	FDD	Fault classification	Merges DL strengths, ↑ accuracy	Complex, high resource use
Hybrid ML-Meta.	MPPT	Robust MPPT tracking	Boosts stability/perf.	More algorithmic overhead

).

4.14. Comparative Synthesis and Hybrid Methodologies

Metaheuristic algorithms and ML/DL techniques offer distinct yet often complementary capabilities for optimizing PV systems. Their relative suitability varies depending on the specific optimization task:

• MPPT under PSC: Metaheuristics excel due to their inherent global search capabilities, enabling them to locate the GMPP effectively in multi-peaked PV landscapes. ML/DL approaches, including ANNs, FLCs, or clustering methods like K-means, aim to learn the optimal control policy or simplify the search space. A key trade-off exists that metaheuristics might require more online computation during tracking but less offline training data, whereas ML models require potentially extensive offline training but can offer faster inference (decision-making) during operation [75,76].
• Layout Optimization: As noted earlier, direct application of metaheuristics seems less documented recently compared to other tasks. Metaheuristics could theoretically explore the combinatorial space of panel placements, while ML might be better suited for rapidly predicting shading effects or energy yield for numerous candidate layouts generated by another process (e.g., geometric or parametric design tools). The complexity of integrating geometric constraints and detailed shading analysis might favor simulation-based optimization or specialized algorithms.
• System Sizing/Design: Metaheuristics are well-suited for optimizing component sizes (PV, battery, etc.), navigating complex trade-offs between cost, reliability, and performance based on system models. ML primarily contributes by providing accurate forecasts of load demand and renewable generation, which serve as crucial inputs to the sizing optimization process performed by metaheuristics or other optimization solvers [49,55].
• Energy Management: Both paradigms are applicable. Metaheuristics can optimize operational schedules based on predictive models of generation, load, and prices. ML/DL, particularly RL or ML-informed MPC, can learn adaptive control strategies directly from data or use high-accuracy forecasts to optimize decisions in real time or near-real time. ML offers advantages when system dynamics are difficult to model accurately or when adapting to unforeseen changes [52].
• Fault Detection and Diagnosis: This domain is heavily dominated by ML/DL due to their exceptional pattern recognition capabilities applied to electrical signatures, environmental data, or thermal images. Metaheuristics primarily play a supporting role by optimizing the structure or hyperparameters of the ML/DL models used for FDD [92].

4.15. Rise of Hybrid Models: Combining Strengths for Enhanced Performance

A prominent trend across the reviewed literature is the increasing development and application of hybrid optimization methodologies. This reflects a growing recognition that combining the strengths of different approaches, whether metaheuristics, ML/DL, or conventional techniques, can overcome the limitations of individual methods and lead to superior performance. This hybridization manifests in several distinct forms.

One approach involves combining similar paradigms, such as in metaheuristic–metaheuristic hybrids, which may leverage the exploration strengths of one algorithm and the exploitation strengths of another, or integrate local search mechanisms to refine solutions found by a global search. Emerging quantum-classical hybrids also fall into this category [102,103]. Another common strategy is the creation of conventional–metaheuristic hybrids. These models augment traditional techniques with metaheuristics, as seen in the GWO-P&O or PSO-P&O algorithms for MPPT. In such cases, the metaheuristic performs an initial global search to locate the region of the global maximum power point, after which the faster conventional method refines the tracking locally [76].

More recently, hybridization has increasingly involved the integration of data-driven techniques. ML/DL–ML/DL hybrids combine different neural network architectures to exploit their complementary capabilities, such as using CNNs for spatial feature extraction from I–V curves or images, paired with RNNs, like LSTM or GRU to capture temporal dependencies in fault diagnosis or forecasting. Another example is the combination of an unsupervised feature extractor, like an SSAE, with a supervised classifier, such as an optimized MLP (OMLP), for advanced fault diagnosis [84,89].

The fusion of metaheuristics with ML/DL represents a particularly active and synergistic area of research. This integration occurs in several ways. First, metaheuristics such as GA, PSO, or the firefly–host optimization (FHO) algorithm are used to automate and enhance the otherwise tedious process of hyperparameter tuning or architecture search for ML/DL models, leading to better predictive or classification performance [51]. Second, ML/DL models are employed to provide accurate forecasts, for instance, of solar irradiance or load demand, which then serve as critical inputs for optimization problems solved by metaheuristics, such as system sizing or energy management scheduling [93]. Finally, these paradigms are directly combined to create hybrid controllers, like ANN-GA or RF-PSO for MPPT, where the ML component may handle pattern recognition while the metaheuristic optimizes the corresponding control action [93].

The underlying rationale for this widespread hybridization is compelling: it allows researchers to create synergistic systems that overcome the inherent limitations of single-paradigm approaches, such as slow convergence, susceptibility to local optima, heavy data dependency, or a lack of adaptability [93]. By combining, for instance, the robust global exploration of metaheuristics with the powerful pattern recognition of ML/DL, or the speed of conventional methods with the global reach of advanced algorithms, these integrated frameworks push the boundaries of performance. The prevalence and success of these hybrid strategies strongly suggest that future breakthroughs in PV optimization are likely to emerge from such integrated systems rather than from isolated techniques. A diagram of a hybrid optimization model can be found in Figure 8.

4.16. Evaluating Approaches Against Dual Objectives: Area/Energy Maximization vs. Efficiency Preservation

Revisiting the core objectives of maximizing spatial area utilization and energy production yield without compromising system efficiency, the reviewed optimization methods contribute in various distinct but interrelated ways. The goal of area maximization is most directly addressed through layout optimization. Here, while the precise role of metaheuristics requires further clarification based on recent literature, machine learning and deep learning (ML/DL) models contribute significantly by providing the accurate yield predictions necessary to evaluate and compare different layout designs. This is especially critical for complex scenarios like building-integrated photovoltaics (BIPV). Effective area maximization inherently demands a careful balance between panel density and potential shading losses, an optimization challenge where predictive modeling is a key enabler [95].

The objective of energy maximization is tackled through multiple avenues. First, advanced MPPT techniques, whether based on metaheuristics, ML/DL, or hybrid models, directly maximize energy harvest by ensuring operation at the GMPP, particularly under challenging partial shading conditions where conventional methods often fail [52]. Second, optimal sizing and design practices ensure that system components are correctly scaled to meet load demands efficiently, which minimizes energy curtailment or unmet load and thus contributes to maximizing the useful energy delivered over the system’s lifetime [52]. Third, predictive energy management, which couples accurate forecasting with optimized control strategies for energy storage and grid interaction, allows for better utilization of generated PV energy, thereby reducing waste and maximizing economic value [49].

Simultaneously, efficiency preservation is intrinsically linked to both energy maximization and loss minimization. A primary contribution in this regard is accurate MPPT, as operating at the GMPP is fundamental to maximizing the conversion efficiency of the PV array under any given conditions; avoiding local maxima directly prevents significant efficiency losses [75]. Another key factor is the prompt identification and rectification of system faults through advanced FDD, which prevents prolonged operation under degraded conditions and thereby preserves system efficiency and maximizes long-term energy yield [92]. Furthermore, techniques that optimize the performance and stability of power electronic converters minimize conversion losses, while efficient system design through optimal sizing and layout mitigates electrical and shading losses [52].

It is important to recognize that maximizing energy yield is often achieved by improving or maintaining efficiency; for instance, better MPPT reduces power loss, and fault detection prevents efficiency degradation. The constraint ”without compromising efficiency” primarily serves to guard against optimization strategies that might achieve a higher instantaneous output at the cost of increased component stress, reduced lifespan, excessive computational overhead, or higher system losses elsewhere. An overly aggressive MPPT algorithm, for example, might track slightly more power but induce damaging oscillations or require disproportionate computational resources. Similarly, packing panels too densely may maximize the nameplate capacity within an area but can reduce the overall energy yield due to increased shading, thus compromising the effective system efficiency. The optimization methods reviewed generally aim to enhance efficiency as a means to maximize useful energy output and economic value, rather than treating efficiency as a potential casualty of maximization efforts, as summarized in Table 3 and

Table 4. Comparative summary of optimization paradigms in PV applications (2020–2025).

Task	Metaheuristics	ML/DL	Hybrid	Key Findings
MPPT (PSC)	✓ Global opt.; ✗ slow, unstable	✓ Fast post-train; ✗ data-hungry	✓ GWO-P&O, ANN-GA	Hybrids outperform standalone; K-means helps zone detection; improved tracking under PSC.
Layout Opt.	✓ Combinatorial fit; ✗ geometry limits	✓ Yield prediction; ✗ not direct search	✓ ML for fitness est.	ML used to evaluate designs; limited hybrid use; more explicit metaheuristic reporting needed.
System Sizing	✓ Multi-obj.; ✗ model sens.	✓ Load/gen. forecast; ✗ no sizing logic	✓ ML + GA/NSGA	Common use of ML inputs in HOMER-based sizing; accurate forecasting improves results.
Energy Mgmt.	✓ Schedule opt.; ✗ real-time issues	✓ RL/MPC; ✗ error propagation	✓ MPC + DL/RL	RL enables adaptiveness; DL essential for MPC forecasts; growing interest in real-time control.
Fault Diagn.	✗ Not direct use	✓ Pattern recog.; ✗ rare fault data	✓ CNN-RNN, AE-MLP	ML/DL dominant (>95% acc.); metaheuristics used for tuning; hybrid DL models perform best.

.

4.17. Critical Analysis of Methods

While traditional methodologies such as linear programming, dynamic programming, and local search methods are effective in simpler PV systems, they have significant limitations when applied to nonlinear and dynamic problems. These approaches rely on idealized assumptions that do not always hold in real-world operating conditions, such as variability in solar radiation. In contrast, AI-based methods, including metaheuristic algorithms and machine learning, show greater potential in handling these complex problems. However, these advanced approaches require a more detailed analysis of their computational costs, data availability, and lack of interpretability, which limits their deployment in resource-constrained systems (

Table 5. Critical comparison of optimization methods in PV systems.

Method	Accuracy	Adaptability	Complexity	Key Insights
Traditional (LP, DP)	✓ Simple cases	✗ Limited to static env.	Low–Medium	Suitable for sizing/planning; fails in dynamic/nonlinear scenarios; low computational load.
Metaheuristics (PSO, GA)	✓ Versatile	✗ Param.-sensitive	Medium–High	Effective in complex optimization; widely used in MPPT/design; tuning affects performance.
Machine Learning (NN, etc.)	✓ High precision	✓ Learns patterns	High	Accurate prediction and control; data-intensive; real-time potential but hard to interpret.

).

Despite the effectiveness of AI-based algorithms, one of their main drawbacks is the computational cost involved. The use of deep learning techniques and neural networks for energy prediction or real-time optimization requires significant computational resources, which can be a major limitation in PV systems with constrained resources, such as those found in rural areas or small installations. Additionally, these methods typically require large datasets for training the models, which can be problematic if data is scarce or of low quality. While metaheuristic algorithms are more computationally efficient, their performance is highly dependent on parameter configuration, which can affect the quality of the solutions found.

One of the biggest barriers to implementing advanced optimization techniques in PV systems is the lack of interpretability of AI models, especially in critical applications where automated decisions can have a significant impact. Although deep neural networks and other AI algorithms can provide accurate solutions, the opacity of their decision-making processes can create mistrust among end-users. To overcome this limitation, it is essential to develop models that are not only effective but also explainable. Explainable AI models (XAI) are crucial for the widespread adoption of these technologies in PV systems.

The hybridization of traditional approaches with AI-based methods, such as combining metaheuristic algorithms with neural networks, has shown promising results in optimizing PV systems. However, this combination does not always lead to substantial improvements in every case. In some scenarios, the additional computational complexity and the time required to tune parameters for both methods may outweigh the benefits. It is crucial to carefully assess when it is appropriate to use hybrid methods and when traditional or simple metaheuristic approaches are sufficient to achieve optimal results.

5. Discussion

This section analyzes the findings of the study and their relevance across various contexts. The analysis is based on a curated set of scientific publications retrieved through a structured bibliographic search, which combined terms related to photovoltaic systems and optimization techniques. From a practical standpoint, the results reinforce the potential of intelligent optimization methods to enhance the efficiency and adaptability of photovoltaic systems, particularly under variable operating conditions. These findings have direct implications for the design of more robust solar infrastructures, capable of effectively responding to challenges such as shading, orientation, and load fluctuations.

New Perspectives on Advances and Trends in Optimization of Photovoltaic Systems

This article examines the evolution of scientific publications related to traditional and algorithmic approaches in PV systems from 2020 onward. As illustrated in Figure 9, the scientific output has shown a progressive shift, with a clear increase in the focus on algorithmic methods, particularly from 2023.

Between 2020 and 2022, traditional PV research dominated over 70% of publications. Since 2023, however, there has been a notable shift toward algorithmic approaches, rising to 43.55% in 2023 and 56.45% in 2024. This trend reflects the growing importance of computational optimization, AI, and heuristic algorithms in solar energy system design and performance enhancement. Early 2025 data show continued growth, suggesting that advanced optimization techniques will dominate future research output. This paradigm shift emphasizes integrating intelligent algorithms into PV system modeling and control, while interest in traditional methods is gradually declining (see Figure 10) [53].

The design of PV support structures now requires a multidisciplinary approach encompassing energy efficiency, material engineering, technical standards, and sustainability. Research from 2020 to 2025 highlights increased focus on advanced concepts such as genetic algorithms, metaheuristics, and machine learning, signaling a move toward more intelligent, resilient, and eco-friendly solar system designs (see Figure 10) [53]. Despite significant advances in hybrid metaheuristic-ML approaches for MPPT, forecasting, and fault detection, challenges remain, including the scarcity of standardized datasets, high computational costs, model interpretability, and bridging simulation-to-reality gaps [104].

Future work must focus on lightweight, XAI models optimized for edge computing, robust data fusion techniques, and rigorous experimental validation. Material selection is crucial, prioritizing durability, recyclability, and mechanical strength, with galvanized steel and anodized aluminum preferred for corrosion resistance and modularity. The structural design process includes project definition, environmental adaptation, material/mechanical design, electrical integration, regulatory compliance (National Electrical Code -NEC-, International Electrotechnical Commission -IEC-, Underwriters Laboratories -UL-), and sustainability and cost analyses ensuring that PV systems are efficient, safe, economically viable, and environmentally responsible [104] (see Figure 10).

Table 6. Comparison of this work against state-of-the-art reviews.

Works	Optimization, Control and Efficiency in PV	Mathematical Modeling, Optimization Techniques and IA	Simulation and Embedded Systems	Classical, Data-Driven Physical Models	AI, Machine Learning, and Algorithms	Application of PRISMA Methodology	Period Search
[105]	X	X		X			2017–2021
[106]	X		X				2016–2022
[107]	X	X	X	X			–
[108]		X	X		X		2018–2023
[109]	X		X	X			–
[110]		X	X		X		2017–2022
Our Work	X	X	X	X	X	X	2019–2025

presents a comprehensive comparison between this work and several recent reviews in the field of photovoltaic (PV) system optimization and modeling. While many of these reviews address relevant topics, such as optimization, control, efficiency, mathematical modeling, and artificial intelligence (AI) techniques, few integrate all these components simultaneously and comprehensively.

For instance, reviews by Kazem et al. [105] and Le et al. [107] combine optimization analysis with mathematical modeling, including classical physical models, yet they do not thoroughly explore the application of systematic review methodologies nor focus on embedded systems and real-time simulation integration. Conversely, Atawi et al. [106] and Zhou et al. [108] emphasize simulation and embedded applications, but lack a comprehensive examination of AI-driven models or a systematic PRISMA-based review.

The adoption of the PRISMA methodology in this work constitutes a significant contribution, ensuring rigor, transparency, and reproducibility in literature selection and analysis, which is an aspect seldom addressed in comparable reviews. Furthermore, the extended search period through 2025 enables inclusion of the most recent advances in this rapidly evolving domain.

A key differentiator of this study is its broad coverage of modeling approaches, spanning from classical physical models to data-driven and advanced AI and machine learning algorithms. This comprehensive scope facilitates a critical comparative evaluation that incorporates criteria such as accuracy, computational complexity, and practical feasibility for implementation in simulation environments or real-time embedded systems.

In summary, this work not only encompasses the core thematic areas addressed by leading reviews but also expands upon them by integrating a rigorous systematic methodology, providing an up-to-date literature analysis, and balancing the evaluation of both traditional and cutting-edge AI techniques. This delivers a valuable contribution to the scientific and technological community engaged in photovoltaic system research and development. A conclusive flowchart summarizing optimization methodologies for Solar PV systems can be seen in Figure 11.

Table 7. Comparative analysis of recent reviews on PV optimization.

Review (Year)	Main Focus	Compared Algorithms	Systematic Methodology	N. of Articles	Addressed Gaps	Reported Limitations	Period Search
[105]	PV system design and modeling	Traditional + GA	No	127	Classical modeling only	No embedded system analysis	2020–2025
[108]	Forecasting and AI methods	ML, DL (CNN, LSTM)	Partial	82	Forecasting focus	No comparison with classical optimization	2020–2024
[93]	MPPT hybrid techniques	GA, PSO, ANN	No	54	GMPP tracking only	Does not analyze computational efficiency	2020–2025
[94]	Fault detection in PV systems	CNN, Bi-GRU	No	112	Fault detection techniques	No computational efficiency analysis	2023–2025
[100]	Optimal tilt and orientation prediction for PV panels	Ensemble Learning	No	150	Optimal tilt prediction	No comparison with traditional optimization methods	2020–2024
[67]	Rooftop PV system optimization under shading	GA	No	200	Shading effects on layout	Limited classical optimization techniques	2020–2023
Our Works	Integrated PV optimization and diagnosis	GA, PSO, ACO, ML/DL	PRISMA + bibliometrics	314	Broad integration (optimization, diagnosis, layout)	High data and computation demand	2019–2025

provides a comparative synthesis between the present work and three recent and representative review studies in the field of photovoltaic (PV) system optimization. This analysis contextualizes the contributions of the present review within the broader research landscape and highlights its distinctive value.

From a thematic scope perspective, prior studies such as Kazem et al. (2022) [105] focus primarily on classical modeling and simulation tools, whereas Zhou et al. (2024) [108] and Salman et al. (2025) [93] concentrate on energy forecasting and MPPT control strategies, respectively. In contrast, the present study addresses a broad and integrated range of optimization domains, including physical modeling, geometric layout design, maximum power point tracking, fault diagnosis, and energy management. This comprehensive coverage constitutes a clear advantage, offering a holistic perspective on PV system performance across the entire lifecycle, from design and configuration to real-time operation and predictive maintenance.

Another major differentiator is the rigorous adoption of a systematic review methodology based on PRISMA, complemented by structured bibliometric analysis using VOSviewer. This dual strategy, absent in the other reviewed works, ensures transparency and reproducibility in the article selection process. As a result, this study incorporates a substantially larger and more representative body of literature (314 original research articles published between 2020 and 2025), thereby enhancing the robustness and generalizability of its findings.

Moreover, the technical analysis conducted here encompasses a comprehensive taxonomy of optimization techniques, ranging from traditional approaches (linear and dynamic programming and gradient-based methods) to metaheuristics (e.g., PSO, GA, ACO, and GWO), hybrid models, and advanced ML and DL frameworks. Unlike prior reviews that merely catalog these techniques, the present work provides a critical and functional comparison, assessing each method based on applicability, computational demand, accuracy, parameter sensitivity, and real-world feasibility. This structured characterization is reflected in Table 2, Table 3 and Table 4, which serve as key reference tools for researchers and practitioners.

Importantly, this study identifies and elaborates on several gaps in the existing literature. These include the lack of standardized evaluation metrics for hybrid algorithms, limited availability of public datasets for ML/DL training and validation, and the absence of integrated approaches that jointly optimize structural, electrical, and diagnostic parameters. To address these shortcomings, the study proposes future research directions centered on XAI, lightweight edge-computing algorithms, and the design of hybrid PV systems optimized for energy efficiency and computational scalability.

The research by Amiri et al. (2024) [94], Khan et al. (2022) [100], and Ren et al. (2023) [67] presents significant advances in the optimization of photovoltaic systems. Amiri et al.’s fault diagnosis approach using CNN and Bi-GRU is promising, but its high computational demand and reliance on large labeled datasets limit its applicability. Khan et al. apply ensemble learning techniques to optimize panel tilt, but their computational load and lack of comparison with classical methods represent limitations. Ren et al. optimize the design of rooftop photovoltaic systems using a GA, but their focus on traditional techniques prevents exploring the advantages of hybrid models that integrate AI and metaheuristics. Overall, while these advanced approaches improve efficiency, computational barriers and the lack of direct comparisons with classical methods remain key challenges.

Table 8. Comparison of optimization studies in photovoltaic systems.

Location/Case Study	Modules	Power (kWp)	PR (%)/Efficiency	Method/Tool Used	Reference
India (Technological Institute)	360	90	79	PVsyst	[111]
Iraq (Residential)	15	4.3	80–85	Genetic Algorithm	[112]
China (PV Greenhouses)	–	–	80–90	Collaborative Optimization	[113]
Sweden (Distributed Storage)	–	–	85–90	Genetic Algorithm	[114]
Pakistan (3 MW Plant)	∼9000	3000	82–85	PVsyst + Critical Parameter Validation	[115]
Spain (Grid-Connected Plant)	1200	450	83–87	Hybrid Optimization (GA + ML)	[116]
Iran (Hybrid PV/T System)	–	–	80–88	AI + Metaheuristic Algorithms	[117]

summarizes several case studies on PV system optimization, highlighting the diversity of approaches applied in different geographical and operational contexts. The comparison includes both traditional tools, such as PVsyst simulations, and modern optimization techniques, including GA, collaborative optimization methods, and hybrid approaches that combine machine learning with physical modeling. In addition, the table incorporates recent studies addressing large-scale PV plants, grid-connected installations, and hybrid PV/T systems. The analysis emphasizes not only the performance ratios or efficiency levels achieved but also the methodologies applied, providing a critical overview of how different techniques contribute to enhancing system design and operation. This comparative perspective underscores the importance of integrating advanced optimization methods while recognizing current challenges such as data scarcity, computational cost, and limited interpretability of models.

Table 9. Critical Comparison of methods and identification of current challenges in PV optimization studies.

Study/Author (Year)	Approach/Compared Methods	Challenges Identified	Key Remarks
Di Leo (2025) [118]	Physical models, statistical approaches, ML, hybrid methods, metaheuristics	Data quality/availability, computational efficiency, adaptability	Systematic review highlighting advantages and limitations of each method; emphasizes robust data and efficient models.
Mamodiya et al. (2025) [119]	MPPT (traditional) vs. Deep Learning hybrids (PINNs + RL + CNN-LSTM on Edge AI)	High computational cost, latency, large datasets, limited interpretability	Achieved 10–15% performance improvement, 40–50% faster response; warns about computational demands and lack of transparency.
Nyangiwe et al. (2025) [120]	Multiple regression vs. ML (SVM, Random Forest, clustering, Deep Learning)	Data scarcity and quality, computational cost, interpretability	Review showing ML outperforming classical techniques; stresses challenges of data access and model explainability.
Abdelsattar et al. (2025) [121]	SVR, regression, decision trees, ensembles (XGBoost, RF, etc.)	Interpretability, adaptability to nonlinear data, environmental variability	Benchmark on ML methods for PV environment forecasting; integration of SHAP for improved interpretability.
Bianchini et al. (2019) [122]	PVUSA model with limited data vs. standard forecasting methods	Lack of meteorological data, model complexity, forecasting accuracy	Efficient low-complexity approach requiring only historical generation and temperature forecasts; useful under scarce data conditions.

provides a critical comparison of recent studies addressing optimization methods applied to PV systems. The selected works analyze a broad spectrum of approaches, ranging from traditional physical and statistical models to advanced machine learning and hybrid metaheuristic techniques. In addition to reporting methodological differences and performance outcomes, the table emphasizes the main challenges identified across the literature, including data scarcity and quality, high computational cost, and the limited interpretability of complex models. These issues remain central barriers to the practical deployment of intelligent optimization strategies in PV systems. By summarizing both the methodological comparisons and the critical challenges, this synthesis highlights the need for robust, explainable, and computationally efficient solutions to ensure more reliable and scalable PV system optimization.

Finally, the present work adopts a realistic and critical stance on the limitations of current methodologies, an aspect often overlooked in other reviews. Explicit discussion is provided on the stochastic variability of metaheuristic algorithms, the data dependency and lack of interpretability in deep learning models, and the computational barriers these techniques face in embedded or resource-constrained platforms (e.g., <2 W power, <500 ms latency). These considerations offer practical insights into the trade-offs involved in deploying intelligent optimization strategies in real-world PV applications.

Taken together, this work positions itself as a comprehensive, methodologically robust, and technically grounded review, contributing not only to the synthesis of current evidence but also to the formulation of a critical framework that bridges academic research, practical implementation, and strategic innovation in the field of photovoltaic system optimization.

6. Conclusions

This work has provided a comprehensive view of optimization methods applied to photovoltaic systems, contrasting traditional techniques with emerging ones based on AI and metaheuristics. Through a detailed analysis of 314 relevant studies, it has been identified that while classical approaches remain useful in the initial design of systems, AI-based methodologies, such as neural networks and evolutionary algorithms, offer significant advantages in dynamic and nonlinear scenarios, such as MPPT under partial shading conditions.

However, important gaps and challenges in the field have been highlighted, requiring urgent attention. In particular, data quality and availability remain a critical challenge for training AI models, limiting their applicability in real-world environments where data may be scarce or of low quality. Additionally, despite advancements in AI models, high computational costs and lack of interpretability in deep learning models continue to be significant barriers for their implementation in photovoltaic systems with limited resources, such as in rural or small-scale applications.

A key area for future research is the development of explainable AI models, which can offer not only accuracy but also clarity in their decision-making processes, thus fostering trust and adoption in critical applications. Moreover, more efficient and lightweight algorithms should be explored to enable real-time optimization of photovoltaic systems, particularly in dynamic conditions such as fluctuations in solar radiation or changes in energy demand.

Hybridization of techniques also emerges as a promising path to address sector challenges. Combining the best of traditional approaches with the flexibility of metaheuristics and the predictive power of AI can lead to more robust and adaptive optimization solutions. However, it is crucial to assess when these combinations truly outperform simpler solutions, especially in applications that do not require such complex optimization.

This paper highlights the need to continue integrating hybrid models that optimize photovoltaic systems not only in terms of energy efficiency but also in terms of sustainability and profitability. For these technologies to be widely adopted, efforts should be made to improve the availability of quality data, reduce computational costs, and promote the development of explainable models that can be practically applied in the industry. This integrated approach will enable the maximization of photovoltaic system efficiency, ensuring their long-term viability as a key renewable energy source in a sustainable future.