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Various maximum power point tracking (MPPT) techniques have been proposed to optimize the efficiency of solar photovoltaic (PV) systems. These techniques differ in several aspects such as design simplicity, convergence speed, implementation types (analog or digital), decision optimal point accuracy, effectiveness range, hardware costs, and algorithmic modes. Choosing the most suitable MPPT controller is crucial in PV system design, as it directly impacts the overall cost of PV solar modules. This paper presents a comprehensive exploration of 64 MPPT techniques for PV solar systems, covering optimization, traditional, intelligent, and hybrid methodologies. A comparative analysis of these techniques, considering cost, tracking speed, and system stability, indicates that hybrid approaches exhibit higher efficiency albeit with increased complexity and cost. Amidst the existing PV system review literature, this paper serves as an updated comprehensive reference for researchers involved in MPPT PV solar system design. Full article

The research process was based on an analysis of an existing building equipped with a heat pump on which photovoltaic panels were installed; then, based on energy consumption, the investment profitability was evaluated. In this research, using the available data, the coefficient of self-consumption of energy from the PV installation, the potential index of the installation’s own needs coverage, and the index of energy use from photovoltaic modules were determined, which in practice is equated with the energy efficiency of the PV installation. The entire investment was subjected to simulation and field tests to determine the energy demand of a single-family building. The main aim of this work was to check whether a system equipped with a heat pump combined with a PV installation is an effective technical solution in the analysed climatic conditions in one of the countries of Central and Eastern Europe. In addition, both positive and negative aspects of renewable energy sources were analysed, including long-term financial savings, energy independence, and reductions in greenhouse gas emissions. It has been shown that the described solution is characterised by high initial costs depending on weather conditions. The installation presented would allow us to avoid 1891 kg/year of CO₂ emissions, which means that with this solution, we contribute to environmental protection activities. Full article

Bangladesh’s electricity use is growing rapidly, but it has limited fossil fuel reserves. This disadvantage makes it harder for the country to provide people in densely populated cities with access to reliable energy. Solar photovoltaic (PV) electricity could solve these problems by making the grid less dependent on fossil fuels, cutting carbon emissions, and encouraging businesses and institutions to switch to cleaner energy sources. This study designs and simulates a 27 kWp grid-connected solar photovoltaic (PV) system for the University of Asia Pacific (UAP) in Dhaka, Bangladesh. The system has 80 SunPower SPR-MAX2-340 modules and one Sunways STT-30KTL-P inverter. It is expected to generate 36,412 kWh of electricity every year with a performance ratio (PR) of 82.42%. The economic analysis indicates that the system is financially profitable, with a levelized cost of energy (LCOE) of 0.0613 USD/kWh and a payback period of 5 years. The environmental assessment also states that the system will reduce emissions by 503.0 tCO₂ over its lifetime. The results indicate that solar PV systems in cities in Bangladesh could be a long-term solution for meeting energy needs. The overall results show that grid-connected solar PV systems can be a viable, long-term solution for meeting Bangladesh’s urban energy needs. Full article

To address the challenges of insufficient robustness and limited feature extraction in photovoltaic module image segmentation under complex scenarios, we propose a high-precision PV module segmentation model (Pv-UNet) that integrates Transformer and improved U-Net architecture. The model introduces a MultiScale Transformer in the encoding path to achieve cross-scale feature correlation and semantic enhancement, combines residual structure with dynamic channel adaptation mechanism in the DoubleConv module to improve feature transfer stability, and incorporates an Attention Gate module in the decoding path to suppress complex background interference. Experimental data were obtained from UAV visible light images of a photovoltaic power station in Yuezhe Town, Qiubei County, Yunnan Province. Compared with U-Net, BatchNorm-UNet, and Seg-UNet, Pv-UNet achieved significant improvements in IoU, Dice, and Precision metrics to 97.69%, 93.88%, and 97.99% respectively, while reducing the Loss value to 0.0393. The results demonstrate that our method offers notable advantages in both accuracy and robustness for PV module segmentation, providing technical support for automated inspection and intelligent monitoring of photovoltaic power stations. Full article

To explore the potential of solar energy in the pursuit of a more sustainable aviation sector, this research examines the feasibility of solar photovoltaic systems for battery recharge of electric or electric hybrid aircraft deployed at four airports in North Africa and North, Central, and South Europe, respectively: Cairo International, London Heathrow, Milan Malpensa, and Rome Fiumicino. Employing PVGIS software with Google Maps, a site-specific photovoltaic array can be designed, optimizing module tilt and orientation to maximize solar energy collection across various climatic conditions. The energy production of the photovoltaic systems at the selected airports is compared to the energy demand required for the annual recharge of the batteries (28 MWh each) used in a widely popular medium-range aircraft, the Airbus A320. Although the calculated amount of energy, allowing for daily capacities ranging from 6 to 10 batteries on average, is insufficient to support the extensive demand associated with the typical air traffic in such airports, the potential of solar energy to decarbonize aircraft seems an appropriate approach to be pursued. Locations with limited solar access necessitate hybrid solutions, especially in sunny regions. Full article

Photovoltaic (PV) systems represent one of the pillars of the global energy transition, yet their reliability and long-term efficiency are constantly threatened by hidden defects and progressive degradation. Early and precise identification of such anomalies is essential for ensuring safety, enhancing performance, and facilitating predictive maintenance plans. Infrared thermography (IRT) is a non-invasive and cost-effective technique for the inspection of PV modules. This study proposes an efficient attentive U-Net architecture for the semantic segmentation of thermographic images, aimed at supporting predictive maintenance and power loss assessment. The model integrates squeeze-and-excitation (SE) and attention gate (AG) modules with atrous spatial pyramid pooling (ASPP), achieving an optimal balance between accuracy and computational complexity. A comprehensive ablation study, including input resolution and module combinations, was conducted on a dataset of 500 thermograms annotated into six defect classes. The proposed configuration (256 × 256 input) achieved a mean Intersection over Union (mIoU) of 81.4% and a macro-F1 score of 87.5%, outperforming U-Net and DeepLabv3+ by over 4 percentage points, with only 5.24 M parameters and an inference time of 118.6 ms per image. These results confirm the suitability of the framework for energy-oriented fault diagnosis and near real-time monitoring of photovoltaic plants. Full article

This work focuses on the development of a diagnostic approach for detecting and localizing sensor faults in an autonomous photovoltaic system. The considered system is composed of a photovoltaic module and a resistive load. However, an adaptation stage formed by a DC/DC voltage boost converter is necessary to transfer energy from the source to the load. The diagnostic scheme is based on a sliding mode observer (SMO) that is robust to uncertainties and parametric variations. The SMO incorporates adaptive gains optimized via parametric adaptation laws, with stability rigorously verified through Lyapunov analysis. The method effectively identifies both independent and simultaneous sensor faults, employing an optimized threshold selection strategy to balance detection sensitivity and false alarm resistance. Simulation results under varying environmental conditions, system parameter fluctuations, and noisy measurement demonstrate the approach’s superior performance, achieving a 20% reduction in mean absolute percentage error (MAPE) and 90% faster settling time compared to existing techniques. These enhancements immediately increase the dependability, efficiency, and lifetime of the PV system, which are critical for lowering carbon emissions and ensuring the economic feasibility of solar energy investments. Key innovations include a novel residual generation mechanism, seamless integration with backstepping sliding mode maximum power point tracking (MPPT) control, and enhanced transient response characteristics. Full article

Solar energy systems have increasingly replaced conventional energy systems, driving global efforts to combat climate change and promote sustainability. This study conducts a comprehensive life-cycle cost analysis (LCCA) of photovoltaic (PV) modules, with a focus on the solar market in Eskisehir, Türkiye. Unlike prior research, this work integrates financial analysis with ecological benefits, offering a localized case study. By leveraging primary data from surveys and government sources, the analyses display that investing in PV equipment generates €883.75 in Net Present Value (NPV) savings through the business-as-usual scenario (−€392 under the worst-case and €2350 under the optimistic scenarios) over a 30-year lifespan, demonstrating the financial viability of these systems. Despite high initial costs, PV modules provide ecological and economic advantages that outweigh maintenance expenses, making them a viable solution for reducing fossil fuel dependence. The findings serve as a guideline for decision-makers, consumers, and producers to foster a sustainable solar energy market in Türkiye and similar developing economies by enabling feasible PV investments through appropriate Feed-in tariff mechanisms. Full article

Organic photovoltaics (OPVs) based on non-fullerene acceptors (NFAs) are rapidly advancing as lightweight, flexible, and low-cost solar technologies, with power conversion efficiencies approaching 20%. To ensure that environmental sustainability progresses symmetrically alongside performance improvements, it is essential to quantify the environmental footprint of these emerging technologies, particularly during early development stages when material and process choices remain adaptable. This study presents a cradle-to-gate life cycle assessment (LCA) of PTB7-Th:ZY-4Cl solar cells, aiming to identify asymmetries in environmental impact distribution and guide eco-efficient optimization strategies. Using laboratory-scale fabrication data, global warming potential (GWP), cumulative energy demand (CED), acidification (AP), eutrophication (EP), and fossil fuel depletion (FFD) were evaluated via the TRACI methodology. Results reveal that electricity consumption in thermomechanical operations (ultrasonic cleaning, spin coating, annealing, and stirring) disproportionately dominates most impact categories, while chemical inputs such as PEDOT:PSS, PTB7-Th:ZY-4Cl precursors, and solvents contribute significantly to fossil fuel depletion. Substituting grid electricity with renewable sources (hydro, wind, PV) markedly reduces GWP, and solvent recovery or replacement with greener alternatives offers further gains. Although extrapolation to a 1 m² pilot-scale module reveals impacts higher than established PV technologies, prospective scenarios with realistic efficiencies (10%) and lifetimes (10–20 years) suggest values of ~150–500 g CO₂-eq/kWh—comparable to fullerene OPVs and approaching perovskite and thin-film benchmarks. These findings underscore the value of early-stage LCA in identifying asymmetrical hotspots, informing material and process optimization, and supporting the sustainable scale-up of next-generation OPVs. Full article

The output power of photovoltaic modules is significantly reduced by solar irradiance shading. To address this issue, innovative strategies for mitigating shading effects have been continuously explored. In this study, detailed research on the edge dust accumulation effect of modules has been conducted. It is found that under vertical installation, when the shading ratio reaches 50%, the output power of full-cell modules decreases by 42%, while that of half-cell modules drops by only 27%. Moreover, when the shading ratio reaches 100%, the output power of full-cell modules declines by nearly 99%. In contrast, half-cell modules are still able to maintain nearly 50% of their output power. These results demonstrate that half-cell modules exhibit significantly better resistance to shading compared to full-cell modules. On the other hand, under a horizontal layout, power degradation for both full-cell and half-cell modules is observed to be approximately 16% when the shading ratio is 25%, and around 36% when the coverage reaches 50%. Experimental results further revealed that shading under horizontal orientation leads to a multi-peak power output profile, which poses a risk of the PV inverter being trapped in local maxima. Overall, half-cell modules demonstrated better resistance to dust-induced shading under both layouts. Based on these findings, novel module design schemes are proposed to enhance resistance to dust accumulation effects. The proposed method can effectively reduce power losses caused by edge dust-induced shading and improve the annual power generation of PV modules, thereby offering technical support for effectively enhancing the operational stability of PV power generation systems. Full article

Agrivoltaic (APV) systems co-locate agricultural production and photovoltaic (PV) electricity generation on the same land to maximize land use efficiency. This study proposes an integrated assessment framework that jointly evaluates crop yield and electricity generation in APV systems. Unlike many previous APV studies that estimated crop responses from empirical PAR–photosynthesis relationships, this framework explicitly couples a process-based rice growth model (DSSAT-CERES-Rice) with irradiance and PV performance simulations (Honeybee-Radiance and PVlib) in a single workflow. The five-stage framework comprises (i) meteorological data acquisition and processing; (ii) 3D modeling in Rhinoceros; (iii) calculation of module front and rear irradiance and crop height irradiance using Honeybee; (iv) crop yield calculation with DSSAT; and (v) electricity generation calculation with PVlib. Using bifacial PV modules under rice cultivation in Gochang, Jeollabuk-do (Republic of Korea), simulations were performed with ground coverage ratio (GCR) and PV array azimuth as key design variables. As GCR increased from 20% to 50%, crop yield reduction (CYR) rose from 12% to 33%, while land equivalent ratio (LER) increased from 128% to 158%. To keep CYR within the domestic guideline of 20% while maximizing land use, designs with GCR ≤ 30% were found to be appropriate. At GCR 30%, CYR of 17–18% and LER of 139–140% were achieved, securing a balance between agricultural productivity and electricity generation. Although PV array azimuth had a limited impact on crop yield and electricity generation, southeast or southwest orientations showed more uniform irradiance distributions over the field than due south. A simple economic assessment was also conducted for the study site to compare total annual net income from rice and PV across GCR scenarios. The proposed framework can be applied to other crops and sites and supports design-stage decisions that jointly consider crop yield, electricity generation, and economic viability. Full article

This study provides a comprehensive analysis of the fire hazards associated with Building-Integrated Photovoltaics (BIPV), using Aluminum Composite Panels (ACP) as a benchmark. Large-scale fire tests, modified from ISO 13785-1, were conducted on vertically installed BIPV modules to observe their fire behavior under conditions simulating a severe fire. The experimental process involved measuring key fire performance indicators, leading to the identification of a cascading failure mechanism. The BIPV modules demonstrated a peak Heat Release Rate (HRR) up to hi times higher (max. 898 kW) and smoke production nearly 10 times greater than the ACP baseline. The analysis reveals a distinct, multi-stage failure sequence that defines the systemic fire hazard of BIPV. Initially, a phenomenon strongly indicative of a chimney effect within the rear air cavity accelerates concealed fire spread. This rapid heating induces thermal stress, leading to extensive specimen damage termed cracking. This cracking event acts as a critical turning point, triggering a rapid release of trapped pyrolyzates and driving the fire to its peak intensity. This chain of events constitutes a unique hazard signature not observed in conventional cladding. The findings conclude that the fire risk of BIPV is a systemic issue, challenging the adequacy of component-level testing and highlighting the need for safety standards that assess the façade as a complete assembly. Full article

Solar photovoltaic (PV) power generation has become an important source of global renewable energy. The photoelectric conversion efficiency of crystalline silicon PV modules decreases as their surface temperature rises, while excessively high operating temperatures can also affect their service life. Therefore, reducing the temperature of photovoltaic modules is one of the effective methods of enhancing their photoelectric conversion efficiency. Passive thermal management methods, such as the use of phase change materials (PCM) and heat pipes (HP), can be used to control the temperature of PV modules, but they manifest the problems of poor thermal conductivity and low heat transfer efficiency at low heat flux density, respectively. On the other hand, previous experimental studies have mostly focused on small-scale non-standard PV cell modules, without considering encapsulation and installation issues in practical applications. Meanwhile, passive cooling technology exhibits strong regional characteristics, with significant variations in temperature control and energy efficiency improvements under different climatic conditions. To address these issues, this paper proposes a novel PV module temperature control unit that couples PCM and HP. Standard commercial PV cell modules are used as experimental subjects, and tests are conducted in four different regions of China to study the adaptability and effectiveness of the coupled PCM and HP control method. The experimental results show that the power generation pattern of PV modules is consistent with the variation in solar radiation intensity. When the operating temperature of the PV module is below 40 °C, the high thermal conductivity of the heat pipe plays a dominant role in dissipating heat. When the operating temperature of PV rises above 40 °C, the phase change material begins to play a role in heat storage and temperature control. Compared to using PCM alone for temperature control, the coupled method further enhances the cooling effect, preventing a sharp temperature increase after the PCM has completely melted, and increases the power generation of PV by 4–5%. The temperature control effect of the PV module is influenced by local ambient temperature and wind speed. The coupled temperature control method exerts a relatively low improvement effect under high-temperature and low-radiation environmental conditions, but it performs better when used under low-temperature and high-radiation environmental conditions. Full article

Driven by the global energy transition and carbon-neutrality goals, virtual power plants (VPPs) are expected to aggregate distributed energy resources and participate in multiple electricity markets while achieving economic efficiency and low carbon emissions. However, the strong volatility of wind and photovoltaic generation, together with the coupling between electric and thermal loads, makes real-time VPP scheduling challenging. Existing deep reinforcement learning (DRL)-based methods still suffer from limited predictive awareness and insufficient handling of physical and carbon-related constraints. To address these issues, this paper proposes an improved model, termed SAC-LAx, based on the Soft Actor–Critic (SAC) deep reinforcement learning algorithm for intelligent VPP scheduling. The model integrates an Attention–xLSTM prediction module and a Linear Programming (LP) constraint module: the former performs multi-step forecasting of loads and renewable generation to construct an extended state representation, while the latter projects raw DRL actions onto a feasible set that satisfies device operating limits, energy balance, and carbon trading constraints. These two modules work together with the SAC algorithm to form a closed perception–prediction–decision–control loop. A campus integrated-energy virtual power plant is adopted as the case study. The system consists of a gas–steam combined-cycle power plant (CCPP), battery storage, a heat pump, a thermal storage unit, wind turbines, photovoltaic arrays, and a carbon trading mechanism. Comparative simulation results show that, at the forecasting level, the Attention–xLSTM (Ax) module reduces the day-ahead electric load Mean Absolute Percentage Error (${\mathrm{M}}_{\mathrm{A}\mathrm{P}\mathrm{E}}$) from 4.51% and 5.77% obtained by classical Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to 2.88%, significantly improving prediction accuracy. At the scheduling level, the SAC-LAx model achieves an average reward of approximately 1440 and converges within around 2500 training episodes, outperforming other DRL algorithms such as Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Under the SAC-LAx framework, the daily net operating cost of the VPP is markedly reduced. With the carbon trading mechanism, the total carbon emission cost decreases by about 49% compared with the no-trading scenario, while electric–thermal power balance is maintained. These results indicate that integrating prediction enhancement and LP-based safety constraints with deep reinforcement learning provides a feasible pathway for low-carbon intelligent scheduling of VPPs. Full article

Maximum power point tracking (MPPT) under partial shading is a nonconvex, rapidly varying control problem that challenges multi-agent policies deployed on photovoltaic modules. We present Fuzzy–MAT3D, a fuzzy-augmented multi-agent TD3 (Twin-Delayed Deep Deterministic Policy Gradient) controller trained under centralized training/decentralized execution (CTDE). On the theory side, we prove that differentiable fuzzy partitions of unity endow the actor–critic maps with global Lipschitz regularity, reduce temporal-difference target variance, enlarge the input-to-state stability (ISS) margin, and yield a global $L_{\infty }$$\gamma$-contraction of fixed-policy evaluation (hence, non-expansive with $\kappa =\gamma <1$). We further state a two-time-scale convergence theorem for CTDE-TD3 with fuzzy features; a PL/last-layer-linear corollary implies point convergence and uniqueness of critics. We bound the projected Bellman residual with the correct contraction factor (for $L^{\infty }$ and $L^2\left(\rho \right)$ under measure invariance) and quantified the negative bias induced by $min\{Q_1,Q_2\}$; an N-agent extension is provided. Empirically, a balanced common-random-numbers design across seven scenarios and 20 seeds, analyzed by ANOVA and CRN-paired tests, shows that Fuzzy–MAT3D attains the highest mean MPPT efficiency (92.0% ± 4.0%), outperforming MAT3D and Multi-Agent Deep Deterministic Policy Gradient controller (MADDPG). Overall, fuzzy regularization yields higher efficiency, suppresses steady-state oscillations, and stabilizes learning dynamics, supporting the use of structured, physics-compatible features in multi-agent MPPT controllers. At the level of PV plants, such gains under partial shading translate into higher effective capacity factors and smoother renewable generation without additional hardware. Full article