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25 pages, 14247 KiB

Open AccessArticle

Energy Efficiency and Environmental Sustainability in Rural Buildings: A Life Cycle Assessment of Photovoltaic Integration in Poultry Tunnels—A Case Study in Central Italy

by Stefano Bigiotti, Carlo Costantino, Alessio Patriarca, Giulia Mancini, Giorgio Provolo, Fabio Recanatesi, Maria Nicolina Ripa and Alvaro Marucci

Appl. Sci. 2025, 15(9), 5094; https://doi.org/10.3390/app15095094 - 3 May 2025

Viewed by 295

Abstract

Livestock buildings in rural areas are increasingly recognized for their environmental impact, yet few studies provide applied, scenario-based evaluations to guide retrofit interventions. While the existing literature acknowledges the environmental burden of livestock facilities, it often lacks operationally grounded analyses applicable to real-world [...] Read more.

Livestock buildings in rural areas are increasingly recognized for their environmental impact, yet few studies provide applied, scenario-based evaluations to guide retrofit interventions. While the existing literature acknowledges the environmental burden of livestock facilities, it often lacks operationally grounded analyses applicable to real-world agricultural contexts. This paper proposes an original integration of experimental climatic monitoring and life cycle assessment (LCA) to evaluate retrofit scenarios for energy efficiency in real poultry farming contexts. Based on an accurate climatic monitoring campaign conducted on-site during the spring and summer periods, relevant data were collected on air temperature, humidity, wind speed, and solar radiation affecting two poultry tunnels in central Italy, highlighting the need for thermal mitigation. The comparison between the observed operational scenario and the hypothesized improved scenario, involving energy supply from photovoltaic sources, evaluated using the PVGIS tool, demonstrated a significant reduction in environmental impact, with a 33.4% decrease in global warming potential and a 26.1% reduction in energy consumption. This study combines experimental on-site climatic data collection with comparative environmental evaluation using LCA methodology. The LCA approach, which guided the entire study, highlighted how the energy efficiency gained through solar panels adequately offsets their production and maintenance costs over the long term. These findings offer a replicable model for energy retrofits in rural livestock facilities, contributing to both environmental goals and rural resilience. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Technologies, Materials and Their Applications)

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34 pages, 2529 KiB

Open AccessArticle

Hybrid Fuzzy–DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels

by Diana Ortiz-Munoz, David Luviano-Cruz, Luis A. Perez-Dominguez, Alma G. Rodriguez-Ramirez and Francesco Garcia-Luna

Appl. Sci. 2025, 15(9), 4869; https://doi.org/10.3390/app15094869 - 27 Apr 2025

Viewed by 226

Abstract

Partial shading conditions reduce the efficiency of photovoltaic (PV) systems by introducing multiple local maxima in the power–voltage curve, complicating Maximum Power Point Tracking (MPPT). Traditional MPPT methods, such as Perturb and Observe (P&O) and Incremental Conductance (IC), frequently converge to local maxima, [...] Read more.

Partial shading conditions reduce the efficiency of photovoltaic (PV) systems by introducing multiple local maxima in the power–voltage curve, complicating Maximum Power Point Tracking (MPPT). Traditional MPPT methods, such as Perturb and Observe (P&O) and Incremental Conductance (IC), frequently converge to local maxima, leading to suboptimal power extraction. This study proposes a hybrid reinforcement learning-based MPPT approach that combines fuzzy techniques with Deep Deterministic Policy Gradient (DDPG) to enhance tracking accuracy under partial shading. The method integrates fuzzy membership functions into the actor–critic structure, improving state representation and convergence speed. The proposed algorithm is evaluated in a simulated PV environment under various shading scenarios and benchmarked against conventional Perturb and Observe P&O and IC methods. Experimental results demonstrate that the Fuzzy–DDPG approach outperforms these classical techniques by achieving a higher tracking efficiency of 95%, compared to 85% for P&O and 88% for IC in average, while also minimizing steady-state oscillations. Additionally, the proposed method reduces tracking errors by up to 7.9% compared to conventional MPPT algorithms. These findings indicate that the combination of fuzzy logic and deep reinforcement learning provides a more adaptive and efficient MPPT solution, ensuring improved energy harvesting in dynamically changing conditions. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Technologies, Materials and Their Applications)

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20 pages, 4235 KiB

Open AccessArticle

Low Voltage Ride-Through Improvement of a Grid-Connected PV Power System Using a Machine Learning Control System

by Altan Gencer

Appl. Sci. 2025, 15(8), 4251; https://doi.org/10.3390/app15084251 - 11 Apr 2025

Viewed by 252

Abstract

The insufficient durability of solar energy systems is an important problem in low-voltage situations in the electrical grid. This problem can cause PV systems to become difficult to operate during periods of low voltage and may disconnect PV systems from electrical grids. In [...] Read more.

The insufficient durability of solar energy systems is an important problem in low-voltage situations in the electrical grid. This problem can cause PV systems to become difficult to operate during periods of low voltage and may disconnect PV systems from electrical grids. In this study, a hybrid protection system combining a DC chopper and a capacitive bridge fault current limiter (CBFCL) and based on a machine learning (ML) approach is proposed as a protection strategy to improve the low voltage ride-through (LVRT) capability of a grid-connected PV power plant (PVPP) system. To forecast the best control parameters using real time, including both the fault and normal operation conditions of the grid-connected PVPP system, the ML approach is trained on historical data. Among 20 classifier algorithms, the Coarse Tree classifier and Medium Gaussian SVM classifier have the best accuracy and F1-score for the DC chopper and DC chopper + CBFCL protection systems. The Medium Gaussian SVM classifier has the highest accuracy (98.37%) and F1-score (99.17%) for the DC chopper and CBFCL protection method among the 20 classifier methods. In comparison to another protection system, the simulation results show that a proposed hybrid protection system using SVM offers optimum protection for the grid-connected PVPP system. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Technologies, Materials and Their Applications)

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20 pages, 2074 KiB

Open AccessArticle

Impact of Environmental Variables on Tilt Selection for Energy Yield Maximization in Bifacial Photovoltaic Modules: Modeling Review and Parametric Analysis

by Riccardo Adinolfi Borea, Vincenzo Cirimele, Francesco Lo Franco, Giosuè Maugeri and Francesco Melino

Appl. Sci. 2024, 14(24), 11497; https://doi.org/10.3390/app142411497 - 10 Dec 2024

Viewed by 1022

Abstract

Among the different photovoltaic technologies, bifacial photovoltaic modules outperform monofacial ones by being able to harvest the rear incident irradiance. In fact, they achieve higher power output under identical operating conditions. Consequently, the transition from monofacial to bifacial photovoltaic modules is progressing in [...] Read more.

Among the different photovoltaic technologies, bifacial photovoltaic modules outperform monofacial ones by being able to harvest the rear incident irradiance. In fact, they achieve higher power output under identical operating conditions. Consequently, the transition from monofacial to bifacial photovoltaic modules is progressing in residential and utility contexts. However, it remains to be fully clarified which installation conditions allow bifacial modules to perform best under different operating conditions. After discussing the different modeling techniques presented in the literature, this paper isolates and evaluates the influence of ground reflectivity, module height, and cloudy weather conditions on the annual incident irradiance and, consequently, the optimal tilt angle of a bifacial photovoltaic module. To focus on the bifacial aspect, each factor is analyzed from the perspectives of the front surface, the back surface, and both. Therefore, different patterns are isolated. The results show that ground reflectivity is key in determining the optimal tilt angle, as it affects the back incident irradiance by up to 431% when compared to a low reflectivity scenario. In contrast, module height and weather conditions do not affect the optimal tilt angle, although they do affect the incident irradiance by up to 5% and 24%, respectively. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Technologies, Materials and Their Applications)

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46 pages, 3794 KiB

Open AccessArticle

Progress in Improving Photovoltaics Longevity

by Tsampika Dimitriou, Nikolaos Skandalos and Dimitrios Karamanis

Appl. Sci. 2024, 14(22), 10373; https://doi.org/10.3390/app142210373 - 11 Nov 2024

Cited by 2 | Viewed by 3113

Abstract

With the increase of photovoltaic (PV) penetration in the power grid, the reliability and longevity of PV modules are important for improving their payback period and reducing recycling needs. Although the performance of PV systems has been optimized to achieve a multi-fold increase [...] Read more.

With the increase of photovoltaic (PV) penetration in the power grid, the reliability and longevity of PV modules are important for improving their payback period and reducing recycling needs. Although the performance of PV systems has been optimized to achieve a multi-fold increase in their electricity generation compared to ten years ago, improvements in lifespan have received less attention. Appropriate operation and maintenance measures are required to mitigate their aging. PV cells and modules are subject to various degradation mechanisms, which impact their long-term performance and reliability. Understanding these degradation processes is crucial for improving the lifetime and sustainability of solar energy systems. In this context, this review summarizes the current knowledge on key degradation mechanisms (intrinsic, extrinsic, and specific) affecting PV modules, as well as on-site and remote sensing methods for detecting PV module defects and the mitigation strategies employed for enhancing their operational lifetime under different climatic conditions in the global environment. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Technologies, Materials and Their Applications)

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21 pages, 7518 KiB

Open AccessArticle

Effects of Microbiota on the Soiling Process of PV Modules in Arid Zones of the Atacama Desert

by Ricardo Ortiz, Douglas Olivares, Luis A. Rojas, Abel Taquichiri, Carlos Portillo, Paris Lavín, Diego Valenzuela, Felipe M. Galleguillos Madrid and Edward Fuentealba

Appl. Sci. 2024, 14(19), 8591; https://doi.org/10.3390/app14198591 - 24 Sep 2024

Cited by 1 | Viewed by 1465

Abstract

Photovoltaic technology has proven to be a reliable, economical, and clean energy source that is capable of adapting to diverse geographical conditions. However, factors such as soiling overshadow these qualities, thus leading to production losses and affecting the profitability of this technology. For [...] Read more.

Photovoltaic technology has proven to be a reliable, economical, and clean energy source that is capable of adapting to diverse geographical conditions. However, factors such as soiling overshadow these qualities, thus leading to production losses and affecting the profitability of this technology. For these reasons, soiling is a highly studied topic, which involves considering the physicochemical characterization of the deposited material, mitigation strategies, effect predictions, and cleaning mechanisms. However, there is a relatively unexplored area related to the microbiological contribution to soiling. The surface of photovoltaic modules, along with the deposited material and local atmospheric factors, fosters favorable conditions for the colonization of microorganisms. These microorganisms influence the soiling mechanisms and optical properties of photovoltaic modules. This work presents a detailed characterization of the microbial diversity present in the soiling deposited on photovoltaic modules installed in the Atacama Desert. Two study sites were defined: Antofagasta and the Solar Platform of the Atacama Desert, which have warm and cold desert climates, respectively. Mineralogical characterization tests, heavy metal analyses, TOC, and inorganic element analyses were conducted on the deposited material. Additionally, the culturable isolates and the metagenomic DNA of the soiling samples and biofilms grown on standard PV glass were characterized using next-generation sequencing. The results show that the deposited soiling contained a microbiological component that had adapted to extreme desert conditions. The presence of the genera Arthrobacter, Kocuria, and Dietzia were identified in the culturable isolates from Antofagasta, while Arthrobacter and Dietzia were obtained from the Solar Platform of the Atacama Desert. The metagenomic DNA was mainly represented by the genera Pontibacter, Noviherbaspirillum, Massilia, Arthrobacter, Hymenobacter, and Deinococcus at Antofagasta. However, at the Solar Platform of the Atacama Desert, the analyzed samples presented DNA concentrations below 0.5 ng/µL, which made their preparation unviable. At the PSDA, the biofilms formed by the genera Peribacillus and Kocuria were identified, whereas the UA showed a greater abundance of bacteria that favored biofilm formation, including those that belonged to the genera Bacillus, Sporosarcina, Bhargavaea, Mesaobacillus, Cytobacillus, Caldakalibacillus, and Planococcus. Based on these results, we propose a soiling mechanism that considers the microbiological contribution to material cementation. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Technologies, Materials and Their Applications)

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11 pages, 3323 KiB

Open AccessArticle

Effect of Methylammonium Iodide (MACl) on MAPbI₃-Based Perovskite UV-C Photodetectors

by Dong Jae Shin, Sangmo Kim and Hyung Wook Choi

Appl. Sci. 2024, 14(14), 6223; https://doi.org/10.3390/app14146223 - 17 Jul 2024

Viewed by 1512

Abstract

In this study, we fabricated deep ultraviolet (DUV) photodetectors based on perovskite thin films doped with halide materials using formamidinium bromide (FABr) and methylammonium iodide (MAI). The device was fabricated using a simple surface engineering technique by post-treating the MAPbI₃ perovskite film [...] Read more.

In this study, we fabricated deep ultraviolet (DUV) photodetectors based on perovskite thin films doped with halide materials using formamidinium bromide (FABr) and methylammonium iodide (MAI). The device was fabricated using a simple surface engineering technique by post-treating the MAPbI₃ perovskite film with an FABr solution. This film acts as a light absorption layer, like a depletion layer with a p-i-n (PIN) structure, with n-type of SnO₂-SDBS and p-type of spiro-OMeTAD. Adding 0.10 M MACl to the MAPbI3 precursor solution during the manufacturing process could effectively reduce the trap density compared with existing films. Films with MACl added in the two-step process can control a wide band gap and improve crystallinity. In addition, the Cl atom has a smaller atomic radius than iodine and a higher electronegativity of 3.16, which can improve phase stability, and the effect of the added Cl⁻ increases the electron mobility of the perovskite, showing a fast response. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Technologies, Materials and Their Applications)

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Review

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63 pages, 2878 KiB

Open AccessReview

A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used

by Carlos M. Travieso-González, Fidel Cabrera-Quintero, Alejandro Piñán-Roescher and Sergio Celada-Bernal

Appl. Sci. 2024, 14(13), 5605; https://doi.org/10.3390/app14135605 - 27 Jun 2024

Cited by 3 | Viewed by 2285

Abstract

The increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. [...] Read more.

The increasing penetration of solar energy into the grid has led to management difficulties that require high accuracy forecasting systems. New techniques and approaches are emerging worldwide every year to improve the accuracy of solar power forecasting models and reduce uncertainty in predictions. This article aims to evaluate and compare various solar power forecasting methods based on their characteristics and performance using imagery. To achieve this goal, this article presents an updated analysis of diverse research, which is classified in terms of the technologies and methodologies applied. This analysis distinguishes studies that use ground-based sensor measurements, satellite data processing, or all-sky camera images, as well as statistical regression approaches, artificial intelligence, numerical models, image processing, or a combination of these technologies and methods. Key findings include the superior accuracy of hybrid models that integrate multiple data sources and methodologies, and the promising potential of all-sky camera systems for very short-term forecasting due to their ability to capture rapid changes in cloud cover. Additionally, the evaluation of different error metrics highlights the importance of selecting appropriate benchmarks, such as the smart persistence model, to enhance forecast reliability. This review underscores the need for continued innovation and integration of advanced technologies to meet the challenges of solar energy forecasting. Full article

(This article belongs to the Special Issue Solar Energy and Photovoltaic Technologies, Materials and Their Applications)

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