Solar

CPVG (Utility-scale photovoltaic generation) is expanding rapidly worldwide, yet its cumulative ecological effects remain insufficiently quantified. This review synthesizes current evidence to clarify how CPVG influences ecosystems through linked mechanisms of energy redistribution, biogeochemical cycling disturbance, and ecological responses. CPVG alters surface radiation balance, modifies microclimate, and disrupts carbon–nitrogen–water fluxes, thereby driving vegetation shifts, soil degradation, and biodiversity decline. These impacts accumulate across temporal scales—from short-term construction disturbances to long-term operational feedbacks—and propagate spatially from local to regional and watershed levels. Ecological outcomes differ substantially among deserts, grasslands, and agroecosystems due to contrasting resilience and limiting factors. Based on these mechanisms, we propose a multi-scale cumulative impact assessment framework integrating indicator development, multi-source monitoring, coupled modelling, and ecological risk tiering. A full-chain mitigation pathway is further outlined, emphasizing optimized siting, disturbance reduction, adaptive management, and targeted restoration. This study provides a systematic foundation for evaluating and regulating CPVG’s cumulative ecological impacts, supporting more sustainable solar deployment. Full article

Smart microgrids are localized energy systems that integrate distributed energy resources, such as photovoltaics (PVs) and battery storage, to optimize energy use, enhance reliability, and minimize environmental impacts. This paper investigates the operation of a smart microgrid installed at the Hellenic Mediterranean University (HMU) campus in Heraklion, Crete, Greece. The system, consisting of PVs and battery storage, operates under a zero feed-in scheme, which maximizes on-site self-consumption while preventing electricity exports to the main grid. With increasing PV penetration and growing grid congestion, this scheme is an increasingly relevant strategy for microgrid operations, including university campuses. A properly sized PV–battery microgrid operating under zero feed-in operation can remain financially viable over its lifetime, while additionally it can achieve significant environmental benefits. The study performed at the HMU Campus utilizes measured hourly data of load demand, solar irradiance, and ambient temperature, while PV and battery components were modeled based on real technical specifications. The study evaluates the system using financial and environmental performance metrics, specifically net present value (NPV) and annual greenhouse gas (GHG) emission reductions, complemented by sensitivity analyses for battery technology (lead–carbon and lithium-ion), load demand levels, varying electricity prices, and projected reductions in lithium-ion battery costs over the coming years. The findings indicate that the microgrid can substantially reduce grid electricity consumption, achieving annual GHG emission reductions exceeding 600 tons of CO₂. From a financial perspective, the optimal configuration consisting of a 760 kWp PV array paired with a 1250 kWh lead–carbon battery system provides a system autonomy of 46% and achieves an NPV of EUR 1.41 million over a 25-year horizon. Higher load demands and electricity prices increase the NPV of the optimal system, whereas lower load demands enhance the system’s autonomy. The anticipated reduction in lithium-ion battery costs over the next 5–10 years is expected to provide improved financial results compared to the base-case scenario. These results highlight the techno-economic viability of zero feed-in microgrids and provide valuable insights for the planning and deployment of similar systems in regions with increasing renewable penetration and grid constraints. Full article

Uncovering the interdependencies among barrier factors and pinpointing the most critical obstacles are essential to overcoming the resistance encountered by photovoltaic (PV) integration into electricity markets. This study first employs grounded theory to identify and categorize the key barriers impeding PV participation, thereby constructing a comprehensive barrier factor model. Subsequently, Interpretive Structural Modeling (ISM) is applied to systematically analyze the interrelations and hierarchical structure among these barriers. The results reveal that: (1) The complex system of PV participation comprises 15 distinct barriers, which can be grouped into 4 overarching categories: economic and cost-related challenges, policy and regulatory uncertainties, technological and infrastructure constraints, and environmental and resource limitations. (2) These barriers form a six-tier hierarchical structure, reflecting their layered influence. (3) Root-level barriers—such as inadequate government fiscal support and the absence of a comprehensive coordination mechanism—play a foundational role in hindering progress. In response, this study proposes policy recommendations, including establishing a unified and effective coordination framework to align renewable energy policies and formulating standardized guidelines for PV panel recycling. Full article

The increasing integration of solar energy into the power grid necessitates robust fault detection and diagnosis (FDD) guidelines to ensure energy continuity and optimize the performance of grid-connected photovoltaic (GCPV) systems. This research addresses a gap in the literature by systematically evaluating machine learning (ML) algorithms for the detection and classification of AC-side faults (inverter and grid faults) in GCPV systems. We utilized three commonly employed algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), and Artificial Neural Networks (ANNs), to develop fault detection models. These models were trained using a monthly electrical dataset obtained from the AYCEM-GES-GCPV power plant in Giresun, Turkiye, and their performance was rigorously evaluated using classification accuracy, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) analyses. The results demonstrate that the algorithms are highly effective in fault detection, with AUC values consistently exceeding the critical threshold. The obtained accuracies for KNN, LR, and ANN were 0.9826, 0.782, and 0.7096, respectively. These findings emphasize the high effectiveness of ML algorithms, with KNN exhibiting the best performance, for identifying AC-side faults in GCPV installations. While the study focused on AC-side fault detection, subsequent work developed a smart card module to identify complex DC side electrical faults and built a PV array for experimental testing. Full article

In Mediterranean cities, high solar radiation combined with limited shading and vegetation intensifies the urban heat island (UHI) phenomenon. As the road network often covers a large portion of the cities’ surfaces and is mostly constructed using asphalt pavements, it can significantly affect the urban microclimate, leading to low thermal comfort and increased energy consumption. Recycled and waste materials are increasingly used in the construction of pavements in accordance with the principle of sustainability for minimizing waste and energy to produce new materials based on a circular economy. The scope of this study is to evaluate the effect of recycled or waste materials used in road pavements on the urban microclimate. The surface and ambient temperature of urban pavements constructed with conventional asphalt and recycled/waste-based mixtures are assessed through simulation. Two study areas comprising large street junctions near metro stations in the city of Thessaloniki, in Greece, are examined under three scenarios: a conventional hot mix asphalt, an asphalt mixture containing steel slag, and a high-albedo mixture. The results of the research suggest that the use of steel slag could reduce the air temperature by 0.9 °C at 15:00, east European summer time (EEST), while the high-albedo scenario could reduce the ambient temperature by 1.6 °C at 16:00. The research results are useful for promoting the use of recycled materials, not only as a means of sustainably using resources but also for the improvement of thermal comfort in urban areas, the mitigation of the UHI effect, and the reduction of heat stress for human health. Full article

Bypass diode failure, particularly in the short-circuit mode, remains an under-addressed reliability issue in photovoltaic (PV) modules, causing severe power suppression and often leading to premature disposal of otherwise functional units. This study presents a non-destructive, field-applicable plug-in repair protocol for restoring modules affected by short-circuited bypass diodes. From twenty-two field-deployed modules, nine were analyzed in detail under healthy, single-fault, and dual-fault conditions. Controlled diode faults were introduced and subsequently repaired using commercially available plug-in bypass diodes. Electroluminescence (EL) imaging, current–voltage (I–V) testing, and extraction of series and shunt resistances were performed before and after repair. Results show that a single shorted diode deactivates one substring, reducing power by ~34–37%, while dual faults suppress over two-thirds of the active area, causing power losses above 67%. After repair, power deviation decreased to <3% for single faults and <7% for dual faults, with shunt resistance increasing by 52–262%, confirming removal of diode-induced leakage paths. Series resistance remained largely unchanged except in modules with irreversible cell-level damage accumulated during prolonged faulty operation. The findings demonstrate that short-circuited bypass diode faults are readily repairable and that component-level intervention can restore module performance, extend operational lifetime, and reduce unnecessary PV recycling. Full article

The deployment of solar and other renewable energy technologies (RETs) plays a central role in the global energy transition and the pursuit of sustainable development. Beyond reducing greenhouse gas emissions, these technologies generate far-reaching societal co-benefits that shape environmental quality, social equity, and economic growth. This study systematically reviews peer-reviewed literature published between 2009 and 2025 to identify, integrate, and assess empirical evidence on how RET deployment contributes to societal welfare. Following the SALSA framework and PRISMA guidelines, 147 studies were selected from Scopus and Web of Science. The evidence reveals a consistent welfare triad: environmental gains (emission and pollution reduction, climate mitigation), social gains (improved health, affordability, energy security, and inclusion), and economic gains (employment and income growth, local development). These benefits are, however, heterogeneous and depend on enabling conditions such as policy stability, financial development, grid integration, innovation capacity, and social acceptance. The review highlights that solar energy, in particular, acts as both an environmental and social catalyst in advancing sustainable welfare outcomes. The findings provide a comprehensive basis for policymakers and researchers seeking to design equitable and welfare-enhancing renewable energy transitions. Full article

While there is currently a significant opportunity for the construction of photovoltaic solar farms in the Southeastern United States, there is also a need for proper spatial planning that has not been adequately addressed by the existing literature. The objective of this study is to examine the adaptability of geographic information system-based multiple criteria decision analysis models developed for foreign contexts to the United States. This was accomplished through the application of a model developed originally for Thailand to the study area of Bibb County, Georgia, United States. Model results were analyzed to identify trends and provide concrete recommendations for future work. Using a six-rank classification scheme, 93% of Bibb County was found to have moderate suitability, while 5% and 2% had moderate-to-low and moderate-to-high suitability, respectively. Of the 11 model criteria, land usage and power line distance were found to have the largest impact on the area’s suitability. Statistical analysis identified positive trends indicating that these criteria explained 21% and 10% of the variance in the model’s output, respectively. Empirical verification proved the model structure to be viable for application in the Southeastern United States; however, additional examination of the model’s results found that there is room to improve the model for the local context. These improvements could potentially be realized through the reweighting of criteria and the re-establishment of evaluation benchmarks, allowing for the development of a truly robust model for the region. Full article

This study investigates the fire behaviour of building-integrated photovoltaic (PV) claddings, focusing on the progression from glass failure to ignition under different cavity conditions. Experimental tests were conducted on two common PV cladding types: bifacial dual-glass (GG) and monofacial glass–plastic (GP) modules. Results revealed that GP modules exhibited faster burning and higher peak heat release rates (HRR), reaching up to 600 kW, while GG modules burned more slowly with peak HRR between 50 and 100 kW. Cavity conditions, including depth, ventilation, and operational energization, were found to be vital in determining glass breakage, occurring between 400 and 550 °C, and cavity ignition and subsequent flame spread. The relationship between cavity fire dynamics and glass breakage suggests the importance of system design, particularly regarding cavity ventilation and flame barriers, for mitigating upward fire propagation. These results establish a basis for advancing numerical fire models through integration of critical parameters such as material properties, glass breakage, cavity ignition, and cavity configuration. This approach supports comprehensive real-scale analysis to guide the development of effective design recommendations, ultimately improving fire safety in PV-integrated construction. Full article

In response to the pressing climate change emergency, the rapid expansion of renewable energies, particularly photovoltaic (PV) power in Spain, is reconfiguring national energy landscapes, thereby necessitating detailed spatial analysis. This study aims to characterise the spatial distribution of PV energy in the country. Specifically, it employed the Administrative Register of Electricity Production Facilities (RAIPEE) database from 2000 to 2023 and a review of Environmental Impact Statements (EIA) from 2014 to 2023 to generate a facility density cartography. Additionally, the spatial statistic Moran’s I was used to detect aggregation patterns. The results demonstrated an aggregation tendency for low and medium power facilities (up to 10 MW), while the distribution of higher-capacity facilities appeared random. Examination of the facility density cartographies suggest significant variability among provinces and distribution trends centred around the country’s main urban regions. This approach to understanding the spatial dynamics of PV energy offers novel and crucial geospatial insights for renewable energy planning. Full article

As the penetration of distributed energy resources (DERs) continues to increase, there is conjecture concerning the power quality implications of the inverters used to interface these DERs with low-voltage (LV) electricity supply networks. As a power electronics converter, inverters are a known source of harmonic emissions. Using a combination of large-scale field measurements, laboratory evaluations of inverter performance, and power system modelling, this study applies an empirical data-driven approach to investigate the impact of small-scale solar PV inverters on LV harmonic distortion magnitudes. This multi-facetted approach, involving field data analysis, laboratory assessments of inverter performance, and power system simulation to evaluate the impact of small-scale DER on harmonic distortion in LV networks, is novel in comparison to other studies, which only utilise one or two of the analysis methods of simulation, laboratory evaluation, or analysis of field measurements but not all three. The analysis of field measurement data collected over the past decade does not indicate any significant changes in harmonic distortion magnitudes that can be attributed to the increasing penetration of DERs. Power system modelling, which incorporates data obtained from laboratory inverter performance evaluations, indicates that, even at very high levels of penetration, the harmonic current emissions from solar PV inverters are only sufficient to add modest levels of harmonic distortion to LV networks, a 0.25% increase in THD for 40% penetration and a 0.62% increase in THD for 100% penetration, providing an explanation for the findings of the field data analysis. Full article

Floating photovoltaic systems represent a promising solution for renewable energy generation, offering an alternative to agricultural land consumption. However, these installations have the potential to exert an effect on the aquatic ecosystem, emphasizing the necessity of effective monitoring strategies also related to system management issues. In this paper, the use of an unmanned surface vehicle, which can also operate as an autonomous surface vehicle, is proposed to overcome many difficulties of maintenance and monitoring in aquatic environments. A review of the extant literature reveals the scarcity of a cohesive monitoring framework for these plants, highlighting the urgent need for standardized guidelines for plant management and water quality monitoring. The implementation of automated plants directly addresses this gap by providing a tool for efficient and sustainable monitoring tasks, enabling, at the same time, aquatic ecosystem protection and energy production optimization. To address these challenges, a low-cost prototype of an autonomous surface vehicle is proposed. Preliminary test results on trajectory control and obstacle recognition are reported. Full article

Efficient utilization of solar energy in equatorial regions depends on accurately determining the optimal tilt and azimuth angles of fixed photovoltaic (PV) systems. This study presents a three-dimensional vector-based methodology that employs Direct Normal Irradiance (DNI) to estimate the mean direction of incident solar flux. Hourly DNI data from 2020–2024 for the city of Guayaquil, Ecuador, were transformed into spatial vectors and integrated to obtain a resultant vector representing the average orientation and elevation of direct solar radiation. The analysis yielded an optimal tilt angle of 5.73° and an azimuth of 59.15°, values consistent with Guayaquil’s equatorial latitude and previous studies conducted in tropical environments. The low tilt angle reflects the persistently high solar elevation typical of equatorial zones, while the slight northeastward orientation deviation corresponds to the asymmetric diurnal distribution of solar irradiance. The main contribution of this work lies in providing a geometrically rigorous and computationally efficient approach capable of synthesizing the directional behavior of solar flux without relying on complex transposition models. The proposed method enhances the optimization of PV system design, urban energy planning, and renewable microgrid modeling in data-scarce contexts, supporting the sustainable development of solar energy in equatorial regions. Full article

Accurate day-ahead solar forecasting is essential for grid stability and energy planning. This study introduces a specialized forecasting framework that enhances accuracy by training models on specific day-to-day sky condition transitions. The framework employs a dual-attention transformer model, which captures complex temporal and feature-wise relationships, using a dataset of approximately 5000 daily sequences from three sites in Mongolia (2018–2024). Our core contribution is a specialized training strategy where the dataset is first classified into nine distinct classes based on the sky condition transition from the previous day to the forecast day, such as ‘Clear’ to ‘Partly cloudy’. A dedicated transformer model is then trained for each transitional state, enabling it to become an expert on that specific weather dynamic. This specialized framework is benchmarked against a naive persistence model, a standard, generalized transformer trained on all data and a ‘cluster-then-forecast’ approach. Results show the proposed approach achieves superior performance improvement compared to baseline models (p < 0.001) across all error metrics, demonstrating the value of modeling inter-day weather dynamics. Furthermore, the framework is extended to probabilistic forecasting using quantile regression to generate 80% prediction intervals, providing crucial uncertainty information for operational decision-making in power grids. Full article

This study evaluates the thermal performance of a prototype vacuum-tube solar cooker adapted to the climatic conditions of the Amazon region, Peru. Four grain types (Zea mays L., Triticum aestivum, Zea mays var. morochon, and Hordeum vulgare) were tested to assess temperature evolution, exposure time, and incident solar radiation. Hordeum vulgare was selected as a food model for calibration due to its well-characterized thermophysical properties and reproducible heating behavior. The results showed individual thermal efficiencies ranging from 19.3% to 35.3%, with an average of 27.3% across the three tubes. The most efficient treatment, obtained with Zea mays L., reached 180 °C under an irradiance of approximately 980 W/m². A direct relationship was observed between solar radiation intensity, exposure time, and thermal efficiency. These findings confirm that the proposed hybrid design combining a cylindrical parabolic collector with vacuum U-tubes achieves higher and more stable performance than conventional box-type cookers. The system allows complete grain cooking without fossil fuels, demonstrating its potential as a sustainable and low-cost solution for rural communities in the Andean Amazonian region, promoting clean energy adoption and reducing environmental impact. Full article

This study investigates the efficacy of large language models (LLMs) in supporting responsible and optimized geographic site selection for large-scale solar energy farms. Using Microsoft Bing (predecessor to Copilot), Google Bard (predecessor to Gemini), and ChatGPT, we evaluated their capability to address complex technical and social considerations fundamental to solar farm development. Employing a series of guided queries, we explored the LLMs’ “understanding” of social impact, geographic suitability, and other critical factors. We tested varied prompts, incorporating context from existing research, to assess the models’ ability to use external knowledge sources. Our findings demonstrate that LLMs, when meticulously guided through increasingly detailed and contextualized inquiries, can yield valuable insights. We discovered that (1) structured questioning is key; (2) characterization outperforms suggestion; and (3) harnessing expert knowledge requires specific effort. However, limitations remain. We encountered dead ends due to prompt restrictions and limited access to research for some models. Additionally, none could independently suggest the “best” site. Overall, this study reveals the potential of LLMs for geographic solar farm site selection, and our results can inform future adaptation of geospatial AI queries for similarly complex geographic problems. Full article

Cr content χ of 0.4 at% for a Cr doped Nd (1 at%): YAG laser rod (LR) gave a higher laser output (I_output) than that of 0.0, 0.7, and 1.0 at% in a specially designed compact solar-pumped laser (SPL) outdoors. I_outputs were measured as a function of an 808 nm pumping laser’s power indoors, changing the transmittance of the output coupler. From the obtained slope efficiencies, round-trip resonator losses Ls for the four χs were estimated, and the best-fit function L(χ) was derived. From the experimentally estimated Cr-to-Nd effective energy transfer efficiency η_Cr→Nd at the four χs, the best-fit function η_Cr→Nd(χ) was derived. Using L(χ), η_Cr→Nd(χ), and a wavelength λ- and χ-dependent absorption coefficient α(λ, χ), inferred from the literature, the power conversion efficiency η_power(χ) under 1 Sun was estimated. The estimated η_power(0.4) and η_power(0.7) were reproduced in experimentally deduced factors at the mode-matching efficiency η_mode = 0.19. The estimated maximum η_power(χ) appeared around χ = 0.2 at%, being 20% higher than that at χ = 0.4 at%. In addition to this, a composite LR (Cr, Nd: YAG core/Gd: YAG cladding) was found to achieve η_mode = 0.68 and η_power = 0.064, ranking among the highest-class SPL η_powers. Full article

Solar panel deployment is vital to generate clean energy and reduce carbon emissions, but sustaining energy output requires regular monitoring and maintenance. This is particularly critical in countries with harsh environmental conditions, such as Qatar, where high dust density reduces solar radiation reaching panels, thereby lowering generating efficiency and increasing maintenance costs. This paper introduces a data-driven model that uses the relationship between generated and consumed energy to track changes in solar panel performance. By applying statistical analysis to real and simulated data, the model identifies when efficiency losses are within the parameters of normal variation (e.g., daily fluctuations) and when they are likely caused by dust accumulation or system ageing. The findings demonstrate that the model provides a reliable and cost-effective way to support timely cleaning and maintenance decisions. It offers decision-makers a practical tool to improve residential solar panel management, reducing unnecessary costs, and ensuring more consistent renewable energy generation. Full article

Nowadays, bifacial photovoltaic (PV) technology has emerged as a key solution to enhance the energy yield of large-scale PV plants, especially when integrated with sun-tracking systems. This study investigates the quantification of bifaciality productivity for two multi-MW PV plants in southern Italy (Sicily) equipped with monocrystalline silicon bifacial modules installed on single-axis east–west tracking systems and aligned in the north–south direction. An optimized energy model was developed at the stringbox level, employing a dedicated procedure including data filtering, clear-sky condition selection, and numerical estimation of bifaciality factors. The model was calibrated using on-field measurements acquired during the first operational months to minimize uncertainties related to degradation phenomena. The application of the model demonstrated that the rear-side contribution to the total energy output is non-negligible, resulting in additional energy gains of approximately 5.3% and 3% for the two plants, respectively. Full article