Search Results (1,410)

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

Photovoltaic (PV) fault detection faces significant challenges in distinguishing subtle defects from complex backgrounds while maintaining reliability across diverse environmental conditions. Traditional approaches struggle with scalability and accuracy limitations, particularly when detecting electrical damage, physical defects, and environmental soiling in thermal imagery. This paper presents SolarFaultAttentionNet, a novel dual-attention deep learning framework that integrates channel-wise and spatial attention mechanisms within a multi-path CNN architecture for enhanced PV fault classification. The approach combines comprehensive data augmentation strategies with targeted attention modules to improve feature discrimination across six fault categories: Electrical-Damage, Physical-Damage, Snow-Covered, Dusty, Bird-Drop, and Clean. Experimental validation on a dataset of 885 images demonstrates that SolarFaultAttentionNet achieves 99.14% classification accuracy, outperforming state-of-the-art models by 5.14%. The framework exhibits perfect detection for dust accumulation (100% across all metrics) and robust electrical damage detection (99.12% F1 score) while maintaining an optimal sensitivity (98.24%) and specificity (99.91%) balance. The computational efficiency (0.0160 s inference time) and systematic performance improvements establish SolarFaultAttentionNet as a practical solution for automated PV monitoring systems, enabling reliable fault detection critical for maximizing energy production and minimizing maintenance costs in large-scale solar installations. Full article

In recent years, the amount of electricity produced by photovoltaic systems in Poland has increased significantly. This paper presents an evaluation of commercial software (PVGIS 5.3, ENERAD, and PVGIS 24) used for simulating energy produced by four photovoltaic installations. The results of the simulation were compared with the real energy production. The installations differ in terms of panel orientation (S, SE, SE-NW), tilt angle (12°, 25°, 37°) and location (roof- or ground-mounted). The average annual electricity production per 1 kW of module power for each installation was as follows: PV1—1104 kWh·kW⁻¹, PV2—1169 kWh·kW⁻¹, PV3—927 kWh·kW⁻¹, and PV4—831 kWh·kW⁻¹. The highest values were recorded for ground-mounted installations facing south. Simulations carried out using computer programs show differences between simulated and real electricity production values of 35–41% for the ENERAD software, 3–13% for the PVGIS 5.3 software, and 3–32% for the PVGIS 24 software. The most accurate forecasts were obtained for the PV2 system in the PVGIS 24 software (MPE 3%, RMSE 12%), and the most unfavorable for the same installation in the ENERAD software (MPE 41%, RMSE 48%). Full article

As interest in PV installation has spiked in recent years, the need for optimizing several factors of PV performance has become crucial. These are tilt angle and solar cell temperature (taking into account ambient temperature) and their effect on solar insolation for solar photovoltaic (PV) systems. The objective of this study is to achieve the optimal tilt angle and cell temperature accordingly by developing a MATLAB program to reach the target of maximizing the received solar insolation. To achieve this, additional solar angles such as the azimuth, hour, latitude angle, declination angle, hour angle, and azimuth angle need to be calculated. By computing the solar insolation for specific regions of interest, specifically the Gulf Cooperation Council (GCC) countries, the desired results can be obtained. Additionally, the study aims to assess the influence of PV cell temperature on the I–V curves of commercially available PV modules, which will provide insights into the impact of temperature on the performance characteristics of PV cells. By employing a developed model, the study examined the combined collective influences of solar received radiation, tilt angle, and ambient temperature on the output power of PV systems in five different cities. The annual optimal tilt angles were found to be as follows: Mecca (21.4° N)—21.48°, Fujairah (25.13° N)—25.21°, Kuwait (29.3° N)—29.38°, Baghdad (33.3° N)—33.38°, and Mostaganem (35.9° N)—2535.98°. Notably, the estimated yearly optimal tilt angles closely corresponded to the latitudes of the respective cities. Additionally, the study explored the impact of ambient temperature on PV module performance. It was observed that an increase in ambient temperature resulted in a corresponding rise in the temperature of the PV cells, indicating the significant influence of environmental temperature on PV module efficiency. Overall, the findings demonstrate that adjusting the tilt angle of PV modules on a monthly basis led to higher solar power output compared to yearly adjustments. These results underscore the importance of considering both solar radiation and ambient temperature when optimizing PV power generation. Full article

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO₂ emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024. Full article

As states within the United States respond to future grid development goals, there is a growing demand for reliable and resilient nighttime generation that can be addressed by low-cost, long-duration energy storage solutions. This report studies the potential of including concentrating solar power (CSP) in the technology mix to support California’s goals as defined in Senate Bill 100. A joint agency report study that determined potential pathways to achieve the renewable portfolio standard set by the bill did not include CSP, and our work provides information that could be used as a follow-up. This study uses a capacity expansion model configured to have nodal spatial fidelity in California and balancing-area fidelity in the Western Interconnection outside of California. The authors discovered that by applying current technology cost projections CSP fulfills nearly 15% of the annual load while representing just 6% of total installed capacity in 2045, replacing approximately 30 GWe of wind, solar PV, and standalone batteries compared to a scenario without CSP included. The deployment of CSP in the results is sensitive to the technology’s cost, which highlights the importance of meeting cost targets in 2030 and beyond to enable the technology’s potential contribution to California’s carbon reduction goals. Full article

The growing volume of decommissioned wind turbine blades (WTBs) poses substantial challenges for end-of-life (EoL) material management, particularly within the composite repurposing and recycling strategies. This study investigates the repurposing of EoL WTB segments in a full-scale demonstrator for a photovoltaic (PV) floating platform. The design process is supported by a calibrated numerical model replicating the structure’s behaviour under representative operating conditions. The prototype reached Technology Readiness Level 6 (TRL 6) through laboratory-scale wave basin testing, under irregular wave conditions with heights up to 0.22 m. Structural assessment validates deformation limits and identifies critical zones using composite failure criteria. A comparison between two configurations underscores the importance of load continuity and effective load distribution. Additionally, a life cycle assessment (LCA) evaluates environmental impact of the repurposed solution. Results indicate that the demonstrator’s footprint is comparable to those of conventional PV-floating installations reported in the literature. Furthermore, overall sustainability can be significantly enhanced by reducing transport distances associated with repurposed components. The findings support the structural feasibility and environmental value of second-life applications for composite WTB segments, offering a circular and scalable pathway for their integration into aquatic infrastructures. Full article

With growing urban populations and rapid technological advancement, major cities worldwide are facing pressing challenges from surging energy demands. Interestingly, substantial unused space within residential buildings offers potential for installing renewable energy systems coupled with energy storage. This study innovatively proposes a grid-connected photovoltaic (PV) system integrated with pumped hydro storage (PHS) and battery storage for residential applications. A novel optimization algorithm is employed to achieve techno-economic optimization of the hybrid system. The results indicate a remarkably short payback period of about 5 years, significantly outperforming previous studies. Additionally, a threshold is introduced to activate pumping and water storage during off-peak nighttime electricity hours, strategically directing surplus power to either the pump or battery according to system operation principles. This nighttime water storage strategy not only promises considerable cost savings for residents, but also helps to mitigate grid stress under time-of-use pricing schemes. Overall, this study demonstrates that, through optimized system sizing, costs can be substantially reduced. Importantly, with the nighttime storage strategy, the payback period can be shortened even further, underscoring the novelty and practical relevance of this research. Full article

The carbon emission reduction mission requires a multifaceted approach, in which green hydrogen is expected to play a key role. The accelerated adoption of green hydrogen technologies is vital to this journey towards carbon neutrality by 2050. However, the energy transition involving green hydrogen requires a data-driven approach to ensure that the benefits are realised. The introduction of testing sites for green hydrogen technologies will be crucial in enabling the performance testing of various components within the green hydrogen value chain. This study involves an areal assessment of a selected test site for the installation of a grid-tied solar PV-green hydrogen-battery storage microgrid system at a factory facility in South Africa. The evaluation includes a site energy audit to determine the consumption profile and an analysis of the location’s weather pattern to assess its impact on the envisaged microgrid. Lastly, a design of the microgrid is conceptualised. A 39 kW photovoltaic system powers the microgrid, which comprises a 22 kWh battery storage system, 10 kW of electrolyser capacity, an 8 kW fuel cell, and an 800 L hydrogen storage capacity between 30 and 40 bars. Full article

To support dual-carbon objectives and enhance the accuracy of rooftop distributed photovoltaic (PV) planning, this study proposes a multidimensional coupled evaluation framework that integrates an improved rooftop segmentation network (CESW-TransUNet), a residual-fusion ensemble, and physics-based shading and performance simulations, thereby correcting the bias of conventional 2-D area–based methods. First, CESW-TransUNet, equipped with convolution-enhanced modules, achieves robust multi-scale rooftop extraction and reaches an IoU of 78.50% on the INRIA benchmark, representing a 2.27 percentage point improvement over TransUNet. Second, the proposed residual fusion strategy adaptively integrates multiple models, including DeepLabV3+ and PSPNet, further improving the IoU to 79.85%. Finally, by coupling Ecotect-based shadow analysis with PVsyst performance modeling, the framework systematically quantifies dynamic inter-building shading, rooftop equipment occupancy, and installation suitability. A case study demonstrates that the method reduces the systematic overestimation of annual generation by 27.7% compared with traditional 2-D assessments. The framework thereby offers a quantitative, end-to-end decision tool for urban rooftop PV planning, enabling more reliable evaluation of generation and carbon-mitigation potential. Full article

China possesses abundant solar energy resources and remains heavily dependent on agriculture. The integration of photovoltaic (PV) power generation with agricultural production has emerged as a strategic pathway to advance China’s ecological transition and dual carbon goals. By 2023, PV power generation represented 21% of the nation’s total installed capacity. The cumulative capacity was projected to reach approximately 887 GW by 2024. The novelty of this study lies in offering a systematic and integrative review of PV agriculture in China. This paper used a combination of field research, case studies, policy analysis, and a comparative evaluation of diverse “PV+” development models. The findings reveal a pronounced spatial imbalance. Western China possesses 42% of the country’s solar energy resources, whereas the eastern provinces of Jiangsu, Zhejiang, and Anhui collectively comprise 37.8% of all PV agricultural projects. Three dominant “PV+” models are identified and categorized as follows: “PV + ecological restoration”, “PV + agriculture, forestry, animal husbandry, and fisheries,” and “PV + facility agriculture.” These models provide multiple benefits. They enhance land use efficiency, stimulate local economic development, and contribute to food security by expanding the supply of essential agricultural products. Based on these insights, the study highlights future priorities in technological innovation, ecological evaluation, intelligent equipment, digitalization, and region-specific policy support. Overall, this research fills a key gap in systematically and comprehensively describing the current development status of photovoltaic agriculture in China. It also offers transferable lessons for sustainable agriculture and global energy transitions. Full article

This study evaluates two approaches for detecting solar photovoltaic (PV) installations across agricultural areas, emphasizing their role in supporting sustainable energy monitoring, land management, and planning. Accurate PV mapping is essential for tracking renewable energy deployment, guiding infrastructure development, assessing land-use impacts, and informing policy decisions aimed at reducing carbon emissions and fostering climate resilience. The first approach applies deep learning-based semantic segmentation to high-resolution RGB orthophotos, using the pretrained “Solar PV Segmentation” model, which achieves an F1-score of 95.27% and an IoU of 91.04%, providing highly reliable PV identification. The second approach employs multitemporal pixel-wise spectral classification using Sentinel-2 imagery, where the best-performing neural network achieved a precision of 99.22%, a recall of 96.69%, and an overall accuracy of 98.22%. Both approaches coincided in detecting 86.67% of the identified parcels, with an average surface difference of less than 6.5 hectares per parcel. The Sentinel-2 method leverages its multispectral bands and frequent revisit rate, enabling timely detection of new or evolving installations. The proposed methodology supports the sustainable management of land resources by enabling automated, scalable, and cost-effective monitoring of solar infrastructures using open-access satellite data. This contributes directly to the goals of climate action and sustainable land-use planning and provides a replicable framework for assessing human-induced changes in land cover at regional and national scales. Full article

Insulation degradation in photovoltaic (PV) cables can cause electrical faults and fire hazards, thereby compromising system reliability and safety. Early detection of precursor signals is crucial for preventive maintenance. However, conventional diagnostic techniques are limited to static assessments and fail to capture early-stage electrical anomalies in real-time. This study investigates the time-series behavior of voltage, current, and temperature in PV cables under thermal stress conditions. Experiments were conducted using TFR-CV cables installed in a vertically stacked and tight-contact configuration. A gas torch was applied for localized heating to induce insulation degradation. A grid-connected testbed with six series-connected PV modules was constructed. Each module was instrumented with PV-M sensors, temperature sensors, and an infrared camera. Data were acquired at 1 Hz intervals. Results showed that cable surface temperature exceeded 280 °C during degradation. The output voltage exhibited transient surges of up to +13.3% and drops of −68%, while the output current decreased by over 20%, particularly in the PV-M3 module. These anomalies, such as thermal imbalance, voltage spikes/dips, and current drops, were closely associated with critical degradation points and are interpreted as precursor signals. This work confirms the feasibility of identifying fire-related precursors through real-time monitoring of PV cable electrical characteristics. The observed correlation between electrical responses and thermal expansion behaviors suggests a strong link to the stages of insulation degradation. Future work will focus on quantifying the relationship between degradation and electrical behavior under controlled environmental conditions. Full article

Driven by the “double carbon” goals, the penetration rate of distributed photovoltaics (PV) in distribution networks has increased rapidly. However, the continuous growth of distributed PV installed capacity poses significant challenges to the carrying capacity of distribution networks. Reinforcement learning (RL), with its capability to handle high-dimensional nonlinear problems, plays a critical role in analyzing the carrying capacity of distribution networks. This study constructs an evaluation model for distributed PV carrying capacity and proposes a corresponding quantitative evaluation index system by analyzing the core factors influencing it. An optimization scheme based on deep reinforcement learning is adopted, introducing the Deep Deterministic Policy Gradient (DDPG) algorithm to solve the evaluation model. Finally, simulations on the IEEE-33 bus system validate the good feasibility of the reinforcement learning approach for this problem. Full article

Soiling is one of the main performance risks for bifacial photovoltaic (PV) technology, particularly in arid environments such as the Atacama Desert, where dust is deposited asymmetrically on the front and rear surfaces of the modules. This study evaluates one year (July 2022 to June 2023) of soiling behavior in bifacial modules installed in fixed-tilt and horizontal single-axis tracking (HSAT) configurations, enabling a comparison to be made between static and moving structures. The average dust accumulation was found to be 0.33 mg/cm² on the front surface and 0.15 mg/cm² on the rear surface of the fixed modules. In contrast, the respective values for the HSAT systems were found to be lower at 0.25 mg/cm² and 0.035 mg/cm². These differences resulted in performance losses of 5.8% for fixed modules and 3.7% for HSAT systems. Microstructural analysis revealed that wetting and drying cycles had formed dense, cemented layers on the front surface of fixed modules, whereas tracking modules exhibited looser deposits. Natural cleaning events, such as fog, dew and frost, only provided partial and temporary mitigation. These findings demonstrate that bifaciality introduces differentiated soiling dynamics between the front and rear surfaces, emphasizing the importance of tailored cleaning strategies and the integration of monitoring systems that consider bifacial gain as a key operational parameter. These insights are crucial for developing predictive models and cost-effective O&M strategies in large-scale bifacial PV deployments under desert conditions. Full article