Search Results (1,506)

This study investigates the electro-thermal transient response of a photovoltaic–thermal (PV/T)–heat pump system under dynamic disturbances to optimize operational stability. A dynamic model integrating a PV/T collector and a heat pump was developed by the transient heat current method, enabling high-fidelity simulations of step perturbations: solar irradiance reduction, compressor operation, condenser water flow rate variations, and thermal storage tank volume changes. This study highlights the thermal storage tank’s critical role. For V_tank = 2 m³, water tank volume significantly suppresses the water tank and PV/T collector temperature fluctuations caused by solar irradiance reduction. PV/T collector temperature fluctuation suppression improved by 46.7%. For the PV/T heat pump system in this study, the water tank volume was selected between 1 and 1.5 m³ to optimize the balance of thermal inertia and cost. Despite PV cell electrical efficiency gains from PV cell temperature reductions caused by solar irradiance reduction, power recovery remains limited. Compressor dynamic performance exhibits asymmetry: the hot water temperature drop caused by speed reduction exceeds the rise from speed increase. Load fluctuations reveal heightened risk: load reduction triggers a hot water 7.6 °C decline versus a 2.2 °C gain under equivalent load increases. Meanwhile, water flow rate variation in condenser identifies electro-thermal time lags (100 s thermal and 50 s electrical stabilization), necessitating predictive compressor control to prevent temperature and compressor operation oscillations caused by system condition changes. These findings advance hybrid renewable systems by resolving transient coupling mechanisms and enhancing operational resilience, offering actionable strategies for PV/T–heat pump deployment in building energy applications. Full article

A smart irrigation system based on soil moisture sensors supported by photovoltaic energy is an innovation to address water use efficiency in the agricultural sector, especially in remote areas. This technology utilizes photovoltaic panels as a renewable energy source to operate water pumps, while soil moisture sensors provide real-time data that is used to automatically manage irrigation according to plant needs. This technology not only increases the efficiency of water and energy use but also supports environmental conservation by reducing dependence on fossil fuels. This research was conducted using a Systematic Literature Review (SLR) approach guided by the PRISMA framework to analyze trends, benefits, and challenges in implementing this technology. The analysis results show that this system offers various advantages, including energy efficiency, reduced carbon emissions, and ease of management through the integration of Internet of Things (IoT) technology. Several challenges remain, such as high initial investment costs, limited network access, and obstacles. Technical matters related to installation and maintenance. Various solutions have been proposed, including providing subsidies for small farmers, implementing radiofrequency modules, and using modular designs to simplify implementation. This study contributes to the development of a conceptual framework that can be adapted to various geographic and socio-economic conditions. Potential further developments include the integration of artificial intelligence and additional sensors to increase efficiency and support the sustainability of the agricultural sector globally. Full article

As energy systems transition toward high shares of variable renewable generation, local energy communities (ECs) are increasingly relevant for enabling demand-side flexibility and self-sufficiency. This shift is particularly evident in the residential sector, where the deployment of photovoltaic (PV) systems is rapidly growing. While mixed-integer linear programming (MILP) remains the standard for operational optimization and demand response in such systems, its computational burden limits scalability and responsiveness under real-time or uncertain conditions. Reinforcement learning (RL), by contrast, offers a model-free, adaptive alternative. However, its application to real-world energy system operation remains limited. This study explores the application of a Deep Q-Network (DQN) to a real residential EC, which has received limited attention in prior work. The system comprises three single-family homes sharing a centralized heating system with a thermal energy storage (TES), a PV installation, and a grid connection. We compare the performance of MILP and RL controllers across economic and environmental metrics. Relative to a reference scenario without TES, MILP and RL reduce energy costs by 10.06% and 8.78%, respectively, and both approaches yield lower total energy consumption and CO₂-equivalent emissions. Notably, the trained RL agent achieves a near-optimal outcome while requiring only 22% of the MILP’s computation time. These results demonstrate that DQNs can offer a computationally efficient and practically viable alternative to MILP for real-time control in residential energy systems. Full article

With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions. Full article

Accurate prediction of maximum voltage is essential for the safe, efficient, and sustainable design of photovoltaic systems, as it defines the maximum allowable number of modules in series. This study examines how the choice of meteorological year affects voltage estimations in high-power PV systems. A comparison is made between maximum voltage results derived from typical meteorological (TMY) years and those based on inter-hourly historical data. The results reveal notable differences, with TMY often underestimating extreme voltage levels. To address this, the study introduces the Extreme Meteorological Year (EMY) model, which uses historical voltage percentiles to better estimate peak voltages and mitigate overvoltage risk. This model has been applied successfully in real PV plant designs. Its performance is assessed using monitoring data from seven PV projects in different regions. The EMY model demonstrates improved accuracy and safety in predicting maximum voltages compared to traditional datasets. Its percentile-based structure enables adaptation to different design criteria, enhancing reliability and supporting more sustainable photovoltaic deployment. Overall, the study underscores the importance of selecting appropriate meteorological data for voltage prediction and presents EMY as a robust tool for improving PV system design. Full article

Mechanical alloying (MA) has been proven to be an energy-efficient synthetic route for the development of high-performance thermoelectric (TE) materials. Higher Manganese Silicide (HMS) phases of the general formula Mn(Si_1−xAl_x)_1.75 (0 ≤ x ≤ 0.05) were prepared by MA implementing a short-time ball-milling process. Powder XRD and SEM analysis were carried out to validate the HMS phases, while small amounts of the secondary phase, MnSi, were also identified, especially for the Al-doped products. Electrical transport properties measurements showed that Al substitution causes an effective hole doping. A remarkable increase in electrical conductivity is observed for the Al-doped phases, while the corresponding reduction in the Seebeck coefficient is indicative of the increase in carrier density. Despite the small percentages of MnSi detected in Al-doped phases, an improvement in TE efficiency is achieved in the series Mn(Si_1−xAl_x)_1.75 (0 ≤ x ≤ 0.05). The 2.5% Al-doped phase exhibits a maximum figure-of-merit (ZT) of 0.43 at 773 K. Moreover, in an effort to utilize recycled silicon byproducts from photovoltaic (PV) manufacturing, Al-doped phases are developed by MA using two types of Si kerf. The two kerf-based products exhibit lower TE efficiencies, due to the increased amounts of the metallic MnSi phase. Full article

Bifacial photovoltaic panels are preferred over monofacial panels due to the ability of their back surfaces to absorb radiation and generate electricity. However, optimizing the rear-side energy contribution remains a critical area of research. This study systematically investigates how four key parameters (albedo, tilt angle, panel height, and mounting configuration) affect rear-side energy generation and overall panel efficiency. In the first scenario, the impact of surface reflectivity was evaluated. High-reflectivity materials such as aluminum (21.2%) and fresh snow (20.5%) significantly increased rear-side energy yield. The second scenario examined tilt angle, showing that increasing the tilt up to 50° enhanced back-side generation, reaching a gain of 5.5%. The third scenario focused on the effect of panel height, revealing a linear relationship with energy generation. The fourth assessed orientation, comparing horizontal and vertical installations. Horizontal mounting provided a higher rear-side energy yield (4.5%) due to increased exposure to ground-reflected radiation. The findings of this study provide important information for the optimization of bifacial photovoltaic panels and the information will provide guidance for easier and more efficient installation of solar power plants. Full article

This study developed a novel folding light shelf system that integrates reflectors, photovoltaic (PV) modules, and adaptive louvers that adjust based on solar altitude, aiming to improve daylight distribution, minimize glare, and reduce energy consumption in office buildings. The research employed an advanced optimization approach, utilizing Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Latin Hypercube Sampling, a highly effective method suitable for managing complex multi-objective scenarios involving numerous variables, to efficiently identify high-performance configurations with increased precision. Key design variables across all three components of the system included angle, width, distance, and the number of folds in the light shelf, along with the number of louvers. The proposed method successfully integrates PV technology into light shelves without compromising their functionality, enabling both daylight control and energy generation. The optimization results demonstrate that the system achieved up to a 15% improvement in useful daylight illuminance (UDI) and a 16% reduction in cooling energy consumption. Furthermore, the PV modules generated 509.5 kWh/year, ensuring improved efficiency and sustainability in building performance. Full article

The electrification of rural medical centers in high Andean areas represents a critical challenge for equitable development due to limited access to reliable energy. Hybrid Renewable Energy Systems (HRESs), which combine solar photovoltaic generation, Battery Energy Storage Systems (BESSs), and backup diesel generators, are emerging as viable solutions to ensure the supply of critical loads. However, their effective implementation requires optimal sizing methodologies that consider multiple technical and economic constraints and objectives. In this study, an optimization model based on metaheuristic algorithms is developed, specifically, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), to identify optimal configurations of an HRES applied to a remote medical center in the Peruvian Andes. The results show that GA achieved the lowest Life Cycle Cost (LCC), with a high share of renewable energy (64.04%) and zero Energy Not Supplied (ENS) defined as the amount of load demand not met by the system, significantly outperforming PSO and ACO. GA was also found to offer greater stability and operational robustness. These findings confirm the effectiveness of metaheuristic methods for designing efficient and resilient energy solutions adapted to isolated rural contexts. Full article

Solar irradiance, which is closely influenced by cloud cover, significantly affects photovoltaic (PV) power generation efficiency. To improve cloud type recognition without relying on labeled data tasks, this paper proposes a self-supervised cloud classification method based on patch rotation prediction. In the Pre-training stage, unlabeled ground-based cloud images are augmented through blockwise rotation, and high-level semantic representations are learned via a Swin Transformer encoder. In the fine-tuning stage, these representations are adapted to the cloud classification task using labeled data. Experimental results show that our method achieves 96.61% accuracy on the RCCD and 90.18% on the SWIMCAT dataset, outperforming existing supervised and self-supervised baselines by a clear margin. These results demonstrate the effectiveness and robustness of the proposed approach, especially in data-scarce scenarios. This research provides valuable technical support for improving the prediction of solar irradiance and optimizing PV power generation efficiency. Full article

Agro-photovoltaics (APV) or agrivoltaic systems integrate crop cultivation with solar energy production, offering a promising solution through the dual-use of land. This two-year study (2023 and 2024) examined the effects of an APV system on rice production. The results indicated that APV arrays created spatially variable light environments, with shadow lengths following predictable solar azimuth patterns and cloudy conditions mitigating shading effects through enhanced diffuse light. Compared with CK (non-shadow area), inter-panel plots (BP) maintained 77% photosynthetic efficiency and 85.4% plant height, whereas the areas beneath the panel showed a significant decrease in the relative chlorophyll content (SPAD values), photosynthesis rates, and yield. BP plots preserved a 78% fruiting rate through adaptive stomatal regulation, whereas LP zones (directly under the low eave) exhibited 35% higher intercellular CO₂ because of the limited assimilation in shading. Rice yield losses were correlated with shading intensity, driven by reduced panicles and grain filling. Moreover, the APV system achieved a high land equivalent ratio of 148–149% by combining 65–66% rice yield with 82.5% photovoltaics output. Based on the microenvironment created by the APV system, optimal crop types and fertilisation are essential for enhancing agricultural yields and improving land use efficiency. Full article

The instability of the energy supply, growing demand and the need to reduce carbon emissions are priority challenges in developing countries such as Ecuador, where power outages affect productivity and generate economic losses. Therefore, solar energy is positioned as a sustainable alternative. The objective of this study is to evaluate a pilot photovoltaic (PV) system for residential housing in coastal areas in the Santa Elena province, Ecuador. The methodology included the following: (i) criteria for the selection of three representative residential housings; (ii) design of a distributed generation system using PVsyst software; and (iii) proposal of strategic guidelines for the design of PV systems. This proposed system proved to be environmentally friendly, achieving reductions of between 16.4 and 32 tonnes of CO₂ in the first 10 years. A return on investment (ROI) of 16 years was achieved for the low-demand (L) scenario, with 4 years for the medium-demand (M) scenario and 2 years for the high-demand (H) scenario. The sensitivity analysis showed that the Levelized Cost of Energy (LCOE) is more variable in the L scenario, requiring more efficient designs. It is proposed to diversify the Ecuadorian energy matrix through self-supply PV systems, which would reduce electricity costs by 6% of consumption (L scenario), 30% (M scenario), and 100% (H scenario). Although generation is concentrated during the day, the net metering scheme enables compensation for nighttime consumption without the need for batteries, thereby improving the system’s profitability. The high solar potential and high tariffs make the adoption of sustainable energy solutions a justifiable choice. Full article

In order to address the challenge of achieving optimal prediction accuracy when a single prediction model faced with changes in meteorological conditions of different weather types, this paper proposes a photovoltaic (PV) power prediction method based on the combination of similar day clustering and convolutional neural network (CNN)-gated recurrent unit (GRU). The Pearson correlation coefficient and Spearman’s correlation coefficient are used to filter out the key features such as total solar radiation and module temperature to construct a new input dataset; the K-means algorithm is used to perform clustering analysis on the data, and the data are classified into sunny, cloudy, and rainy days; the spatial correlation features of the meteorological factors are extracted by using the convolutional neural network (CNN), and the CNN-GRU model is established by combining with the gated recurrent units (GRUs). The PV output power is predicted based on the PV power data and the corresponding meteorological data from a place in Ningxia, collected during June to August 2020, and the method proposed in the article is tested. Validation results show that, compared to other models, the model proposed in this paper reduces MAE and RMSE by 66.1% and 65.7% on average under three different weather type scenarios, and improves R² by 19.8% on average. This verifies that the model has high prediction accuracy and generalization ability, achieving better results in PV output power prediction. The CNN-GRU model demonstrates superior capability in modeling short- and long-term dependencies compared to other deep learning hybrid approaches, while also achieving higher computational efficiency and faster training convergence. Full article

Perovskite solar cells (PSCs) have emerged as a promising contender in photovoltaics, owing to their rapidly advancing power conversion efficiencies (PCEs) and compatibility with low-temperature solution processing techniques. Single-junction architectures reveal inherent limitations imposed by the Shockley–Queisser (SQ) limit, motivating adoption of a dual-absorber structure comprising Cs₄CuSb₂Cl₁₂ (CCSC) and Cs₂TiI₆ (CTI)—lead-free perovskite derivatives valued for environmental benignity and intrinsic stability. Comprehensive theoretical screening of 26 electron/hole transport layer (ETL/HTL) candidates identified SrTiO₃ (STO) and CuSCN as optimal charge transport materials, producing an initial simulated PCE of 16.27%. Subsequent theoretical optimization of key parameters—including bulk and interface defect densities, band gap, layer thickness, and electrode materials—culminated in a simulated PCE of 30.86%. Incorporating quantifiable practical constraints, including radiative recombination, resistance, and FTO reflection, revised simulated efficiency to 26.60%, while qualitative analysis of additional factors follows later. Furthermore, comparing multiple algorithms within this theoretical framework demonstrated eXtreme Gradient Boosting (XGBoost) possesses superior predictive capability, identifying CTI defect density as the dominant impact on PCE—thereby underscoring its critical role in analogous architectures and offering optimization guidance for experimental studies. Collectively, this theoretical research delineates a viable pathway toward developing stable, environmentally sustainable PSCs with high properties. Full article

Solar photovoltaic (PV) power is an important force in promoting the transformation of the energy structure. An increase in PV panel temperature reduces open-circuit voltage and fill factor, thereby increasing the recombination of internal charge carriers and leading to a decrease in the output power of PV systems. When PV panels operate in the environment, high solar intensity may rapidly heat PV panels to very high temperature levels, converting a significant proportion of solar energy into waste heat. Waste heat further reduces the efficiency of PV panels. Therefore, effective cooling methods are important in improving the electrical performance and reliability of PV systems. For different types of PV panel cooling methods, many research works have been conducted. Aiming at providing a relatively valuable reference for future work on PV panel cooling methods, this paper presents a comprehensive review of existing research on cooling methods for PV panels. Relevant issues of eight types of PV panel cooling methods are introduced, including working principles, typical research, advantages, disadvantages, existing problems, and future research directions. Full article