Search Results (1,281)

Electric vehicle charging stations (EVCSs) are increasingly deployed as grid-interactive energy assets that combine power electronic converters, sensing devices, communication interfaces, photovoltaic (PV) generation, battery energy storage systems (BESS), and multiple charging ports. This complexity creates reliability challenges because abnormal behavior may originate from electrical, thermal, sensing, communication, port-level, or grid-side sources. This paper proposes a fault-aware decision-support framework for renewable-powered EVCSs using multi-source explainable learning. The framework integrates electrical, thermal, session/port, grid/PV/BESS, and communication/data-quality indicators into a unified health-monitoring representation. Supervised models diagnose known fault classes, anomaly-detection models flag unknown or anomalous events, and a source-level explainability layer supports candidate-source interpretation and maintenance-oriented risk mapping. A scenario-controlled EVCS benchmark is developed with PV generation, BESS operation, grid import, charging-port behavior, communication/data-quality indicators, and six injected fault/anomaly categories. An extended 180-day benchmark further assesses longer-horizon operation, seasonal/weather diversity, drift/ageing proxies, and event-level behavior. The strongest closed-set classifier, LightGBM with class weights, achieved 98.45% accuracy and 0.9792 macro-F1, while the Random Forest model used for explainability and decision-layer analysis achieved 97.35% accuracy and 0.9626 macro-F1. Full multi-source monitoring improved Random Forest macro-F1 from 0.6922 under electrical-only monitoring to 0.9626, demonstrating within the controlled benchmark the diagnostic value of heterogeneous EVCS observability. Open-set performance was source dependent: sensor/measurement and communication/data anomalies were more detectable, whereas thermal/cooling and port/session unknowns remained difficult at the selected threshold. Under nominal scenario-based response assumptions, unavailable port hours and unmet charging energy decreased by 49.01% and 38.33%, respectively, relative to reactive operation; sensitivity analysis showed that these outcomes depend on intervention effectiveness and response delay. These findings establish controlled-benchmark feasibility for explainable multi-source EVCS decision support. Field validation using charger telemetry, maintenance-confirmed labels, and operator-calibrated response policies remains necessary. Full article

For large-scale photovoltaic plant inspection, UAV-based infrared–visible real-time detection can combine thermal abnormality information with appearance and structural cues. This is useful for improving inspection and maintenance efficiency. However, in real UAV inspection, differences in sensor resolution, field of view, and flight attitude can cause weak misalignment between the two modalities. Since complex image registration is difficult to perform before real-time inference, this misalignment can affect cross-modal feature fusion and defect localization. To address this problem, this paper proposes Frequency-Aware Fusion YOLO (FAF-YOLO) for dual-modal photovoltaic defect detection. We also build a real-scene infrared–visible dual-modal photovoltaic defect dataset, named DM-PV, which covers six defect categories related to thermal anomalies and external environmental interference. FAF-YOLO is based on a dual-branch YOLO detection framework. The C3k2-DPRG module is used to enhance defect boundaries, local details, and neighborhood context. The Frequency-aware Selective Fusion (FSF) module models low-frequency structural information and high-frequency detail responses separately, which reduces edge ghosting and background mis-fusion caused by weak misalignment. A Multi-Scale Differentiated Decoupled Head is then used to handle scale-specific prediction and improve small-defect localization and regional-anomaly discrimination. Experimental results show that FAF-YOLO achieves 92.5% Precision, 86.7% Recall, 91.7% ${\mathrm{mAP}}_{50}$, and 61.4% ${\mathrm{mAP}}_{50:95}$ on the DM-PV dataset. It outperforms several mainstream dual-modal detection methods and has lower parameters and computational complexity. Further tests for real-time inspection show that the proposed method keeps more stable performance under weak misalignment perturbations. It also reaches an inference speed of 33 FPS on the Jetson Orin Nano edge platform, which verifies its effectiveness and deployability for UAV-based real-time photovoltaic inspection. Full article

To address the renewable energy hosting capacity problem in regional power grids under security, stability, and power supply adequacy constraints, this paper proposes an energy storage configuration method that coordinates demand–response, synchronous condensers, grid-forming renewable energy retrofits, and storage. First, wind and photovoltaic output scenarios are generated by clustering historical data while considering wind–solar correlations, and a time-of-use-price-based demand–response model is established to improve the net-load profile. Second, a three-layer optimization framework is constructed. The upper layer determines the rated power and energy capacity of the storage system and minimizes storage investment and operation and maintenance costs. The middle layer performs source–grid–load–storage coordinated dispatch under the storage boundary provided by the upper layer and minimizes the total operating costs while embedding node voltage and short-circuit ratio security checks. The lower layer maximizes renewable energy hosting capacity through incremental capacity iteration and determines the maximum admissible renewable capacity subject to reliability, security, and stability constraints. A normalized normal constraint (NNC) method is adopted to solve the multi-objective three-layer model. Case studies based on an improved IEEE 118-bus system show that the coordinated measures can significantly increase hosting capacity, reduce storage demand, and improve voltage and frequency security. Full article

In practical photovoltaic grid-tied four-leg inverter systems (PV-GTFLIs), changes in radiation, temperature, and grid voltage magnitude cause significant disturbances, including DC-link voltage disturbance and power unbalance, which can impact the system’s dynamic responses, control performance, grid power quality, efficiency, and reliability. To deal with these problems, this article proposes an improved active disturbance rejection control (ADRC) methodology for optimizing DC-link voltage regulation in PV-GTFLIs. The proposed ADRC approach incorporates a nonlinear high-gain observer (NHGO) within the external DC-link voltage control loop to estimate and mitigate disturbances. The suggested ADRC approach adopts the NHGO instead of the traditional extended state observer due to its superior characteristics, which include excellent dynamic responses, rapid and accurate disturbance estimation and rejection, and enhanced resilience against measurement noise. Thus, it enhances the stability of the DC bus voltage, enhances the system’s dynamic responses, improves its steady-state performance, increases its ability to reject voltage disturbances, and improves the reliability of the PV-GTFLI, as well as reducing the cost and size. The effectiveness of the proposed ADRC approach based on the NHGO is confirmed through software-in-the-loop real-time validation tests, including changes in irradiation and PV cell temperature, sag in grid voltage amplitude, and internal uncertainties, using the OPAL real-time digital simulator. Full article

Utility-scale ground-mounted photovoltaic development is expanding across alpine dryland grasslands, creating engineered microhabitats with contrasting shading regimes, surface exposure, and soil hydro-physical conditions. Microhabitat-specific responses of vegetation and soil carbon pools remain poorly quantified in these systems. We assessed carbon-pool responses in the Talatan photovoltaic park on the northeastern Qinghai–Tibet Plateau using 109 plot-level observations across five microhabitats: reference grassland (REF), fixed-panel shaded microhabitat (FS), fixed-panel interspace microhabitat (FI), horizontal single-axis tracking microhabitat (HSA), and tilted single-axis tracking microhabitat (TSA). We estimated aboveground biomass carbon (AGB-C), belowground biomass carbon (BGB-C), 0–30 cm soil organic carbon stock (SOC stock), and total ecosystem carbon storage (TEC). Microhabitat contrasts were evaluated relative to REF, and standardized association models were used to examine relationships between SOC stock, soil moisture, soil fines, and vegetation carbon pools. Carbon responses differed by microhabitat position and carbon-pool compartment. FS showed the clearest vegetation carbon contrast, with AGB-C 29.2% lower than REF and BGB-C 29.8% lower with borderline statistical support. In contrast, FS showed smaller, more uncertain contrasts for SOC stock (−3.2%) and TEC (−6.3%). The SOC stock association model explained 46% of the variance, with positive coefficients for soil moisture (β = 0.496), soil fines (β = 0.256), AGB-C (β = 0.239), and BGB-C (β = 0.171). These findings indicate that photovoltaic carbon assessment in alpine dryland grasslands should distinguish microhabitat position, carbon-pool compartment, and soil hydro-physical background to identify where carbon responses occur and through which carbon pools they are expressed. Full article

Organic semiconductors offer a potential class of materials for organic photovoltaic (OPV) applications due to their tunable optoelectronic properties and low-cost processing. A methodical DFT/TD-DFT study of a library of organic donor–π–acceptor (D–π–A) compounds based on triphenylamine donors, thiophene-based π-bridges, and benzothiadiazole/malononitrile acceptors is presented in this work, with the goal of rationalizing the structure–property relationships governing their photovoltaic behavior. CAM-B3LYP calculations were used to analyze the role of donor, bridge, and acceptor units in modulating frontier-orbital alignment, charge-transfer character, and optical absorption properties, as well as to evaluate the active-layer thickness in the estimation of the light-harvesting efficiency. The results, which are intended as internal comparative descriptors rather than predictive device efficiencies, reveal that the most pronounced bathochromic shifts and most favorable optical responses are not simply associated with the strongest donor or acceptor moieties, but rather arise from an optimal balance between frontier-orbital delocalization and charge-transfer character across the molecular framework. A preliminary assessment of photovoltaic descriptors suggests that the proposed computational workflow may provide useful guidelines for the descriptor-guided design and screening of next-generation organic photovoltaic materials. Full article

This paper presents an advanced load frequency regulation strategy for interconnected thermal power systems integrated with renewable energy sources and energy storage systems. A two-area non-reheat thermal power system is investigated, where photovoltaic generation is incorporated in Area 1 and wind turbine generation in Area 2 to assess the impact of renewable penetration on system dynamics and frequency stability. To improve the dynamic response under varying operating conditions, a novel multi-stage TDn(1+PIDn) controller is proposed. The TDn stage enhances transient shaping, while the PIDn stage provides superior damping and steady-state accuracy. The controller parameters are optimally tuned using the pied kingfisher optimizer (PKO) and compared with particle swarm and grey wolf-based optimizers. Furthermore, vanadium redox flow batteries, superconducting magnetic energy storage, and hydrogen–air fuel cells are integrated into the hybrid system to mitigate frequency oscillations caused by renewable intermittency. Offline simulations and real-time validation using the OPAL-RT OP4512 simulator are conducted under different dynamic scenarios. The obtained results demonstrate that the proposed PKO-TDn(1+PIDn) method achieves the best transient performance, for example, reducing the $∆F_1$ overshoot, undershoot and settling time by 28%, 15% and 7.5%, respectively, relative to its closest-performing counterpart while attaining the minimum ITAE value of 0.037309. Consistent improvements are observed across the key performance metrics, confirming the robustness and effectiveness of the scheme for modern hybrid power systems. Full article

The main objective of the current study is to assess the techno-economic feasibility, climate change adaptability, and sustainability of photovoltaic energy systems in six large hospitals in Turkey (Adana, Başakşehir, Bursa, Elazig, Gaziantep, and Yozgat) to achieve United Nations recommendations as Sustainable Development Goal 7 (affordable and clean energy) and Sustainable Development Goal 13 (climate action). This study aims to determine the impact of long-term climate change on the availability of photovoltaic (PV) energy resources. To achieve this goal, this research was conducted through a multi-step approach combining (1) the detection of long-term climate trends using linear regression on the TerraClimate database, (2) the spatial analysis of photovoltaic solar energy potential using high-resolution satellite imagery (Google Maps) for roof suitability and parking areas, (3) the estimation of photovoltaic electricity generation and the calculation of the capacity factor, (4) the application of the Response Surface Methodology (RSM) based on NASA Giovanni data to model the nonlinear reciprocal relationships between precipitation (R), aerosol optical thickness (AOT), photovoltaic solar energy production, and (5) the techno-economic analysis using the Levelized energy cost (LCOE), payback period, and CO₂ emission reductions. The results show statistically consistent warming trends across all sites with trends for Tmax ranging from +0.0205 to +0.0268 °C/year and for Tmin from +0.0208 to +0.0300 °C/year. The temperature of PV cells increases at a rate of +0.0197 °C/year and the wind speed decreases by −0.0031 to −0.0149 m/s/year, which indicates a reduction in convective cooling. Solar radiation, on the other hand, is relatively constant with small trends ranging from +0.0002 to +0.0566 W/m²/year, and confirms the consistent solar resource availability. Seasonal PV resource potential varies from ~70–95 W/m² in winter to 290–310 W/m² in summer. Furthermore, the installed PV capacities are between 6 MW (Yozgat) and 47 MW (Başakşehir) with capacity factors of 17.0–19.7% and payback periods of 4.31–4.88 years. RSM models have high explanatory power (R² = 0.57–0.74) with AOT as the most important negative driver of PV performance. Consequently, the results show that while the solar resource of Türkiye is stable and highly exploitable, PV efficiency is increasingly determined by climate-induced thermal stress and reduced wind cooling. The study highlights the economic viability, environmental advantages, and strategic relevance of PV systems at hospitals for resilient, low-carbon healthcare infrastructure in future climate scenarios. Full article

To overcome the limitations of conventional homogeneous materials in floating photovoltaic (FPV) systems, this review critically examines advanced material-s design strategies and fabrication approaches that enable enhanced durability, reliability, and performance under coupled hydro-mechanical and environmental loading conditions. Although FPV systems provide a scalable solution to land scarcity, their long-term operation is challenged by wind–wave interactions, persistent moisture exposure, thermal cycling, and corrosion-induced degradation, and biofouling, requiring materials capable of accommodating spatially varying and interacting stressors. Functionally graded materials (FGMs) are explored as a promising solution, enabling continuous variation in the composition and microstructure to tailor local mechanical, thermal, and chemical properties within a single structure. Recent research highlights growing interest in additive manufacturing and gradient design, but limited focus on FPV and marine applications. Emphasis is placed on additive manufacturing as a key fabrication route for realizing complex gradient architectures with high precision and design flexibility. Recent advances demonstrate notable improvements in corrosion resistance, fatigue performance, and stress distribution, leading to the enhanced structural integrity and service life of FPV systems. The review further organizes FGM concepts within a design–process–property–performance framework and discusses their application in key FPV subsystems. This review further evaluates the role of multi-physics modelling and digital twin frameworks in linking processing conditions with in-service performance under realistic operating environments. The potential of computational intelligence approaches for predicting thermo-mechanical response, damage evolution, and reliability of graded structures is also discussed. Additionally, emerging challenges related to gradient characterization, standardization, technology qualification and scalability, and large-scale deployment are critically discussed, highlighting key directions for future research and technological development. Overall, FGMs and advanced manufacturing show strong potential to improve FPV durability, fatigue resistance, and corrosion performance. Full article

This study evaluates the techno-economic viability of bifacial photovoltaic (PV) systems in Uribia, La Guajira—the department that holds Colombia’s strongest solar resource, with a mean global horizontal irradiance near 5.6 kWh/m²/day and seasonal peaks above 6.0. Despite this endowment, the country’s installed solar capacity remains far below its potential, largely because developers lack the site-specific financial risk analyses that investment decisions require. To address this, we pair stochastic Monte Carlo simulation with a set of machine learning surrogate models and quantify how Colombia’s Law 1715 fiscal incentives—VAT exclusion and accelerated depreciation—reshape the Levelized Cost of Electricity (LCOE) of bifacial PV systems under realistic climatic variability. Drawing on six years of daily meteorological data, we model bifacial PERC performance under two ground-albedo conditions: the natural site value ($\alpha =0.125$) and an optimized surface ($\alpha =0.30$). The results are consistent and encouraging. Under the Law 1715 tax shields, the mean LCOE settles at 0.0588 USD/kWh, and even the 95% Value-at-Risk (VaR) of 0.0638 USD/kWh stays below the prevailing Colombian industrial tariff across every climatic realization evaluated—evidence that the fiscal framework does as much to compress downside risk as it does to lower the average cost. Ground-albedo optimization proved to be the decisive lever: raising $\alpha$ from 0.125 to 0.30 through low-cost surface preparation shortens the payback period to roughly four years and lets the bifacial configuration overtake the cumulative net present value of the monofacial baseline before year seven. The surrogate models tell a complementary story about the structure of the problem. The non-linear algorithms—Support Vector Regression, Gradient Boosting, a Multi-Layer Perceptron and Gaussian Process Regression—reproduce the Monte Carlo response surface almost exactly ($R^2\ge 0.99$, 0.994–0.997), whereas linear models trail at $R^2\approx 0.90$–$0.94$, a gap that quantifies just how strongly the techno-economic drivers of LCOE interact. Full article

Conventional photovoltaic performance indicators provide useful benchmarking but do not determine whether production deficits arise from expected weather and module effects or from unresolved plant-side behaviour. This study develops a residual performance diagnostic framework for data-limited grid-connected photovoltaic plants and applies it to two adjacent fixed-tilt crystalline-silicon systems, each rated at 99.36 kW_p, in South Corfu, Greece, over 2016–2023. An expected-energy baseline was constructed using hourly irradiance and meteorological data from the Photovoltaic Geographical Information System after explicit correction for incidence-angle effects, irradiance-level response, module temperature, and first-order air-mass and spectral effects. Measured alternating-current energy delivered to the grid, obtained from the Hellenic Electricity Distribution Network Operator, was then compared with the corrected expected energy through a weather-adjusted system-efficiency indicator and its complementary residual-loss coefficient. The explicit module and weather loss envelope remained close to 9.01%, with temperature as the largest modelled component. Residual losses increased from 10.76% to 14.53% in Plant 1 and from 10.51% to 16.26% in Plant 2. The fitted apparent annual declines were 0.453 percentage points per year for Plant 1 and 0.609 percentage points per year for Plant 2. A generation-hour uncertainty analysis based on independent observations from the Hellenic National Meteorological Service gave expanded uncertainties of ±5.80% for expected energy and ±5.89% for the system-efficiency indicator. The framework is therefore presented as an uncertainty-bounded screening method for prioritizing inspection and maintenance, not as a root-cause diagnostic or a formal performance-loss-rate assessment. Full article

Both temperature and solar radiation cause variations in photovoltaic output power. Nevertheless, each variation has a maximum power point, which represents the photovoltaic output’s maximum efficiency. Photovoltaic power must be managed at the highest point in order to achieve optimal efficiency. This can be accomplished by employing a converter to regulate the photovoltaic output voltage at the maximum power. This study proposes a quadratic boost converter (QBC) to control photovoltaic output power by using the Deep Recurrent Neural Network (DRNN) algorithm. The goal of the DRNN is to decrease ripple at the maximum point and speed up the time to reach the maximum power point. The QBC is designed to obtain a higher DC output voltage than a regular boost converter, so it can eliminate the use of a step-up transformer if the photovoltaic is connected to an inverter. The proposed method is applied to a 50 Wp solar panel with an Arduino microcontroller as the controller device. The experimental results demonstrate that the DRNN algorithm-based QBC effectively controls the solar panel output power at the maximum point with a smoother ripple and a faster response. The QBC is also able to produce higher-voltage output according to its characteristics. Full article

Rooftop bifacial photovoltaic (PV) performance depends on both the optical properties of the roof surface and the installation geometry. This simulation-based study assessed a planned rooftop PV installation on a school building in Brno, Czech Republic, using PV*SOL simulations and statistical post-processing in R. The principal dataset comprised 238 bifacial scenarios combining seven spatially distinct roof zones assigned nominal albedo values from 8% to 80%, 17 module tilts from 0° to 80°, and row spacings of 1.00 and 2.10 m. Because each nominal albedo was associated with a different physical roof zone, the results represent combined roof-zone, reflectance and geometry responses rather than the isolated causal effect of albedo. The highest annual specific yield was obtained in the roof-zone scenario assigned 70% nominal albedo. The maximum was 1296.60 kWh/kWp at 1.00 m spacing and 35° tilt and 1359.34 kWh/kWp at 2.10 m spacing and 50° tilt. Under the fixed 25° reference configuration, the bifacial system outperformed the matched monofacial reference in all seven scenarios, with bifacial gain ranging from 7.57% to 11.23%. An empirical response-surface model reproduced the complete simulation grid with adjusted $R^2=0.980$ and RMSE of 13.94 kWh/kWp. For the representative 6.0 kWp subarray, the maximum lifetime incremental avoided-emission difference relative to the matched 8% reference was 10.35 t CO₂. Within the predefined Monte Carlo ranges, the highest-ranked scenarios produced positive incremental net present value in all 5000 sampled combinations. The findings show that roof reflectance should be evaluated together with tilt, spacing and spatial roof context. They do not establish a universally optimal albedo or rooftop configuration. Full article

Real-time grid monitoring of photovoltaic (PV) systems with battery storage requires continuous access to voltage, current, and power states. Traditional simulation models obtain these responses by calculating switching events, which limits the scale of real-time simulation. This paper proposes a Multi-Feature Dynamic Reconstruction (MFDR) method that combines environmental inputs, averaged converter states, and frequency-domain electrical variables. On the DC side, PV output is calculated from irradiance and temperature, while the bidirectional battery converter is represented by a low-frequency reconstruction model. On the AC side, dynamic phasors are used to convert the grid-connected inverter into a frequency-domain Norton equivalent for reconstructing its port voltage and current responses. Controller hardware-in-the-loop tests are conducted under different operating conditions. The reported peak and normalized tracking errors of the evaluated transient quantities remain below 3%. In the four-core IEEE 118-bus case, the average per-core CPU utilization decreases from 70.43% to 41.08%, while the maximum step execution time decreases from 38 μs to 27 μs. The model with 1069 state variables also operates within the fixed 50 μs simulation step. The results show that the MFDR method reduces the computational demand of real-time grid monitoring while retaining the voltage, current, and power responses. Full article

As solar photovoltaic (PV) infrastructure expands across open landscapes, understanding its belowground effects in grassland ecosystems is important for sustainable solar development. PV panels can create spatially heterogeneous microsites by altering light availability, precipitation redistribution, and soil environmental conditions, but their effects on soil bacterial community structure remain insufficiently understood. Here, we investigated soil physicochemical properties and bacterial communities across four PV-associated microsites: undisturbed grassland outside the PV array (Control), the front edge of the panel (FE), beneath the center of the panel (BP), and the uncovered interspace between adjacent panel rows (IS). Soil bacterial communities were characterized using 16S rRNA gene sequencing. Most measured soil physicochemical properties did not differ significantly among microsites. Available potassium was the only variable showing an overall microsite effect and was significantly lower in FE and BP than in the Control. Shannon index and Chao1 richness did not differ significantly among microsites. In contrast, Bray–Curtis-based principal coordinate analysis (PCoA) showed that FE samples occupied a relatively distinct ordination position, whereas Control, BP, and IS samples partially overlapped; permutational multivariate analysis of variance (PERMANOVA) indicated a significant overall difference in bacterial community composition among microsites. At the phylum level, no dominant bacterial phylum differed significantly among microsites after false discovery rate (FDR) correction. Redundancy analysis (RDA) indicated that alkali-hydrolyzable nitrogen and available potassium were the measured soil variables most closely associated with bacterial community composition at the operational taxonomic unit level in the final model. Together, these results indicate that differences among PV-associated microsites were detected in overall bacterial community composition but not in Shannon index or Chao1 richness. Alpha-diversity and community-composition analyses therefore provide complementary information for evaluating belowground bacterial responses to PV development in grasslands. Full article