Search Results (8,343)

Lead halide-based perovskite solar cells have gained significant attention from academia and the photovoltaic industry due to their exceptional optical and electrical characteristics. The primary problem with Pb-based perovskite pertains to its toxicity and solubility in water within the external environment. These concerns regarding hazards to the environment are constraining the application of lead-based perovskite in both consumer and industrial contexts. To offer a viable alternative to lead-based hazardous perovskite solar cells, we examined an inverted (p-i-n) perovskite structure with three distinct absorber layers based on cesium bismuth halides (Cs₃Bi₂I₉, Cs₃Bi₂Cl₉, Cs₃Bi₂Br₉) and conducted a comparative analysis utilizing SCAPS-1D software (version 3.3.08). The comparison analysis of our design against starting parameters indicated that the optimal power conversion efficiency (PCE) of 10.01% was recorded for Cs₃Bi₂I₉, 7.56% for Cs₃Bi₂Br₉, and 4.34% for Cs₃Bi₂Cl₉. Following careful optimization of the thickness of charge-transport layers (CTLs), doping concentrations of CTLs, and all three absorber layers, the overall efficiencies of the three inverted structures were enhanced from 10.01% to 14.08% for Cs₃Bi₂I₉, from 4.34% to 5.28% for Cs3Bi2Cl9, and from 7.56% to 11.05% for Cs₃Bi₂Br₉, respectively. The other performance enhancement, open-circuit voltage, increased from 1.08 V to 1.37 V for Cs₃Bi₂I₉, from 1.26 V to 1.47 V for Cs₃Bi₂Cl₉, and from 1.20 V to 1.47 V for Cs₃Bi₂Br₉. This comparative analysis of proposed perovskite devices demonstrates that Cs₃Bi₂X-based perovskite devices possess significant potential to replace conventional hazardous solar cells in the renewable and clean energy sectors. Full article

Remediating complex-contaminated soils demands the synergistic optimization of efficiency, cost-effectiveness, and carbon emission reduction. Currently, ultra-high-temperature thermal desorption technology is mature in terms of principle and laboratory-scale performance; however, ongoing efforts are focusing on achieving stable, efficient, controllable, and cost-optimized operation in large-scale engineering applications. To address this gap, this study aimed to (1) verify the energy efficiency and economic benefits of removing over 98% of target pollutants at a 7.5 × 10⁴ m³ contaminated site and (2) elucidate the mechanisms underlying parallel scale–technology dual-factor cost reduction and energy–carbon–cost optimization, thereby accumulating case experience and data support for large-scale engineering deployment. To achieve these objectives, a “thermal stability–chemical oxidizability” classification criterion was developed to guide a parallel remediation strategy, integrating ex situ ultra-high-temperature thermal desorption (1000 °C) with persulfate-based chemical oxidation. This strategy was implemented at a 7.5 × 10⁴ m³ large-scale site, delivering robust performance: the total petroleum hydrocarbon (TPH) and pentachlorophenol (PCP) removal efficiencies exceeded 99%, with a median removal rate of 98% for polycyclic aromatic hydrocarbons (PAHs). It also provided a critical operational example of a large-scale engineering application, demonstrating a daily treatment capacity of 987 m³, a unit remediation cost of 800 CNY·m⁻³, and energy consumption of 820 kWh·m⁻³, outperforming established benchmarks reported in the literature. A net reduction of 2.9 kilotonnes of CO₂ equivalent (kt CO₂e) in greenhouse gas emissions was achieved, which could be further enhanced with an additional 8.8 kt CO₂e by integrating a hybrid renewable energy system (70% photovoltaic–molten salt thermal storage + 30% green power). In summary, this study establishes a “high-temperature–parallel oxidation–low-carbon energy” framework for the rapid remediation of large-scale multi-contaminant sites, proposes a feasible pathway toward developing a soil carbon credit mechanism, and fills a critical gap between laboratory-scale success and large-scale engineering applications of ultra-high-temperature remediation technologies. Full article

Increasing regulatory and societal pressures to reduce environmental impacts have led the industry to adopt more robust evaluation methods. This study assessed the potential impacts of quick-change abrasive discs—short-life-cycle products made from aluminium oxide, zirconia, and ceramic gel. The evaluation used a cradle-to-grave life cycle assessment (LCA) in accordance with ISO 14040 and 14044. The functional unit examined was a 0.29 m² abrasive sheet containing 180 discs, with an average use time of 10 min per disc. Environmental impacts were estimated in SimaPro 9.2 using the ReCiPe Midpoint (H) method and the Ecoinvent 3.6 database. Results indicated that the highest impacts were marine ecotoxicity (49.5%, 0.67–0.74 kg 1,4-DCB eq), freshwater ecotoxicity (32.8%, 0.52–0.58 kg 1,4-DCB eq), human carcinogenic toxicity (10.4%, 0.37–0.44 kg 1,4-DCB eq), non-carcinogenic toxicity (3.6%, 6.9–7.9 kg 1,4-DCB eq), and terrestrial ecotoxicity (2.0%, 27–33 kg 1,4-DCB eq), primarily resulting from raw material production and the high consumption of electricity and fuel during manufacturing. Improvement strategies, such as changes in disc geometry and the integration of photovoltaic systems, reduced impacts by 14–27%. Additional measures addressed energy efficiency, local supplier development, and user awareness for responsible use and disposal. Full article

Building-integrated photovoltaics (BIPVs) can benefit not only from transparent but also from opaque modules that maximize light capture. We present haze-assisted luminescent solar concentrators (HALSCs) that integrate scattering and luminescence in multilayer designs. Polymer–liquid crystal composites with embedded dyes form micron-scale domains that act as broadband Mie scattering centers, while the dye provides spectral conversion. Monte Carlo ray-tracing simulations and experiments reveal that edge-emitted intensity increases with haze thickness but saturates beyond a threshold; segmenting the same thickness into multiple thinner layers enables repeated scattering, markedly enhancing side-guided emission. When coupled with crystalline silicon solar cells, multilayer HALSCs converted this optical advantage into enhanced photocurrent, with triple-layer devices nearly doubling output relative to transparent controls. These findings establish opacity–luminescence coupling and multilayer haze engineering as effective design principles, positioning HALSCs as practical platforms for advanced BIPVs and optical energy-management systems. Full article

Electronic communication and industrial protocols are critical to the reliable operation of modern electrical grids and Distributed Energy Resources (DERs). Communication loss between devices in renewable power plants can lead to significant revenue losses and jeopardize operational safety. While current control and automation systems for renewable plants are primarily based on the IEC 61131-3 standard, it lacks defined communication frameworks, leading most deployments to depend on Original Equipment Manufacturer (OEM)-specific protocols. The IEC 61499 standard, in contrast, offers a reference model for distributed automation systems, introducing Service Interface Function Blocks (SIFBs) and high-level communication abstractions that enable hardware-independent integration. This study proposes adopting the IEC 61499 standard for DER automation systems to enhance interoperability and flexibility among plant components. A photovoltaic power plant gateway is developed on a virtualized platform using open-source tools and libraries, including Python version 3, libmodbus version 3.1.7, and open62541 version 1 The implemented gateway successfully interfaces with industry-validated software applications, including UAExpert and Matrikon OPC Unified Architecture (OPC UA) clients, demonstrating the feasibility and effectiveness of IEC 61499-based integration in DER environments. Full article

The integration of renewable energy sources, particularly photovoltaic (PV) solar, is increasingly challenged by the limited flexibility and storage capacity of actual energy systems. Hydrogen produced via renewable-powered electrolysis offers a promising pathway to address these constraints. This paper explores hydrogen blending into the natural gas grid as a systemic solution to enhance power system flexibility and support renewable (PV) expansion. Methodologically, the analysis is based on actual grid flow dynamics rather than static averages, identifying network nodes with stable gas demand as the most suitable for hydrogen injection. The novelty of this study lies in framing power-to-gas coupling as an operational flexibility tool rather than a storage-only option, and in quantifying its potential contribution to PV deployment. The methodology is applied to the Italian energy system, chosen as a representative case of high PV penetration and gas dependency. Analysis indicates that under current regulatory constraints (up to 5% hydrogen blending), the additional PV capacity that could be effectively integrated remains limited, resulting in modest reductions in natural gas consumption (<1%) and CO₂ emissions (~0.3%). However, the approach demonstrates the conceptual and methodological relevance of treating gas networks as dynamic elements of an integrated power-to-gas system. Hydrogen blending thus emerges as a transitional but essential step toward future multi-energy integration under evolving regulatory and economic frameworks. 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

This study investigates the use of Si and CdTe-based Timepix3 detectors for photovoltaic energy conversion using solar X-rays and other high-energy electromagnetic radiation in space. As space missions increasingly rely on miniaturized platforms like CubeSats, power generation in compact and radiation-prone environments remains a critical challenge. Conventional solar panels are limited by size and spectral sensitivity, prompting the need for alternative energy harvesting solutions—particularly in the high-energy X-ray domain. A novel CubeSat-compatible payload design incorporates a UV-visible filter to isolate incoming X-rays, which are then absorbed by semiconductor detectors to generate electric current through ionization. Laboratory calibration was performed using Fe-55, Ba-133, and Am-241 sources to compare spectral response and clustering behaviour. CdTe consistently outperformed Si in detection efficiency, spectral resolution, and cluster density due to its higher atomic number and material density. Equalization techniques further improved pixel threshold uniformity, enhancing spectroscopic reliability. In addition to experimental validation, simulations were conducted to quantify the expected energy conversion performance under orbital conditions. Under quiet-Sun conditions at 500 km LEO, CdTe absorbed up to 1.59 µW/cm² compared to 0.69 µW/cm² for Si, with spectral power density peaking between 10 and 20 keV. The photon absorption efficiency curves confirmed CdTe’s superior stopping power across the 1–100 keV range. Under solar flare conditions, absorbed power increased dramatically, up to 159 µW/cm² for X-class and 15.9 µW/cm² for C-class flares with CdTe sensors. A time-based energy model showed that a 10 min X-class flare could yield nearly 1 mJ/cm² of harvested energy. These results validate the concept of a compact photovoltaic payload capable of converting high-energy solar radiation into electrical power, with dual-use potential for both energy harvesting and radiation monitoring aboard small satellite platforms. Full article

This paper presents an economic–environmental power dispatch approach for a grid-connected microgrid (MG) with photovoltaic (PV) generation and battery energy storage systems (BESSs). The problem was formulated as a multiobjective optimization problem with functions such as minimizing fixed and variable generation costs, power losses, and CO${}_2$ emissions. This study addresses the problem of intelligent energy management in microgrids with PV generation and BESSs to optimize their performance based on multiple criteria. This study focuses on optimizing the Energy Management System (EMS) with metaheuristic algorithms to achieve practical implementation with simpler algorithms to solve a complex optimization problem. This study employs four multiobjective optimization algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II), Harris Hawks Optimization (HHO), multiverse optimizer (MVO), and Salp Swarm Algorithm (SSA), which are classified as robust techniques for obtaining Pareto fronts. The computational resources employed to simulate the problem are presented. The optimal dispatch obtained from the Pareto front achieved reductions of 0.067% in fixed costs, 0.288% in variable costs, 3.930% in power losses, and 0.067% in CO${}_2$ emissions, demonstrating the effectiveness of the proposed approach in optimizing both economic and environmental performance. The SSA stood out for its stability and computational efficiency, establishing itself as a promising method for energy management in urban and rural microgrids (MGs) and providing a solid framework for optimization in alternating current systems. Full article

The article analyses the hosting capacity of low-voltage (LV) power grids for connecting distributed energy sources (DER), mainly photovoltaic installations (PV), considering technical limitations imposed by power system operating conditions. The main objective of the research was to develop a simple equation that enables the quick estimation of the maximum power of an energy source that can be safely connected at a given point in the network without causing excessive voltage rise or overloading the transformer and line cable. The analysis was performed on the basis of relevant calculation formulas and simulations carried out in DIgSILENT PowerFactory, where a representative low-voltage grid model was developed. The network model included four transformer power ratings (40, 63, 100, and 160 kVA) and four cable cross-sections (25, 35, 50, and 70 mm²), which made it possible to assess the impact of these parameters on grid hosting capacity as a function of the distance from the transformer station. Based on this, the P_HCI indicator was developed to determine the hosting capacity of a low-voltage network, using only the transformer rating and the length and cross-section of the line for the calculations. A comparison of the results obtained using the proposed equation with detailed calculations showed that the approximation error does not exceed 15%, which confirms the high accuracy and practical applicability of the proposed approach. Full article

This paper examines the sustainability of photovoltaic (PV) panel recycling through a case study in Greece. It traces the evolution of PVs and outlines the main construction characteristics, emphasizing that although PV systems reduce greenhouse gas emissions, they also generate substantial end-of-life (EoL) waste containing both valuable and potentially hazardous materials. The study estimates Greece’s annual PV waste generation and evaluates its environmental, social, and economic impacts. It focuses on advanced disassembly and recycling methods by PV types and calculates material-recovery rates. Using national installation data from 2009–2023, the analysis quantifies the potential mass of recoverable materials and assesses the sustainability of PV recycling in terms of environmental protection, public health, and economic feasibility. Results show high recovery rates: silicon (85%), aluminum (100%), silver (98–100%), glass (95%), copper (97%), and tin (32%). Although current recycling economics remain challenging, the environmental and health benefits are significant. This research contributes to the existing literature by providing the first detailed quantification of recoverable raw materials embedded in Greece’s PV stock and by highlighting the need for technological innovation and supportive policies to enable a circular and sustainable solar economy. Full article

The precise identification of photovoltaic power stations is essential for advancing the assessment of energy infrastructure and for the efficient management of land resources. To address the need for spatially explicit data on photovoltaic (PV) development in arid and semi-arid regions amid green energy transitions, particularly in the context of identification challenges induced by the widespread distribution of bare ground, this study optimized a remote sensing-based identification method integrating Principal Component Analysis (PCA), automated sampling via Google Earth Engine (GEE), and deep learning models, and applied it to Qinghai Province, one of China’s largest PV regions. The results showed that HRNetv2 (validation Dice = 0.9463) outperformed UNet (0.9328), Attention UNet (0.9399), and HRNet + OCR (0.9184) in small-sample (1871 training samples) PV segmentation; the PV installed area during 2020–2024 accounted for 63.5% of the total pre-2024 area (~607 km²), exceeding the cumulative area before 2019, with projects predominantly distributed in areas with elevation less than 2500 m and slope less than 2°; bare land dominated PV land use (88.7%), followed by grassland (6.9%) and shrubland (3.9%), and PV construction contributed to desert greening by modifying microclimates. The study concludes that its optimized method effectively supports PV spatial identification, and the revealed PV distribution and land use patterns provide scientific guidance for synergistic PV development and ecological conservation in arid regions, while acknowledging limitations in generalizability to other regions due to Qinghai-specific data, suggesting future algorithm refinement and expanded research scales. Full article

With the rapid development of renewable energy, surface defect detection of photovoltaic panels has become an important link in improving photoelectric conversion efficiency and ensuring safety. However, there are various types of surface defects on photovoltaic panels with complex backgrounds, and traditional detection methods face challenges such as low efficiency and insufficient accuracy. This article proposes a lightweight improved model AE-YOLO (YOLOv11+Adown +ECA) based on YOLOv11, which improves detection performance and efficiency by introducing a lightweight dynamic down-sampling module (Adown) and an Efficient Channel Attention (ECA). The Adown module reduces the complexity of computational and parameters through steps such as average pooling preprocessing, channel dimension segmentation, branch feature processing, and feature fusion. The ECA mechanism enhances the model’s response to defect sensitive feature channels and improves its ability to discriminate low contrast small defects through adaptive average pooling, one-dimensional convolution, and sigmoid activation. The experimental results indicate that the AE-YOLO model performs well on the PVEL-AD dataset. mAP@0.5 reached 90.3%, the parameter count decreased by 18.7%, the computational load decreased by 19%, and the inference speed reached 259.56 FPS. The ablation experiment further validated the complementarity between Adown and ECA modules, providing an innovative solution for real-time and accurate defect detection of photovoltaic panels in industrial scenarios. Full article

This study investigates the optimal operation of distribution systems incorporating Photovoltaic (PV) units, Electric Vehicle Charging Stations (EVCSs), and DSTATCOM devices using the Starfish Optimization Algorithm (SFOA). The main goal of the SFOA is to minimize a combined function that encompasses three key objectives: reducing system losses, increasing PV capacity, and enhancing EVCS power. By applying the SFOA within a multi-objective optimization framework, the optimal locations and sizes of PV units, EVCSs, and DSTATCOMs are identified to meet these objectives. This study analyzes and compares several case studies with different numbers of EVCSs, focusing on the operation of a modified 51-bus distribution system over 24 h. Results show that PV hosting energy increases to 21.73, 23.83, and 29.22 MWh for cases with 1, 2, and 3 EVCSs, respectively. EVCS energy also rises to 12.41, 19.50, and 37.23 MWh for the same cases. The corresponding optimized DSTATCOM reactive powers are 11.02, 12.02, and 13.74 MVarh. Throughout all cases, system constraints—such as voltage limits, utility current, and power flow equations—remain within acceptable ranges. The findings demonstrate the SFOA’s effectiveness in optimizing distribution systems with various devices, ensuring efficient operation and meeting all key objectives while adhering to system constraints. Full article

Photovoltaic (PV) power forecasting combined with energy storage systems (ESS) is critical for grid stability and renewable energy optimization. Machine learning (ML) techniques have shown promise in improving PV forecast accuracy and ESS operation. However, the intersection of PV forecasting and ESS control remains underexplored, warranting a systematic review of recent advances and evaluation of ML effectiveness in PV–ESS integration. To assess research trends in ML-based PV forecasting with ESS (scientometric analysis), synthesize state-of-the-art ML approaches for PV–ESS forecasting (systematic review), and quantify their overall predictive performance via meta-analysis of the coefficient of determination (R²). A comprehensive search of Scopus (2010–2025) was conducted following PRISMA 2020 guidelines. Studies focusing on ML-based PV power forecasting integrated with ESS were included. Multiple reviewers screened the records and extracted data. Study quality was appraised using Joanna Briggs Institute checklists. A random-effects meta-analysis of R² was performed to aggregate model performance across studies. The search identified 227 records; 50 studies were included in the review and 5 in the meta-analysis. Publications grew rapidly after 2018, indicating increased interest in PV–ESS forecasting. Deep learning models and hybrid architectures were the most frequently studied and outperformed traditional methods, while integrating PV forecasts with ESS control consistently improved operational outcomes. Common methodological limitations were noted, such as limited external validation and non-standardized evaluation metrics. The meta-analysis found a pooled R² ~0.95 (95% CI) with no heterogeneity (I² = 0), and no evidence of publication bias. ML-based forecasting significantly improves PV–ESS performance, underscoring AI as a key enabler for effective PV–ESS integration. Future research should address remaining gaps and explore advanced approaches to further enhance PV–ESS outcomes. Full article