Search Results (13,479)

The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation. Full article

Growing interest in sustainable hydrogen production has brought renewed attention to photoelectrochemical (PEC) water splitting as a promising route for direct solar-to-chemical energy conversion. This study explores how integrating hematite (α-Fe₂O₃) and cupric oxide (CuO) photoelectrodes with a series of nickel-based co-catalysts can improve photoelectrochemical activity. Photoanodic (NiO_x, NiFeO_x, NiWO₄) and photocathodic (Ni, NiCu, NiMo) co-catalysts were synthesized via co-precipitation and mechanochemical methods and characterized through X-ray Diffraction (XRD), X-ray Fluorescence (XRF), Transmission Electron Microscopy–Energy Dispersive X-ray Spectroscopy (TEM-EDX), Scanning Electron Microscopy–Energy Dispersive X-ray Spectroscopy (SEM-EDX), X-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) gas-adsorption analyses to clarify their crystallographic, morphological, and compositional properties, as well as their surface chemistry and textural properties (surface area and porosity). Electrochemical tests under 1 SUN illumination showed that NiO_x significantly improves the photocurrent of hematite photoanodes. Among the cathodic co-catalysts, NiMo demonstrated the best performance when combined with CuO photocathodes. For both photoelectrodes, an optimal co-catalyst loading was identified, beyond which performance declined due to potential charge transfer limitations and light attenuation. These findings highlight the critical role of co-catalyst composition and loading in optimizing the efficiency of PEC systems based on earth-abundant materials, offering a pathway toward scalable and cost-effective hydrogen production. Full article

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

Accurate short-term photovoltaic (PV) power generation forecasting is critical for the stable integration of renewable energy into the grid. This study proposes a Transformer model enhanced with an adaptive sparse self-attention (ASSA) mechanism for PV power forecasting. The ASSA framework employs a dual-branch attention structure that combines sparse and dense attention paths with adaptive weighting to effectively filter noise while preserving essential spatiotemporal features. This design addresses the critical issues of computational redundancy and noise amplification in standard self-attention by adaptively filtering irrelevant interactions while maintaining global dependencies in Transformer-based PV forecasting. In addition, a deep feedforward network and a feature refinement feedforward network (FRFN) adapted from the ASSA–Transformer are incorporated to further improve feature extraction. The proposed algorithms are evaluated using time-series data from the Desert Knowledge Australia Solar Centre (DKASC), with input features including temperature, relative humidity, and other environmental variables. Comprehensive experiments demonstrate that the ASSA models’ accuracy in short-term PV power forecasting increases with longer forecast horizons. For 1 h ahead forecasts, it achieves an R${}^2$ of 0.9115, outperforming all other models. Under challenging rainfall conditions, the model maintains a high prediction accuracy, with an R${}^2$ of 0.7463, a mean absolute error of 0.4416, and a root mean square error of 0.6767, surpassing all compared models. The ASSA attention mechanism enhances the accuracy and stability in short-term PV power forecasting with minimal computational overhead, increasing the training time by only 1.2% compared to that for the standard Transformer. Full article

Transparent building envelopes significantly increase energy demands due to low thermal resistance and solar heat gain, while conventional double-skin facades may lead to overheating and high cooling loads in the summer. This study proposes a novel earth-to-air heat exchanger (EAHE)-assisted ventilated double-skin facade (VDSF) system utilizing low-grade shallow geothermal energy for year-round thermal regulation of transparent building envelopes. A numerical model of this coupled system was developed and validated to estimate the thermal performance of the EAHE-assisted VDSF system in a hot-summer-and-cold-winter climate. Parametric study was conducted to investigate the impact of some key design parameters on thermal performance of the EAHE-assisted VDSF system and further reveal recommended design parameters of this coupled system. The results indicate that the EAHE-VDSF system reduces annual accumulated cooling loads by 20.3% to 76.5% and heating loads by 19.6% to 47.1% in comparison to a conventional triple-glazed, non-ventilated facade. The cavity temperature of the VDSF decreases by 15 °C on average in the summer, effectively addressing the overheating issue in DSFs. The proposed coupled EAHE-VDSF system shows promising energy-saving potential and ensures stability and consistency in the thermal regulation of transparent building envelopes. Full article

This study explores a hybrid advanced electrochemical oxidation process (EAOP) intensified by solar irradiation and ozone for the treatment of wastewater containing COVID-19-related pharmaceuticals. Pilot-scale trials were performed in a 30 L compound parabolic collector (CPC)-type photoreactor with a boron-doped diamond (BDD–BDD) electrode configuration. Under optimal conditions (50 mg L⁻¹ paracetamol, 0.05 M Na₂SO₄, 0.50 mM Fe²⁺, pH 3.0, and 60 mA cm⁻²), the solar photoelectro-Fenton (SPEF) process achieved 78% chemical oxygen demand (COD) reduction within 90 min, with catechol and phenol detected as the main aromatic intermediates. When applied to a four-drug mixture (dexamethasone, paracetamol, amoxicillin, and azithromycin), the solar photoelectro-Fenton (SPEF–ozone (O₃)) system reached 60% degradation and 41% COD removal under solar conditions. The results highlight the synergistic effect of ozone and solar energy in enhancing the electrochemical oxidation process (EAOP) performance and demonstrate the potential of these processes for scalable and sustainable removal of pharmaceutical contaminants from wastewater. Full article

In concentrated solar power (CSP) systems, structural materials face severe corrosion challenges induced by molten chlorides, with the corrosion severity being highly dependent on the salt composition. This study systematically compares the corrosion behavior of two representative superalloys, Inconel 625 and SS321, in binary NaCl–KCl and ternary MgCl₂–NaCl–KCl molten salts at 700 °C. The corrosion products and microstructural features were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM) equipped with energy-dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD), in combination with static exposure tests to elucidate the underlying mechanisms. The results show that in NaCl–KCl molten salts, both alloys primarily form Cr₂O₃ as the protective product. However, the corrosion scale of SS321 is porous, whereas Inconel 625 develops a dense NiCr₂O₄ inner layer, exhibiting superior corrosion resistance. In the MgCl₂–NaCl–KCl molten salt system, Cr₂O₃ is replaced by a dense MgO layer forms on Inconel 625, coupled with Mo surface enrichment, which significantly inhibits Cr depletion and leads to a notably reduced corrosion rate relative to the binary salt. In contrast, the transformation of Cr₂O₃ on SS321 into porous MgCr₂O₄ exacerbates intergranular corrosion, resulting in a substantial degradation of corrosion resistance. This study elucidates the distinct corrosion pathways and mechanisms of different alloys in binary and ternary chloride salts, providing important guidance for the selection of molten salt compositions and corrosion-resistant structural materials in CSP applications. Full article

Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs. Full article

In South Korea, sunlight rights and daylight rights are legally distinguished, yet no standardized methodology exists for their quantitative assessment. Current evaluations of sunlight rights are narrowly defined, relying on the duration of direct solar penetration at the window center during the winter solstice, while excluding reflected and diffuse light. This restrictive approach has led to confusion among both researchers and legal practitioners, as it diverges from daylighting evaluations that account for indoor brightness and energy performance. The recent enactment of regulations to secure solar access in schools has further intensified disputes between educational institutions striving to protect students’ visual comfort and developers seeking to maximize building potential. To address this gap, this study proposes an evaluation framework tailored to the Korean context. A reference classroom model representative of standard Korean schools was developed, and simulations were conducted by introducing obstructing building masses to block direct sunlight. The methodology evaluated key variables, including time of day and solar altitude, and analyzed daylighting performance and lighting-related energy consumption under obstructed conditions. The results show that blocking sunlight through south-facing windows reduces daylighting performance by 89% to 98%, leading to additional reliance on artificial lighting, with energy use increasing between 128 Wh and 768 Wh. These findings underscore the limitations of current legal interpretations based solely on sunlight duration and highlight the necessity of adopting performance-based evaluation methods. Protecting school sunlight rights through such approaches is essential to enhancing classroom visual environments and reducing energy demand. Full article

The wind–solar–storage–thermal combined power generation system is one of the key measures for China’s energy structure transition, and rational capacity planning of each generation entity within the system is of critical importance. First, this paper addresses the uncertainty of wind and photovoltaic (PV) power outputs through scenario-based analysis. Considering the diversity of generation entities and their complex interest demands, a bi-level capacity optimization framework based on game theory is proposed. In the upper-level framework, a game-theoretic method is designed to analyze the multi-agent decision-making process, and the objective function of capacity allocation for multiple entities is established. In the lower-level framework, multi-objective optimization is performed on utility functions and node voltage deviations. The Nash equilibrium of the non-cooperative game and the Shapley value of the cooperative game are solved to study the differences in the capacity allocation, economic benefits, and power supply stability of the combined power generation system under different game modes. The case study results indicate that under the cooperative game mode, when the four generation entities form a coalition, the overall system achieves the highest supply stability, the lowest carbon emissions at 30,195.29 tons, and the highest renewable energy consumption rate at 53.93%. Moreover, both overall and individual economic and environmental performance are superior to those under the non-cooperative game mode. By investigating the capacity configuration and joint operation strategies of the combined generation system, this study effectively enhances the enthusiasm of each generation entity to participate in the energy market; reduces carbon emissions; and promotes the development of a more efficient, environmentally friendly, and economical power generation model. Full article

This study explores the use of mid-infrared (MIR) free-electron laser (FEL) irradiation as a tool for tailoring the surface properties of electrochemically synthesized TiO₂—graphene quantum dots (QDs). The QDs, prepared in colloidal form via a cost-effective electrochemical method in a KCl—citric acid medium, were exposed to MIR wavelengths (5.76, 8.02, and 9.10 µm) at the Kyoto University FEL facility. Post-irradiation measurements revealed a pronounced inversion of zeta potential by 40–50 mV and approximately 10% reduction in hydrodynamic size, indicating double-layer contraction and ionic redistribution at the QD—solvent interface. Photoluminescence spectra showed enhanced emission for GQDs and TiO₂/GQD composites, while Tauc analysis revealed modest bandgap blue shifts (0.04–0.08 eV), both consistent with trap-state passivation and sharper band edges. TEM confirmed intact crystalline structures, verifying that FEL-induced modifications were confined to surface chemistry rather than bulk lattice damage. Taken together, these results demonstrate that MIR FEL irradiation provides a resonance-driven, non-contact method to reorganize ions, suppress defect states, and improve the optoelectronic quality of QDs. This approach offers a scalable post-synthetic pathway for enhancing electron transport layers in perovskite solar cells and highlights the broader potential of photonic infrastructure for advanced nanomaterial processing and interface engineering in optoelectronic and energy applications. Full article

HVAC systems are becoming increasingly important around the world due to the increasing need for climatization in recent years. While district heating systems have been used for a long time, district cooling systems tend to be something that is only reserved for large buildings, making decentralized cooling flourish, shaping the idea of considering it as the first choice when it comes to cooling devices, disregarding the efficiency of larger systems. This article compares two technologies for district energy solutions. One option features single-stage absorption chillers using solar thermal technologies (Fresnel collectors) for heat, while the other uses high-efficiency compression chillers with photovoltaic technologies. Parametric studies were used to determine system sizes and considerations were taken to perform such as comparison. This paper concludes that compression chillers are the better option for cooling systems with variable demand while absorption chillers are a good choice for systems with constant demand, like data centers, especially when there is a high-temperature heat source available. Full article

Reliable power generation from solar cells is critical for spacecraft operation. High-energy laser irradiation poses a significant threat, as it can potentially cause irreversible damage to solar cells, which is difficult to detect remotely using conventional techniques such as radar or optical imaging. Spectral detection offers a potential approach through unique “spectral fingerprints,” but the spectral characteristics of laser-damaged solar cells remain insufficiently documented. This study investigates the scattering spectral characteristics of triple-junction GaAs (Gallium Arsenide) solar cells subjected to nanosecond pulsed laser irradiation to establish spectral signatures for damage assessment. GaAs solar cells were irradiated at varying energy densities. Bidirectional Reflectance Distribution Function (BRDF) spectra (400–1200 nm) were measured. A thin-film interference model was used to simulate damage effects by varying layer thicknesses, thereby interpreting experimental results. The results demonstrate that as the laser energy density increases from 0.12 to 2.96 J/cm², the number of absorption peaks in the visible range (400–750 nm) decreases from three to zero, and the oscillation in the near-infrared range vanishes completely, indicating progressive damage to the GaInP (Gallium Indium Phosphide) and GaAs layers. This study provides a spectral-based approach for remote assessment of laser-induced damage to solar cells, which is crucial for satellite health monitoring. Full article

The transition to sustainable electricity grids, particularly in countries with high renewable potential, such as Brazil, requires integrated assessments of hybrid and single-source configurations. This study analyzed the technical and economic feasibility of hybrid plants and isolated wind and solar systems in the Brazilian Northeast, focusing on Macaíba (RN) and Casa Nova (BA), regions characterized by high resource availability. The work addresses a gap in the literature by integrating detailed technical modeling and financial analysis of hybrid configurations, considering both local and operational constraints. Hourly simulations were performed using the HyDesign software (v1.1.0), with optimization based on the ratio between net present value (NPV) and invested capital (CAPEX), covering seven different scenarios by location, including hybrid combinations and systems with solar trackers. The results indicated that systems with solar tracking achieved superior economic performance. In Macaíba, the optimal configuration was the hybrid scenario with trackers, which increased the NPV/CAPEX by 27.69% compared to the relevant baseline. In Casa Nova, the best solution was the pure solar plant with trackers, which increased the NPV/CAPEX by 50.0% compared to fixed solar. Hybridization showed moderate gains in scenarios without tracking. It is concluded that while solar trackers are highly beneficial, the optimal plant configuration (pure solar or hybrid) is site-specific and depends on the local renewable resource profile. Notably, battery storage was not economically justified under the evaluated cost assumptions. The study contributes to the planning of renewable projects in contexts of high source complementarity. Full article

Achieving optimal daylighting in buildings necessitates complex and expensive control systems. This research addresses this challenge by proposing a simple and more practical solution: a parametric louver system based on rotating slats controlled by stepper motors, powered by an Integrated Circuit platform (Arduino board), which can translate the digital figures (the rotation angles) to a physical action. The system automatically adjusts the slats in accordance with solar altitudes and reflects them to specific targets over the ceiling. This ensures a uniform and comfortable distribution of daylight throughout a room. This system was developed using Grasshopper as the parametric software, with future control planned via a user-friendly mobile app through a preliminary prototype. This daylighting system prioritises human visual comfort while targeting a significant 53% reduction in electrical lighting energy consumption. The system aims to enhance occupant well-being to significantly increase energy savings, making it a compelling solution for sustainable building design. Full article