Irradiance Fluctuations Research Articles

Non-photochemical quenching may contribute to the dominance of the pale mat-forming lichen Cladonia stellaris over the sympatric melanic Cetraria islandica.

The mat-forming fruticose lichens Cladonia stellaris and Cetraria islandica frequently co-occur on soils in sun-exposed boreal, subarctic, and alpine ecosystems. While the dominant reindeer lichen Cladonia lacks a cortex but produces the light-reflecting pale pigment usnic acid on its surface, the common but patchier Cetraria has a firm cortex sealed by the light-absorbing pigment melanin. By measuring reflectance spectra, high-light tolerance, photosynthetic responses, and chlorophyll fluorescence in sympatric populations of these lichens differing in fungal pigments, we aimed to study how they cope with high light while hydrated. Specimens of the two species tolerated high light equally well but with different protective mechanisms. The mycobiont of the melanic species efficiently absorbed excess light, consistent with a lower need for its photobiont to protect itself by non-photochemical quenching (NPQ). By contrast, usnic acid screened light at 450-700nm by reflectance and absorbed shorter wavelengths. The ecorticate usnic species with less efficient fungal light screening exhibited a consistently lower light compensation point and higher CO2 uptake rates than the melanic lichen. In both species, steady state NPQ rapidly increased at increasing light with no signs of light saturation. To compensate for less internal shading causing light fluctuations with a larger amplitude, the usnic lichen photobiont adjusted to changing light by faster induction and faster relaxation of NPQ rapidly transforming excess excitation energy to less damaging heat. The high and flexible NPQ tracking fluctuations in solar radiation probably contributes to the strong dominance of the usnic mat-forming Cladonia in open lichen-dominated heaths.

Turbulence-radiation interaction in large eddy simulations of turbulent reactive flows: a review

Turbulence-radiation interaction (TRI) arises from the highly non-linear coupling between fluctuations of radiative intensity and fluctuations of temperature and chemical composition of the medium. It is well recognized that neglecting turbulent fluctuations of the latter scalars when computing the mean radiation field, as is standard in Reynolds-Averaged Navier-Stokes (RANS) simulations, leads to mispredictions of the radiative heat fluxes and the radiant fraction in as much as one order of magnitude. Conversely, the importance of TRI in large eddy simulations (LES)—i.e., the effect of subfilter-scale (SFS) fluctuations on the filtered radiation field—has only started to be investigated in the last decade. The present text reviews the knowledge gathered so far on the subject. The instantaneous and the spatially filtered forms of the radiative transfer equation (RTE) are presented, and the unresolved correlations resulting from the filtering operation are discussed. Models for accounting for SFS-TRI are surveyed, with the most popular ones (namely, probability density function-based methods and empirical approximations) reviewed in detail. An appraisal of the literature on the importance and characteristics of SFS-TRI is performed, with special attention given to turbulent jet diffusion flames and pool fires, which are the two configurations for which SFS-TRI has been studied the most. The main findings of these studies are summarized, and recommendations are given on directions of future works on the subject.

Shunt Active Parallel Filter, Grid Photovoltaic System

This paper introduces a study focused on managing a photovoltaic system connected to the electrical grid. The primary components of this system include solar arrays linked via a DC bus to an inverter situated on the grid side. Fluctuations in solar irradiance and temperature are swift, prompting the integration of maximum power point tracking (MPPT) within the inverter’s control mechanism. The energy produced by the photovoltaic system is fed into the grid. This transfer is achieved through a proficient DC/AC conversion process, wherein the MPPT is integrated into the inverter’s operation to regulate the levels of active and reactive power injected into the grid. The paper also delves into the employment of the Space Vector Modulation (SVM) control technique for the DC-AC inverter. It covers the implementation of a Shunt Active Power Filter (SAPF) with a three-phase four-wire configuration, consisting of four legs and adopting a split capacitor topology. Furthermore, the paper includes an exploration of the instantaneous power theory and the utilization of hysteresis block control for the SAPF. The findings of this study are demonstrated and analyzed using Matlab/Simulink software.

Probing Physicochemical Performances of 3D Printed Carbon Fiber Composites During 8‐Month Exposure to Space Environment

AbstractCarbon fiber reinforced polymers (CFRPs) offer exceptional properties that make them highly relevant in the aerospace industry, such as high thermal conductivity and an outstanding strength‐to‐weight ratio. Advances in additive manufacturing have expanded the aerospace applications of CFRPs, even allowing for in‐space fabrication of complex structures. Understanding the stability of CFRPs in the harsh conditions of low Earth orbit (LEO) is crucial. LEO exposes materials to extreme environmental factors, such as vacuum, radiation, atomic oxygen, and temperature fluctuations, which can accelerate degradation. To investigate the space‐environment effect on material, changes in properties of 3D‐printed CFRPs are compared with CFRPs made through forging and conventional compression molding. Surface analyses examine morphological, chemical, and matrix composition changes, along with an evaluation of mechanical integrity. Remarkably, the naked 3D printed CFRPs withstood 8 months of LEO exposure similar to the compression molded CFRP samples, with changes in chemical properties limited to the sample's outer surface. Further, despite no protective coatings are used, limited surface erosion and no variation in mechanical strength are observed. These results provide relevant information for the development and deployment of novel 3D printed CFRPs materials for a wide spectrum of terrestrial and space applications.

LAW OF LARGE NUMBERS OF PROBABILITY THEORY AND FORMATION OF THE ENERGY SPECTRUM OF SYNCHROTRON RADIATION OF RELATIVISTIC ELECTRON IN THE STORAGE RING

The fluctuations of synchrotron radiation (SR) energy losses during the motion of relativistic electrons in the uniform magnetic field in a storage ring are considered. The influence on the formation of the energy spectrum of the SR photon flux of the duration of registration of SR relativistic electrons is analyzed. It has been established that with the increase in the duration of temporal registration, the energy spectrum of the SR photon flux is regularly transformed into the asymptotically normal density in accordance with the law of large numbers of probability theory. It is determined that the main mechanism in the formation of the energy spectrum is successive convolutions of the current spectra.

The Frequency of Occurrence of Pre-Flare Fluctuations in Microwave Radiation of Active Regions

The Frequency of Occurrence of Pre-Flare Fluctuations in Microwave Radiation of Active Regions

Enhanced Convolutional Neural Network for solar radiation nowcasting: All-Sky camera infrared images embedded with exogeneous parameters

Electrical power production by renewable energy sources is unpredictable in nature and this may cause imbalance between power generation and demand. Therefore, an accurate prediction of solar radiation is crucial for the stability and efficient management of electric grid. This study focuses on very short-term forecasts of solar radiation with a horizon in the range of 5–15 min. In this paper, a Convolutional Neural Network is proposed that uses sequences of infrared images captured by an All-Sky Imager to forecast the Global Horizontal Irradiance on different time horizon. A real case study, exploiting six months of high-resolution data, is analyzed. Additionally, an innovative technique, the Enhanced Convolutional Neural Network (ECNN), is proposed in which exogenous data, as the solar radiation measurement, is encoded in terms of colored pixels in the upper corner of the images. Considering the naïve persistence method as a baseline, a clear improvement across the key metrics has been noted with the proposed methodology. A deeper analysis of the results reveals that the proposed models are more accurate than persistence when high fluctuations of solar radiation are experienced. In that case, the ECNN achieves a forecast skill exceeding 19% for all the tested forecast horizons.

Enhancing photovoltaic performance through solar radiation splitting: A beam splitter-assisted hybrid approach with 2-D tracking and PCM integration

Solar energy is an abundant and environmentally friendly power source; however, its conversion efficiency is limited by the mismatch between the response of photovoltaic (PV) and the solar spectrum. This paper proposes a novel hybrid system that splits solar radiation into visible and thermal components using a beam splitter. Through experimental and theoretical analyses, the study explores the integration of a phase change material (PCM) packed bed with a PV cell as a hybrid system to optimize energy conversion. The experiment is carried out using a 2D tracker. The concentration ratio, determined based on the maximum concentration ratio (MCR) supported by the PV cell, is established as the optimum point. For Silicon PV, the maximum concentration ratios are determined to be 8 and 16 sun for the cases of using and not using the beam splitter, respectively. The ratios of thermal and visible energy, measured at a wavelength of 700 nm, are observed to be 60.66 % and 39.33 % respectively. Under MCR conditions, in the PV system alone, the generated power reaches approximately 850 W/m2, while in the hybrid splitting configuration, it increases to around 1200 W/m2, resulting in a 43 % increase in the system's net power when employing the beam splitter. Furthermore, the study reveals that the utilization factor remains constant at 0.13 across all irradiation ranges for PV-alone systems. In contrast, for the PV/PCM hybrid splitting system, the utilization factor exhibits an increasing trend with higher solar irradiation intensities, reaching values of 0.31, 0.41, and 0.46 for irradiation ranges of 900, 950, and 1000 W/m2, respectively. The findings of this study suggest the potential of using a beam splitter in combination with a 2-D tracker and PCM-packed bed to increase PV efficiency. The proposed configuration demonstrates a substantial enhancement in total net power output, offering a promising avenue for advancing solar energy conversion technologies. Adding PCM to the system has proven to be beneficial, as it addresses the fluctuations in solar radiation and may enhance the total system efficiency.

Flower production decreases with warmer and more humid atmospheric conditions in a western Amazonian forest.

Climate models predict that everwet western Amazonian forests will face warmer and wetter atmospheric conditions, and increased cloud cover. It remains unclear how these changes will impact plant reproductive performance, such as flowering, which plays a central role in sustaining food webs and forest regeneration. Warmer and wetter nights may cause reduced flower production, via increased dark respiration rates or alteration in the reliability of flowering cue-based processes. Additionally, more persistent cloud cover should reduce the amounts of solar irradiance, which could limit flower production. We tested whether interannual variation in flower production has changed in response to fluctuations in irradiance, rainfall, temperature, and relative humidity over 18 yrs in an everwet forest in Ecuador. Analyses of 184 plant species showed that flower production declined as nighttime temperature and relative humidity increased, suggesting that warmer nights and greater atmospheric water saturation negatively impacted reproduction. Species varied in their flowering responses to climatic variables but this variation was not explained by life form or phylogeny. Our results shed light on how plant communities will respond to climatic changes in this everwet region, in which the impacts of these changes have been poorly studied compared with more seasonal Neotropical areas.

A new hybrid model for photovoltaic output power prediction.

Recently, with the development of renewable energy technologies, photovoltaic (PV) power generation is widely used in the grid. However, as PV power generation is influenced by external factors, such as solar radiation fluctuation, PV output power is intermittent and volatile, and thus the accurate PV output power prediction is imperative for the grid stability. To address this issue, based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), improved artificial rabbits optimization (IARO) and convolutional bidirectional long short-term memory (CBiLSTM), a new hybrid model denoted by CEEMDAN-IARO-CBiLSTM is proposed. In addition, inputs of the proposed model are optimized by analyzing influential factors of PV output power with Pearson correlation coefficient method. In order to verify the prediction accuracy, CEEMDAN-IARO-CBiLSTM is compared with other well-known methods under different weather conditions and different seasons. Specifically, for different weather conditions, MAE and RMSE of the proposed model decrease by at least 0.329 and 0.411, 0.086 and 0.021, and 0.140 and 0.220, respectively. With respect to different seasons, MAE and RMSE of the proposed model decrease by at least 0.270 and 0.378, 0.158 and 0.209, 0.210 and 0.292, and 1.096 and 1.148, respectively. Moreover, two statistical tests are conducted, and the corresponding results show that the prediction performance of CEEMDAN-IARO-CBiLSTM is superior to other well-known methods.

A passive continuous flow pasteurisation process designed for solar application. Part I: Indoor experiments for thermal characterization and bacteria inactivation

Within the framework of the WHO's universal sanitation policy, water disinfection processes with low manufacturing and maintenance costs must be developed. This is the case with solar pasteurisation, the effectiveness of which, at 50–70 °C, has been already demonstrated in the literature. To be fully acceptable, the process must be appropriately energy efficient and be able to adapt to natural fluctuations in solar radiation. An experimental indoor test platform under controlled conditions, with thermal powers supplied similar to those of a future solar application with a standard thermal panel, was designed. Experimental results highlighted the specific thermal energy consumption (SECtherm), which is highly dependent on the effectiveness of a deliberately oversized heat recovery exchanger. The operation under both stationary and dynamic regimes underscored the role of the thermostatic valve which autonomously calibrated the flow of the fluid according to the injected available power. For the temperature range sufficient to inactivate microorganisms [50–70 °C], the specific thermal energy consumption reached values between 3.5 and 6 kWhtherm.m−3. The bacterial indicators, Escherichia coli and Enteroccocus, currently present in surface waters in the south of France, were no longer detected (<10 MPN.100 mL−1) after one-minute of pasteurisation at 55 °C and 60 °C, respectively.

Rapid irradiance fluctuations occur in a greenhouse: Quantification and implication

Rapid irradiance fluctuations occur in a greenhouse: Quantification and implication

Impact of Solar Radiation on Luminaires and Energy Efficiency in Isolated Residential Photovoltaic Systems

This research centers on the implementation of photovoltaic systems in residential applications, coupled with battery-based energy storage, and evaluates their efficiency in generating energy, specifically for lighting in buildings. The methodology hinges on detecting interharmonic signals to characterize potentially disruptive frequencies and identify the origins of various failures. Multiple case studies are presented to validate the method’s efficacy, including one involving fluorescent lamp circuits and another examining variations in solar radiation during the summer season. Real-world experiments are conducted in a residential setting, and the results are thoroughly analyzed. Various types of interharmonic generation behaviors are demonstrated, which are influenced by fluctuations in solar radiation and the appropriate installation of solar panels. The findings reveal that the absence of solar radiation below 300 W/m2 in a photovoltaic system relying on energy storage adversely affects interharmonics in luminaires installed within a residential space.

Changes in solar resource intermittency and reliability under Australia’s future warmer climate

The dependency of photovoltaic (PV) power generation on meteorological parameters can impact power production due to weather-induced variability. During the day, fluctuations in radiation introduce intermittency in power generated, raising reliability and grid stability issues at higher penetration levels. Long-term future resource assessment provides an effective tool for estimating resource reliability and future intermittency essential for pre-feasibility site assessments around the world. Australia has high solar power capacity, with several solar farms in operational and developmental stage. Using Australia as a case study, this research aims to understand Australia's solar resource distribution and variability using regional climate model projections under a high emission scenario. Results indicate an abundance of solar resource power density in Australia, especially in the North (450-500Wm−2). The solar resource will be more reliable in Eastern Australia in the future with ∼ 5% increase in resource density. Results suggest reduction in intermittency (∼20-minute lull periods) in the East with increase in clear-sky days/year in the future (∼20 days/year). Resource assessment of Sun Cable and New England solar farm located in Australia, revealed the future scope of increase in clear-sky days at the sites. This long-term future solar variability analysis can help identify regions in Australia where PV systems will be least susceptible to losses due to intermittency. Furthermore, this study will help in critical decision-making processes like planning storage systems, site selection, opportunities to create hybrid solar farms with the co-existence of solar and wind technology, etc., to mitigate the risks associated with future intermittent PV power generation.

Hybrid SSA-PSO based intelligent direct sliding-mode control for extracting maximum photovoltaic output power and regulating the DC-bus voltage

Photovoltaic (PV) energy has become a considerably more attractive source of power, with costs continually declining. Along with the implementation of this sustainable energy system, the power supply stability should be extensively investigated, particularly in off-grid systems where electricity storage becomes a limitation. The performance of the standalone photovoltaic battery system (SPVBS) is mainly reliant on the DC-bus voltage and its capacitor. The shortcomings of utilizing a small film DC-bus capacitor in SPVBS include the instability and low-frequency ripple of DC-bus voltage and the system's dynamic MPPT techniques performance. Therefore, this study proposes a new technique of regulating the various system components that stabilizes the DC-bus voltage, extracts stable output power despite harsh sudden load and radiation fluctuations, and maintains the system's functionality with or without the battery storage system (BSS). The proposed approach uses a hybrid salp swarm algorithm and particle swarm optimization (SSA-PSO) combined with an intelligent direct sliding mode controller (DSMC) to eliminate the instability and ripples of DC-bus voltages. Moreover, it employs DSMC for bidirectional DC-DC converter and boost converters to enhance the system's dynamic MPPT techniques capabilities. Four different modes are considered according to the energy availability, demand, and battery's state of charge (SOC). Additionally, this easy-to-use technique keeps the DC-bus voltage stable even when the battery storage system is not available. Simulation and experimental results revealed the resilience performance of the proposed intelligent SSA-PSO-DSMC technique in terms of DC-bus regulation, output power extracted, and current harmonics reduction compared to PSO, SSA, cuckoo search optimization (CSO), and grey wolf optimization (GWO).

Back propagation neural network based proportional-integral hybrid control strategy for a solar methane reforming reactor

To handle the impact of solar radiation fluctuations on the solar reactor, a new proportional-integral (PI) hybrid control strategy based on back propagation neural network (BPNN) is designed in this study to achieve stable operation. Firstly, the BPNN model optimized by genetic algorithm is trained using reactor model simulation data. Then, the BPNN model is combined with PI feedback control to obtain a BPNN-based PI feedforward-feedback hybrid control strategy for regulating the inlet flow rate. Finally, the effectiveness of the control strategy during transient operation under different disturbances is tested. The results show that during actual solar radiation disturbance, the integral square errors (ISE) of outlet hydrogen fraction and methane conversion for BPNN-based PI hybrid control are 7.03 × 10−6 and 1.05 × 10−4, respectively. Compared to traditional PI feedback control, the new hybrid control strategy represents a reduction of 94% and 91% in ISE, indicating that the strategy can significantly improve production stability.

Fuzzy logic controller-based enhancement for grid-connected large-scale PV systems

: In recent years, considerable scholarly attention has been directed toward photovoltaic (PV) power generation, driven by the imperative to ameliorate the ecological ramifications inherent in conventional electricity generators. The economic viability of PV generation is intrinsically contingent upon both the cost-efficiency of the cells employed and the quantum of energy that the arrays have the capacity to deliver over their operational lifespan. This scholarly exposition elucidates an exploratory inquiry into the intricate realm of modeling and simulating solar PV systems, conjoined with the distribution grid via the employment of a Fuzzy Logic Controller (FLC). The inquiry delves into the nuanced realm of the influence wielded by fluctuations in solar radiation upon the system's operational efficacy, concurrently proffering a novel energy storage framework engineered to counterbalance the power quality predicaments stemming from the intermittent nature of solar energy. The empirical manifestations gleaned from simulations evince the system's conceptual prowess, effectuating an amelioration in the comprehensive operational dexterity of the solar PV system, thereby embellishing energy capture efficiency and mollifying the perturbing power quality predicaments. A comparative investigation, encompassing varied conditions, substantiates the prowess of the proposed methodology vis-à-vis the fixed perturbation Maximum Power Point Tracking (MPPT) algorithm. Empirical observations emanating from these investigations conspicuously affirm the feasibility and distinct advantages underpinning the purview of the proposed methodology.

Exploring carbon sequestration in broad-leaved Korean pine forests: Insights into photosynthetic and respiratory processes

A comprehensive understanding of carbon assimilation and sequestration in broad-leaved Korean pine forests is crucial for accurately estimating this significant aspect of temperate forests at a regional scale. In this study, we introduced a high-temporal resolution model designed for carbon assimilation insights at the plot scale, focusing on specific parameters such as leaf area dynamics, vertical leaf distribution, photosynthetically active radiation (PAR) fluctuations, and the photosynthetic traits of tree species. The findings reveal that most tree species in broad-leaved Korean pine forests exhibit an inverted U-shaped pattern in leaf area dynamics, with shorter leaf drop periods than leaf expansion events. Leaf distribution varies significantly among different canopy heights, with approximately 80 % of the leaves above 15 m. PAR decreases as canopy height decreases, with PAR at 25 m accounting for about 60 % of the PAR above the canopy. Our framework incorporates a leaf-scale light-response curve and empirical photosynthesis-temperature relationships to estimate forest carbon assimilation on daily and hourly scales accurately. Using the model, we assess the gross primary productivity (GPP), leaf net photosynthetic assimilation (LNPA), and carbon increment (ΔC) of broad-leaved Korean pine forests from 2017 to 2020. The results demonstrate GPP, LNPA, and ΔC values of 21.4 t·ha−1·a−1, 17.4 t·ha−1·a−1, and 4.0 t·ha−1·a−1, respectively. Regarding efficiency, GPP, LNPA, and ΔC per square meter of leaf per year are 179 g, 146 g, and 33 g, respectively. Notably, tree species in the canopy layer of the forest exhibit significantly higher efficiency than those in the understory layer. This research significantly contributes to our understanding of carbon cycling and the responses of forest ecosystems to climate change. Moreover, it provides a practical tool for forest management and the development of carbon sequestration strategies.

Sequential stochastic and bootstrap methods to generate synthetic solar irradiance time series of high temporal resolution based on historical observations

Generating synthetic solar irradiance data through computational methods has become an attractive approach, especially when obtaining accurate measurements from historical observations or satellite-derived estimates is not feasible. Since solar photovoltaic energy production is influenced by variable weather conditions, having reliable solar irradiance data is crucial for evaluating the performance of photovoltaic systems accurately. In this paper, we propose two methods, namely stochastic method, and bootstrap method, to generate one-day synthetic solar irradiance data at a minimum 60-minute time resolution. Both techniques consider dynamic meteorological behavior and maintain the physical significance of the observed data, effectively capturing the intermittent nature of solar irradiance. Validations were performed for five sites with different climate conditions and significant geographical separation, and different time resolutions, through various metrics, including variability, statistical distribution, and energy production, which demonstrated mean average percentage errors as low as 2.1% for intraday irradiance fluctuations, statistical distribution goodness-of-fit up to 80%, and discrepancy for energy production close to 2.4%. results verify the applicability of the proposed methods regardless the time resolution, the location of the measured data and the procedure to record it (i.e., from ground weather stations or satellite-derived estimates). We also evaluated the computational performance of the proposed methods, synthetic, where the elapsed time to generate the synthetic data takes less than a minute to generate a hundred one-day solar irradiance time series. The novelty of the proposed methods is that it is only needed the information of a single month to generate representative synthetic solar irradiance one-day sequences generated for five climate conditions without the need of a training process (i.e., iterative adjustments of the parameters), being useful for pre-feasibility studies of the photovoltaic project and to train machine learning algorithms that deals with time series. To encourage the reproducibility of the research presented in this paper, the proposed methods to generate synthetic solar irradiance data are freely available in a well-documented GitHub repository from https://github.com/salazarna/synthetic-irradiance-sequence.

Brief windows with more favorable atmospheric conditions explain patterns of Polylepis reticulata tree water use in a high-altitude Andean forest.

Polylepis trees occur throughout the Andean mountain region, and it is the tree genus that grows at the highest elevation worldwide. In the humid Andes where moisture is rarely limiting, Polylepis trees must adapt to extreme environmental conditions, especially rapid fluctuations in temperature, ultraviolet radiation and vapor pressure deficit (VPD). However, Polylepis' water-use patterns remain largely unknown despite the importance of understanding their response to microclimate variation to determine their capacity to maintain resilience under future environmental change. We conducted a study in a Polylepis reticulata Kunth forest in the Ecuadorian Andes to evaluate its tree water-use dynamics and to identify the main environmental drivers of transpiration. Tree sap flow was monitored simultaneously with soil volumetric water content (VWC) and microclimate during 2 years for trees growing in forest edge and interior locations. We found that sap flow was primarily controlled by VPD and that VWC exerted a secondary role in driving sap flow dynamics. The highest values for sap flow rates were found when VPD>0.15kPa and VCW<0.73cm3cm-3, but these threshold conditions only occurred during brief periods of time and were only found in 11% of our measurements. Moreover, these brief windows of more favorable conditions occurred more frequently in forest edge compared with forest interior locations, resulting in edge trees maintaining 46% higher sap flow compared with interior trees. Our results also suggest that P. reticulata has a low stomatal control of transpiration, as the sap flow did not decline with increasing VPD. This research provides valuable information about the potential impacts of projected future increases in VPD due to climate change on P. reticulata water-use dynamics, which include higher sap flow rates leading to greater transpirational water loss due to this species' poor stomatal control.