Direct Normal Solar Irradiance Research Articles

Research on Optimization Design Based on Heliostat Field

This study aims to deeply explore the mathematical modeling and performance optimization of solar heliostat fields, in order to improve their energy output efficiency. The solar heliostat field is an innovative solar heating system that focuses solar radiation on the absorption tower through a reflector, thereby generating high-temperature heat energy. This article mainly discusses the performance analysis and parameter optimization of the heliostat field to achieve optimal performance while meeting the rated power requirements. A simulated solar model was established using a three-dimensional Cartesian coordinate system, taking into account the effects of solar altitude angle and direct normal irradiance (DNI). The model in this paper comprehensively considers the effects of shadow occlusion loss, cosine efficiency, atmospheric transmittance, collector truncation efficiency, and mirror reflectance on the mirror field. The specific values of each parameter were calculated using model algorithms such as the HFLCAL model, To provide a basis for accurate performance analysis and solve the problem of parameter optimization, this article defines an objective function aimed at improving the energy utilization efficiency of the heliostat field by adjusting the parameters of the reflector, in order to achieve optimal performance. The above parameters are determined by establishing a genetic algorithm to define constants and initial parameters, and initializing the overall layout of the heliostat field to calculate the optical efficiency of the heliostat. In each iteration, the optimal heliostat field layout can be recorded. The final optimal solution includes parameters such as the position coordinates of the absorption tower, heliostat size, installation height, number of heliostats, and heliostat position.

Influences of Cloud Microphysics on the Components of Solar Irradiance in the WRF-Solar Model

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance.

Development and assessment of artificial neural network models for direct normal solar irradiance forecasting using operational numerical weather prediction data

Accurate operational solar irradiance forecasts are crucial for better decision making by solar energy system operators due to the variability of resource and energy demand. Although numerical weather prediction (NWP) models can forecast solar radiation variables, they often have significant errors, particularly in the direct normal irradiance (DNI), which is especially affected by the type and concentration of aerosols and clouds. This paper presents a method based on artificial neural networks (ANN) for generating operational DNI forecasts using weather and aerosol forecasts from the European Center for Medium-range Weather Forecasts (ECMWF) and the Copernicus Atmospheric Monitoring Service (CAMS), respectively. Two ANN models were designed: one uses as input the predicted weather and aerosol variables for a given instant, while the other uses a period of the improved DNI forecasts before the forecasted instant. The models were developed using observations for the location of Évora, Portugal, resulting in 10 min DNI forecasts that for day 1 of forecast horizon showed an improvement over the downscaled original forecasts regarding R2, MAE and RMSE of 0.0646, 21.1 W/m2 and 27.9 W/m2, respectively. The model was also evaluated for different timesteps and locations in southern Portugal, providing good agreement with experimental data.

Prediction of direct normal irradiation using a new empirical sunshine duration-based model

In this work, we are interested in presenting a new approach allowing us to express the Direct Normal solar Irradiation (DNI) according to the sunshine duration essentially. This choice is justified by the fact that in addition to the sunshine, duration has a strong correlation with solar irradiation, it is measured in many radiometric stations. Some clear sky models with modifications developed exclusively here are made valid for all types of sky. The proposed model is compared with one of the intelligent models such as the Support Vector Regression (SVR) for daily data from Ghardaïa.

Local climate variability associated to sustainable agriculture at rain shadow areas of mount Rinjani and Tambora in West Nusa Tanggara

Climate is driven by solar energy received on the earth surface and local factor, such as mountains. The solar energy affects climate variables namely, temperature, atmospheric pressure, relative humidity, wind and rain. In addition, mountains perform rain shadow areas and affect variability of local climate. A descriptive research was conducted to study variability of those variables associated to direct normal solar irradiation (DNI), and its possible impact on sustainable agriculture at rain shadow areas of Mt. Rinjani in Lombok and Tambora in Sumbawa, West Nusa Tenggara. The results show that the DNI was relative similar at both sides of the Mt. Rinjani which was 13.15±2.6 MJ m−2day−1 on average. While at Mt. Tambora, the DNI at rain shadow area (southern part) was higher than northern part which were 15.6±2.07 MJ.m−2.day−1 and 13.6±3.2 MJ m−2.day−1 respectively. The DNI was relatively high in low lands and it successively decreased through medium and high lands. ETo, air and soil surface temperature were relatively high at rain shadow areas. In conclusion, the intensity of DNI was not significantly affected by aspect factor of incoming light relative to the Mt Rinjani and Mt. Tambora. The mountains had affected the variability of local climate and performed rain shadow areas to the north from Mt Rinjani and to the south from Mt. Tambora, where there had established typical dry land agriculture of semi arid tropics.

Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas

As a crucial issue in renewable energy, accurate prediction of direct normal solar irradiance (DNI) is essential for the stable operation of concentrated solar power (CSP) stations, especially for those in arid desert areas. In this study, in order to fully explore the laws of climate change and assess the solar resources in arid desert areas, we have proposed a mixed multi-pattern regression model (MMP) for short-term DNI prediction using prior knowledge provided by the clear-sky solar irradiance (CSI) model and time series patterns of key meteorological factors mined using PR-DTW on different time scales. The contrastive experimental results demonstrated that MMP can outperform existing DNI prediction models in terms of three recognized statistical metrics. To address the challenge of limited data in arid desert areas, we presented the T-MMP model involving combined transfer learning and MMP. The experimental results demonstrated that T-MMP outperformed MMP in DNI prediction by exploiting the significant correlation between meteorological time series patterns in similar areas for data augmentation. Our study provided a valuable prediction model for accurate DNI prediction in arid desert areas, facilitating the economical and stable operation of CSP plants.

AEROSOL OPTICAL DEPTH FROM SPECTRAL DIRECT NORMAL IRRADIANCE MEASUREMENTS IN MONTEVIDEO, URUGUAY

Abstract. Aerosols are liquid or solid particles with diameters between 2.5 and 10 µm suspended in the lower layers of the atmosphere. Aerosol Optical Depth (AOD) is a relevant parameter that quantifies their concentration in the atmosphere. It is usually estimated from sun photometer measurements at specific wavelengths. The objective of this work is to implement a simple inversion algorithm to retrieve AOD at six different wavelengths (340, 380, 440, 500, 675 and 870 nm) using solar direct normal spectral irradiance ground measurements from a relatively low cost collimated spectroradiometer (EKO MS-711) at a low-altitude site in Montevideo, Uruguay. The results obtained are compared with AERONET products for the same site, including AOD and Angström coefficient. The results of AOD for all wavelengths show a consistent negative mean bias (MBD, unitless), between −0.005 and −0.015, and dispersion (RMSD, unitless) between 0.021 and 0.015 (to be compared to a mean reference AOD of 0.097). These metrics improve considerably for very clear days, MBD up to ± 0.001 and RMSD under 0.007 (to be compared to a mean reference AOD of 0.058). These results are considered to be a first step in implementing the methodology and acquiring local knowledge about AOD retrievals using relatively simple instrumentation.

Minute-Scale Models for the Diffuse Fraction of Global Solar Radiation Balanced between Accuracy and Accessibility

The separation models are tools used in solar engineering to estimate direct normal (DNI) and diffuse horizontal (DHI) solar irradiances from measurements of global solar irradiance (GHI). This paper proposes two empirical separation models that stand out owing to their simple mathematical formulation: a rational polynomial equation. Validation of the new models was carried out against data from 36 locations, covering the four major climatic zones. Five current top minute-scale separation models were considered references. The tests were performed on the final products of the estimation: DNI and DHI. The first model (M1) operates with eight predictors (evaluated from GHI post-processed measurements and clear-sky counterpart estimates) and constantly outperforms the already established models. The second model (M2) operates with three predictors based only on GHI measurements, which gives it a high degree of accessibility. Based on a statistical linear ranking method according to the models’ performance at every station, M1 leads the hierarchy, ranking first in both DNI and DHI estimation. The high accessibility of the M2 does not compromise accuracy; it is proving to be a real competitor in the race with the best-performing current models.

Calculation of surface spectral irradiance using the Geant4 Monte Carlo toolkit

The spectral direct normal irradiance (DNI) of the surface spectral irradiance, with a spectral resolution of 1 nm in the range of 280–1600 nm, and 5 nm in the range of 1600–3000 nm, has been calculated for the first time with the Geant4 Monte Carlo simulation toolkit using the US Standard Atmosphere 1976 model and the reference extraterrestrial solar spectrum SOLAR-ISS. The atmospheric attenuation of DNI has been studied with and without aerosols. In the presence of aerosols corresponding to 10 km visibility, the total solar DNI attenuation can reach about 84% in the UV domain, 56% in the visible domain and 35.08% in the rest of the infrared domain. The Geant4 results have been compared to those calculated by the MYSTIC code of LibRadtran package and the Beer–Lambert law under the same atmospheric conditions. The average relative transmittance difference between Geant4 and MYSTIC is of the order of few percent in the entire solar spectrum. In the case of atmosphere with aerosols corresponding to 23 km visibility, the most important average of the relative difference between Geant4 and MYSTIC transmittances is about 7.5% in the UV domain and stays between 1.19% and, 3.31% in the rest of the spectrum. More importantly, in this last case of the atmosphere, the values of the spectral transmittance of Geant4 remain intermediate between those of MYSTIC and Beer–Lambert law in the entire visible range up to 800 nm.

Efficient hydrogen production system with complementary utilization of methane and full-spectrum solar energy

Solar hydrogen production by integration of photovoltaic cells (PV) and electrolytic water splitting offers the ability to simultaneously store intermittent solar energy and produce hydrogen. However, the solar-to-hydrogen efficiency is limited by the inability of PV cells to utilize solar energy in the long-wavelength spectrum. On the other hand, solar energy in the short-wavelength spectrum with high activation ability is underutilized in solar thermochemical methane steam reforming (MSR). To address these issues, this paper proposes a hydrogen production system that integrates electrolytic water splitting with a thermochemical MSR and employs a Rankine Cycle (RC) for balancing electricity and heat for water electrolysis. This system enables the efficient conversion of full-spectrum solar energy into hydrogen while minimizing the underutilization of high-grade solar energy. The thermodynamic and environmental performances of the proposed system are evaluated and compared with those of a reference system - a full-spectrum solar hydrogen production system without fossil fuels. Results show that the proposed system produces 2.21–2.71 times more hydrogen than the reference system when solar direct normal irradiance (DNI) ranges from 200–1000 W/m2. It exhibits higher energy efficiency, exergy efficiency, and solar-to-hydrogen efficiency than the reference system. Furthermore, the proposed system can reduce approximately 15.0 kg of CO2 emissions when producing 1 kg of hydrogen. This study provides a promising approach to efficiently using solar energy and fossil fuels for generating hydrogen.

Measuring Concentrated Solar Radiation Flux in a Linear Fresnel-Type Solar Collector

Linear Fresnel solar collectors are a promising and emerging solution to contribute to renewable heat supply in industrial processes with thermal energy demand in the medium temperature range (<250 °C). An innovative linear Fresnel collector (LFC) prototype has been designed, patented, and built at the Plataforma Solar de Almería (PSA), Spain. This work presents the applied methodology, experimental device, and results obtained in the measurement of the flux density of concentrated solar radiation in the focal plane of the solar collector. The experimental results confirm that an average flux density of (9.8 ± 0.6) kW/m2 was obtained with a direct normal solar irradiance of (870 ± 10) W/m2 in tests performed in May 2002, which is a result similar to that obtained in optical simulations of the system.

A Unit Commitment Model Considering Feasibility of Operating Reserves under Stochastic Optimization Framework

Grid integration of renewable resources such as solar and wind energy can significantly raise the level of uncertainty in power systems, making the scheduled operation of generating units difficult. Therefore, the importance of operating reserves is more emphasized to prevent disruption by sudden changes in outputs of generators. In this paper, a stochastic unit commitment (UC) model to reflect uncertainty due to a large amount of renewable resources is proposed, in which upward and downward operating reserves are deployed simultaneously, and feasibility of the reserves is examined to make the deployed reserves supplied reliably. Uncertain parameters considered in the model are wind power availability, solar direct normal irradiance, and electric load. Two-stage stochastic programming is applied to the mathematical formulation, where UC decisions including dispatch are modeled as non-anticipative variables at the first stage, and redispatch decisions to serve realized electric demand are made at the second stage as recourse. By solving the UC problem, feasible and reliable stochastic UC and dispatch solutions can be provided to power system operators.

Characterization of absolute cavity radiometers for traceability to SI of solar irradiance

Solar-type cavity radiometers are instruments of the highest metrological level for measuring solar direct normal irradiance. To ensure their traceability and performance, they are periodically compared to the World Group of Standards, which realizes the World Radiometric Reference (WRR), in the International Pyrheliometer Comparisons (IPCs). Additionally, they can be characterized in an absolute way, with direct traceability to SI units and with their measurement uncertainty calculated. This paper describes the different techniques and procedures applied for the characterization and calibration of solar cavity radiometers, with the main results obtained to date in the case of an Automatic Hickey–Frieden (AHF) radiometer. Voltmeters, resistors, temperature sensors and the area of the precision apertures have been calibrated, while the effective absorptance, temperature coefficients, optical scattering and non-equivalence factor have been evaluated. The temperature dependence of the electrical current in the cavity heater has also been analysed. The resulting corrections obtained for the AHF by characterization are compatible with the WRR factors obtained by this instrument in the past IPCs. An uncertainty of 0.42% (k = 1) has been obtained, and this paper discusses further improvements that may be able to reduce this figure to the desired expanded uncertainty of U = 0.1% (k = 2).

Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery

Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery

Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network

Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m−2 s−1, mean absolute error MAE = 150.24 μ mol of photons m−2 s−1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.

Development and Experiments on a Batch-Type Solar Roaster—An Innovative Decentralized System for Coffee Roasting

About 70% of the harvested coffee is exported to the industrialized nations for value addition due to lack of processing and logistic facilities in developing coffee producer countries, thus leaving behind a marginal economic return for the growers. This research was conducted to investigate the roasting capacity of an innovatively developed batch-type directly solar radiated roasting system for the decentralized processing of coffee using solar energy. Central composite rotatable design (CCRD) was employed to design the experiments to optimize the coffee roasting process. Experimental results revealed that with an average solar direct normal irradiance (DNI) of 800 W/m2, the roaster was capable of roasting a batch of 2 kg coffee beans in 20, 23, and 25 min subjected to light roasts, medium roasts, and dark roasts, respectively at a drum speed of two revolutions per minute (rpm). The batch-type solar roaster has the capacity to roast 28.8–36 kg of coffee beans depending on dark to light roasting conditions on a clear sunny day with DNI ranging from 650 to 850 W/m2. The system thermal efficiency during coffee roasting was determined to be 62.2%, whereas the roasting efficiency at a corresponding light roast, medium roast, and dark roast was found to be 97.5%, 95.2%, and 91.3%, respectively. The payback period of the solar roaster unit was estimated to be 1038 working sunshine hours, making it viable for commercialization.

Solar Energy Potential Evaluation. Case of Study: M'Sila, an Algerian Province

Solar energy is the most important green energy source that may be used to address the problems caused by conventional energy sources. The research in this paper aims to evaluate the solar energy potential of M'Sila, an Algerian internal region, and to determine which technology, among those currently available on the market, is adequate for harnessing that potential for electricity production (PV and CSP). This assessment was based on data (daily temperature and daily solar irradiation) collected at M'Sila weather station for one year and from the NASA site for 34 years from 1984 to 2017. These data were treated with excel 2016 software to determine the total horizontal, diffuse and direct normal solar irradiation. During the study period, the total solar irradiation falling on a horizontal surface in M'Sila (GHI) was estimated to be 1.79 MWh/m2/year. The diffuse solar irradiation incident on a horizontal surface (DHI) and direct normal irradiation, respectively, were estimated to be 594.88 kWh/m2/year, 2117.93 kWh/m2/year. M'Sila is a favourable land for the installation of concentrating solar power plants and photovoltaic panels due to its significant potential energy. The parabolic trough system is recommended. While polycrystalline PV modules should be promoted and installed in M'Sila.

Effects of geometric and operating parameters on thermal performance of conical cavity receivers using supercritical CO2 as heat transfer fluid

In this paper, a comprehensive optical-thermal numerical model of the conical cavity receiver using supercritical CO2 (S–CO2) as the heat transfer fluid (HTF) is established by combining ray tracing with computational fluid dynamics method, which is verified with published data in the literature. Based on the model, the effects of critical geometric parameters (tube inner diameter, aspect ratio and cone angle ratio) and operating parameters (solar direct normal irradiance (DNI), HTF inlet mass flow rate and inlet temperature) on the thermal performance of the receiver are discussed thoroughly. The results indicate that a decrease in the tube inner diameter is beneficial to improve the optical-thermal performance of the receiver. When the aspect ratio and cone angle ratio are 1.5 and 0.25, respectively, the optical-thermal conversion efficiencies reach their optima, which are 75.13% and 75.30%, respectively. Moreover, for the S–CO2 receiver with optimal geometric parameters, the HTF outlet temperature increases linearly with increasing DNI and inlet temperature. Increasing mass flow rate can improve the optical-thermal conversion efficiency, but once the flow rate exceeds 0.04 kg/s, it has little effect on the efficiency improvement. These results can provide a reference for the design and operation of the S–CO2 conical cavity receiver.

CFD simulation of thermal performance of hybrid oil-Cu-Al2O3 nanofluid flowing through the porous receiver tube inside a finned parabolic trough solar collector

In this study, we perform numerical simulations to investigate the thermal and flow characteristics of a parabolic trough solar collector equipped with a porous receiver tube and internal longitudinal fins. The heat transfer medium is a synthetic oil-Cu-Al2O3 hybrid nanofluid. We examine the thermal characteristics of the nanofluid in response to variations in several system parameters. We find that at Reynolds numbers between 5 × 103 and 5 × 105, increasing the volume fraction of Cu nanoparticles can increase the temperature gain at the exit of the receiver tube by 6.4%. Furthermore, the temperature gradient in the cross-section of the collector increases as the direct normal solar irradiance decreases. Increasing the volume fraction of Cu and Al2O3 nanoparticles by 0.02 at low Reynolds numbers is found to increase the thermal efficiency by 12.2% with 5.2% adverse effects on the friction factor; Cu nanoparticles are found to be three times more effective than Al2O3 nanoparticles. For Darcy numbers between 0.01 and 0.1, we find that higher permeability has only a minor adverse effect on the thermal efficiency, but can reduce the friction factor to a third of its original value. These findings can be used to improve the efficiency of solar collectors.

Influence of non-steady transient heat flux on flow boiling heat transfer and pressure drop in horizontal pipes

Transient heat fluxes imposed on solid surfaces can significantly affect the heat transfer and pressure drop of flow boiling processes in diverse fields ranging from microprocessor electronics cooling to waste-heat recovery and large-scale direct steam generation in concentrated solar applications. A series of simulated transients were applied in this experimental study to investigate the saturated flow boiling of R-245fa in a horizontal pipe. The test section consisted of an 8.31 mm inner diameter, 800 mm long heated pipe. The imposed transient conditions involved spatially uniform but temporally varying heat fluxes imposed on the pipe. A baseline condition with a saturation temperature of 35°C, a heat flux of 7.5 kW/m2 and a mass flux of 200 kg/m2s was considered over a vapour quality range from 0.10 to 0.85. Motivated by direct steam generation application case studies and based on actual solar direct normal irradiation data, reductions with an amplitude of 75% of the baseline heat flux were imposed over a period of 30 s. The waveform types were step, triangular and sinusoidal pulses, and were applied in a controlled fashion. It was found that during the step perturbation, the heat transfer coefficient was approximately 30% lower than the steady state condition. The triangular and sinusoidal perturbations resulted in heat transfer coefficients that were 8% lower than that of the steady state. The pressure gradient through the test section was unaffected by the imposed perturbations.