Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data

Annual variations of solar radiation at the Earth’s surface may be strong and could seriously harm the return of investment for solar energy projects. This paper analyzes the long-term variability of broadband surface solar radiation based on 18 years of three-hourly satellite observations from the International Satellite Cloud Climatology Project (ISCCP). Direct normal irradiance (DNI) and global horizontal irradiance (GHI) at the surface are derived through radiative transfer calculations, using different physical input parameters describing the actual composition of the atmosphere. Validation of DNI is performed with two years of high resolution Meteosat-derived irradiance. Monthly averages show an average mean bias deviation of −1.7%. Results for DNI from the 18-year time series indicate strong and significant increases for several regions in the subtropics up to +4 W/m² per year, with exception of Australia, where a small decrease in DNI of -1 W/m² per year is observed. Inter-annual variability for DNI is very strong and sometimes exceeds 20%. Comparisons of calculations with and without volcanic aerosol reveal a decrease of up to 16% in annual averages due to volcano eruptions. Changes in GHI are much smaller and less significant. Results show a maximum increase of 0.8 W/m² per year and an annual variability of less than 4%. Volcano eruptions reduce annual averages of GHI by less than 2.2%. The two reanalysis data sets investigated differ strongly from each other and are far off the validated results derived from satellite data. Trends are weaker and less significant or even of opposite sign.     

Hourly solar irradiance time series forecasting using cloud cover index

We apply time series analysis to forecast next hour solar irradiance including cloud cover effects. Three forecasting methods are proposed using different types of meteorological data as input parameters, namely, global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), direct normal irradiance (DNI) and cloud cover. The first method directly uses GHI to forecast next hour GHI through additive seasonal decomposition followed by an Auto-Regressive Integrated Moving Average (ARIMA) model. The second method forecasts DHI and DNI separately using additive seasonal decomposition followed by an ARIMA model and then combines the two forecasts to predict GHI using an atmospheric model. The third method considers cloud cover effects. An ARIMA model is used to predict cloud transients. GHI at different zenith angles and under different cloud cover conditions is constructed using nonlinear regression, i.e., we create a look-up table of GHI regression models for different cloud cover conditions. All three methods are tested using data from two weather stations in the USA: Miami and Orlando. It is found that forecasts using cloud cover information can improve the forecast accuracy.     

The aerosol effect on direct normal irradiance in Europe under clear skies

The effect of spatial and temporal variability of aerosol optical depth (AOD) on direct normal irradiance (DNI) under clear skies is studied, with the synergetic use of satellite and ground-based data as well as calculations from a radiative transfer model. The area of interest is Europe; data from May to September during 13 years (2000–2012) are analyzed. The aerosol effect on DNI is high in areas influenced by desert dust intrusions and intense anthropogenic activities, such as the Mediterranean basin and the Po Valley in Italy. In May, the attenuation of DNI from aerosols, over these areas, can reach values up to 35% and 45% respectively, which corresponds to 4 and 6 kWh m⁻² per day. In most areas, even for periods with lower values of AOD, the attenuation of DNI is found to be around 20%, which corresponds to about 2–3 kWh m⁻² less received DNI per day, compared to the corresponding value on an aerosol clean day. However, the DNI has increased during the recent years, due to the decreasing tendency of AOD over most areas of Europe. The increase is around 6–12%, which corresponds to an amount of 0.5–1.25 more kWh m⁻² received per day, compared to a clean day. The percentage differences of daily DNI from the corresponding monthly climatological value reveals that day-to-day differences (due to AOD changes) from the monthly mean, by ±20%, can occur. The significance of the aerosol changes in Europe reveals the necessity for near real-time measurements or forecasts of AOD when reliable estimations of DNI are required.     

New challenges in solar energy resource and forecasting in Greece

Aerosols and clouds are the most important constituents in the atmosphere that affect the incoming solar radiation, either directly through absorbing and scattering processes or indirectly by changing the optical properties and lifetime of clouds. Under clear skies, aerosols become the dominant factor that affect the intensity of solar irradiance reaching the ground. Under cloudy skies, the high temporal and spatial variability of cloudiness is the key factor for the estimation of solar irradiance. In this study, recent research activities related to the climatology and the prediction of solar energy in Greece are presented with emphasis on new challenges in the climatology of global horizontal irradiance (GHI) and direct normal irradiance (DNI), the changes of DNI due to the decreasing aerosol optical depth and the short-term (15–240 min) forecasts of solar irradiance with the collaborative use of neural networks and satellite images.     

Evaluation of decomposition models of various complexity to estimate the direct solar irradiance over Belgium

Solar energy production is directly correlated to the amount of radiation received at a given location. Appropriate information on solar resources is therefore very important for designing and sizing solar energy systems. Concentrated solar power projects and photovoltaic tracking systems rely predominantly on direct normal irradiance (DNI). However, the availability of DNI measurements from surface observation stations has proven to be spatially too sparse to quantify solar resources at most potential sites. Satellite data can be used to calculate estimates of direct solar radiation where ground measurements do not exist. Performance of decomposition models of various complexity have been evaluated against one year of in situ observations recorded on the roof of the radiometric tower of the Royal Meteorological Institute of Belgium in Uccle, Brussels. Models were first evaluated on a hourly and sub-hourly basis using measurements of global horizontal irradiance (GHI) as input. Second, the best performing ground-based decomposition models were used to extract the direct component of the global radiation retrieved from Meteosat Second Generation (MSG) images. Results were then compared to direct beam estimations provided by satellite-based diffuse fraction models and evaluated against direct solar radiation data measured at Uccle. Our analysis indicates that valuable DNI estimation can be derived from MSG images over Belgium regardless of the satellite retrieved GHI accuracy. Moreover, the DNI retrieval from MSG data can be implemented on an operational basis.     

Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances

This work proposes a novel forecast methodology for intra-hour solar irradiance based on optimized pattern recognition from local telemetry and sky imaging. The model, based on the k-nearest-neighbors (kNN) algorithm, predicts the global (GHI) and direct (DNI) components of irradiance for horizons ranging from 5 min up to 30 min, and the corresponding uncertainty prediction intervals. An optimization algorithm determines the best set of patterns and other free parameters in the model, such as the number of nearest neighbors. Results show that the model achieves significant forecast improvements (between 10% and 25%) over a reference persistence forecast. The results show that large ramps in the irradiance time series are not very well capture by the point forecasts, mostly because those events are underrepresented in the historical dataset. The inclusion of sky images in the pattern recognition results in a small improvement (below 5%) relative to the kNN without images, but it helps in the definition of the uncertainty intervals (specially in the case of DNI). The prediction intervals determined with this method show good performance, with high probability coverage (≈90% for GHI and ≈85% for DNI) and narrow average normalized width (≈8% for GHI and ≈17% for DNI).     

Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain)

In this work, we evaluate the reliability of three-days-ahead global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts provided by the WRF mesoscale atmospheric model for Andalusia (southern Spain). GHI forecasts were produced directly by the model, while DNI forecasts were obtained based on a physical post-processing procedure using the WRF outputs and satellite retrievals. Hourly time resolution and 3 km spatial resolution estimates were tested against ground measurements collected at four radiometric stations along the years 2007 and 2008. The evaluation was carried out independently for different forecast horizons (1, 2 and 3 days ahead), the different seasons of the year and three different sky conditions: clear, cloudy and overcast. Results showed that the WRF model presents considerable skill in forecasting both GHI and DNI, overall, better than a trivial persistence model. Nevertheless, both MBE and RMSE values presented a marked dependence on the sky conditions and season of the year. Particularly, for 24 h lead time, the MBE of the forecasted GHI was 2% for clear-skies and 18% for cloudy conditions. However, the MBE of the forecasted DNI increased up to about 10% and 75% for clear and cloudy conditions, respectively. Regarding RMSE values, in the case of forecasted GHI, results ranged from below 10% under clear-skies to 50% for cloudy conditions. In the case of forecasted DNI, RMSE ranged from 20% to 100% for clear and cloudy skies, respectively. This proved the higher sensitivity of DNI to the sky conditions. In general, an increment of the MBE and RMSE values with the cloudiness was observed. This reflects a still limited ability of the WRF model to properly forecast cloudy conditions compared to clear skies. Nevertheless, the model was able to accurately forecast steep changes in the sky (cloudiness) conditions. Finally, WRF performed considerable better than the persistence model for clear skies both for GHI and DNI, with relative RMSE values about a half. However, for cloudy conditions, performance was similar.     

Using direct normal irradiance models and utility electrical loading to assess benefit of a concentrating solar power plant

The objective of this paper was to determine if three different direct normal irradiance (DNI) models were sufficiently accurate to determine if concentrating solar power (CSP) plants could meet the utility electrical load. DNI data were measured at three different laboratories in the United States and compared with DNI calculated by three DNI models. In addition, utility electrical loading data were obtained for all three locations. The DNI models evaluated were: the Direct Insolation Simulation Code (DISC), DIRINT, and DIRINDEX. On an annual solar insolation (e.g. kW h/m²) basis, the accuracy of the DNI models at all three locations was within: 7% (DISC), 5% (DIRINT), and 3% (DIRINDEX). During the three highest electrical loading months at the three locations, the monthly accuracy varied from: 0% to 16% (DISC), 0% to 9% (DIRINT), and 0% to 8% (DIRINDEX). At one location different pyranometers were used to measure GHI, and the most expensive pyranometers did not improve the DNI model monthly accuracy. In lieu of actually measuring DNI, using the DIRINT model was felt to be sufficient for assessing whether to build a CSP plant at one location, but use of either the DIRINT or DIRINDEX models was felt to be marginal for the other two locations due to errors in modeling DNI for utility peak electrical loading days – especially for partly cloudy days.     

A case study to prepare for the utilization of aerosol forecasts in solar energy industries

Precise aerosol information is indispensable in providing accurate clear sky irradiance forecasts, which is a very important aspect in solar facility management as well as in solar and conventional power load prediction. In order to demonstrate the need of detailed aerosol information, direct irradiance derived from Aerosol Robotic Network (AERONET) ground based measurements of aerosol optical depth (AOD) was compared in a case study over Europe to irradiance calculated using a standard aerosol scenario. The analysis shows an underestimation of measurement-derived direct irradiance by the scenario-derived direct irradiance for locations in Northern Europe and an overestimation for the Mediterranean region.Forecasted AOD of the European Dispersion and Deposition Model (EURAD) system was validated against ground based AERONET clear sky AOD measurements for the same test period of February 15th to 22nd, 2004. For the time period analyzed, the modelled AOD forecasts of the EURAD system slightly underestimate ground based AERONET measurements. To quantify the effects of varying AOD forecast quality in their impact on the application in solar energy industry, measured and forecasted AOD were used to calculate and compare direct, diffuse, and global irradiance. All other influencing variables (mainly clouds and water vapour) are assumed to be modelled and measured correctly for this analysis which is dedicated to the specific error introduced by aerosol forecasting. The underestimated AOD results in a mean overestimation of direct irradiance of +28 W/m² (+12%), whereas diffuse irradiance is generally underestimated (−19 W/m² or −14%). Mean global irradiance values where direct and diffuse irradiance errors compensate each other are very well represented (on average +9 W/m² or +2%).     

Validation of the NSRDB-SUNY global horizontal irradiance in California

Satellite derived global horizontal solar irradiance (GHI) from the SUNY modeled dataset in the National Solar Radiation Database (NSRDB) was compared to measurements from 27 weather stations in California during the years 1998-2005. The statistics of spatial and temporal differences between the two datasets were analyzed and related to meteorological phenomena. Overall mean bias errors (MBE) of the NSRDB-SUNY indicated a GHI overprediction of 5%, which is smaller than the sensor accuracy of ground stations. However, at coastal sites, year-round systematic positive MBEs in the NSRDB-SUNY data up to 18% were observed and monthly MBEs increased up to 54% in the summer months during the morning. These differences were explained by a tendency for the NSRDB-SUNY model to overestimate GHI under cloudy conditions at the coast during summer mornings. A persistent positive evening MBE which was independent of site location and cloudiness occurred at all stations and was explained by an error in the time-shifting method applied in the NSRDB-SUNY. A correction method was derived for these two errors to improve the accuracy of the NSRDB-SUNY data in California.     

Solar variability of four sites across the state of Colorado

Solar Global Horizontal Irradiance (GHI) fluctuates on both short (seconds to hours) and long (days to months) timescales leading to variability of power produced by solar photovoltaic (PV) systems. Under a high PV penetration scenario, fluctuations on short time scales may require a supplementary spinning power source that can be ramped quickly, adding significant external cost to PV operation. In order to examine the smoothing effect of geographically distributed PV sites, GHI timeseries at 5 min resolution at four sites across the state of Colorado were analyzed. GHI at the four sites was found to be correlated due to synchronous changes in the solar zenith angle. However, coherence analysis showed that the sites became uncorrelated on time scales shorter than 3 h, resulting in smoother average output at short time scales. Likewise, extreme ramp rates were eliminated and the spread in ramp rate magnitude was significantly reduced when all four sites were averaged. Nevertheless, even for the averaged output, high frequency fluctuations in PV power output are relatively larger in magnitude than fluctuations expected from wind turbines. Our results allow estimation of the ancillary services required to operate distributed PV sites.     

Viability of a concentrated solar power system in a low sun belt prefecture

Concentrating solar power (CSP) is considered as a comparatively economical, more efficient, and large capacity type of renewable energy technology. However, CSP generation is found restricted only to high solar radiation belt and installed where high direct normal irradiance is available. This paper examines the viability of the adoption of the CSP system in a low sun belt region with a lower direct normal irradiance (DNI). Various critical analyses and plant economics have been evaluated with a lesser DNI state. The obtained results out of the designed system, subjected to low DNI are not found below par, but comparable to some extent with the performance results of such CSP plants at a higher DNI. The analysis indicates that incorporation of the thermal energy storage reduces the levelized cost of energy (LCOE) and augments the plant capacity factor. The capacity factor, the plant efficiency, and the LCOE are found to be 32.50%, 17.56%, and 0.1952 $/kWh, respectively.     

A simple and efficient procedure for increasing the temporal resolution of global horizontal solar irradiance series

The intermittent nature of instantaneous solar radiation has a considerable impact on the nonlinear behavior of solar energy conversion systems. The time resolution of the Numerical Weather Prediction Models (NWPM) or satellite derived solar irradiance data are typically limited to 1-h (at best 15-min). Unfortunately, this resolution is not sufficient in the design and performance of many solar systems. In this study, a new methodology has been developed to increase the temporal resolution of GHI series from 1-h to 1-min. This methodology uses the clearness index k_t (the ratio of GHI to top-of-atmosphere irradiance on the same plane) to characterize the GHI high-frequency dynamics from a 1-year measurement campaign at a given site. The evaluation of the method with 2 years of measured data in different climatic zones has resulted in KSI(%) (Kolmogorov–Smirnov test Integral parameter) and normalized root mean square deviation values below 8.0% and 1.7% respectively for each month, with negligible bias. Indicators of overall performance show an excellent agreement between measured and modeled 1-min GHI data for each month: average values for Nash-Sutcliffe efficiency, Willmott index of agreement and Legates coefficient of efficiency are found to be 0.94, 0.99 and 1.00, respectively.     

An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records

Temporal solar variability significantly affects the integration of solar power systems into the grid. It is thus essential to predict temporal solar variability, particularly given the increasing popularity of solar power generation globally. In this paper, the daily variability of solar irradiance at four sites across Australia is quantified using observed time series of global horizontal irradiance for 2003–2012. It is shown that the daily variability strongly depends on sky clearness with generally low values under a clear or overcast condition and high values under an intermittent cloudiness condition. Various statistical techniques are adopted to model the daily variability using meteorological variables selected from the ERA-Interim reanalysis as predictors. The nonlinear regression technique (i.e. random forest) is demonstrated to perform the best while the performance of the simple analog method is only slightly worse. Among the four sites, Alice Springs has the lowest daily variability index on average and Rockhampton has the highest daily variability index on average. The modelling results of the four sites produced by random forest have a correlation coefficient of above 0.7 and a median relative error around 40%. While the approach of statistical downscaling from a large spatial domain has been applied for other problems, it is shown in this study that it generally suffices to use only the predictors at a single near point for the problem of solar variability. The relative importance of the involved meteorological variables and the effects of clearness on the modelling of the daily variability are also explored.     

Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning

We propose novel smart forecasting models for Direct Normal Irradiance (DNI) that combine sky image processing with Artificial Neural Network (ANN) optimization schemes. The forecasting models, which were developed for over 6 months of intra-minute imaging and irradiance measurements, are used to predict 1 min average DNI for specific time horizons of 5 and 10 min. We discuss optimal models for low and high DNI variability seasons. The different methods used to develop these season-specific models consist of sky image processing, deterministic and ANN forecasting models, a genetic algorithm (GA) overseeing model optimization and two alternative methods for training and validation. The validation process is carried over by the Cross Validation Method (CVM) and by a randomized training and validation set method (RTM). The forecast performance for each solar variability season is evaluated, and the models with the best forecasting skill for each season are selected to build a hybrid model that exhibits optimal performance for all seasons. An independent testing set is used to assess the performance of all forecasting models. Performance is assessed in terms of common error statistics (mean bias and root mean square error), but also in terms of forecasting skill over persistence. The hybrid forecast models proposed in this work achieve statistically robust forecasting skills in excess of 20% over persistence for both 5 and 10 min ahead forecasts, respectively.     

Geometric optimization model for the solar cavity receiver with helical pipe at different solar radiation

In consideration of geometric parameters, several researches have already optimized the thermal efficiency of the cylindrical cavity receiver. However, most of the optimal results have been achieved at a fixed solar radiation. At different direct normal irradiance (DNI), any single optimal result may not be suitable enough for different regions over the world. This study constructed a 3-D numerical model of cylindrical cavity receiver with DNI variation. In the model of a cylindrical cavity receiver containing a helical pipe, the heat losses of the cavity and heat transfer of working medium were also taken into account. The simulation results show that for a particular DNI in the range of 400 W/m² to 800 W/m², there exists a best design for achieving a highest thermal efficiency of the cavity receiver. Besides, for a receiver in constant geometric parameters, the total heat losses increases dramatically with the DNI increasing in that range, as well as the temperature of the working medium. The thermal efficiency presented a different variation tendency with the heat losses, which is 2.45% as a minimum decline. In summary, this paper proposed an optimization method in the form of a bunch of fitting curves which could be applied to receiver design in different DNI regions, with comparatively appropriate thermal performances.     

基于太阳能热驱动甲烷重整制氢的燃料电池发电系统性能研究

该文采用Aspen Plus软件建立膜反应器重整制氢及燃料电池模型,根据拉萨某日太阳能直接辐射强度（DNI）变化计算太阳能可供使用的能量,作为外热源输入重整系统,并分析反应温度、水碳比（S/C）及DNI对该系统各性能指标的影响,性能指标包括甲烷转化率、H₂收率、电池功率及电压、太阳能转换为氢能的效率。结果表明：反应温度为500 ℃,S/C为2.5时有利于太阳能甲烷湿重整反应;系统日性能结果显示在某日10:00—20:00时,电池输出功率120 kW,太阳能-化学能转化效率0.368,系统发电效率0.225。     

Applying the kriging method to predicting irradiance variability at a potential PV power plant

One-second irradiance data from forty-five sensors spaced over a one-mile square section of land were analyzed to characterize the short-term (1-s to 1-min) variability of the solar resource in Northern Arizona. The geostatistical interpolation model known as kriging was applied to our data set to better understand the method's strengths and weaknesses in accurately predicting the variations in the irradiance over this relatively small section of land. Of particular interest was to investigate the ability of the kriging method to show the variation in solar irradiance over the section of land as compared to that measured by the sensors. When using data from all the sensors as input to the prediction method, kriging performed very well compared to the sensors. However, because it is unlikely to have a large number of sensors to characterize the variability at a prospective solar site, it was also of interest to investigate how many sensors are required as input to the kriging technique in order to generate a reliable prediction. Solar data from four characteristic periods (related to the four seasons) were analyzed, and different sensor configurations, consisting of subsets of the actual sensor array, were employed using the method to demonstrate the number of sensors required to correctly characterize the short-term irradiance variability at the site. Using four measurement stations as input to the kriging method was shown to reasonably represent the variability in the 1-s to 1-min timescales.     

Effects of atmospheric aerosol on the direct normal irradiance on the earth's surface

Direct normal irradiance (DNI) plays a key role on the quantity and rate of hydrogen production. The accurate calculation of DNI has very important significance for low-cost hydrogen economy and efficient utilization of solar energy. This study mainly takes account of the influence of atmospheric aerosol on DNI and the experimental tests. The main idea of this paper is: obtaining the distribution characteristics of aerosol particles in the atmosphere and the optical depth of aerosol spectrum based on inversion method of ground observation station data; calculating the attenuation coefficient of solar spectrum with classical Mie scattering theory and particle system radiation characteristics; calculating aerosol attenuation coefficient under full spectrum, namely the aerosol correction factor (defined as the ratio of the attenuation coefficient of aerosol atmosphere to standard atmosphere under full spectrum) with Planck model, Rosseland model and Planck–Rosseland model respectively; choosing with the theoretical calculation model of aerosol correction factor based on the solar spectrum radiation calculated by SMARTS software; verifying the accuracy of this theoretical model with experimental DNI in city Harbin. The results show that there is a good agreement with a minimum variation of 3.08% and a maximum variation of 9.97%.     

A power-rating model for crystalline silicon PV modules

A model for the performance of generic crystalline silicon photovoltaic (PV) modules is proposed. The model represents the output power of the module as a function of module temperature and in-plane irradiance, with a number of coefficients to be determined by fitting to measured performance data from indoor or outdoor measurements. The model has been validated using data from 3 different modules characterized through extensive measurements in outdoor conditions over several seasons. The model was then applied to indoor measurement data for 18 different PV modules to investigate the variability in modeled output from different module types. It was found that for a Central European climate the modeled output of the 18 modules varies with a standard deviation (SD) of 1.22%, but that the between-module variation is higher at low irradiance (SD of 3.8%). The variability between modules of different types is thus smaller than the uncertainty normally found in the total solar irradiation per year for a given site. We conclude that the model can therefore be used for generalized estimates of PV performance with only a relatively small impact on the overall uncertainty of such estimates resulting from different module types.