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Technical report site assessment of solar resource for a csp plant. corrections with ground measured data

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Technical report site assessment of solar resource for a csp plant. corrections with ground measured data

Technical report site assessment of solar resource for a csp plant. corrections with ground measured data

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  • 1. TECHNICAL REPORT SOLAR RESOURCE ASSESSMENT Update: IrSOLaV data corrected with ground measurements XXXXX CSP SOLAR PROJECT (COUNTRY) (Morocco) April 25, 2014
  • 2. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 3 of 66 TECHNICAL REPORT: SOLAR RESOURCE ASSESSMENT: XXX CSP SOLAR PROJECT (Country) DATE: April 25, 2014 AUTHORS: IrSOLaV S. L. (INVESTIGACIONES Y RECURSOS SOLARES AVANZADOS). CUSTOMER: Customer
  • 3. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 4 of 66
  • 4. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 5 of 66 INDEX 1 INTRODUCTION........................................................................................................................................... 7 1.1 Main objective.......................................................................................................................................... 7 1.2 Data needed ............................................................................................................................................ 7 1.3 Methodology for solar radiation derived from satellite images ................................................................ 7 1.4 Brief summary of IrSOLaV methodology................................................................................................. 8 2 AVAILABLE DATA FOR SOLAR RESOURCE ASSESSMENT ............................................................... 11 2.1 Measurements at the site of the project ................................................................................................ 11 2.2 METEONORM....................................................................................................................................... 12 2.3 NASA ..................................................................................................................................................... 13 2.4 SOLAR-MED-ATLAS............................................................................................................................. 14 2.5 PV-GIS................................................................................................................................................... 15 2.6 CM SAF-CLIMATE-DWD ...................................................................................................................... 16 2.7 SoDa Database (HELIOCLIM-3V4)....................................................................................................... 17 2.8 IrSOLaV raw estimations....................................................................................................................... 18 3 CORRECCTION OF SATELLITE ESTIMATIONS WITH GROUND MEASUREMENTS.......................... 20 4 IRSOLAV DATABASE ............................................................................................................................... 30 4.1 Uncertainties of IrSOLaV corrected estimations ................................................................................... 30 4.2 Global horizontal irradiance (GHI) in LOCATION PROJECT................................................................ 30 4.3 Direct normal irradiance (DNI) in LOCATION PROJECT...................................................................... 36 5 CORRECTED IRSOLAV DATABASE COMPARED TO MEASUREMENTS............................................ 43 6 METEOROLOGICAL PARAMETERS........................................................................................................ 49 7 INTER-ANNUAL VARIABILITY OF SOLAR RADIATION......................................................................... 53 8 UNCERTAINTY OF ESTIMATES............................................................................................................... 55 9 TYPICAL SOLAR RADIATION YEAR ....................................................................................................... 56 9.1 GHI and DNI radiation values ............................................................................................................. 59 10 STATISTICAL ANALYSIS OF THE LONG TERM..................................................................................... 61 11 CONCLUSIONS AND COMMENTS........................................................................................................... 62 12 REFERENCES............................................................................................................................................ 64
  • 5. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 6 of 66
  • 6. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 7 of 66 1 INTRODUCTION 1.1 Main objective The main objective of this report is to analyze the solar resource available and to produce the corresponding typical solar radiation year for a specific site in Country, selected to host a solar thermal power plant with a capacity of XXX MW. The solar resource analysis applies to a site with geographic coordinates: Latitude XX.XX N, Longitude XX.XX W, 1260 meters of altitude, hereinafter referred to as LOCATION. 1.2 Data needed The solar radiation is a meteorological variable measured only in few measurement stations and during short and, on most occasions, discontinuous periods of times. The lack of reliable information on solar radiation, together with the spatial variability that it presents, leads to the fact that developers do not find appropriate historical databases with information available on solar resource for concrete sites. This lack provokes in turn serious difficulties at the moment of projecting or evaluating solar power systems. Among the possible different approaches to characterize the solar resource of a given specific site they can be pointed out the following:  Data from nearby stations. This option can be useful for relatively flat terrains and when distances are less than 10 km far from the site. In the case of complex terrain or longer distances the use of radiation data from other geographical points is absolutely inappropriate.  Interpolation of surrounding measurements. This approach can be only used for areas with a high density of stations and for average distances between stations of about 20-50 km [Pérez et al., 1997; Zelenka et al., 1999]. Solar radiation estimation from satellite images is currently the most suitable approach. It supplies the best information on the spatial distribution of the solar radiation and it is a methodology clearly accepted by the scientific community and with a high degree of maturity [McArthur, 1998]. In this regard, it is worth to mention that BSRN (Baseline Surface Radiation Network) has among its objectives the improvement of methods for deriving solar radiation from satellite images, and also the Experts Working Group of Task 36 of the Solar Heating and Cooling Implement Agreement of IEA (International Energy Agency) focuses on solar radiation knowledge from satellite images. 1.3 Methodology for solar radiation derived from satellite images Solar radiation derived from satellite images is based upon the establishment of a functional relationship between the solar irradiance at the Earth’s surface and the cloud index estimated from
  • 7. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 8 of 66 the satellite images. This relationship has been previously fitted by using high quality ground data, in such a manner that the solar irradiance-cloud index correlation can be extrapolated to any location of interest and solar radiation components can be calculated from the satellite observations for that point. 1.4 Brief summary of IrSOLaV methodology The methodology of IrSOLaV uses two main inputs to compute hourly solar irradiance: the geostationary satellite images and the information about the attenuating properties of the atmosphere. The former consists of one image per hour offering information related with the cloud cover characteristics. The latter is basically information on the daily Linke turbidity which is a very representative parameter to model the attenuating processes which affects solar radiation on its path through the atmosphere, mainly the aerosol optical depth and water vapor column. The methodology applied has undoubtedly been accepted by the scientific community and its main usefulness is in the estimation of the spatial distribution of solar radiation over a region. Its maturity is guaranteed by initiatives like the establishment in 2004 of a new IEA (International Energy Agency) task known as “Solar Radiation Knowledge from Satellite Images” or the fact that the measuring solar radiation network BSRN (Baseline Surface Radiation Network) promoted by WMO (World Meteorological Organization) has as its main objectives for the improvement of solar radiation estimation from satellite images models. Solar radiation estimation from satellite images offered is made from a modified version of the renowned model Heliosat-3, developed and validated by CIEMAT with more than thirty radiometric stations in the Iberian Peninsula. Over this first development, IrSOLaV has generated a tool fully operational which is applied on a database of satellite images available with IrSOLaV (temporal and spatial resolution of the data depends on the satellite covering the region under study). It is worthwhile to point out that tuning-up and fitting of the original methodology in different locations of the World have been performed and validated with local data from radiometric stations installed in the region of interest. This way, it may be considered that the treatment of the information from satellite images offered by IrSOLaV is an exclusive service. Even though the different research groups working in this field are making use of the same core methodologies, there are several characteristics that differ depending on the specific objectives pursued. Therefore, the main differences between the IrSOLaV/CIEMAT and others, like the ones applied by PVGis or Helioclim are:  Filtering of images and terrestrial data. Images and data used for the fitting and relations are thoroughly filtered with procedures developed specifically for this purpose.
  • 8. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 9 of 66  Selection of albedo for clear sky days. The algorithm used to select albedos for clear sky days provides a daily sequence that is different for every year; however the other methodologies use a unique monthly value.  Introduction of characteristic variables. The relation developed by IrSOLaV/CIEMAT includes new variables characterizing the climatology of the site and the geographical location, with a significant improvement of the results obtained for global and direct solar radiation.  Global horizontal irradiance is estimated by relating the clear sky index with the cloud index, the cloud index distribution and the air mass (Zarzalejo et al., 2009).  Ground albedo is estimated by a moving window of about 20 days that comprises images of the central instants in terms of co-scattering angle (Zarzalejo, 2005). This method allows the daily computation of the ground albedo.  Direct normal irradiance for non-clear sky situations is calculated using the Louche conversion function (Louche et al., 1991) and DirIndex model which takes into account daily values of AOD at 550nm and water vapour column obtained from MODIS satellite and MACC database.  Clear sky days are identified (Polo et al., 2009) and estimated separately by the ESRA transmittance model (Rigollier et al., 2000). Besides, as some clear sky models behave better in some locations and other depending in local climatic conditions of the sites, SOLIS and REST2 clear sky models are also tested.  Input of daily of values of Aerosol optical depth (AOD) 500nm and column water vapor content estimated from MODIS satellite for the period from 2000 to 2012. The resolution of the dataset is 1º by 1º and it has a global coverage.  Daily Linke turbidity factor is estimated by the Ineichen correlation from AOD at 550 nm obtained from MODIS Aqua and Terra satellite (Ineichen, 2008), MISR satellite and MACC model. Water vapour obtained from NCEP and MODIS satellite. Application of a method to fit the angular dependence of the sun and satellite and the ground albedo estimations {Polo, 2012 1000423 /id}. In classical Heliosat-3 method the potential overestimation of cloud index under some situations for high reflective (deserted regions mainly) sites could lead to noticeable underestimation of the surface solar irradiance. The uncertainty of the estimation comparing with hourly ground pyranometric measurements is expressed in terms of the relative root mean squared deviation (RMSD). Different assessments and benchmarking tests can been found at the available literature concerning the use of satellite images (Meteosat and GOES) on different geographic sites and using different models [Pinker y Ewing, 1985; Zelenka et al., 1999; Pereira et al., 2003; Rigollier et al., 2004; Lefevre et al., 2007]. The uncertainty for hourly values is estimated around 20-25% RMSD and in a daily basis the uncertainty of the models used to be about 13-17%. It is important to mention here the contribution given by
  • 9. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 10 of 66 Zelenka in terms of distributing the origin of this error, concluding that 12-13% is produced by the methodology itself converting satellite information into radiation data and a relevant fraction of 7- 10% because of the uncertainty of the ground measurements used for the comparison. In addition Zelenka estimates that the error of using nearby ground stations beyond 5 km reaches 15%. Because of that his conclusion is that the use of hourly data from satellite images is more accurate than using information from nearby stations located more than 5 km far from the site. The IrSOLaV methodology is based on the work developed in CIEMAT by the group of Solar Radiation Studies. The model has been assessed for about 30 Spanish sites with the following uncertainty data for global horizontal irradiance:  About 12% RMSD for hourly values  Less than 10% for daily values  Less than 5% for annual and monthly means. The model has been modified for a better estimation of solar radiation with clear sky, leading to an important improvement in the accuracy of the model [Polo, 2009; Polo et al., 2009b]. The uncertainties in the raw estimated values of GHI and DNI have been calculated by comparison with a set of ground measurements located in region XXXX, in terms of root mean squared difference (RMSD), and the deviations of the estimations with respect to the measurements are quantified by the mean bias difference (MBD). Uncertainties and deviations are summarized in Table 1 and Table 2, respectively. Table 1. Relative uncertainties (RMSD) [%] of GHI and DNI by IrSOLaV methodology. Hourly Daily Monthly Yearly GHI 18 12 5 4 DNI 40 25 15 9 Table 2. Relative deviations (MBD) [%] of GHI and DNI by IrSOLaV methodology. Hourly Daily Monthly Yearly GHI 1 0 1 0 DNI -1 0 0 0 This improved model is the one applied to the LOCATION site in this report. IrSOLaV also provides data online through its web portal SOLAR EXPLORER at http://www.solarexplorer.info/.
  • 10. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 11 of 66 2 AVAILABLE DATA FOR SOLAR RESOURCE ASSESSMENT For the site of this study, various sources of solar radiation data are discussed, both from databases of terrestrial measurements and from satellite image processing. 2.1 Measurements at the site of the project Three components of the solar radiation (global horizontal (GHI), diffuse horizontal (DIF) and direct normal irradiance (DNI)) are measured in the station of LOCATION using two pyranometers and one pyrheliometer. The time step of the measurements is 10 minutes and the temporal reference is Coordinated Universal Time (UTC + 0h, Longitude of reference 0º). Global solar radiation is defined as the solar radiation received on a horizontal surface in a solid angle of 2π steradians. As a result of the interaction of sunlight with the atmosphere, the global horizontal irradiation (GHI) is the sum of the direct normal irradiance (DNI) (which has not interacted with atmospheric components and therefore has not changed in its angle of incidence) and the diffuse component (DIF) (result of atmospheric dispersion processes and it can be assumed to come from all points of the sky and has no predominant direction). These three components, (GHI, DNI and DIF) are related to each other by using the following expression, where ϴ is the zenith angle. cosDNIDIFGHI  (1) The monthly and yearly measurements of GHI, DNI and DIF are presented in Table 3, Table 4 and Table 5, respectively. Table 3. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) GHI METEOROLOGICAL- STATION. METEOROLOGICAL-STATION GLOBAL HORIZONTAL IRRADIANCE (kWh m-2 day-1 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Yearly 2011 2012 2013 AVG
  • 11. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 12 of 66 Table 4. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) DNI METEOROLOGICAL- STATION. METEOROLOGICAL-STATION DIRECT NORMAL IRRADIANCE (kWh m-2 day-1 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Yearly 2011 2012 2013 AVG Table 5. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) DIF METEOROLOGICAL- STATION. METEOROLOGICAL-STATION DIFFUSE HORIZONTAL IRRADIANCE (kWh m-2 day-1 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Yearly 2011 2012 2013 AVG With nearly three years of measurements in the station, average annual global irradiance (GHI) is xxxxx kWh m-2 year-1 and direct normal irradiance (DNI) is xxxxx kWh m-2 year-1 . Ground data measurements will be used below to adapt the satellite time-series estimations to the site climatic conditions. 2.2 METEONORM METEONORM is a comprehensive meteorological reference, incorporating a catalogue of meteorological data and calculation procedures for solar applications and system design at any desired location in the world. It is based on the expertise acquired over 23 years in the development of meteorological databases for energy applications. The most important features of METEONORM are climatological data of more than 8055 weather stations, measured parameters (monthly means of global radiation, temperature, humidity, precipitation, days with precipitation, wind speed and direction, sunshine duration), interpolation models to calculate mean values for any site in the world, calculation of radiation for inclined surfaces with updated models, etc. In this report we have used METEONORM version 6. The results from METEONORM for LOCATION (Table 6) show an average annual global irradiance (GHI) of xxxxxx kWh m-2 year-1 .
  • 12. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 13 of 66 Table 6. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated GHI and DNI on horizontal surface. Radiometric data from METEONORM. LOCATION PROJECT TMY from METEONORM Month GHI (kWh m-2 day-1 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual xxxxxxx 2.3 NASA NASA, through its Science Mission Directorate, has long supported satellite systems and research providing data important to the study of climate and climate processes. NASA Surface meteorology and Solar Energy (SSE) dataset include long-term estimates of meteorological quantities and surface solar energy fluxes. These satellite and modeled based products have been shown to be accurate to provide reliable solar and meteorological resource data over regions where surface measurements are sparse or nonexistent, and offer two unique features - the data is global and, in general, contiguous in time. Monthly and yearly values of GHI provided by NASA are summarized in the Table 7.
  • 13. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 14 of 66 Table 7. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated GHI (NASA). LOCATION PROJECT (NASA) GLOBAL HORIZONTAL IRRADIANCE (kWh m-2 day-1 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 AVG The results of NASA for LOCATION show an average annual global radiation of xxxxx kWh m-2 day-1 . NASA also provides the yearly DNI which is in the range xxxx-xxxxx kWh m-2 year-1 . 2.4 SOLAR-MED-ATLAS SOLAR-MED-ATLAS gives freely values of GHI and DNI on their web portal http://www.solar-med- atlas.org. The results of SOLARGIS for LOCATION show an average annual global horizontal irradiation of xxxx kWh m-2 year-1 and direct normal irradiation of xxxxx kWh m-2 year-1 . For the period from 1992 to 2010. Table 8 shows single yearly values for each year from 1992 to 2010 and long-term yearly average.
  • 14. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 15 of 66 Table 8. Yearly sum (kWh m-2 year-1) GHI and DNI estimated from satellite (Solar Med Atlas). Year GHI DNI 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 MEAN 2.5 PV-GIS PVGIS is a geographical information system which offers modeled values of solar radiation and temperature in Europe and Africa, based on ten-year average data from a large number of measuring stations. Within PVGIS a relation for the conversion efficiency of crystalline silicon PV modules at varying irradiance and temperature is used to obtain values for the loss of efficiency at any location. The computational approach used in PVGIS to construct the solar radiation database is based on a solar radiation model r.sun, and the spline interpolation techniques s.surf.rst and s.vol.rst that are implemented within the open-source GIS software GRASS. The r.sun model algorithm uses the equations published in the European Solar Radiation Atlas. The model estimates beam, diffuse and reflected components of the clear-sky and real-sky global irradiance/irradiation on horizontal and inclined surfaces. The total daily irradiation (Wh m-2 ) is computed by the integration of the irradiance
  • 15. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 16 of 66 values (W m-2 ) that are calculated at a time step of 15 minutes from sunrise to sunset. The data from 566 meteorological stations are monthly averages of measurements over the period 1981-1990. The results of PVGIS for LOCATION shows an average annual global horizontal irradiation of xxxx kWh m-2 day-1 and average annual direct normal irradiation of xxxx kWh m-2 day-1 . Table 9 shows the results for yearly and monthly averages from PVGIS: Table 9. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated global irradiation on horizontal surface (GHI) and direct normal irradiation (DNI). Data from PVGIS. LOCATION PROJECT (PVGIS) Month GHI (kWh m-2 day-1 ) DNI (kWh m-2 day-1 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual 2.6 CM SAF-CLIMATE-DWD CM SAF aims at the provision of satellite-derived geophysical parameter data sets suitable for climate monitoring. CM SAF provides climatologies for Essential Climate Variables (ECV), as required by the Global Climate Observing System (GCOS) implementation plan in support of the United Nations Framework Convention on Climate Change (UNFCCC). The CM SAF data products are categorized in monitoring data obtained in near real time and data sets based on carefully intersensor calibrated radiances. The homogenous sets of high-quality data help scientists to investigate climate variability and long-term changes in the climate mean state. The products are derived from several instruments on-board meteorological operational satellites in geostationary and polar orbit as the Meteosat and EUMETSAT Polar System satellites, respectively.
  • 16. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 17 of 66 The results of CMSAF-CLIMATE for LOCATION shows an average annual global horizontal irradiation of xxxx kWh m-2 day-1 and average annual direct normal irradiation of xxxx kWh m-2 day-1 . Table 10 shows the results for yearly and monthly averages from CMSAF-CLIMATE. Table 10. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated global irradiation on horizontal surface (GHI) and direct normal irradiation (DNI). Data from CM SAF-CLIMATE-DWD. LOCATION PROJECT (CM SAF-CLIMATE-DWD) Month GHI (kWh m-2 day-1 ) DNI (kWh m-2 day-1 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual 2.7 SoDa Database (HELIOCLIM-3V4) The project SoDa (Solar Radiation Data) answers the needs of industry and research for information on solar radiation parameters with a satisfactory quality. A prototype service has been developed that integrate and efficiently exploits diverse networked information sources to supply value-added information in a selected number of environmental applications. The project began on January 1st, 2000 and ended in March 2003. The project SoDa aims at responding to the strong multi-disciplinary needs for information on solar radiation. It represents a real innovation. Advanced information and communication technologies have been used to supply high quality value-added information that match the actual customer needs. The methodology was user-driven with a large involvement of users in the project. A WWW-based service has been developed and demonstrated which realize the integration of information sources of different natures within a smart network. These sources include databases containing solar radiation parameters and other relevant information, including algorithms and end-users applications; some of them may
  • 17. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 18 of 66 originate from an advanced processing of remote sensing images. Before the SoDa service was made available, these resources were available separately. Table 11 and Table 12 show single yearly values of GHI and DNI for year 2004 and 2005 and yearly average. Table 11. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated global radiation on horizontal surface (GHI). Data from SoDa. HELIOCLIM-3V4 LOCATION PROJECT (SoDa) GLOBAL HORIZONTAL IRRADIANCE (kWh m-2 day-1 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual 2004 2005 AVG Table 12. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated direct normal irradiation (DNI). Data from SoDa. HELIOCLIM-3V4. LOCATION PROJECT (SoDa) DIRECT NORMAL IRRADIANCE (kWh m-2 day-1 ) JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual 2004 2005 AVG The results of SoDa for LOCATION PROJECT show an average annual global radiation of xxxx kWh m-2 year-1 and average annual direct normal irradiation of xxx kWh m-2 year-1 . 2.8 IrSOLaV raw estimations GHI and DNI as estimated by IrSOLaV methodology has been discussed in a previous version of this report. Nevertheless, monthly and yearly values for the period 1994 to 2013 are shown again in Table 13 and Table 14. Notice that years 2003 and 2004 have been removed due to the fact of the transition from Meteosat First Generation to Meteosat Second Generation satellites, which caused a decrease in available images and therefore produced non-realistic low irradiances. Table 13. Monthly averages (kWh m-2 day-1) and yearly sums (kWh m-2 year-1) for estimated global irradiance. Averages over the 20-year period is also shown.
  • 18. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 19 of 66 LOCATION PROJECT (IrSOLaV Database) GLOBAL HORIZONTAL IRRADIANCE (kWh m-2 day-1 ) YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual 1994 1995 1996 1997 1998 1999 2000 2001 2002 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 AVG Table 14. Monthly average (kWh m-2 day-1) and yearly sums (kWh m-2 year-1) for estimated direct normal irradiance. Averages over the 20-year period are also shown. LOCATION PROJECT (IrSOLaV)
  • 19. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 20 of 66 DIRECT NORMAL IRRADIANCE (kWh m-2 day-1 ) YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual 1994 1995 1996 1997 1998 1999 2000 2001 2002 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 AVG 3 CORRECCTION OF SATELLITE ESTIMATIONS WITH GROUND MEASUREMENTS The strength of satellite estimations is its capability to provide long-term time series of a solar component. However, the signal received is representative for an extended region, and it means a loss of sensitivity to variability of cloud information, aerosols, water vapor and shading, which affects
  • 20. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 21 of 66 strongly to DNI estimations. Hence, deviations are expected when comparing estimations with pinpoint measurements. Besides, the coarse spatial resolution of aerosol and water vapor databases used for satellite estimations makes difficult to describe the special characteristics of a specific site. GHI estimations are less affected by this lack of sensitivity. IrSOLaV estimations are based on Heliosat-3 methodology which has been developed and validated using ground measurements from stations spread along the European geography. When estimations are performed in regions where the methodology has not been validated, it is essential to have ground measurements that provides knowledge on the spectral response of the satellite in the region. Hence, in order to improve the quality of IrSOLaV data, the estimations are correlated to ground measurements, allowing the decrease of the bias and deviations of the estimations with respect to the measurements and a better agreement between frequency distribution functions. From this correlation, a correction for the aerosols and ground reflectivity is obtained, which better represent the local conditions of the site. The correlation has taken into account a dedicated filtering of the ground measurements, and only daytime values have been used. The improvement of the correction is analyzed by comparing with the measurements. This comparison is made in two ways: - Standard: The validation of the estimations ˆy against measurements y for N hours (ti=1..N) are done mainly with bias difference (MBD) and the root mean squared difference (RMSD). The mean bias difference (MBD) is defined by:   1 1 ˆMBD ( ) ( ) N i i i y t y t N      2 1 1 ˆ(t ) (t ) N i i i RMSD y y N    The corresponding relative differences are MBD(%) and rRMSD(%):
  • 21. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 22 of 66 1 MBD MBD(%) 100 ( ) / RMSD RMSD(%) 100 N i i y y t y N y                  Another parameter used in the validation is the correlation coefficient (R), giving information on the linear relation between measurements and estimations. - Distributions: Additional measures for model quality based on the analysis of cumulative distribution function (CDF) are used. A comprehensive approach to an analysis of the deviations of measured and modelled CDFs which is based on the Kolmogorov-Smirnov (KS) test and defines new parameters to quantify the similarity of the two CDFs. Although there are several statistical tests and ways of evaluating the goodness of a model, the KS test has the advantage of making no assumption about the data distribution, and is thus a non-parametric, distribution-free test. The KS test tries to determine if two datasets differ significantly. The test consists of comparing the distribution of a dataset to a reference distribution. This can be done by converting the list of data points to an unbiased estimator S(xi) of the CDF, i = 1..N, N is the population size. The KS statistic D is defined as the maximum value of the absolute difference between the two CDFs:  max (x ) Ri iD S x  Where R(xi) is the CDF of the reference dataset. Thus, if the D statistic is lower than the threshold value VC, the null hypothesis that the two datasets come from the same distribution cannot be rejected. The critical value depends on N and is calculated for a 99% level of confidence as: 1.63 , 35cV N N   This test detects smaller deviations in cumulative distributions than the χ2 test does. However, instead of using the original one, an extended KS test is used, in which the distances between the CDFs are calculated over the whole range of the variable x, i.e. the solar radiation. A discretization in n = 1…m levels is applied here. In the following m = 100 intervals as a reasonable choice are
  • 22. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 23 of 66 used. Greater order of magnitude for m is not recommended since it implies more computational cost for no improvement in the accuracy of the result. The interval distance p is thus defined as: max min , 100 x x p m m    where xmax and xmin are the extreme values of the independent variable. Then, the distances between the CDFs are defined, for each interval, as:      min min, 1 ,n j j jD S x R x x x n p x np        The representation of the values Dn, along with the critical value, gives the complete testing behaviour of the CDF with respect to the reference over the whole range. However, although application of the KS test contributes valuable information, it only materializes in the acceptance or rejection of the null hypothesis. In the next sections new parameters are proposed, which, based on the estimation of the distance between the two CDFs for the sets compared, define quantitative measures that can be used to rank models. Kolmogorov – Smirnov test Integral, parameter KSI The KSI parameter (Kolmogorov-Smirnov test Integral) is defined as the integral of the area between the CDFs for the two sets. The unit of this index is the same for the corresponding magnitude, the value of which depends on it. The KSI is defined as the integral: max min x nx KSI D dx  As Dn is a discrete variable and the number of integration intervals is identical in all cases, trapezoidal integration is possible over the whole range of the independent variable x. A percentage of KSI is obtained by normalizing the critical area, acritical: max min % *100 x nx critical D dx KSI a   where acritical is calculated as:
  • 23. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 24 of 66 acritical = Vc* (xmax-xmin) and Vc is the critical value for the level of confidence selected and (xmax, xmin) are the extreme values of the independent variable. Normalization to the critical area enables the comparison of different KSI values from different tests. The minimum value of the KSI index is zero, in which case, it can be said that the CDFs of the two sets compared are the same. Figure 1 shows the distribution of estimated (left panel) and corrected (right panel) hourly GHI versus measured. Notice the improvement at low values, while it remains nearly unchanged for larger GHI. Analogously, Figure 2 shows the distributions obtained for hourly DNI where it can be seen the reduction of the deviations and a better agreement below 500Wh. In all cases, black line represents the perfect correlation. 0 200 400 600 800 1000 1200 0 200 400 600 800 1000 1200 HOURLY GHI SCATTER PLOT: IRSOLAV RAW VS GROUND HOURLY GROUND MEASURED DATA HOURLYIRSOLAVRAWDATA 0 200 400 600 800 1000 1200 0 200 400 600 800 1000 1200 HOURLY GHI SCATTER PLOT: IRSOLAV CORRECTED VS GROUND HOURLY GROUND MEASURED DATA HOURLYIRSOLAVCORRECTEDDATA Figure 1. Hourly GHI (Wh/m2) raw estimated (left) and corrected (right) versus measured.
  • 24. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 25 of 66 0 200 400 600 800 1000 1200 0 200 400 600 800 1000 1200 HOURLY DNI SCATTER PLOT: IRSOLAV RAW VS GROUND HOURLY GROUND MEASURED DATA HOURLYIRSOLAVRAWDATA 0 200 400 600 800 1000 1200 0 200 400 600 800 1000 1200 HOURLY DNI SCATTER PLOT: IRSOLAV CORRECTED VS GROUND HOURLY GROUND MEASURED DATA HOURLYIRSOLAVCORRECTEDDATA Figure 2. Hourly DNI (Wh/m2) raw estimated (left) and corrected (right) versus measured. If we focus on the agreement between measured data distribution and estimated and corrected, we see more clearly the improvement of the correction. Figure 3 shows the Kolmogorov-Smirnov test for GHI and DNI. The right panels show the clear improvement of the corrections with respect the raw estimations, except for largest values of GHI. The statistic Dn is below the threshold for almost all DNI values range except for some values around 1000Wh, where Dn is slightly larger. This way, we can conclude that the similarities between measured and satellite corrected DNI are high.
  • 25. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 26 of 66 0 200 400 600 800 1000 1200 0 0.005 0.01 0.015 0.02 0.025 GHI Wh/m2 Dn Statistical Kolmogorov Smirnov Test for hourly GHI GHI Raw 0 200 400 600 800 1000 1200 0 0.005 0.01 0.015 0.02 0.025 GHI Wh/m2 Dn Statistical Kolmogorov Smirnov Test for hourly GHI GHI Corrected 0 200 400 600 800 1000 1200 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 DNI Wh/m2 Dn Statistical Kolmogorov Smirnov Test for hourly DNI DNI Raw 0 200 400 600 800 1000 1200 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 DNI Wh/m2 Dn Statistical Kolmogorov Smirnov Test for hourly DNI DNI Corrected Figure 3. Kolmogorov-Smirnoff test for GHI (upper panels) and DNI (lower panels). Raw estimations are plotted in left panels and corrections are shown in right panels. Figure 4 shows the distribution of a comparison of estimated, corrected and measured radiation values, where it can be observed that the distribution for raw GHI can hardly be improved, but the results for DNI indicate a better agreement between corrected and measured DNI.
  • 26. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 27 of 66 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 0 20 40 60 80 100 120 140 160 180 200 220 GHI Wh/m2 Frequencyofocurrence Hourly GHI ground Hourly GHI IrSOLaV Raw 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 0 20 40 60 80 100 120 140 160 180 200 220 GHI Wh/m2 Frequencyofocurrence Hourly GHI ground Hourly GHI IrSOLaV Corrected 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 0 50 100 150 200 250 300 DNI Wh/m2 Frequencyofocurrence Hourly DNI ground Hourly DNI IrSOLaV Raw 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 0 50 100 150 200 250 300 DNI Wh/m2 Frequencyofocurrence Hourly DNI ground Hourly DNI IrSOLaV Corrected Figure 4. Histograms for GHI (upper panels) and DNI (lower panels), raw estimations (left) and corrections (right) are shown. Red lines represent IrSOLaV data and ground measurements are shown in blue bars. To conclude with the comparison between raw estimations, corrections and measurements, Figure 5 shows the corresponding CDFs. Again, it is clear the overall improvement achieved for DNI, while for GHI the improvement is obtained for values below 900Wh/m2 , getting a light deviation over 100Wh/m2 .
  • 27. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 28 of 66 0 200 400 600 800 1000 1200 0 0.2 0.4 0.6 0.8 1 GHI Wh/m2 F(x) CDF GHI Hourly GHI ground measurements Hourly GHI IrSOLaV Raw 0 200 400 600 800 1000 1200 0 0.2 0.4 0.6 0.8 1 GHI Wh/m2 F(x) CDF GHI Hourly GHI ground measurements Hourly GHI IrSOLaV corrected 0 200 400 600 800 1000 1200 0 0.2 0.4 0.6 0.8 1 DNI Wh/m2 F(x) CDF DNI Hourly DNI ground measurements Hourly DNI IrSOLaV Raw 0 200 400 600 800 1000 1200 0 0.2 0.4 0.6 0.8 1 DNI Wh/m2 F(x) CDF DNI Hourly DNI ground measurements Hourly DNI IrSOLaV corrected Figure 5. CDF for GHI (upper panels) and DNI (lower panels), raw estimations (left) and corrections (right) are shown. Red lines represent ground measurements and IrSOLaV data is shown in blue lines. The achieved improvement in the satellite estimations that the correction has brought can be well represented by the calculation of the correlation index with respect to the measurements, the mean bias and root mean squares for hourly, daily and monthly values (Table 15, Table 16) and the KSI parameter (Table 17).
  • 28. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 29 of 66 Table 15. Mean bias difference (MBD) of the comparison of raw and corrected estimations with measured data. MBD Solar Component Hourly Daily Monthly Yearly GHI raw xx Wh/m2 xx Wh/m2 xx kWh/m2 xx kWh/ m2 xx % xx % xx % xx % GHI corrected xx Wh/m2 xx Wh/m2 xx kWh/m2 xx kWh/m2 xx % xx % xx % xx % DNI raw xx Wh/m2 xx Wh/m2 xx kWh/m2 xx kWh/m2 xx % xx % xx % xx % DNI corrected xx Wh/m2 xx Wh/m2 xx kWh/m2 xx k Wh/m2 Xx % Xx % Xx % Xx % Table 16. Root mean square (RMSD) of the comparison of raw and corrected estimations with measured data. RMSD Solar Component Hourly Daily Monthly Yearly GHI raw xx Wh/m2 xx Wh/m2 xx kWh/m2 xx kWh/m2 xx % xx % xx % xx % GHI corrected xx Wh/m2 xx Wh/m2 xx kWh/m2 xx kWh/m2 xx % xx % xx % xx % DNI raw xx Wh/m2 xx Wh/m2 xx kWh/m2 xx kWh/m2 xx % xx % xx % xx % DNI corrected xx Wh/m2 xx Wh/m2 xx kWh/m2 xx kWh/m2 xx % xx % xx % xx % Table 17. Correlation index (R) and KSI index of the comparisons of raw estimations and corrected values with measured data. R KSI Solar Component Hourly Hourly GHI raw 0.xx xx.xx GHI corrected 0.xx xx.xx DNI raw 0.xx xx.xx DNI corrected 0.xx xx.xx
  • 29. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 30 of 66 4 IRSOLAV DATABASE This section shows the results of applying the methodology developed by IRSOLAV/CIEMAT, for the specific location called LOCATION. The methodology makes use of Meteosat satellite images for the period 1994-2014, generating cloudiness index in the site, and then estimates global radiation and direct normal radiation. After the raw estimation, the correlation with ground measurements is performed, and a correction for the estimations is obtained. A number of statistically representative parameters are obtained, such as monthly and annual averages of global and direct solar radiation, which summarize the availability of solar energy resource on the LOCATION site. 4.1 Uncertainties of IrSOLaV corrected estimations The uncertainties of the estimated GHI and DNI from satellite images have no single source. On the one hand, there are systematic uncertainties due to the estimation methodology itself, i.e., spectral response of the satellite’s radiometer, radiative transfer model and input atmospheric data. On the other hand, there are uncertainties related to the differences between estimations and ground measurements, which are considered as the true values of radiation (Table 15, Table 16 and Table 17). Uncertainties related to the WMO classification of the pyranometers used in the measurement has been taken into account. For the specific site of LOCATION, the uncertainties related to GHI and DNI, for long-term annual values are of 2.5% and 3%, respectively. Stochastic effects such as volcanic eruptions or climatic change effects on the radiation that reaches the Earth surface have not been taken into account. 4.2 Global horizontal irradiance (GHI) in LOCATION PROJECT The yearly average of GHI resource obtained from the treatment of satellite images over the period 1994-2013 is xxxx kWh m-2 yr-1 . Figure 6 shows the monthly averages of global radiation on horizontal surface at the site of LOCATION. Monthly and yearly values are summarized in Table 18.
  • 30. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 31 of 66 Table 18. Monthly average (kWh m-2 day-1) and yearly sums (kWh m-2 year-1) for estimated global horizontal irradiance as corrected by ground measurements. Averages over the 20-year period are also shown. LOCATION PROJECT (IrSOLaV Database) GLOBAL HORIZONTAL IRRADIANCE (kWh m-2 day-1 ) YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual 1994 1995 1996 1997 1998 1999 2000 2001 2002 2005 2006 2007 2008 2009 2010 2011 2012 2013 AVG The distribution of the monthly means of GHI is plotted in Figure 6. The Box-Whisker diagram in Figure 7 shows the main moments of the distribution of daily global radiation for all the period analyzed (median, 25th and 75th percentile). The value corresponds to an annual-average daily radiation of xxxx Wh m-2 day-1 . The good behavior of the radiation is observed during the year 2007, that qualifies as the best year of the series of 18 years studied and that agrees with the information coming from the previously analyzed databases.
  • 31. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 32 of 66 Monthly mean of daily global horizontal irradiance (GHI) (kWh/m 2 day) Month Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 3 4 5 6 7 8 9 Figure 6. Monthly average of the global radiation on horizontal surface from the database of satellite. 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 years kWh/m2 Figure 7. Year-on-year distribution of the global daily radiation (GDR). Box-Whisker diagram.
  • 32. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 33 of 66 Regarding the analysis of the information in hourly base, Figure 8 shows the hourly contours of radiation on average over the 20 years. It is noted that the hourly average over the 18 years, between 11 and 14 hours in April and May, exceeds 800 Wh m-2 . Figure 9 presents the inverse cumulative distribution of hours for different levels of GHI. Month Hour 100 100 200 200 300 300 400 400 500 500 600 700 800 900 1e+003 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 6 8 10 12 14 16 18 Figure 8. Monthly distribution of the average of global hourly radiation.
  • 33. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 34 of 66 0 200 400 600 800 1000 1200 0 10 20 30 40 50 60 70 80 90 100 Wh/m2 [%] Figure 9. Inverse cumulative distribution of the percentage of hours for global horizontal radiation levels. Figure 10 shows Box-Whisker diagrams of monthly global radiation year on year for each month. Notice the low values obtained for 2003 and 2004 (years 10 and 11, respectively), which have been eventually removed from the study.
  • 34. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 35 of 66 1000 2000 3000 4000 5000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year JAN kW/m2 /day 1000 2000 3000 4000 5000 6000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year FEB kW/m2 /day 2000 4000 6000 8000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year MAR kW/m2 /day 0 2000 4000 6000 8000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year APR kW/m2 /day 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year MAY kW/m2 /day 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year JUN kW/m2 /day
  • 35. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 36 of 66 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year JUL kW/m2 /day 2000 4000 6000 8000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year AUG kW/m2 /day 0 2000 4000 6000 8000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year SEP kW/m2 /day 1000 2000 3000 4000 5000 6000 7000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year OCT kW/m2 /day 0 1000 2000 3000 4000 5000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year NOV kW/m2 /day 0 1000 2000 3000 4000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year DEC kW/m2 /day Figure 10. Year-on-year distribution of the monthly global radiation (DDR). Box-Whisker diagram. 4.3 Direct normal irradiance (DNI) in LOCATION PROJECT This section presents the results obtained from the analysis of direct normal solar radiation at LOCATION site.
  • 36. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 37 of 66 Table 19 shows the yearly and monthly average values of direct normal radiation at LOCATION. The annual average irradiance potential during the period 1994-2013 is xxxx kWh m-2 yr-1 . LOCATION. For the 18 years studied, the irradiances are reaching always approximate values in the range from xxxx to xxxx kWh m-2 yr-1 . The cumulated average value corresponds to a typical average day of xxxxx Wh m-2 day-1 . This value can be considered as representative for the whole period 1994-2013. Table 19. Monthly average (kWh m-2 day-1) and yearly sums (kWh m-2 year-1) for estimated direct solar radiation as corrected by ground measurements. Averages over the 20-year period are also shown. LOCATION PROJECT (IrSOLaV) DIRECT NORMAL IRRADIANCE (kWh m-2 day-1 ) YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual 1994 1995 1996 1997 1998 1999 2000 2001 2002 2005 2006 2007 2008 2009 2010 2011 2012 2013 AVG The distribution of the monthly means of DNI is plotted in Figure 11, and the Box & Whisker plot is shown in Figure 12, where despite the noticeable variable of the annual DNI through the years the quartile size appearing in the Box & Whisker shows a general homogeneous behaviour. Figure 13 shows the hourly contours of radiation on average over the 20 years. Figure 14 presents the inverse cumulative distribution of hours for different levels of DNI, suggesting the convenience of designing a Solar Thermal Plant of 900 W m-2 , since this value is an inflection point. The percentage of operational hours with a DNI level beyond 900 Wm-2 is below 10%. Figure 15 shows year-on-year distribution of the monthly DNI. Notice the anomalous values for years 2003 and 2004, which have been finally removed from the study.
  • 37. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 38 of 66 Monthly mean of daily direct normal irradiance (DNI) (kWh/m 2 day) Month Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 3 4 5 6 7 8 9 10 11 Figure 11. Monthly average of the direct radiation on normal surface from the database of satellite. 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 years kWh/m2 Figure 12. Year-on-year distribution of the daily direct radiation (DDR). Box-Whisker diagram.
  • 38. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 39 of 66 Month Hour 100 100 200 200 300 300 400 400 500 500 600 600 700 700 700 800 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 6 8 10 12 14 16 18 Figure 13. Monthly distribution of the average of hourly direct radiation. 0 200 400 600 800 1000 1200 0 10 20 30 40 50 60 70 80 90 100 Wh/m2 [%] Figure 14. Inverse cumulative distribution of the percentage of hours for direct normal radiation levels.
  • 39. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 40 of 66 0 2000 4000 6000 8000 10000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year JAN kW/m2 /day 0 2000 4000 6000 8000 10000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year FEB kW/m2 /day 0 2000 4000 6000 8000 10000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year MAR kW/m2 /day 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year APR kW/m2 /day 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year MAY kW/m2 /day 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year JUN kW/m2 /day
  • 40. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 41 of 66 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year JUL kW/m2 /day 0 2000 4000 6000 8000 10000 12000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year AUG kW/m2 /day 0 2000 4000 6000 8000 10000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year SEP kW/m2 /day 0 2000 4000 6000 8000 10000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year OCT kW/m2 /day 0 2000 4000 6000 8000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year NOV kW/m2 /day 0 2000 4000 6000 8000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # year DEC kW/m2 /day Figure 15. Year-on-year distribution of the monthly direct radiation (DDR). Box-Whisker diagram. Figure 16 shows how the yearly sums of DNI for the period 1994-2013 are distributed. Values around xxxxx kWh/m2 /year arise as the most probable according to the histogram.
  • 41. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 42 of 66 2200 2300 2400 2500 2600 2700 2800 2900 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 DNI (kWh/m2 /year) relativefrequency Figure 16. Probability distribution of yearly values of satellite derived DNI (kWh/m2/year) as corrected by measurements
  • 42. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 43 of 66 5 CORRECTED IRSOLAV DATABASE COMPARED TO MEASUREMENTS In this section, only the period of coincidences with ground measurements, i.e., 2011 January to 2013 October is considered. The quantities to compare are based in clearness indices. The clear sky index (Kt) defined as: sin( ) t o GHI K I h  where GHI is the horizontal global irradiance, Io is the solar constant, and h the solar elevation angle. Clear sky index (Kc) is the defined as: c CS GHI K G  Where Gcs is the global horizontal irradiance for clear sky conditions. A similar test can be done with the beam clearness index Kb defined as: 0 b DNI K I  In order to use the clearness index as a reliable sky condition descriptor, Perez et al. (1990) modified this parameter to make it independent of the solar elevation angle. The formulation is the following:   * 1.41.031exp 0.1 (0.9 9.4 ) t t K K AM       where AM is the optical air mass as defined by Kasten (1980). Next graphics present values of Kt, Kc, Kt * and Kb against solar elevation for filtered measurements and IrSOLaV estimations. We can see that the form of the data is ok as expected.
  • 43. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 44 of 66 0 10 20 30 40 50 60 70 80 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Hourly Kt=GHI/Io.sin(h) comparison Solar Elevation ClearnessindexKt=GHI/Io.sin(h) Hourly Kt IrSOLaV Data Hourly Kt from Ground Data Figure 17. Clearness index of GHI values (Wh/m2) for LOCATION. 0 10 20 30 40 50 60 70 80 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Hourly Kt prima comparison Solar Elevation ClearnessindexKt* Hourly Kt* IrSOLaV Data Hourly Kt* from Ground Data Figure 18. Clearness index of GHI values (Wh/m2) for LOCATION.
  • 44. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 45 of 66 0 10 20 30 40 50 60 70 80 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Hourly Kc=GHI/Gcs comparison. Solar Elevation ClearskyindexKc=GHI/Gcs Hourly Kc IrSOLaV Data Hourly Kc Ground Data Figure 19. Clear sky index of GHI values (Wh/m2) for LOCATION. 0 10 20 30 40 50 60 70 80 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Hourly Kb=DNI/Io comparison. Solar Elevation BeamclearnessindexKb=DNI/Io Hourly Kb IrSOLaV Data Hourly Kb Ground Data Figure 20. Beam clearness index of DNI values (Wh/m2) for LOCATION.
  • 45. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 46 of 66 Next graphics show monthly average values and single values for each year of measurements (dashed line) and IrSOLaV satellite estimations (solid line) of GHI and DNI. As we can see, there is an underestimation of satellite derived data. Monthly GHI for each year Month (kWh/m2 day) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 1 2 3 4 5 6 7 8 9 10 2011 2012 2013 Figure 21. Distribution of monthly daily-average (kWh m-2 day-1) of measurements and IrSOLaV database for each year (2011-2013) for GHI. Solid lines are IrSOLaV corrected estimations; dashed lines are ground measurements.
  • 46. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 47 of 66 Averaged Monthly GHI: 2011-2013 period. Month (kWh/m2 day) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 1 2 3 4 5 6 7 8 9 10 IrSOLaV corrected Ground Figure 22. Distribution of average monthly daily-average (kWh m-2 day-1) of measurements and IrSOLaV database (2011-2013) for GHI. Monthly DNI for each year Month (kWh/m2 day) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 1 2 3 4 5 6 7 8 9 10 2011 2012 2013 Figure 23. Distribution of monthly daily-average (kWh m-2 day-1) of measurements and IrSOLaV database for each year (2011-2013) for DNI. Solid lines are IrSOLaV corrected estimations; dashed lines are ground measurements.
  • 47. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 48 of 66 Averaged Monthly DNI: 2011-2013 period. Month (kWh/m2 day) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 1 2 3 4 5 6 7 8 9 10 IrSOLaV corrected Ground Figure 24. Distribution of average monthly daily-average (kWh m-2 day-1) of measurements and IrSOLaV database (2011-2013) for DNI.
  • 48. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 49 of 66 6 METEOROLOGICAL PARAMETERS This section presents the local climatic conditions of LOCATION. Table 20 shows for each month: daily average, minimum and maximum for air temperature, daily average relative humidity, pressure, wind speed, wind direction and monthly accumulated precipitation rate. Yearly means are presented in the last row of the table. Table 20. Monthly and yearly average for meteorological parameters. LOCATION PROJECT (IrSOLaV) Meteorological Parameters Mean Temp ºC Min Temp ºC Max Temp ºC Mean RH % Mean Press Mean WS m/s Mean WD º Acu Precip January February March April May June July August September October November December YEAR Next figure presents the wind rose of hourly wind speed and wind direction. As we can see the predominance of wind directions is in the Northeast to Southwest sector. 2% 4% 6% WEST EAST SOUTH NORTH 0 - 2 2 - 4 4 - 6 6 - 8 8 - 10 10 - 12 12 - 14 14 - 16 16 - 18 Wind Speed (m/s) Figure 25. Wind rose of hourly Wind Speed (m/s) and Wind Direction (º) in LOCATION.
  • 49. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 50 of 66 The next figures present the box whisker diagram of hourly meteorological parameters for each month of the year: air temperature, average relative humidity, pressure, wind speed, wind direction and monthly accumulated precipitation rate. -5 0 5 10 15 20 25 30 35 40 45 1 2 3 4 5 6 7 8 9 10 11 12 Month ºC Box-plot diagram of hourly temperature by month Figure 26. Box Whisker of hourly temperature (ºC) for each month of the year in LOCATION. 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 Month % Box-plot diagram of hourly relative humidity by month Figure 27. Box Whisker of hourly relative humidity (%) for each month of the year in LOCATION.
  • 50. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 51 of 66 925 930 935 940 945 950 955 960 965 1 2 3 4 5 6 7 8 9 10 11 12 Month [hPa] Box-plot diagram of hourly barometric presure by month Figure 28. Box Whisker of hourly barometric pressure (hPa) for each month of the year in LOCATION. 0 2 4 6 8 10 12 14 16 1 2 3 4 5 6 7 8 9 10 11 12 Month m/s Box-plot diagram of hourly wind speed by month Figure 29. Box Whisker of hourly wind speed (m/s) for each month of the year in LOCATION.
  • 51. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 52 of 66 0 50 100 150 200 250 300 350 1 2 3 4 5 6 7 8 9 10 11 12 Month º Box-plot diagram of hourly wind direction by month Figure 30. Box Whisker of hourly wind direction (º) for each month of the year in LOCATION. 0 0.5 1 1.5 2 2.5 3 x 10 -3 1 2 3 4 5 6 7 8 9 10 11 12 Month kg/m2s Box-plot diagram of hourly precipitation rate by month Figure 31. Box Whisker of accumulated hourly precipitation rate (kg/m2s) for each month of the year in LOCATION.
  • 52. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 53 of 66 7 INTER-ANNUAL VARIABILITY OF SOLAR RADIATION The weather conditions of a location changes in cycles and has also a stochastic nature (for example. NAO oscillation, El Niño,…). This way, yearly solar radiation can deviate from the long- term average in the range of few percent in the case of GHI and up to 30% for DNI. This section presents the estimation of inter-annual variability. The uncertainty of DNI is highest if only one single year is considered, but when averaged for a longer period it is averaged and approximated to the long-term average. The inter-annual variability is calculated from the unbiased standard deviation of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) over 18 years, considering, in the long-term, the normal distribution of the annual sums. Figure 32 shows time series of yearly values of GHI and DNI for the period 1994-2013. Several features are observed that deserve a brief analysis. The gap of 2003-2004 clearly distinguishes two different behaviors of yearly values for both GHI and DNI coming from the different satellite families MFG (1994-2002) and MSG (2005-2013). On the one hand, the inter-annual variability is larger, more realistic, for MSG than for MFG, as a consequence of the better spatial resolution gained for MSG. On the other hand, the DNI has, on average, larger values for MSG than for MFG. This is also due to the coarser spatial resolution of MFG, which turns into a loss of sensitivity to cloud coverage variability, aerosols and water vapor, but also due to the worse spatial resolution and accuracy of the aerosols and water vapor databases for the MFG period. These factors highly influence the DNI estimation, but do not affect the GHI, as it can be observed in the time series. Figure 32. Yearly GHI and DNI for the period 1994-2013 including average (blue line) and standard deviation (shaded region) (kWh/m2). Table 21, Table 22, Table 23 and Table 24 show an expectation of GHI and DNI values that is to be exceeded at P90 for a consecutive number of years. The variability for a number of years (n) is calculated from the standard deviation. stanardard deviation variability n  The uncertainty related to the inter-annual variability is characterized by P90, i.e. 90% probability of excess, and this is calculated from the variability, multiplying it by 1.282, since a normal distribution for yearly values is considered.
  • 53. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 54 of 66 Table 21. Annual sum of GHI that should be exceeded with 90% probability in the period of 1 to 10 years. Years 1 2 3 4 5 6 7 8 9 10 Variability [±%] 2.34 1.65 1.35 1.17 1.04 0.95 0.88 0.82 0.78 0.74 Uncertainty P(90) [±%] 3.00 2.12 1.73 1.50 1.34 1.22 1.13 1.06 1.00 0.94 Minimum Gh at P90 [kWh/m2 ] xxxx xxxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx Table 22. Annual sum of GHI that should be exceeded with 90% probability in the period of 11 to 20 years. Years 11 12 13 14 15 16 17 18 19 20 Variability [±%] 0.70 0.67 0.65 0.63 0.61 0.58 0.57 0.55 0.54 0.52 Uncertainty P(90) [±%] 0.90 0.87 0.83 0.80 0.77 0.75 0.72 0.71 0.69 0.67 Minimum Gh at P90 [kWh/m2 ] xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx Table 23. Annual sum of DNI that should be exceeded with 90% probability in the period of 1 to 10 years. Years 1 2 3 4 5 6 7 8 9 10 Variability [±%] 6.26 4.43 3.62 2.80 2.55 2.36 2.21 2.09 1.98 1.88 Uncertainty P(90) [±%] 8.03 5.68 4.63 4.01 3.59 3.28 3.03 2.84 2.68 2.53 Minimum Bn at P90 [kWh/m2 ] xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx
  • 54. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 55 of 66 Table 24. Annual sum of DNI that should be exceeded with 90% probability in the period of 11 to 20 years. Years 11 12 13 14 15 16 17 18 19 20 Variability [±%] 1.81 1.74 1.68 1.61 1.57 1.51 1.48 1.44 1.43 1.40 Uncertainty P(90) [±%] 2.42 2.32 2.23 2.15 2.07 2.00 1.95 1.89 1.84 1.80 Minimum Bn at P90 [kWh/m2 ] xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx Last tables show consequences of inter-annual variability if yearly GHI and DNI for different number of consecutive years is estimated. Following, few examples show how this information can be interpreted: i. GHI inter-annual variability of 2.34% has to be considered for any single year. In other words, assuming that the long-term average is xxxx kWh/m2 , it is expected (at 90% probability) that annual Global Horizontal Irradiation exceeds at any single year value of xxxx kWh/m2 , ii. Within a period of three consecutive years, it is expected at P90 that annual average of DNI exceeds value of xxxx kWh/m2 ; iii. For a period of 20 years, it is expected at P90 that due to inter-annual variability, the estimates of the long-term annual DNI average may be off within the range of +-1.40%. Thus assuming that the estimate of the long-term average is xxxx kWh/m2 , it can be expected at P90 that due to variability of weather, it should be at least xxxx kWh/m2 . We must point out that the prediction of long-term solar radiation is based on the analysis of the recent historical data. Future weather changes may include: man-induced or natural events such as volcano eruptions, which may have impact on the long-term prediction. 8 UNCERTAINTY OF ESTIMATES This section presents the cumulative uncertainty of estimations as a combination of the inter-annual variability (Section 7) and the uncertainty of satellite estimations once they have been corrected with ground measurements (Section 4.1). Values are presented for single year, 10 years and 20 years.at P90 probability for GHI (Table 25) and DNI (Table 26).
  • 55. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 56 of 66 Table 25. Cumulative uncertainty of the corrected GHI yearly means for 1, 10 and 20 years in LOCATION. Uncertainty Cumulative uncertainty Yearly value [kWh/m2 ] Minimum value for 1 year assuming a percentile 90 for the estimations 3.00% 3.91% xxx Minimum value for 10 years assuming a percentile 90 for the estimations 0.95% 2.67% xxxx Minimum value for 20 years assuming a percentile 90 for the estimations 0.67% 2.59% xxxx Table 26. Cumulative uncertainty of the corrected DNI yearly means for 1, 10 and 20 years in LOCATION. Uncertainty Cumulative uncertainty Yearly value [kWh/m2 ] Minimum value for 1 year assuming a percentile 90 for the estimations 8.03% 8.57% xxxx Minimum value for 10 years assuming a percentile 90 for the estimations 2.54% 3.93% xxx Minimum value for 20 years assuming a percentile 90 for the estimations 1.80% 3.50% xxxx 9 TYPICAL SOLAR RADIATION YEAR Simulation of solar thermal power production systems is a tool of high interest in various phases of design and development of any project. They will require, among others, climatological data to define the climatic environment in which the site is located. The approaches to the definition of the climatic environment have evolved in line with the requirements of the different simulation programs and the availability of climatic data. A first approximation may be the annual series of hourly values called Short Reference Year (Lund, 1985) available to several European countries (Lund, 1985). This type of time information cannot be considered "sufficiently typical" versus so-called Typical Meteorological Year (TMY) due mainly to the requirements / availability of data needed and the selection method used. The TMY is formed by a set of hourly data including solar radiation, temperature, humidity and wind, over a 1-year period, that is, 8760 records of the main climatic variables. It is formed, in the strict sense, by the concatenation of selected months from specific years (i.e., January '96 + February '97 + March '02, etc). The criterion used for the selection of these months is adopted according to their applicability for the simulation of solar systems (concentrated solar systems, CPC, PV, buildings). Therefore, it can be said that there is no standard method for their generation. In any case, depending on the necessities of the end-user of the TMY and data availability, it is possible to choose the fair method from the one suggested by different authors and published in the scientific
  • 56. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 57 of 66 journals. Whatever the method used for getting the TMY of the site, it should be representative of the climatic evolution of the different variables included. From the dataset available for the site of LOCATION PROJECT, hourly data of solar radiation over a period of 12 years, a modified version of the empirical methodology proposed by the Sandia National Laboratories (Hall et al., 1978) is applied. The basic requirements for the use of such a method are:  Databases with global solar irradiation over horizontal surface, dry bulb temperature and any of the variables that define the moisture content of the atmosphere (relative humidity, wet bulb temperature, dew temperature ,...), direction and wind speed.  Sampling period equal to or less than one hour.  Database with a minimum of 10 years. In the method proposed by Hall the hourly data available are processed and a daily database is built, consisting of 13 meteorological parameters:  Ambient temperature (maximum, minimum, average and variation).  Relative humidity or temperature or dew-wet temperature (maximum, minimum, average, and oscillation).  Wind speed (maximum, minimum, average, and oscillation).  Cumulated global solar radiation on horizontal surface. Each month of the year is examined separately and the TMY is formulated in the same way or applying same methodology as TMM (Typical Meteorological Month). The process to obtain the TMMs is done by comparing the cumulative frequency distribution function (CDF) of each parameter for a given month (i.e. January 2000) with the CDF corresponding to the set of all similar months of the whole period (i.e. January 2000-2010). The comparison is performed using the Finkelstein- Schafer statistic, FS, that quantifies the discrepancy for any particular month versus the set of the same month for all the years (Filkenstein and Schafer, 1971). 1 1 n i i FS n     (1) Where i is the absolute difference between the CDF for a particular month and the CDF for the set of same months and n is the number of days of such a month.
  • 57. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 58 of 66 FS equation is calculated for each month and for each of the 13 parameters. Since these parameters are not equally important, appropriate weighting factors, wj, are applied to each of them and a statistical value for the whole set is calculated, WS, as the following weighted sum: 13 1 j j j WS w FS    (2) The weighting factors for each one of the 13 analyzed parameters depend on the type of application to be given to the TMY. A "typical month" is considered that one who minimizes the statistic WS. The methodology presented is one of the most commonly used during the last twenty years [Pissimanis et al., 1988; Zarzalejo et al., 1995; de Miguel y Bilbao, 2005; Yang et al., 2007] for all types of systems depending on the weighting factors chosen. For the selection of the typical year for the site of LOCATION PROJECT, only the solar radiation variable is used. Table 27 shows the results produced by the statistical WS for the three years that better characterize the series. From this information it is possible to select the "typical month" among all available months. Table 27. WS statistical standard for the three best candidates. Year/Month JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
  • 58. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 59 of 66 2010 2011 2012 2013 The final selection corresponds to the following sequence of months/years: TMY= {01/200x, 02/200x, 03/201x, 04/199x, 05/200x, 06/201x, 07/200x, 08/199x, 09/200x, 10/199x, 11/199x, 12/200x} 9.1 GHI and DNI radiation values Table 28 shows monthly daily-average values of the months selected to build the TMY and the values corresponding to the average of 18 years. These data is plotted in Figure 33, where it can be seen how the profiles of the TMY fit adequately to the average profiles of the whole series, in both global radiation on horizontal surface as direct radiation on normal surface. Table 28. Daily values of the months selected for the TMY and average of 18 years (kWh m-2 day-1). Total values in (kWh m-2 year-1). GHI DNI Month Year TMY AVG TMY AVG JAN 2000 FEB 2009 MAR 2011 APR 1996 MAY 2006 JUN 2012 JUL 2001 AUG 1995 SEP 2006 OCT 1994 NOV 1995 DEC 2000 Total xxxx xxxx xxxx xxxx
  • 59. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 60 of 66 Figure 33. Distribution of monthly daily-average (kWh m-2 day-1) of TMY and IrSOLaV database (1994-2013). for GHI and DNI.
  • 60. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 61 of 66 10 STATISTICAL ANALYSIS OF THE LONG TERM The long term analysis of the solar resource for LOCATION PROJECT is based on the estimations of the complementary values to the percentiles. Let’s denote Pi as the i-th percentile of a given population. Thus, P75 is the 75-percentile of the sample and it means that the probability of finding a value equal or less than P75 is just 75%. The complementary of the percentile will be denoted as pi, in such a way that p75 will represent the same value as P25, and the meaning is that the probability of finding a value equal or higher than p75 is just the 75%. A statistical analysis based upon the p values in the sense of complementary values to the percentiles is presented in this section. The p50, p75, p95 and p99 of the monthly values for global horizontal and direct normal irradiation are shown in Table 29. Table 29. Monthly values of p50, p75, p90 and p99 for GHI and DNI (kWh m-2day-1) from IrSOLaV database. IrSOLaV Database P50 P75 P90 P99 DIFFERENCE P50 AND P75 DIFFERENCE P50 AND P90 DIFFERENCE P50 AND P99 GHI xxxx xxxx xxxx xxxx 1.82 % 2.43 % 2.61 % DNI xxxx xxxx xxxx xxxx 1.82 % 6.37 % 6.70 % From the yearly time series provided by SOLAR MED ATLAS, we have calculated the percentiles p50, 090 and p99 which are shown in Table 30. Table 30. Monthly values of p50, p75, p90 and p99 for GHI and DNI (kWh m-2day-1) from Solar Met Atlas database. SOLAR_MET_ATLAS (1992-2010) P50 P75 P90 P99 DIFFERENCE P50 AND P75 DIFFERENCE P50 AND P90 DIFFERENCE P50 AND P99 GHI xxxx xxxx xxxx xxxx 1.14 % 1.46 % 2.37 % DNI xxxx xxxx xxxx xxxx 1.26 % 2.45 % 2.85 %
  • 61. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 62 of 66 11 CONCLUSIONS AND COMMENTS The main conclusions that can be extracted from this work are summarized in the following paragraphs. Solar radiation has been measured in LOCATION radiometric station from MASEN and data for the period from 1st January 2011 to 31th October 2013 are available. For the period measured, the annual global irradiance (GHI) is xxxx kWh m-2 year-1 and annual direct normal irradiance (DNI) is xxxx kWh m-2 year-1 . The yearly values from different free radiometric database are the following: o METEONORM: the yearly value of GHI estimated is xxxx kWh m-2 year-1 . o NASA: the yearly value of GHI estimated is xxxx kWh m-2 year-1 and DNI estimated is in the range xxxx-xxxx kWh m-2 year-1 . o SOLAR-MED-ATLAS: the yearly value of GHI estimated is xxxx kWh m-2 year-1 and DNI estimated is xxxx kWh m-2 year-1 . o PVGIS: the yearly value of GHI estimated is xxxx kWh m-2 year-1 and DNI estimated is xxxx kWh m-2 year-1 . o CAMSAF-CLIMATE: the yearly value of GHI estimated is xxxx kWh m-2 year-1 and DNI estimated is xxxx kWh m-2 year-1 . o SODA (HELIOCLIM-3V4): the yearly value of GHI estimated is xxxx kWh m-2 year-1 and DNI estimated is xxxxx kWh m-2 year-1 . Estimations of hourly time series of global horizontal and direct normal irradiance have been performed with IrSOLaV methodology for LOCATION site during the period from 1994 to 2013. The methodology has used Meteosat satellite images and daily AOD at 550nm from MISR satellite and daily water vapor information from NCEP. The conclusions from the period of 18 years of data studied are the following:  The average annual value of global solar radiation is xxxx kWh m-2 year-1 . The average annual value of direct solar radiation is xxxx kWh m-2 year-1 .  The values of cumulative global solar radiation and direct normal for the typical meteorological year (TMY) are xxxx kWh m-2 year-1 and xxxxx kWh m-2 year-1 respectively.  P50, P75, P90 and P99 is estimated to be xxxx kWh m-2 year-1 , xxxxx kWh m-2 year-1 , xxxx kWh m-2 year-1 and xxxxx kWh m-2 year-1 respectively for annual GHI.
  • 62. SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 63 of 66  P50, P75, P90 and P99 is estimated to be xxxx kWh m-2 year-1 , xxxx kWh m-2 year-1 , xxxx kWh m-2 year-1 and xxxx kWh m-2 year-1 respectively for annual DNI.
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