Typical Meteorological Year Report for CSP, CPV and PV solar plants


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Typical Meteorological Year Report for CSP, CPV and PV solar plants

  1. 1. TECHNICAL REPORT TMY October 18th , 2011
  4. 4. SOLAR RESOURCE ASSESSMENT: Page 5 of 17 INDEX 1 INTRODUCTION........................................................................................................................................... 6 1.1 Main objective.......................................................................................................................................... 6 1.2 Data needed ............................................................................................................................................ 6 1.3 Methodology for solar radiation derived from satellite images ................................................................ 7 1.4 Brief summary of IrSOLaV methodology................................................................................................. 7 2 TYPICAL SOLAR RADIATION YEAR FOR PROJECT .............................................................................. 10 2.1 Global and direct radiation values ......................................................................................................... 12 3 STATISTICAL ANALYSIS OF THE LONG TERM ...................................................................................... 14 4 REFERENCES............................................................................................................................................ 15
  5. 5. SOLAR RESOURCE ASSESSMENT: Page 6 of 17 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 -, selected for hosting a solar thermal power plant. The solar resource analysis applies to a site with geographic coordinates: Latitude x S, Longitude x E, and located in the province of Northern Cape, hereinafter referred to as PROJECT. 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.
  6. 6. SOLAR RESOURCE ASSESSMENT: Page 7 of 17 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 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
  7. 7. SOLAR RESOURCE ASSESSMENT: Page 8 of 17 pursued. Therefore, the main differences between the IrSOLaV/CIEMAT and others, like the ones applied by PVGis or Helioclim are:  Selection of the working window. The correlations developed by IrSOLaV/CIEMAT are focused on the Iberian Peninsula, and in particular in Spain, making use of 30 equidistant meteo-stations in this territory. However the other groups use stations distributed among all Europe and the resulting relations are applied to all the territory.  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.  Selection of albedo for clear sky. The algorithm used for selection of clear sky albedos 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. The uncertainty of the estimation comparing with hourly ground pyranometric measurements is expressed in terms of the relative root mean squared error (RMSE). 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% RMSE 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 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% RMSE for hourly values  Less than 10% for daily values
  8. 8. SOLAR RESOURCE ASSESSMENT: Page 9 of 17  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]. This improved model is the one applied to the PROJECT site in this report.
  9. 9. SOLAR RESOURCE ASSESSMENT: Page 10 of 17 2 TYPICAL SOLAR RADIATION YEAR FOR PROJECT 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 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 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:
  10. 10. SOLAR RESOURCE ASSESSMENT: Page 11 of 17  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. 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
  11. 11. SOLAR RESOURCE ASSESSMENT: Page 12 of 17 [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. 2.1 Global and direct radiation values For the selection of the typical year for the site of PROJECT, only the solar radiation variable is used. Table 11 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 1: WS statistical standard for the three best candidates Year/Month JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1994 0.231 0.335 0.205 1995 0.086 1996 0.273 1997 0.295 1998 0.132 0.138 1999 0.210 0.207 0.233 2000 0.115 0.061 0.171 0.278 0.058 2001 0.120 2002 0.204 0.094 2003 0.222 0.202 2004 0.191 0.129 0.104 0.217 0.302 2005 0.161 0.286 2006 0.117 0.149 0.108 2007 0.123 2008 0.309 2009 0.240 0.215 2010 0.251 The final selection corresponds to the following sequence of months/years: TMY= {01/2007, 02/2000, 03/2004, 04/2002, 05/2003, 06/2000, 07/2001, 08/2000, 09/2003, 10/1999, 11/1994, 12/2000} Table 12 shows monthly daily-average values of the months selected to build the TMY and the values corresponding to the average of 17 years. The same data is plotted in Figure 13, where it can
  12. 12. SOLAR RESOURCE ASSESSMENT: Page 13 of 17 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 2: Daily values of the months selected for the TMY and average of 17 years (kWh m -2 day -1 ). Total values in (kWh m -2 year -1 ) Global Direct Month Year TMY AVG TMY AVG JAN 2007 7.41 7.49 7.31 7.31 FEB 2000 6.91 6.78 6.79 6.67 MAR 2004 6.01 5.96 6.43 6.37 APR 2002 5.09 5.03 6.43 6.44 MAY 2003 4.30 4.22 6.81 6.57 JUN 2000 3.94 3.88 6.96 6.8 JUL 2001 4.20 4.19 7.11 7.09 AUG 2000 5.19 5.16 7.66 7.55 SEP 2003 6.03 6.25 7.28 7.7 OCT 1999 6.99 7.09 7.45 7.62 NOV 1994 7.64 7.82 8.08 8.18 DEC 2000 8.08 8.18 8.18 8.52 Total 2175 2193 2625 2621 Figure 13: Distribution of monthly daily-average (kWh m -2 day -1 ) of TMY and database IrSOLaV (1994-2010). for GHI and DNI irradiance.
  13. 13. SOLAR RESOURCE ASSESSMENT: Page 14 of 17 3 STATISTICAL ANALYSIS OF THE LONG TERM The long term analysis of the solar resource for 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 and p90 of the monthly values for global horizontal and direct normal irradiation are shown in Table 13. It can be pointed out the proximity of the p50 values with the TMY estimates. Table 3: Monthly values of p50, p75 and p90 for global horizontal and direct normal irradiation (kWh m -2 day -1 ) and their corresponding yearly values (kWh m -2 year -1 ) P50 P75 P90 Month G DNI G DNI G DNI JAN 7.43 7.18 7.00 6.41 6.94 6.25 FEB 6.88 6.79 6.47 5.92 6.04 5.32 MAR 6.08 6.53 5.62 5.44 5.38 5.25 APR 5.02 6.43 4.86 6.07 4.52 5.25 MAY 4.20 6.64 4.14 6.20 3.95 5.82 JUN 3.94 6.96 3.83 6.56 3.64 6.23 JUL 4.20 7.14 4.09 6.84 3.94 6.35 AUG 5.19 7.59 5.02 7.11 4.92 6.91 SEP 6.27 7.57 6.05 7.31 5.85 6.98 OCT 7.07 7.53 6.97 7.33 6.73 7.02 NOV 7.95 8.28 7.60 7.82 7.32 7.09 DEC 8.17 8.68 8.05 8.07 7.75 7.72 Yearly 2192 2649 2111 2460 2029 2313
  14. 14. SOLAR RESOURCE ASSESSMENT: Page 15 of 17 4 REFERENCES De Miguel, A., and Bilbao, J., 2005. Test reference year generation from meteorological and simulated solar radiation data. Solar Energy 78, 695-703. Cony, M. Martín, L., Polo, J., Marchante, R., and Navarro, A.A. 2011. Sensitivity of satellite derived solar radiation to the temporal variability of aerosol input. SolarPACES, Granada, Spain. Cony, M., Martin, L., Marchante, R., Polo, J., Zarzalejo, L.F., Navarro, A.A., 2011. Global horizontal irradiance and direct normal irradiance from HRV images of Meteosat Second Generation. Geophysical Research, 10373. Austria. Cony, M., Zarzalejo, L.F., Polo, J., Marchante, R., Martín, L., Navarro, A.A., 2010. Modelling solar irradiance from HRV images of Meteosat Second Generation. Geophysical Research Abstract, Vol. 12, EGU2010-4292. Vienna, Austria. Espinar, B., Ramirez, L., Drews, A., Beyer, H.G., Zarzalejo, L.F., Polo, J., Martin, L., 2009. Analysis of different comparison parameters applied to solar radiation data from satellite and German radiometric stations. Solar Energy, Vol 83, 1, 118-125. Lefevre, M., Wald, L., and Diabate, L., 2007. Using reduced data sets ISCCP-B2 from the Meteosat satellites to assess surface solar irradiance. Solar Energy 81, 240-253. Martín, L., Cony, M., Polo, J., Zarzalejo, L.F., Navarro, A., and Marchante, R., 2011. Global Solar and Direct Normal Irradiance Forecasting Using Global Forecast System (GFS) and Statistical Techniques. SolarPACES, Granada, Spain. Martin, L., Zarzalejo, L.F., Polo, J., Navarro, A.A., Marchante, R., and Cony, M., 2010. Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning, Solar Energy, Vol 84, 10, 1772-1781. Martín, L., Cony, M., Navarro, A.A., Zarzalejo, L.F., and Polo, J., 2010. Estación de recepción de imágenes del satélite Meteosat Segunda Generación: Arquitectura Informática y Software de Proceso. Informe Técnico CIEMAT, Vol. 1200, 1135-9420, NIPO: 471-10-014-8. McArthur, L. J. B., 1998. Baseline Surface Radiation Network (BSRN). Operations manual V1.0. Serie: World Climate Research Programme. Secretariat of the World Meteorological Organization, Geneva (Switzerland). Meyer, R., Hoyer, C., Schillings, C., Trieb, F., Diedrich, E., and Schroedter, M., 2004. SOLEMI: A new satellite-based service for high-resolution and precision solar radiation data for Europe, Africa and Asia. DLR (Germany). Pereira, E. B., Martins, F., Abreu, S. L., Beyer, H. G., Colle, S., and Perez, R., 2003. Cross validation of satellite radiation transfer models during SWERA project in Brazil. Ponencias de: ISES solar world Congress 2003, Göteborg (Sweden).
  15. 15. SOLAR RESOURCE ASSESSMENT: Page 16 of 17 Pérez, R., Seals, R., and Zelenka, A., 1997. Comparing satellite remote sensing and ground network measurements for the production of site time specific irradiance data. Solar Energy 60, 89-96. Pinker, R. T., and Ewing, J. A., 1985. Modeling surface solar radiation: model formulation and validation. Journal of Climate and Applied Meteorology 24, 389-401. Pissimanis, D., Karras, G., Notaridou, V., and Gavra, K., 1988. The generation of a "typical meteorological year" for the city of Athens. Solar Energy 40, 405-411. Polo, J., Zarzalejo, L.F., Cony, M., Navarro, A.A., Marchante, R., Martin, L., and Romero, M., 2011. Solar radiation estimations over India using Meteosat satellite images. Solar Energy, Vol. 85, 2395-2406. Polo, J., Zarzalejo, L.F., Salvador, P., and Ramirez, L., 2009. Angstrom turbidity and ozone column estimations from spectral solar irradiance in a semi-desertic environment in Spain, Solar Energy, Vol 83, 2, 257-263. Polo, J., 2009. Optimización de modelos de estimación de la radiación solar a partir de imágenes de satélite. PhD presented at Complutense University of Madrid (Spain). Polo, J., Zarzalejo, L. F., Martin, L., Navarro, A. A., and Marchante, R., 2009a. Estimation of daily Linke turbidity factor by using global irradiance measurements at solar noon. Solar Energy 83, 1177-1185. Polo J., Zarzalejo, L.F., and Ramirez, L., 2008. Solar radiation derived from satellite images, pp. 449-461. Contenido en: Modeling Solar Radiation at the Earth Surface. Editado por: Viorel Badescu. Springer-Verlag, Polo, J., Zarzalejo, L.F., Ramirez, L., and Espinar, B., 2006. Iterative filtering of ground data for qualifying statistical models for solar irradiance estimation from satellite data, Solar Energy, Vol 80, 3, 240-247. Ramírez, L., Zarzalejo, L. F., Polo, J., and Espinar, B., 2004. Modelización de la radiación solar a escala regional: Tratamiento de imágenes de satélite para cálculo de la radiación solar global en España. 2º Congreso Internacional Ambiental del Caribe, Cartagena de Indias (Colombia). Rigollier, C., Albuisson, M., Delamare, C., Dumortier, D., Fontoynot, M., Gaboardi, E., Gallino, S., Heinemann, D., Kleih, M., Kunz, S., Levermore, G., Major, G., Martinoli, M., Page, J., Ratto, C., Reise, C., Remund, J., Rimoczi-Pall, A., Wald, L., and Webb, A., 2000a. Explotaition of distributed solar radiation databases through a smart network: the project SoDa. Ponencias de: The 2000 EuroSun Congress, Copenhagen (Denmark). Rigollier, C., Bauer, O., and Wald, L., 2000b. On the clear sky model of the ESRA -- European Solar Radiation Atlas -- with respect to the heliosat method. Solar Energy 68, 33-48. Rigollier, C., Lefèvre, M., and Wald, L., 2004. The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy 77, 159-169. Yang, L., Lam, J.C., and Liu, J., 2007. Analysis of typical meteorological years in different climates of China. Energy Conversion and Management 48, 654-668.
  16. 16. SOLAR RESOURCE ASSESSMENT: Page 17 of 17 Zarzalejo, L. F., Polo, J., Martín, L., Ramírez, L., and Espinar, B., 2009. A new statistical approach for deriving global solar radiation from satellite images. Solar Energy 83, 480-484. Zarzalejo, L.F., 2005. Estimaciones de la irradiancia global horaria a partir de imágenes de satélite. Desarrollo de modelos empíricos. PhD presented at Universidad Complutense de Madrid. Zarzalejo, L. F., Tellez, F., Palomo, E., and Heras, M. R., 1995. Creation of Typical Meteorological Years (TMY) for Southern Spanish cities. Ponencias de: International Symposium Passive Cooling of Buildings, Athens (Greece). Zelenka, A., Perez, R., Seals, R., and Renne, D., 1999. Effective accuracy of satellite-derived hourly irradiances. Theoretical and Applied Climatology 62, 199-207.