Irsolav catalogue

CATALOGUE 2012 Auditing Forecast Consulting Quality AnalysisSolar Irradiance Knowledge Radiation Estimation Technical Support Web Portal Services

ABOUT IRSOLAVIrSOLaV is a spin-off company sponsored by CIEMAT and Parque Científico de Madrid, highly specializedin offering "consultancy and technical services on solar resource evaluation and characterization, applyingmethodologies developed by researchers at CIEMAT since 1985. In particular, IrSOLaV is conductingpioneering services on DNI estimation from satellite images for many CSP projects under development inSpain, India and elsewhere since 2007.The activity of IrSOLaV is based on research, consulting, promoting and teaching related with the estimationand analysis of solar energy in potential locations.IrSOLaV has the methodology to offer hourly and sub-hourly values of global and direct normal solarirradiance (and other solar tracked projected components) from 1994 to the present for any location. Theanalysis of solar energy systems are based on a detailed study and simulation of the thermal and electricalproduction of the CSP, CPV or PV plant.IRSOLAV TEAMIrSOLaV team has a world class experienced team in the solar energy sector. Its members are researcherswith broad experience and have participated in numerous national and international projects. They come fromtop companies and institutions in the solar energy sector like CIEMAT, Plataforma Solar de Almeria (PSA) andUniversity Complutense of Madrid. IrSOLaV team has provided engineering and technical consultancy formore than 500 MW CSP and PV projects. 2

METHODOLOGYAmong the products offered, solar radiation estimated from satellite images is the most important. It isessential to consider that solar radiation estimation from geostationary satellite images is a suitable tool,taking into account temporal and spatial distribution and availability of representative time series, to estimatesolar resource in locations where there is no previous ground historic radiometric records. The use ofestimations from satellites is better than nearby ground measurements when they are separated by morethan 30 km from the location where solar energy system is planned.The methodology of IrSOLaV uses two main inputs to compute hourly solar irradiance: the geostationarysatellite images and the information about the attenuating properties of the atmosphere. The former consistsof one image per hour offering information related with the cloud cover characteristics. The latter is basicallyinformation on the daily Linke turbidity which is a very representative parameter to model the attenuatingprocesses which affects solar radiation on its path through the atmosphere, mainly the aerosol optical depthand water vapor column.The methodology applied has undoubtedly been accepted by the scientific community and its mainusefulness is in the estimation of the spatial distribution of solar radiation over a region. Its maturity isguaranteed by initiatives like the establishment in 2004 of a new IEA (International Energy Agency) taskknown as “Solar Radiation Knowledge from Satellite Images” or the fact that the measuring solar radiationnetwork 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. 3

METHODOLOGYSolar radiation estimation from satellite images offered is made from a modified version of the renownedmodel Heliosat-3, developed and validated by CIEMAT with more than thirty radiometric stations in the IberianPeninsula. Over this first development, IrSOLaV has generated a tool fully operational which is applied on adatabase of satellite images available with IrSOLaV (temporal and spatial resolution of the data depends onthe satellite covering the region under study). It is worthwhile to point out that tuning-up and fitting of theoriginal methodology in different locations of the World have been performed and validated with local datafrom radiometric stations installed in the region of interest. This way, it may be considered that the treatmentof the information from satellite images offered by IrSOLaV is an exclusive service.The precision of the estimation over validation data (hourly pyranometric data from thirty stations) is around12% RMSE and -0.2% MBE. It is necessary to take into account that this level of quality is uniform in all thestations analyzed with a margin between ±5% in terms of RMSE and ±4% in terms of MBE. The errors fordaily values are less than 10% RMSE and for annual and monthly means less than 5% RMSE.IrSOLaV also offers the possibility to simulate energetic production of solar thermal power plants from adefined design. The analysis of annual production is done with the STEC (Solar Thermal ElectricityComponents) library in TRNSYS workspace. CIEMAT took part in the development of the STEC library withinthe activities of the International Cooperation Program SolarPACES which belongs to the International EnergyAgency. The involvement of CIEMAT was in the development of an important part of the types of such librarywhich describes specific components of the solar plant, such as solar thermal storage, turbine, heat transfer,etc. CIEMAT has also participated in the benchmarking of the types to qualify models with real data of solarcomponents in operation. CIEMAT and IrSOLaV cooperate in the exploitation of such library to study annualproduction of CSP power plants. 4

METHODOLOGYIrSOLaV also offers services for the quality analysis of data and the monitoring of solar radiation measured onground radiometric stations. This service can be done from the specific moment that the radiometric stationbegins operation or it could be done on historical measurements already recorded. The prices and deadlinesoffered are referenced to the treatment of solar radiation from a complete radiometric station (global, beamand diffuse registered components). These studies could be extended to other meteorological parameters.SATELLITESThere are two main satellite orbits: Geostationary Earth Orbiting Satellites (GEO) and Low Earth OrbitingSatellites (LEO). GEO satellites hover over a single point at an altitude of about 36,000 kilometers and tomaintain constant height and momentum, a geostationary satellite must be located over the equator. LEOsatellites travel in a circular orbit moving from pole to pole, collecting data in a swath beneath them as theearth rotates on its axis. In this way, a polar orbiting satellite can “see” the entire planet twice in a 24 hourperiod. 5

SATELLITESGEO satellites orbit in the earths equatorial plane at a mean height of 36,000 km. At this height, the satellitesorbital period matches the rotation of the Earth, so the satellite seems to stay stationary over the same pointon the equator. Since the field of view of a satellite in geostationary orbit is fixed, it always views the samegeographical area, day and night. This is ideal for making regular sequential observations of cloud patternsover a region with visible and infrared radiometers. High temporal resolution and constant viewing angles arethe defining features of geostationary imagery. Currently, IrSOLaV uses GEO satellites images from MeteosatFirst Generation (Meteosat-7), Meteosat Second Generation (MSG) and GOES as well as atmospheric datafrom Terra and Aqua Polar (LEO) satellites.The main advantages in the use of images from GEO satellites are the following: • The GEO satellite sees simultaneously large areas of terrain, allowing it to know the spatial distribution of the information, as well as, determine the relative differences between one zone to the other • When the information available (satellite images) belongs to the same area, it is possible to study the evolution of the values in one pixel of the image, or in a specific geographic zone. • It is possible to know past situations when there are satellite images recorded and stored previously. 6

DATABASEIrSOLaV has a database of satellite images of excellent quality and updated by a receiving station. Thenew images received are filtered before its storage in a fully automatic process. The data warehouse ofIrSOLaV is composed of the following satellite images which covers different regions of the planet:MFG: The Meteosat First Generation (MFG) are a set of satellites which provides the Indian Ocean DataCoverage (IODC) service covering the region shown in the centered image further down. These set ofsatellites were previously located over the position 0º of latitude covering Europe, Africa, ArabianPeninsula and some parts of Brazil (see figure further down on the right). The current near real-time dataare rectified to 57.50 E and it provides imagery data 24 hours a day from the three spectral channels of themain instrument, the Meteosat Visible and InfraRed Imager (MVIRI), every 30 minutes. The three channelsare in the visible, infrared, and water vapor regions of the electromagnetic spectrum. The IrSOLaV-CIEMAT database stores MFG images for IODC from 1999 to the present and also for the latitude 0degrees (previous position) for the period from 1994 to 2005. Campo de visión del satélite Meteosat. 7

DATABASEMSG: The Meteosat Second Generation satellite is a significantly enhanced system to the previous version ofMeteosat (MFG). MSG consists of a series of four geostationary meteorological satellites that operateconsecutively. The MSG system provides accurate weather monitoring data through its primary instrumentthe Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to observe the Earth in12 spectral channels. The temporal resolution of the satellite is 15 minutes and the spatial resolution is 1km atNadir Position (over latitude 0 and longitude 0).The radiometric and geometric non-linearity errors of the imagery data are corrected to solve any mistakes inthe acquisition from the sensor. The data are accompanied with the appropriate ancillary information thatallows the user to calculate the geographical position and radiance of any pixel. The IrSOLaV-CIEMATdatabase stores MSG images from 2006 to the current period (latitude 0 deg). 8

DATABASEGOES (The Geostationary Operational Environmental Satellite): The United States of America operatestwo meteorological satellites in geostationary orbit over the equator. Each satellite views almost a third of theEarths surface: one monitors North and South America and most of the Atlantic Ocean, the other NorthAmerica and the Pacific Ocean basin. GOES-12 (or GOES-East) is positioned at 75º W longitude on theequator, while GOES-11 (or GOES-West) is positioned at 135º W longitude on the equator. Both operatetogether to produce a full-face picture of the Earth, day and night. Coverage extends approximately from 20ºW longitude to 165º E longitude. The GOES satellites are able to observe the Earth disk with five spectralchannels. The IrSOLaV-CIEMAT database contain GOES images from 2000 to the present. 9

DATABASEMTSAT (Multi-functional Transport Satellite): The MTSAT series fulfills a meteorological function for theJapan Meteorological Agency and an aviation control function for the Civil Aviation Bureau (CAB) of theMinistry of Land, Infrastructure and Transport (MLIT). The MTSAT series succeeds the GeostationaryMeteorological Satellite (GMS) series as the next generation of satellites covering East Asia and the WesternPacific.MTSAT provides information to more than 30 countries and territories in the region, including 1) imagery formonitoring the distribution/motion of clouds, 2) sea surface temperatures and 3) distribution of water vapor.The MTSAT series provides imagery for the Northern Hemisphere every 30 minutes in contrast to theprevious hourly rate, enabling JMA to more closely monitor typhoon and cloud movement. 10

DATABASEMODIS: The Moderate Resolution Imaging Spectroradiometer is a key instrument aboard of the Terra (EOSAM) and Aqua (EOS PM) satellites. The orbit of Terra around the Earth is timed so that it passes from Northto South across the equator in the morning, while Aqua passes from South to North over the equator in theafternoon. Terra and Aqua view the entire Earths surface with a frequency from 1 to 2 days, acquiring data in36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications on NASA web). Thesedata improve our understanding of global dynamics and processes occurring on the ground, oceans, andlower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earthsystem models able to predict global change accurately enough to assist policy makers in making sounddecisions concerning the protection of our environment.The effect of the atmospheric turbidity on solar radiation is applied in IrSOLaV-CIEMAT model by using thedaily values of Linke Turbidity factor from MODIS Terra and Aqua satellites and daily values of AOD (AerosolOptical Depth) at 550 nm and of water vapour column. Aerosol Optical Depth Linke Turbidity factor 11

BENCHMARKING OF PRODUCTS TO ESTIMATE SOLAR RADIATION DATAGROUND MEASURED DATA: There are several instruments to measure the components of solar radiationat different wavelengths. If instruments are correctly operated, calibrated and maintained this is the bestsource of information for site specific radiometric data. Due to uncertainties in the instruments, errorsexpected may be around 8% in terms of RMSE for instantaneous values.SYNTETHIC GENERATED DATA: This data is generated to meet specific needs or certain conditions thatmay not be found in the original, real data. In the case that there is no measured data we can generate dataartificially following general statistical properties of solar radiation, like monthly mean and auto-correlationfunction. There is no way to compare this data with real measured data because this data is artificiallygenerated and doesnt take into account what happens in reality. Ground radiometric stations used in Meteonorm in Europe and India Legend Meteorological stations with irradiation measurements (GHI) Meteorological stations without irradiation measurement (GHI interpolated) 12

BENCHMARKING OF PRODUCTS TO ESTIMATE SOLAR RADIATION DATADATA ESTIMATED FROM SATELLITE: This is the best source of information to know accurately the valueof long-term solar radiation in case there is no local measurements available. The errors for hourly, daily andmonthly means are 12%, 10% and less than 5% respectively as stated before.REANALYSIS: The reanalysis data set is a continually updating gridded data set representing the state of theEarths atmosphere, incorporating observations and numerical weather prediction (NWP) model output.A meteorological reanalysis is a meteorological data assimilation project which aims to assimilate historicalobservational data spanning an extended period, using a single consistent assimilation (or "analysis") schemethroughout implemented in the NWP model. The reanalysis data errors in terms of relative RMSE are higherthan 30%. Source: C.F.U.A. Scientific Committee 13

SUMMARY IRSOLAV PRODUCTSServices offered by IrSOLaV for solar energy projects STEPS CONTENT IrSOLaV Products Site Selection & Existing Ground Data Pre-Feasibility Existing Modelled Data DNI and GHI maps Long-Term annual average Ground Measured data Existing Ground Data Long-Term averages Feasibility Inter-annual variability Existing Modelled Data AOD variability TMY prevision Weather variables from ground station and reanalysis Existing Ground Data Solar Resource Reports Due Diligence Acquired Ground Data Radiometric Quality Checking Existing Modelled Data Study of diurnal an seasonal patterns Grid integration study for time of day pricing Synthetic 10 minutes data Electricity production audit report Project Acceptance & Acquired Ground Data Radiometric data quality assurance Systems Operation and Remote Sense Monitoring and assurance of the system performance Maintenance Modelled Data in daily operation Relate site observations to original predictions Forecast data 14

PRODUCTS SOLAR RADIATION ESTIMATION DERIVED FROM SATELLITE IMAGES ANALYSIS REPORT AND POWER PLANT PRODUCTION AUDITING OF SOLAR POWER PLANTS METEOROLOGICAL DATA QUALITY REPORTS AD HOC SOLUTIONS FOR SOLAR ENERGY APPLICATIONS FORECAST SOLAR IRRADIANCE CSP/PV SITE SELECTION USING GIS INFORMATION 15

SOLAR RADIATION ESTIMATION DERIVED FROM SATELLITE IMAGESIrSOLaV has the methodology to offer time series of solar irradiance for: • Europe: from 1994 to the present (MFG + MSG). • Africa: from 2006 to the present (MSG). • America: from 2000 to the present (GOES). • Asia: from 1999 to the present (IODC).The analysis of solar energy systems are based on the detailed study and simulation of solar energy powerplants to evaluate thermal and electrical production of the plant using the solar irradiance long-termestimations from satellite.For any specific site, the process of obtaining solar irradiance time series includes: a complete statisticalanalysis of the satellite imagery database, analysis of the monthly and annual solar irradiance satelliteestimations comparing them with ground data available in the zone nearby. The time series that can bedelivered are global horizontal (GHI) and direct normal irradiances DNI (with tracking in one and two axis ifrequired). Besides, to characterize the long-term dynamics of solar radiation for any location we providetypical meteorological years (TMY).Data of solar radiation for any location is provided in electronic format (Excel, ASCII, EPW or any otherformat requested). 16

SOLAR RADIATION ESTIMATION DERIVED FROM SATELLITE IMAGESBased on the estimations derived from satellite we deliver a standard report , which includes, in addition tothe Typical meteorological Year (TMY). The standard report contains the following parts: • Summary of the main characteristics on solar radiation derived from satellite images for the period of 12 years. Tables and figures of monthly means and hourly profiles of two solar radiation components, global horizontal and others, to be selected depending on the technology (global normal, direct normal or direct with one-axis-tracking). • Comparison with the available measurements in the surroundings. • Summary and results of the hourly TMY. • Comments and conclusions IR1.0 – Time series for a historical period  Yearly, monthly and daily sums  Hourly average, 30, 15 and 10 minutes IR2.0 – TMY  TMY hourly time series  TMY 10 minute interval time series IR2.3 – Standard report  Report + TMY hourly time series 17

ANALYSIS REPORT AND POWER PLANT PRODUCTIONThis product is based on the simulation of electrical production of a designed solar energy power plant withany specific configuration. This yield report is made with a solar radiation typical meteorological year (TMY)or the whole solar resource data series for the CSP site. In the case of a photovoltaic plant, the gains andlosses are evaluated in a technical report. It includes solar resources analysis, with and without tracking,peak power of photovoltaic system and performance.AUDITING OF SOLAR POWER PLANTSThe experts report for solar energy promotions is a product for financial institutions which require ananalysis and evaluation of solar resource to guarantee financing to promoters. This report offers anindependent quality review and assessment of solar resource and power production issued by a third-party.The annual performance analysis of solar thermal power plants is made with the library STEC (SolarThermal Electricity Components of TRNSYS) . IR3.0 – Yield report  CSP  PV  Solar resource and yield reports (with TMY) 18

METEOROLOGICAL DATA QUALITY REPORTSAnalysis and monitoring of meteorological data measured in radiometric stations to asses its quality: (1)Quality report of historic measured data, (2) monthly monitoring report and (3) online monitoring of datameasured in meteorological stations.AD HOC SOLUTIONS FOR SOLAR ENERGY APPLICATIONSThis is a fully customized product depending on the customer needs which could vary from advising, specificdevelopments and “ad hoc” courses for promoters and consultants about characterization and solar radiationmeasurements and treatment of radiometric data. IR4.0 – Online monitoring time series estimated from satellite  Monthly delivery of time series estimated from satellite: 12 months  Near real time delivery of time series estimated from satellite: 12 months IR5.0 – Quality report  Historic report  Monthly monitoring of measurement solar radiation data 19

FORECAST SOLAR IRRADIANCESolar power plants need reliable information concerning the resource for different tasks: planning, operation,control and maintenance. For each one of these tasks, IrSOLaV offers different temporal time steps andforecasting horizon which covers most of the necessities of the solar power plant operators.The “nowcasting” horizon is a form of very short-range weather forecasting, covering only a very specificarea. A “nowcast” is loosely defined as a forecast for the coming 6-hour period, based on very detailedobservational data like radiometers, pyrheliometers, satellite images or sky cameras between others. Thesepredictions are suitable for the operation and control of power plants. For the electric energy planning,IrSOLaV offers hourly predictions of GHI and DNI with a temporal forecasting horizon of 72 hours usingstatistical and physical models. The maintenance of the plant needs information of the solar resource with ahorizon of more than 3 days. IR6.0 – Forecasts  Forecasting hourly resolution 72 hours temporal horizon  Hourly temporal resolution (prediction horizon < 6 hours )  30, 15 and 10 minutes temporal resolution (< 6 hours )  Solar irradiance ramps prediction  Forecast medium and long term (more than 3 days) 20

CSP/PV SITE SELECTION USING GIS INFORMATIONSite selection for CSP and PV technologies is a key important factor to warranty the efficiency andprofitability of the plant. There are many variables involved which make the decision process highly complex.Using GIS tools, processing of satellite images and third party information, IrSOLaV can assist projectdevelopers to choose the best sites for their solar energy power plants. The information used in theprocesses is GHI and DNI, land cover, slopes, proximity to high transmission capacity lines, localtransmission capacity, conservation and environmental impact issues, agricultural concerns, localregulations, ownership, land prices, water supply and proximity to density populated cities among othervariables which influence the Levelized Cost of the Energy (LCOE). IR7.0 – GIS Data  Site selection for PV and CSP technologies  GHI and DNI solar resource maps in different formats (paper, digital, GEO web portal) 21

WEB INFORMATIONIrSOLaV web portal offer an automated access to the data via web services. IrSOLaV operates a set ofonline services (www.solarexplorer.info). SolarExplorer is a web system that offers fast access to solar geo-databases, interactive maps and simulation tools for planning and performance assessment of solar energyand especially photovoltaic systems. 22

PRICES TABLEPrices (Euro value) applies for one site and without VAT (VAT is applicable only in the EU).For delivery on “priority”: 10% - 25% extra charges, depending on requested delivery schedule.Abbreviations (parameters) GHI Global Horizontal Irradiance DNI Direct Normal Irradiance VAR Others variable  Air temperature over surface (TEMP)  Relative humidity (RH)  Wind speed (WS)  Wind direction (WD)  Pressure (PRE)  Diffuse Horizontal Irradiance (DIFF)  Global over a surface that rises at an angle (tilted plane GTL) and Solar angle (Theta).Note: this pricelist is valid from 01 January 2012 until 31 December 2012. 23

IRSOLAV S.L.Calle Santiago Grisolia nº2 (PTM) – 28760Tres Cantos (Madrid) (+34) 911263612 ph (+34) 666467855 mobwww.irsolav.com / info@irsolav.comPARTNERSCOLLABORATIONS 24

CUSTOMERS 25

PROJECTS (2007-2011)• Abraxa: Solar energy resource standard report for a PV plant. (2007)• Ibereólica Solar: Solar energy resource standard report for 2 CSP plants. (2007)• Renovalia: Solar energy resource standard report for one of the biggest PV plants. (2007)• CENER: 12 years of hourly data for 2 CSP plants. (2008)• Grupotec: Solar energy standard report for 7 CSP plants. (2008)• Hamilton Sundstrand Corporation: Standard report for a CSP plant. (2008)• Ibereólica Solar: Informe estándar y auditoria para una planta de CET. (2008)• ISOLUX: Preliminary DNI reports (monthly means for 12 years), for 25 plants. (2008)• Proener: Solar energy resource standard report for 9 CSP plants. (2008)• SENER: Solar energy resource standard report for a CSP plant. (2008)• Torresol Energy: Solar energy resource standard report for a CSP plant. (2008)• Iberdrola Ingeniería: 12 years of hourly data for 2 CSP plants. (2009)• Abantia: Electricity producion report for a CSP plant. (2009)• Ibereólica Solar: TMY and ground radiometric data analysis for 4 CSP plants. (2009)• Solar Reserve: Solar energy resource standard report for 7 CSP plants. (2009)• Abantia: Electricity producion report for a CSP plant. (2010)• Iberdrola Renovables: 12 years of hourly data for a PV plant. (2010)• SOCOIN: TMY for 2 CSP plants. (2010)• Solar Reserve: Standard report for 3 CSP plants in Italy and Greece. (2010)• Sunborne: Standard report for 2 CSP plants and a regional map in India. (2010)• Abantia: Auditory of the production of a operating PV plant. (2011)• Iberdrola Renovables: hourly data for 12 PV plants. (2011)• CHILE: Standard report for a PV plant and regional solar map in Arica. (2011) 26

REFERENCESCony, M., Martin, L., Marchante, R., Polo, J., Zarzalejo, L.F., Navarro, A.A., 2011. Global horizontal irradiance and direct normalirradiance 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 ofMeteosat 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 comparisonparameters applied to solar radiation data from satellite and German radiometric stations. Solar Energy, Vol 83, 1, 118-125.Martin, L., Zarzalejo, L.F., Polo, J., Navarro, A.A., Marchante, R., Cony, M., 2010. Prediction of global solar irradiance based ontime 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., Polo, J., 2010. Estación de recepción de imágenes del satélite MeteosatSegunda Generación: Arquitectura Informática y Software de Proceso. Informe Técnico CIEMAT, Vol. 1200, 1135-9420, NIPO:471-10-014-8.Polo, J., Zarzalejo, L.F., Cony, M., Navarro, A.A., Marchante, R., Martin, L., Romero, M., 2011. Solar radiation estimations overIndia using Meteosat satellite images. Solar Energy, Vol. 85, 2395-2406.Polo, J., Zarzalejo, L.F., Salvador, P., Ramirez, L., 2009. Angstrom turbidity and ozone column estimations from spectral solarirradiance in a semi-desertic environment in Spain, Solar Energy, Vol 83, 2, 257-263.Polo, J., Zarzalejo, L.F., Ramirez, L., Espinar, B., 2006. Iterative filtering of ground data for qualifying statistical models for solarirradiance estimation from satellite data, Solar Energy, Vol 80, 3, 240-247.Zarzalejo, L.F., 2005. estimaciones de la irradiancia global horaria a partir de imágenes de satélite. Desarrollo de modelosempíricos. PhD presented at Universidad Complutense de Madrid. 27

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