DNICast THEME [ENERGY ] [Methods for the estimation of the Direct Normal Irradiation (DNI)] Grant agreement no:

Transcription

1 DNICast Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies THEME [ENERGY ] [Methods for the estimation of the Direct Normal Irradiation (DNI)] Grant agreement no: Deliverable Nr.: 5.5 Project coordinator: OME Name of the organization: ARMINES Submission date:31/1/2017 Revision date: 19/5/2017 Revision date: 18/07/2017 Deliverable title: Best practice guideline for DNI Nowcasting WP leader: CENER Authors: T. Ranchin with contribution of Stefan Wilbert, Marion Schroedter-Homscheidt and Yves-Marie Saint-Drénan Version nr.: 3.0 Disclaimer: The information and views set out in this report are those of the author(s) and do not necessarily reflect the official opinion of the European Union. Neither the European Union institutions and bodies nor any person acting on their behalf may be held responsible for the use which may be made of the information contained therein.

2 Table of contents Table of contents Introduction Recommended terms and definitions General recommendations for choosing a nowcasting scheme Nowcasting of general meteorological parameters Nowcasting of Aerosols Nowcasting of clouds with different methods a. Using Satellites b. Using NWP c. Using sky cameras Nowcasting of circumsolar radiation Nowcasting of DNI value Nowcasting of DNI maps Summary and future steps Annexes a. Annex 1: Definitions of nowcasting-related terms b. Annex 2: Specifications of a nowcasting system... 19

3 1. Introduction The DNICast Project aims at the development of nowcasting methods for direct normal irradiance as a support for concentrating solar technologies. This document proposes a set of recommendations for DNI nowcasting. As many nowcasting methods are still under development or not fully operational, this document is not a detailed description of how to produce a nowcast. Only guidelines for basic steps of DNI nowcasting are discussed and a simplified view of the resulting experience acquired during the DNICast project for the practionners and developers of concentrating solar plants are presented. Ground, model and satellite based methods are covered including their combination. The guidelines include clouds and also the treatment of aerosols and circumsolar radiation. This deliverable also proposes an overview of the outcomes of the project for a non-specialist and indicates where to find the relevant information for the different topics related to nowcasting of DNI. 2. Recommended terms and definitions Definitions used for DNI nowcasting are complex. There is no consensus on terms and definitions; the literature uses a lot of different terms. Therefore, this document starts with a summary of recommended terminology. Figure 1: Atmospheric processes affecting DNI Direct normal radiation refers to solar photons that reach the surface without being scattered or absorbed. The physical quantity is measured with a pyrheliometer or calculated corresponding to the penumbra function of a specified pyrheliometer. This instrument has to be installed in a tracker to follow the path of the sun and small tracking errors have to be expected. The radius of the solar disk has a yearly average of In order to avoid the tracking errors on DNI measurements, the World Meterorological Organization (WMO) promulgated the size of the small solid angle for DNI measurements to be 2.5 half angle ( srad -1 ). For most accurate CST modeling, the sunshape (the broadband azimuthal average radiance profile, normalized with respect to the radiance at the center of the sun) up to an angular distance α out from sun center. This limit angle characterizes the assumed limit of the circumsolar region. If the sunshape can be included in the model, it must be complemented by providing the corresponding DNIcirc(αout).

4 This variable is defined as the normal irradiance caused by the radiation observed within αout around the sun s center (half angle), irrespective of whether or not the photons were scattered. DNIcirc(αout) can be calculated from the experimental DNI and the sunshape. αout usually has to be the greatest angle for which the sunshape provides useful irradiance to the CST system. Depending on the solar plant model used, it is not possible to specify both DNI and its corresponding sunshape, since usually only DNI is known through measurement or modeling. In such cases, errors occur if the actual sunshape for the input DNI is different from the sunshape that is assumed within the model. Usually, such models assume that the user enters the experimental DNI into the model. Instead of doing so, the user might provide a different DNI, DNIcirc(αacc) in order to reduce the error of the simple plant model. The angle αacc is the acceptance angle of the CST collector. The acceptance angle is the largest incidence angle for which all or almost all rays on the aperture reach the receiver. DNI nowcasting is usually based on the nowcasting of several atmospheric properties and radiative transfer calculations for the nowcasted parameter combination. The main parameters involved in such radiative transfer calculations are optical depths of atmospheric components. For nowcasting applications, especially the aerosol optical depth (AOD, τaeros) and the cloud optical depth (COD, τclouds) are of relevance since they may change rapidly also within a time interval of a few hours. For solar tower technologies the fraction of AOD in the lowest 200 m above ground needs to be separated from the total AOD. A vertical profile resolution of at least 50 m is required for the raytracing of DNI reaching the solar tower after being reflected in the heliostat field. Most prominent for DNI is the distinction of optically thin ice clouds (cirrus with sub-groups of cirrocumulus and cirrostratus) from optically thick water or mixed ice/water phase clouds all noncirrus clouds belong to the latter class. Therefore, cloud phase and particle effective radius need to be known besides COD. Nowcasting is typically understood as the forecast within the upcoming 6 hours. The nomenclature of different forecast horizons is presented in Figure 1. In Deliverable 2.1. Methods for the estimation of the Direct Normal Irradiation (DNI) a set of definitions of nowcasting related terms were set up. These definitions, recalled in Annex 1, will be used in this report. D2.1. also provided requirements for a nowcasting system for CSP and CPV. They are synthetized by two tables that are extracted from D2.1 and provided in Annex 2.

5 Figure 1. Definitions on forecast horizons as used in the electricity grid operations (blue) and the meteorological sector (red). Forecast lead time is given on a non-linear temporal axis (source: DLR). A nowcasting system is a technical system that delivers a prediction of selected meteorological parameters within a forecast horizon of several hours. Overall, a nowcasting system is characterized by the nowcast horizon, the temporal resolution, the refresh rate, the time lag, the spatial resolution, and their accuracies. Although the direct normal irradiance is the key driver for plant output, several boundary conditions like ambient temperature, air humidity, wind speed, and wind direction have a more or less significant impact on the plant output. Ambient temperature and humidity influence the thermal conditions for the heat transformers and the cooling elements in the power plant. Wind speed, and especially wind gusts can results in a security shut down to avoid strong mechanical loads on the power plant s solar field. Also, maintenance work has to be stopped in case of wind gusts above a certain threshold. Therefore, it is beneficial to obtain nowcasting values also for these quantities. For the solar nowcasting application, several terms are required to describe the specifications for a nowcasting system: The characteristic length refers to a spatial extension of the plant or a plant subelement that is characteristic for the nowcasting. Instead of the characteristic length, a characteristic area can alternatively be defined. Depending on the type of nowcasting application this can either be the extension of the whole solar field or of a distinct sub-system in the field, e.g. a single collector or collector module. The extension of the whole solar installation is described by the term solar field side length or simply side length. The characteristic length is related to the spatial resolution of the nowcasted DNI information. Whereas some DNI nowcasting applications make use of a solar field averaged value, others require a spatially resolved DNI map over the target region. The map is usually represented by a matrix with m times n entries that represent the DNI over some specific area on the ground that represents, or is "associated" with the target area. The width and length of this associated area define the spatial resolution. In certain cases, the associated area can be of a non-rectangular shape (e.g. circular sectors, or the area corresponding to one pixel of a satellite image). The characteristic time tchar is a typical reaction time of the solar installation to any disturbance imposed on it. The selection of an appropriate characteristic time depends on the individual application. For many hydraulic systems like line focusing solar thermal power plants the throughput

6 time is a good indication for the characteristic time if the reaction of the whole solar field is in the key element of the application. The throughput time is the time needed by a fluid particle to travel from the inlet to the outlet of the installation. Typical values range from several seconds up to 30 minutes. It can also be interpreted as an acceptable time-averaging interval. The temporal resolution should be capable of resolving features in a wide range of the characteristic times. Therefore, the temporal resolution should be at least the characteristic time or better. For all nowcasting applications, the required DNI values represent an averaged value over a certain time interval. The nowcasting s temporal resolution defines the length of these time intervals. A temporal resolution of 5 min means that nowcasted values are provided for the lead time intervals 0 5 min, 5 10 min, min, etc. It is important for the nowcasting system to represent the average over the time interval and not just an instantaneous value at some instant. There are other temporal parameters of relevance for a nowcasting system: The refresh rate of the nowcasted DNI information describes the frequency at which the prediction values are updated. It is a function of the accuracy reached by any nowcasting approach and technical parameters as the time lag between observations or the availability of a reliable nowcasting. The time lag can be caused by either the need for significant processing times or by spinoff times in those models that need some forecasted time before they provide reliable results. Forecast lead time is the time between the start of the forecast and the occurrence of a forecasted value. In our case, it is the time between 0 and 4 hours after the start of a forecast. In short-term forecasts it is typically the time between 12 and 72 hours after the first valid hour of the forecast. The accuracy of the nowcasting system usually refers to the expected deviation of the predicted DNI values. In the case that uncertainties can be approximated with a standard normal distribution, 68.3 % of the range. The stated uncertainties always refer to a well-defined temporal resolution and forecast interval. The formulas use to determine errors should be stated. Overall, a nowcasting system is characterized by the nowcast horizon, the temporal resolution, the refresh rate, the time lag, the spatial resolution, and their accuracies. DNICast project considers two different types of application for DNI nowcasting. In the first one, only one DNI value (averaged over the whole solar plant area) is needed in order to predict the overall thermal or electrical output of the system. Such information helps various decisions processes regarding the optimization of start-up or shut-down, the use of thermal storage or co-firing, and electricity sales. Characteristic times have been identified for different applications, from which the required nowcasting horizon and the temporal resolution are derived. Since these time constants strongly differ between concentrating solar thermal power plants (CSP) and concentrating solar photovoltaic plants (CPV), only application-specific parameters are found. In Annex 2 some tables are provided with an overview of typical values for selected applications. These are in the range of 1 to 4 hours of nowcasting horizon with a temporal resolution down to several minutes. The second type of application relies on spatially-resolved DNI inside the solar field ( DNI map ). Such information can be used by solar-field control systems to stabilize the plant operation at all times, especially when clouds are passing by. Stabilized conditions in the plant help to increase the overall efficiency and

7 lifetime of the plant. Depending on technology, a spatial resolution up to about 20 x 20 m, in conjunction with a high temporal resolution of up to 10 s, is desired. The nowcast horizon is then typically about 10 min. Although this report focusses on DNI maps, other forms may exist to present spatially resolved DNI data (e.g. cloud shapes together with speed vector). For several applications, the nowcasting of DNI variability or volatility is considered as beneficial for plant operation. We recommend a kind of volatility index for this purpose that can be provided together with the temporally averaged DNI values. For all concentrating solar technologies, the effect of circumsolar radiation has to be considered also for the nowcasting since large sunshapes reduce the usable solar irradiation significantly. In addition to the already mentioned nowcasting values, information on complementary meteorological parameters like wind speed and direction, ambient temperature, relative humidity or aerosol situation close to the ground, increases the quality of the plant output prediction. 3. General recommendations for choosing a nowcasting scheme This section summarizes general requirements for a good nowcasting system. Recommendations are given about which questions are to be asked to forecasting service providers by nowcast users. Since the field of view of ground-based systems is limited to a few kilometers, only a short-term forecast of up to minutes is possible, depending on cloud velocity. On the other hand, satellite- and NWP-based methods cannot nowcast DNI for the first minutes after the nowcast is created, but will provide extension of the nowcast window to several hours. Furthermore, only ground-based systems can provide the high spatio-temporal resolution required for some applications (see deliverable 2.1 for examples). Recommendations: For the time being, the application of various forecasting methods is recommended (In-situ sensors, All Sky Imager, satellite and NWP). Any nowcasting scheme should be validated at various locations and with various. meteorological conditions covering the conditions that are relevant for the site of interest. Nowcasted data should be reported with uncertainty information. In-situ measurements and their real-time integration within any nowcasting scheme will dramatically improve the results, notably for the lower time horizons. 4. Nowcasting of general meteorological parameters The meteorological parameters of interest for nowcasting in addition to DNI are: Wind speed and direction, ambient temperature and relative humidity. These parameters are delivered by numerical weather predictions (NWP) models. As these parameters are of interest at the scale of the solar farms, high resolution NWP can be relevant for their nowcasting. National or local meteorological institutes are usually able to deliver such a set of parameters. In an operational nowcasting system, NWP such as WRF or GFS can be used to provide all the meteorological parameters of interest, but more precise models can alternatively be used too. Recommendation: High spatial resolution in numerical weather prediction is recommended for meteorological parameters such as wind speed, temperature or relative humidity.

8 5. Nowcasting of Aerosols The DNI nowcast accuracy is influenced by clouds, aerosol optical depth (AOD) and the optical depth of the other atmospheric constituents. Hence an accurate DNI nowcast requires an accurate forecast of these parameters. Descriptions of fundamentals of atmospheric science related to aerosol processes that control DNI can be found in deliverable 2.2, which focus on aerosols. Aerosols may largely determine DNI on a cloud free day. The magnitude of interaction is a function of their size, composition, and concentration, all of which are constantly undergoing rapid alterations and are quantified by AOD. There are many types of aerosols, ranging from natural sea salt, sand and dust, biogenic marine sulphates and terrestrial vegetation organics to anthropogenic sulphates, nitrates, organics and black carbon. Of these, sand and dust mobilized by winds from arid regions with little vegetation is very important for DNI in the North African, Middle East and European regions as well as parts of Asia. In particular, dust storms can cause severe degradation of visibility as well as fogging/abrasion of mirrors reflecting solar radiation or degradation of storage vessels when deposited on the Earth s surface. In recognition of the importance of sand and dust aerosols for DNI, human health and weather/climate, the World Meteorological Organization (WMO) has established the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) to forecast sand and dust properties for two regions of the world: (i) North Africa, the Middle East and Europe and (ii) Asia. SDS-WAS products from many different forecast models in both regions are accessible on the web ( The area of interest of DNICast, which involve Spain, Portugal, and the adjacent northern Africa, is included in the northern Africa, Middle East and Europe (NA-ME-E) regional node. The next table presents the list of products of interest and their web links. In Deliverable 2.2, a detailed description of SDS-WAS models and their forecast products is given. Information is also provided on how to retrieve forecasts and analyses from the SDS-WAS website or from the developer of each model. Recommendation: Aerosol forecasts providing total and dust aerosol optical depths are highly relevant to DNI nowcasting. Forecasts providing only mass concentrations or dust loads are less interesting, since further assumptions on optical properties and the vertical profile of aerosols are required to provide the optical extinction in radiative transfer calculations. Recommendation: Aerosol forecast models making use of data assimilation of satellite-based aerosol observations are more reliable with respect to large-scale dust storm events. This has been shown in Deliverable 2.2 for the EURAD-IM model within DNICast. Data assimilation is routinely applied by a number of aerosol forecast services. It is recommended to ask for data assimilation experiment results when choosing an aerosol forecast provider.

9 From Deliverable 2.2. Table 6 : Features of SDS-WAS model products taken from each model s individual site. Only forecasted products related to DNICast are considered. AOD forecasts are marked in green, other aerosol parameters are marked in orange. Model Name Parameter Latitude Longitude Spatial Resolution Forecast Temporal Updated Data format (degrees) (degrees) (degrees) Horizon (h) Resolution (h) Visual NetCDF Other MACC- ECMWF AOD different wavelengths 90 S - 90 N 180 W E Daily YES YES GRIB BSC- DREAM8b Dust Optical Depth (550 nm) Dust Conc. (kg m -3 ) Dust Load (kg m -2 ) Emission Rate (kg m 2 s -1 ) CHIMERE Sea Salt Conc. (μg m -3 ) Dust Conc. (μg m -3 ) PM2.5 (μg m -3 ) PM10 (μg m -3 ) SKIRON Dust Load (kg m -2 ) Dust Conc. (kg m -3 ) Cloud coverage TAU/DREAM- 8b Dust Load (kg m -2 ) Sea salt Conc. (kg m -3 ) 11 S - 72 N 63W E Daily YES YES N/A 10 N - 60 N 40 W- 60 E Daily YES NO 10 S - 70 N 90 W- 60 E Daily YES NO 15 N - 50 N 20 W - 45 E Daily at 12:00 (UTC) NAAPS AOD 90 S - 90 N 180 W E YES NO ASCII EMA- RegCM4 Dust Load (kg m -2 ) Dust Conc. (kg m -3 ) 5 S - 70 N 15 W - 75 E Daily YES NO TSMS/BSC- DREAM8b GEOS-5 NMMB/BSC- Dust NGAC Dust Optical Depth (550 nm) Dust Load (kg m -2 ) Dust Conc. (kg m -3 ) AOD (550 nm) Aerosol Conc. (kg m -3 ) Cloud Coverage Dust Optical Depth (550 nm) Dust Conc. (kg m-3) AOD (550 nm) Aerosol Conc. (kg m -3 ) 5 N - 60 N 10 W - 70 E 72 3 Daily YES NO 90 S - 90 N 180 W E hours YES YES 3 S - 70 N 31 W - 71 E Daily YES YES 90 S - 90 N 180 W E h Daily YES NO GRIB2 YES NO

10 Recommendation: Depending on the location, the temporal variability of aerosols may be low. In regions with low variabilities in time and space of the aerosols, their amount can be monitored either by an aerosol observation or by the pyrheliometer. Only in regions with high spatial and temporal variabilities, where a larger number of significant dust events is expected, a forecasting and nowcasting scheme is recommended. Measurements of aerosol optical depth Recommendation: For DNI nowcasting system, there is a need for good quality measurements on a regular basis with storage of these measurements. This will enable to the qualification and quantification of unncertainties of the nowcasted values and built improved models for nowcasting. In regions with low spatial and temporal aerosol variability, the nowcasting of aerosols can be replaced by the monitoring of aerosols. As an example, using the COSMO-MUSCAT model system and IASI-based satellite dust observations, spatial and temporal correlation lengths maps were generated (see deliverable 2.4 and 2.5) showing the way to do it. Different means of measurements exists for obtaining information about Aerosols. The reader can refer to pages of the deliverable 2.2. Depending of the location and the spatial and temporal variabilities, an existing station as e.g. from the Aerosol Robotic NETwork (AERONET) on the European Aerosol Research Lidar Network (EARLINET) may be sufficient. Alternatively, the satellitebased observations from the Moderate-resolution Imaging Spectroradiometer (MODIS) may be sufficient in some locations. In case of non-availability of suitable aerosol observations in the vicinity of the power plant, a dedicated observation system at the power plant site may be required. Sky imagers are also instruments of interest for use in a nowcasting system. Deliverable 2.5 provides some elements about an experiment realized for using them as tools for measurements of aerosols to derive the aerosol optical properties from the analysis of the spectral characteristics of all-sky images. According to the relevant literature, the synergetic use of Red (R), Green (G), Blue (B) intensities from the sky images, results from radiative transfer models and training methods are able to provide quite accurate results (error ~ 15-20%). The uncertainties in the retrieval of AOD are site specific and related to the sky camera type not only because of different CCD sensors but also because of using entrance optics of different quality. 6. Nowcasting of clouds with different methods a. Using Satellites In Deliverable 3.6 different solutions are proposed for nowcasting clouds based on images from geostationary satellites. The nowcasting of clouds is an intermediary step towards the nowcasting of the DNI value. For the nowcasting, often the instrument SEVIRI from Meteosat Second Generation (MSG) is utilized. DLR-PA employs two algorithms to separate optical thickness for ice and water clouds from MSG. The

11 advection velocity is determined with an optical flow method from consecutive images. The development of convective cells is treated separately. DLR-DFD introduces a receptor-like approach, where the derived optical thickness for ice and water/mixed phase clouds from MSG is analyzed in several sectors around a point of interest. The advection velocity is determined from consecutive images. In order to utilize MSG/SEVIRI in numerical weather prediction, SMHI has implemented two different aspects of the satellite information in the data assimilation scheme of its forecasting model. Firstly, the radiances of two infrared channels of SEVIRI are assimilated for cloud-free conditions. Secondly, the cloud mask together with cloud top temperature and cloud base height are calculated using the EUMETSAT Satellite Application Facility to NoWCasting & Very Short Range Forecasting (SAFNWC) MSG software. This cloud information is then used for an innovative cloud initialization procedure Recommendation: According to comparison results in deliverable D3.8 for the location of the Plataforma Solar de Almeria, the different approaches have interesting results for the nowcasting of clouds. The final validation will help to confirm their potential within DNI nowcasting approaches. b. Using NWP NWP models were developed for representing the physical processes of the atmosphere and their behavior from global scale to local scale, with a temporal horizon up to ten days ahead. Hence, they have the potential to predict clouds and their evolutions. In operational mode, compared to the needs for DNI nowcasting, their spatial resolution is coarse (25 km at the continental scale, 5 to 3 km at country level). Their temporal resolution is 3 to 6 hours, with in the best case a 15 min resolution. Their performances in terms of nowcasting of clouds are low because they were not developed with this aim. Nevertheless, they have a real added value when used for forecast (6 hours horizon and more). These limitations can be addressed when using high resolution NWP models or models integrating downscaling approaches (such as WRF). Hence, their use for clouds nowcast is of interest due to their high potential when using very high spatial and temporal resolution and their abilities to represent the physical conditions. No such an operational system is actually available in meteorological institutes, and more developments are still needed (see deliverable 4.3) to reach the requirements for DNI nowcasting. But some private companies can provide such a service. c. Using sky cameras The method to detect the cloud-free or cloudy pixels is based on Blue (B), Red (R) and Green (G) values of each pixel. The thresholds for each band are empirically determined and should be adjusted to the specific location of the solar farm. A method for cloud classification was developed (see D3.1. for more details) for globally not at a pixel basis classifying clouds in five types: No cloud Clear Sky (Cloud Cover < 10%) Cumulus Cirrus - Cirrostratus Cirrocumulus Altocumulus Stratocumulus - Stratus

12 Cumulonimbus and nimbostratus cases were excluded from classification algorithm due to appropriate photos inadequacy. Every cloud category is split in multiple sub categories in order to cover maximum possible variations based on solar zenith angle, cloud coverage, visible fraction of the solar disk. The classification algorithm provides two possible implementations of cloud type classification: Global cloud classification: classification of dominant cloud type. Grid-based cloud detection: a classification of the cloud type is provided per grid element in the image. A Second classification technique provides additional characteristics per grid element: No cloud Clear Sky Thin cloud Thick cloud This second classification is of interest namely in case of simultaneous existence of different cloud types. For example, a grid-element can be classified as a thick cloud while it may be part of cumulus or stratocumulus. A third option is to classify the clouds based on their DNI transmittance derived from the combination of DNI measurements with a pyrheliometer and an All Sky Imagers. The two aforementioned classifications can be used to assign an estimated DNI transmittance. Recommendations: Clouds should not only be treated as completely opaque objects, but some classification in terms of transmittance should be applied. The use of a binary representation of clouds is not sufficient. See part Nowcasting of DNI maps for description of a way to do it. With the state of the art on sky camera applied to DNI nowcasting, the added value of sky cameras are mainly in simple clouds conditions (advective case with single layers of clouds). The efficiency of the use of sky camera is improved when they are positioned not only within the solar farms but outside it to avoid to be dazzled by the direct sun. 7. Nowcasting of circumsolar radiation The circumsolar ratio CSR = CSR(αcir) is defined as the normal irradiance coming from an annular region around the Sun divided by the normal irradiance from this circumsolar region and the sun disk. αcir the opening half angle of the region around the Sun forming the annulus. The circumsolar radiation relevant for CST applications is mainly caused by forward scattering of sunlight by aerosol or thin cirrus layers. If these particles are evenly distributed horizontally, the radiance decreases with angular distance from the Sun. The steepness and shape of this angular gradient depends on the particles shape, size, mass load and extinction coefficient, which in turn depends on their refractive index. For all concentrating solar technologies, the effect of circumsolar radiation has to be considered also for the nowcasting since large sun shapes reduce the usable solar irradiation significantly and thus reduce the DNI usable.

13 Hence it is necessary to nowcast the circumsolar radiation to correct the DNI nowcasting. Deliverable 2.7 give some inputs about the modeling and nowcasting of circumsolar radiation and proposed a parametrized method. The method allows parameterizing circumsolar radiation in an accurate way by avoiding cpu time consuming 3D radiative transfer calculations. At the same time, the method requires the representation of the atmospheric components in one atmospheric column by means of two sets of two optical parameters (optical thickness and effective radius), one set for ice clouds and one set for aerosol. The knowledge of cloud and aerosol optical properties not only allows computing circumsolar radiation but also DNI in a consistent way. The method is applicable to whole-sky imager data, to satellite products and to NWP model output. In all cases, the parameters provided by these tools will have to be translated into optical properties that can be used for the determination of the apparent optical thickness. To stick to the original idea of a physically based method for the determination of circumsolar radiation the DNICAST team makes the effort to derive these necessary optical parameters from physical properties of clouds and aerosol whenever possible. In general, an underestimation of CSR is observed using this method. For a more detailed description of the results, the three groups of nowcasting methods used in the DNICast approach (whole-sky imagers, satellite instruments, weather models) are reviewed in Deliverable 2.8. For the determination of CSR from cloud observations of whole-sky imagers it turned out that the derivation of optical thickness from information of cloud coverage of the circumsolar region is not able to reproduce the CSR variability. To improve this result, one should either tune the method to find out more reasonable connections between cloud coverage and cloud optical properties or exploit additional knowledge about radiation extinction through the cloud to the surface camera (not available at the moment). For aerosol instead, the determination of the aerosol optical thickness from whole-sky cameras produced a very high correlation coefficient (0.8) under cloud free conditions. Satellite products, mainly of cloud optical properties, from geostationary (MSG/SEVIRI) and polar orbiting instruments (MetOp/IASI) show generally speaking a good potential for the determination of CSR. Of course, geostationary data comes with a good temporal resolution that enables the forecast of CSR while forecasts of CSR from polar orbiting platforms surely require sophisticated methods to deal with one to three observations per day at selected sites (and is not part of the DNICast project). Both CSR forecast methods by DLR-IPA and Meteotest based produce (Pearson) correlation coefficients (with CSR surface measurements) in the range The DLR-IPA method has been applied to one year of measurements (May April 2012, i.e. temporally separated from the sampling period used here) and resulted in a correlation coefficient of A shorter time series (1 May June 2011) could reach a correlation coefficient of 0.58 and this same short time period resulted in an increased correlation coefficient of 0.67 when satellite data was visually screened for a better detection of low liquid water clouds. This shows that the CSR from forecast satellite data shows a very good correlation with surface measurements. The satellite-based method by DLR-DFD using MetOp/IASI showed a very high correlation coefficient of approx. 0.7 when only cloudy days were observed.

14 For weather models, the creation of the required input parameters is much more complicated due to 1) the lack of optical parameters and 2) the provision of vertical profiles of physical cloud properties. The method seems to provide realistic results but also in this case it underestimates CSR. With respect to the uncertainties related to the effective radius and microphysical model for ice clouds, it could be favorable to test different parameterizations to check whether the observed underestimation of CSR can be alleviated. Recommendation: Circumsolar radiation should be taken into account for the plant modelling and the DNI reported from a nowcasting system should include the circumsolar radiation in a way similar to pyrheliometer measurements. Otherwise a nowcasting sytem must specify exactly how circumsolar radiation is included or not in the nowcasted DNI. Ideally the CSR should be forecasted together with the DNI. However, currently the uncertainty of DNI nowcasts is noticeably higher than the error caused by a wrong estimation of the CSR. Hence, nowcasting a time series of the CSR next to the DNI is currently not an obligation. However, the CSR should always be considered to avoid systematic errors of DNI nowcasts that would occur if the circumsolar radiation is neglected completely. 8. Nowcasting of DNI value The scale of a solar farm is often lower of the spatial resolution of models and satellite images. Hence even if these solutions provide maps of DNI, at the scale of the farm, only one value is given for the whole area. Hence it will be considered as nowcasting of a DNI Value. As the field of view of ground based systems is limited to a few kilometers, only a short-term forecast of up to 30 minutes is possible, depending on the cloud velocity. Satellite imagery based nowcasting methods allow the extension of the forecast window to several hours. The satellites of interest for nowcasting of DNI are geostationary satellites used for meteorological applications. Meteosat Second Generation (MSG) is a series of geostationary satellites operated by EUMETSAT. Their primary mission is the continuous observation of the earth s full disk for the observation and forecasting of weather phenomena. For this purpose the 12-channel imager SEVIRI (Spinning Enhanced Visible and Infrared Imager) has been developed. Additionally a broadband high resolution visible (HRV) channel is integrated, which covers only a part of the earth s full disk with a higher spatial resolution of 1 km at the sub-satellite point. The repetition rate is usually 15 min, but can be reduced to 5 min in rapid-scan-mode for a part of the disk. Three methods are proposed in deliverable 3.6 for satellite-based DNI nowcasting. The first one proposed to link the Clearness-Index (CI) derived from MSG with wind derived from a NWP at the heights of the clouds. The CI map pixels are propagated forward along the wind trajectories for the next 4 hours. Ground measurements are included for post processing. CI-to-DNI conversion is applied to obtain the nowcasting of DNI. The second one derived optical thickness for ice and water/mixed phase clouds from MSG around the point of interest (the solar farm) and the advection velocities of these clouds are determined from consecutive satellite images allowing the nowcasting of DNI.

15 The third one is based on a data assimilation scheme of satellite information in a forecasting model. Firstly, the radiances of two infrared channels of MSG are assimilated for cloud-free conditions. Secondly, the cloud mask together with cloud top temperature and cloud base height are calculated using the EUMETSAT Satellite Application Facility to NoWCasting & Very Short Range Forecasting (SAFNWC) MSG software. Then the nowcasting of DNI is derived. The proposed methods provide nowcasting of DNI value for an area comprised between 3 km and 1 km (for the high resolution area covered by MSG). 9. Nowcasting of DNI maps Within DNICast nowcasting methods based on ground measurements and all-sky imagers were developed to provide very short term (0 30 min) forecasting of high temporal resolution 1-min or less time series of spatially resolved (10 m) maps of direct normal irradiance (DNI). The use of hemispherical sky images from all-sky cameras for nowcasting purpose requires first geometric and radiometric calibration. Indeed, geometric calibration is notably very important for the 3D reconstruction using multiple allsky imagers in stereoscopic mode and the localization of the Sun position in a cloudy situation. The radiometric calibration is also important, namely to ease the clouds detection and classification within the largest field of view (FoV) of the camera and for the larger range of sun elevation angle. With two or more calibrated all-sky cameras at different geographic locations in a large solar plant, it is possible to determine on a real-time basis the position and the base height of the detected clouds and therefore to provide 3D map of clouds. With this time series of 3D cloud maps, position and speed of cloud shadow can be estimated, thus enabling spatially resolved DNI nowcasting. Deliverable 3.1 describes the different approaches developed for nowcasting DNI maps. The different stages required at each time of acquisitions of two or more all-sky images in stereoscopic mode are described in the following. The two cameras and their corresponding images are noted hereinafter A and B. At the instant t, the different stages are: Cloud detection in image A: detection of cloudy pixels associated with cloud characteristics derived from the RGB information. Estimation of the cloud base heights for the cloudy pixels in A thanks to stereoscopic photogrammetry based on pixel matching between the images A and B. Estimation of the 10-m resolution map of cloud shadows on the ground at the instant t. Estimation of the 3d speed of the clouds. Forecasting of the map of cloud shadows on the ground at the instant t+t for a given time horizon t. Forecasting of the DNI map at the instant t+t.

16 All the stages listed here are fully detailed in Deliverable 3.1. To simplify the presentation, once the 3D modeling of the clouds and the forecast of the map of the clouds shadows obtained, the DNI is computed from the cloud-shadow map and the corresponding map of cloud characteristics and a time series of modelled DNI under clear-sky conditions is obtained. Two approaches are used to obtain it, based on modeling (through the ESRA model or the McClear model from the Copernicus Atmosphere Monitoring Service). Two approaches for the computation of the DNI map have been tested: the forecasted DNI map is simply defined with the binary mask of cloud shadow the forecasted DNI map is defined with the binary mask of cloud shadow and affine regressions The set of regression parameters per cloud type is determined by linear least-square regression during a training period with concomitant pyrheliometer measurements of DNI and corresponding time series of cloud-shadow mask and cloud-type map for the location of the ground measurement in the reference local frame. The quality and validation of the proposed approaches are reported in WP4 deliverables. 10. Summary and future steps Within this report a set of recommendations was provided on the use of the different tools for DNI nowcasting. These recommendations have to be considered as linked with the actual knowledge and achievements built during the DNICast project. Nevertheless, the work done has revealed a set of new fields to explore and some points that needed to be explored in a deeper way. Some points are crucial for the actual and future quality of the nowcast that can be produced by any system: For the time being, the application of various forecasting methods is recommended (In-situ sensors, All Sky Imager, satellite and NWP). Any nowcasting scheme should be validated at various locations and with various meteorological conditions covering the conditions that are relevant for the site of interest. Nowcasted data should be reported with uncertainty information. In-situ measurements and their real-time integration within any nowcasting scheme will dramatically improve the results notably for the lower time horizons. All nowcasts being made in an operational system should be stored in the power plant database. Only this allows any evaluations by the research & development team afterwards. Some tracks to the future of the work proposed during the DNICast project can be already determined, even if all activities are not yet achieved. The integration of all measurements and all the nowcastings of the different parameters of interest for being able to nowcast DNI with a good accuracy is the major challenge for obtaining an operational tool. All the work done during this project is focused on this objective. This definition of characteristics of such an integrated system is not straight forward and will need more interactions with operators of the solar farms.

17 During the project the second workshop with end-users explored the nowcasting of aerosols for the surface boundary layer (0-200 m). The benefits of the different approaches proposed are clear. But their operational application is not yet fully defined and easy to apply. They have a great potential and their use in an integrated system should be explored in a more detailed way. In all the DNI nowcasting systems, it should be underlined that there is a need for good quality measurements on a regular basis with storage of these measurements in order to be able to qualify and quantify the quality of the nowcasted values and built improved models for nowcasting. It is highly recommended to refer to Deliverable 4.2 for all matters related to DNI measurements that are keys for a good nowcasting of DNI.

18 11. Annexes a. Annex 1: Definitions of nowcasting-related terms Nowcast horizon: the time period for which the nowcasting system delivers predictions. Nowcasting: forecast up to 6 hours (WMO). DNICast Nowcasting: Forecast up to 4 hours. Very short-term forecasts: provide forecasts up to 12 hours. Short-term forecast: forecast from 12 to 72 hours. Medium term forecasts: forecast up to days ( hours). Long-term forecasts: cover monthly or seasonal predictions. Nowcasting application: technical system that makes use of the nowcasting information in order to improve the operation of a solar power plant. Characteristic length lchar: refers to a spatial extension of the plant or a plant sub-element that is characteristic for the nowcasting. Characteristic area: Depending on the type of nowcasting application this can either be the extension of the whole solar field or of a distinct sub-system in the field, e.g. a single collector or collector module. Characteristic time tchar: typical reaction time of the solar installation to any disturbance imposed on it. Nowcasting s temporal resolution: DNI values represent an averaged value over a certain time interval. Nowcasting s temporal resolution defines the length of these time intervals. A temporal resolution of 5 min means that nowcasted values are provided for the lead time intervals 0 5 min, 5 10 min, min, etc. Refresh rate of the nowcasted DNI information: describes the frequency with which the prediction values are updated. Time lag: the gap between observations and the availability of a reliable nowcasting. Forecast lead time: the time between the start of the forecast and the occurrence of a forecasted value. In our case it is the time between 0 and 4 hours after the start of a forecast. Accuracy of the nowcasting system: usually refers to the expected deviation of the predicted DNI value in relation to the real DNI value.

19 b. Annex 2: Specifications of a nowcasting system Specifications of a nowcasting system for selected CSP applications Application type Operations support Operational stability DSG-specific stability Nowcast horizon 1 Temporal resolution Spatial resolution Ideal minimum Volatility index Nowcast horizon 2 accuracy (1) 1 2 h 10 min Solar field 10 20% optional up to 4 h 25 min area average 15 25% (typical 1-3 km side length) 30 min 3 min Solar field 5 15% optional 75 min 8 min area average 10 25% (typical 1-3 km side length) 5 min 10 s Image with 5 10% - 8 min 45 s pixel size of % 60 m 3 10% - R&D 0 min 5 20 s Image with pixel size of 5 20 m Operations support Operational stability DSG-specific stability R&D Operational decisions on plant level like - initiating start-up and shut-down - usage of storage and backup energy Prediction of electricity production for optimized marketing. Optimizing the operational stability onplant level in terms of power, temperature and pressure transients by - forward-looking control loops - just in time activation of backup energy (storage or fossil) Optimizing the stability of direct steam generation solar field operation by forwardlooking control of solar field sub-systems like loop mass flow, injection mass flow. High accuracy knowledge of DNI distribution for research and development at test and demonstration plants.

20 Specifications of a nowcasting system for selected CPV applications. Values represent the ideal case ( wish list ) for best results. Lower quality nowcasting can be used but will reduce the achievable result. Application type Grid connected CPV plants: Start-up and shut-down decissions CPV plants with batteries for self consumption. Storage and load management CPV plants with buffers used to mitigate short term PV output intermittency PV energy estimation of CPV plants High accuracy knowledge of DNI distribution for research and development at test and demonstration plants Nowcast horizon Temporal resolution Spatial resolution 1 hour 5 min From 5 to 10 m for CPV plants with individual inverters From 100 m to 1000 m for CPV plants with central inverters From 15 minutes to 4 hours From 1 min to 4 hours From a few seconds to 4 hours 5 min From 5 to 10 m for CPV plants with individual inverters From 100 m to 1000 m for CPV plants with central inverters From 1 min to 5 min From 1 second to 5 minutes From 5 to 10 m for CPV plants with individual inverters From 100 m to 1000 m for CPV plants with central inverters From 5 to 10 m for CPV plants with individual inverters From 100 m to 1000 m for CPV plants with central inverters 1 min 0.5 min Image with pixel size of 10 cm Ideal minimum accuracy % 10 20% 10 20% 10 20% 10 20%