The conventional method of initial heliostat calibration by sequentially pointing the heliostats one by one onto a target is a very slow process. The use of unmanned airborne vehicles (UAVs) is a possible approach to developing a less time consuming procedure for the initial setup of the field control system and elimination of offset in light beam pointing. This paper presents a UAV based method for measuring the heliostat offset in two stages. The first stage, preliminary and less accurate, creates a three-dimensional model of the mirror facet corners in order to estimate the heliostat orientation. The estimated orientation from the first stage is a prerequisite for the second stage of measurement, in which the highly accurate heliostat orientation is derived from deflectometrically measuring the mirror shape. While work is still ongoing to fully implement the final steps of the second stage, the measuring principles has been demonstrated and partially validated for the fast stage.

Spatially and temporally highly resolved direct normal irradiance (DNI) maps of the solar field can be used to significantly improve the operation of concentrating solar power (CSP) plants. Further improvements are expected if DNI nowcasts are also used for the plant operation. Currently, the most widely used method to create spatially resolved DNI nowcasts is based on all sky imagers. All sky imagers take photos of the whole sky above the CSP plant and detect clouds to derive the DM map. Such all sky imager systems are promising, but for the calculation of DNI maps the cloud height and shape must be determined. Depending on the conditions, these processing steps can be error prone. Shadow cameras avoid these complex tasks. Mounted on an elevated position, they take photos of the ground in which cloud shadows can he detected. The RUB values of the photos and one measurement station m the camera's field of view are used to derive the DNI for each pixel of the photo. So far, a shadow camera system was mounted on a solar tower, covering an area of about 4 km2. However, mirror surfaces and ground shaded by structures seen in the images cannot be evaluated with the method. Together with the low spatial extension, this so far excluded the shadow camera approach from the application in utility scale CSP plants. To overcome these limitations, a shadow camera is mounted on a mountain range. The evaluation method is adapted to the large distances between the camera and the ground and the results are compared to ground based DNI measurements. Considering that a simple surveillance camera is used the results are satisfying. The method is promising for tower plants and other solar power plants located close to mountain ranges or high buildings and can also help to provide highly resolved forecasts of global irradiance for areas with distributed solar energy systems.

Concentrating Solar Power (CSP) plants are often being built in regions with high dust loads such as the Middle East and North Africa. Current soiling measurement methods in solar fields are labor and cost intensive and they only cover small measurement spots. Even a high number of manual measurements can only cover a minimal portion of the total solar field mirror surface. Airborne measurement solutions such as Qlly have been developed in recent years to measure the geometry and tracking accuracy of the large CSP mirror areas. Soiling on solar mirrors is qualitatively visible in raw QFly measurement images. In this study we present a method to automatically derive the mirror cleanliness from aerial images taken with QFly. We present scattering investigations that correct for the directional particle scattering, illumination and camera viewing angles. A promising correlation was found between the thus corrected camera RGB signals and reference reflectance measurements that shows an RMSD of 3% for the investigated dust.

A new measurement system able to measure the shape accuracy of complete mirrored heliostat modules in series production was developed. The applied deflectometry measurement method is based on the reflection of regular patterns in the minor surface and their distortions due to mirror surface deviations. The measurement system's key features are its high spatial resolution, its low global measurement uncertainty of less than 0.2 mrad and its total measurement and evaluation time of few minutes. The measurement process is contact-free and completely automatic, which allows a 100% optical quality control of the production of the heliostat modules for a typical solar tower power plant. The system is validated by measuring a flat reference surface and by comparison to manual photogrammetry measurements. This makes the new measurement system, called QDec-M-Helio, a valuable tool for final geometric quality control of heliostats in series production, especially for new generation heliostats which obtain its final curvature during a bonding process between mirror and support structure from a precise jig.

The rapid growth of solar power generation and the variable nature of the solar resource pose challenges for our electricity grids. Forecasting future changes in the irradiance might help to cost-efficiently manage this variability both for photovoltaic and concentration solar plants as well as grids with high solar penetrations. So far, for shortest-term forecasts with lead times of a few minutes, all-sky imager based nowcasting systems are used. However, due to the complexity of dynamically changing 3d cloud shapes as well as certain geometrical effects such as self-occlusion or near-horizon saturation, all-sky imager based nowcasting systems exhibit inherent weaknesses. Here, we present a novel system to generate shortest-term solar forecasts, which is located at Plataforma Solar de Almería in southern Spain. This approach is based on downward-facing cameras (shadow cameras), taking images of the ground. From these images, spatially resolved irradiance maps are derived. By tracking cloud shadows, future irradiances are predicted. A demonstration system is achieved, which provides shortest-term forecasts for the next 2 min. To the best of our knowledge, this is the first time such a system is developed. We benchmark several possible algorithmic approaches on 16 days and compare the deviations to a state-of-the-art all-sky imager based nowcasting system on 22 days. The root-mean-squared deviation (RMSD) of this shadow camera based nowcasting system for direct normal irradiance (DNI) and 1-min temporal averages is 15.6 % for lead times of 2 min (MAD, DNI: 9.6 %). In comparison to an all-sky imager system, this is an improvement as the all-sky imager system only reaches 22.0 % RMSD and 14.8 % MAD (both DNI). This demonstrates the feasibility and attractiveness in terms of accuracy of the proposed concept.

Downward-facing shadow cameras might play a major role in future energy meteorology. Shadow cameras directly image shadows on the ground from an elevated position. They are used to validate other systems (e.g. all-sky imager based nowcasting systems, cloud speed sensors or satellite forecasts) and can potentially provide short term forecasts for solar power plants. Such forecasts are needed for electricity grids with high penetrations of renewable energy and can help to optimize plant operations. In this publication, two key applications of shadow cameras are briefly presented.

Solar irradiance nowcasts can be derived with sky images from all sky imagers (ASI) by detecting and analyzing transient clouds, which are the main contributor of intra-hour solar irradiance variability. The accuracy of ASI based solar irradiance nowcasting systems depends on various processing steps. Two vital steps are the cloud height detection and cloud tracking. This task is challenging, due to the atmospheric conditions that are often complex, including various cloud layers moving in different directions simultaneously. This challenge is addressed by detecting and tracking individual clouds. For this, we developed two distinct ASI nowcasting approaches with four or two cameras and a third hybridized approach. These three systems create individual 3-D cloud models with unique attributes including height, position, size, optical properties and motion. This enables us to describe complex multi-layer conditions. In this paper, derived cloud height and motion vectors are compared with a reference ceilometer (height) and shadow camera system (motion) over a 30 day validation period. The validation data set includes a wide range of cloud heights, cloud motion patterns and atmospheric conditions. Furthermore, limitations of ASI based nowcasting systems due to image resolution and image perspective constrains are discussed. The most promising system is found to be the hybridized approach. This approach uses four ASIs and a voxel carving based cloud modeling combined with a cloud segmentation independent stereoscopic cloud height and tracking detection. We observed for this approach an overall mean absolute error of 648 m for the height, 1.3 m/s for the cloud speed and 16.2 degrees for the motion direction.

The solar-thermal parabolic trough power plant Andasol 3 (AS3) near Granada/Spain operated by Marquesado Solar SL (MQS) was commissioned in autumn 2011. The installed capacity of 49.9 MWel in combination with thermal energy storage (TES) capacity for 7.5 hours at full load results in a net annual energy production of more than 165 GWh(1) (Dinter and Gonzalez 2014). The German Aerospace Center (DLR) has developed a tool for airborne characterization of entire parabolic trough plants. The approach called QFly(SURVEY) uses an unmanned aerial vehicle (UAV) equipped with a high resolution digital still camera and delivers effective mirror slope deviation and the absolute orientation of the optical axis of each solar collector element (SCE). In order to validate and demonstrate QFly(SURVEY), a comprehensive measurement campaign was undertaken in the AS3 power plant in cooperation with MQS between 2016-10-24 and 2016-11-14. The main objective was to demonstrate the advantages of airborne solar field characterization in terms of rapid data acquisition, negligible interference with plant operation, and without the need of any additional installation of measurement equipment in the solar field. QFly(SURVEY) provides accurate quantitative measures of optical performance of the solar field and supports the maximization of the thermal energy collected from the solar field by identifying low performing areas and the causes for optical losses.

Accurate nowcasts of the direct normal irradiance (DNI) for the next 15 min ahead can enhance the overall efficiency of concentrating solar power (CSP) plants. Such predictions can be derived from ground based all sky imagers (ASI). The main challenge for existing ASI based nowcasting systems is to provide spatially distributed solar irradiance information for the near future, which considers clouds with varying optical properties distributed over multiple heights. In this work, a novel object oriented approach with four spatially distributed ASIs is presented. One major novelty of the system is the application of an individual 3D model of each detected cloud as a cloud object with distinct attributes (height, position, surface area, volume, transmittance, motion vector etc.). Frequent but complex multilayer cloud movements are taken into account by tracking each cloud object separately. An extended validation period at the Plataforma Solar de Almeria (PSA) on 30 days showing diverse weather conditions resulted in an average relative mean absolute error (relMAE) of around 15 % for a medium lead time of 7.5 minutes and a temporal average of 15 minutes. Further reductions of the relMAE were achieved by spatial aggregation, with a relMAE of 10.7 % for a lead time of 7.5 minutes, a field size of 4 km(2) and a temporal average of 1 minute (during one day). Nowcasting systems described in the literature reach similar deviations but were often validated only for a few days based on a single ground measurement station, which confirms the good performance and the high applicability of the presented system. Three implementations of the system exist already demonstrating the market maturity of the system.

All-sky imager based systems can be used to measure a number of cloud properties. Configurations consisting of two all-sky imagers can be used to derive cloud heights for weather stations, aviation and nowcasting of solar irradiance. One key question for such systems is the optimal distance between the all-sky imagers. This problem has not been studied conclusively in the literature. To the best of our knowledge, no previous in-field study of the optimal camera distance was performed. Also, comprehensive modeling is lacking. Here, we address this question with an in-field study on 93 days using 7 camera distances between 494 m and 2562 m and one specific cloud height estimation approach. We model the findings and draw conclusions for various configurations with different algorithmic methods and camera hardware. The camera distance is found to have a major impact on the accuracy of cloud height determinations. For the used 3 megapixel cameras, cloud heights up to 12,000 m and the used algorithmic approaches, an optimal camera distance of approximately 1500 m is determined. Optimal camera distances can be reduced to less than 1000 m if higher camera resolutions (e.g. 6 megapixel) are deployed. A step-by-step guide to determine the optimal camera distance is provided.

Two basci approaches exist to derive receiver solar absorptance. The first apporach is to measure an enthalpy difference of a medium inside the receiver and the receiver itself by irradiating it with lamps or the sun. The second Approach is to meaure directly the reflectance of glass envelope and absorptance of the selective coating. While the first Approach ususally includesthe whole Receiver assembly, whish is important for product qualification, the second Approach is a more direct measurement of the properties of the Receiver which have a Major influence on reciever performance. The indoor OptiRec and ElliRec test benches, the outdoor two-axis tracking SolarRec or KONTAS test benches or a typical one.axis tracking parabolic trough can be used to measure the temperature increase of the fluid flow inside the receivers.

Prior to commercial operation, large solar Systems in utility-size power plants Need to pass performance acceptance tests conducted by the engineering, procurement, and construction (EPC) contractor or owners. Test procedures for this purpose must yield results of a high Level of accuracy consistent with good Engineering knowledge and pryctice, guided by international test Standards when available. Parabolic trough and solar tower systems are typical of such solar Systems. The power block and Balance of plant, which may be included in the performance test, are conventional technology. Performance tests are also useful during technology development, optimization, and monitoring.

Because of the cloud‐induced variability of the solar resource, the growing contributions of photovoltaic plants to the overall power generation challenges the stability of electricity grids. To avoid blackouts, administrations started to define maximum negative ramp rates. Storages can be used to reduce the occurring ramps. Their required capacity, durability, and costs can be optimized by nowcasting systems. Nowcasting systems use the input of upward‐facing cameras to predict future irradiances. Previously, many nowcasting systems were developed and validated. However, these validations did not consider aggregation effects, which are present in industrial‐sized power plants. In this paper, we present the validation of nowcasted global horizontal irradiance (GHI) and direct normal irradiance maps derived from an example system consisting of 4 all‐sky cameras (“WobaS‐4cam”). The WobaS‐4cam system is operational at 2 solar energy research centers and at a commercial 50‐MW solar power plant. Besides its validation on 30 days, the working principle is briefly explained. The forecasting deviations are investigated with a focus on temporal and spatial aggregation effects. The validation found that spatial and temporal aggregations significantly improve forecast accuracies: Spatial aggregation reduces the relative root mean square error (GHI) from 30.9% (considering field sizes of 25 m2) to 23.5% (considering a field size of 4 km2) on a day with variable conditions for 1 minute averages and a lead time of 15 minutes. Over 30 days of validation, a relative root mean square error (GHI) of 20.4% for the next 15 minutes is observed at pixel basis (25 m2). Although the deviations of nowcasting systems strongly depend on the validation period and the specific weather conditions, the WobaS‐4cam system is considered to be at least state of the art.

Nowcasting of high resolution maps of direct normal irradiance (DNI) is of interest to efficiently operate Concentrated Solar Power plants. The paper presents a state-of-the-art and innovative methodology, developed in the framework of the FP7 DNICast project, to derive nowcasting of DNI maps from fish-eye cameras in stereoscopic mode. This methodology has been applied at the Plataforma Solar de Almeria: fish-eye cameras at distances from each other between 500 m and 900 m have been used in stereoscopic mode to produce nowcasted 1-min time series of decametric DNI maps.