Soiling is responsible for a loss of 3%–4% of the potential global solar power production that is estimated to rise because of increased deployment in dusty environments [Ilse et al., Joule 3, 2303–2321 (2019)]. For effective mitigation strategies and site selection soiling ratio data is crucial. Soiling can be measured directly by comparing the power or short circuit current of a soiled PV cell or module to that of a clean photovoltaic (PV) device of the same model. In recent years, a number of indirect soiling sensors such as Kipp and Zonen's DustIQ and Atonometric's MARS were introduced. These sensors do not use PV cells to determine the soiling ratio. Indirect soiling sensors derive soiling based on information such as particle concentration and size. The soiling loss of PV systems shows a strong angle of incidence (AOI) dependent pattern for sunny conditions. This pattern is not reproduced by indirect sensors such as DustIQ and MARS. We developed a method to adapt the soiling measurement of indirect soiling sensors using the AOI and the Linke turbidity. The adaptation is based on an analysis of the AOI dependence of the reference cell-derived soiling ratio. Linke turbidity is derived from irradiance measurements. The method reduces the root mean square error between the soiling ratios detected by the DustIQ and a colocated pair of PV reference cells from 0.7% to 0.2%. It can improve cumulative performance calculations that use indirect soiling measurements as an input.

The detection and segmentation of clouds in images taken by ground based cameras is of utmost importance for a large number of applications including all-sky imager based nowcasting systems which optimize solar power plant operation, calculation of the global irradiance, estimation of the cloud base height and support of optical satellite downlink operations. Many approaches to segment clouds in camera images are published. However, comparisons of different approaches are not frequently conducted. Here, we address this question by benchmarking six different cloud segmentation algorithms on images taken by an off-the-shelf surveillance camera. The six different algorithms include (1) a color-channel threshold-based algorithm, (2) a Clear Sky Library (CSL) based approach, (3) a region growing algorithm, (4) the Hybrid thresholding algorithm (HYTA), and a (5) novel, HYTA-based development named HYTA+. Furthermore, (6) a deep convolutional neural network (FCN) is adapted via transfer learning to this problem. The segmentation results of algorithms (1) to (5) are compared to 829 manually segmented reference images. The segmentation algorithms are benchmarked on a test dataset which is divided into 16 meteorological categories. These categories cover different Linke turbidity values, solar positions and cloud cover situations. Results show that three out of the six presented segmentation methods (CSL, HYTA+ and FCN) achieve overall accuracy values above 90%. These approaches outperform the other methods and correctly segment images with a higher consistency. Fixed threshold based methods, as the multicolor criterion, HYTA or the region growing algorithm fail under certain meteorological conditions. The FCN based segmentation (6) is tested on 160 images where it delivers the best overall pixel-by-pixel accuracy of 97.0%.

The continuously growing penetration of intermittent electricity sources will increase the future demand for dispatchable power plants, which balance out fluctuations within the electrical grids. Parabolic trough power plants with thermal energy storages could be one renewable solution for regions with a high yearly direct normal irradiance (DNI) sum, but in order to compete against other renewable as well as non-renewable technologies, the economic competitiveness must be ensured. Price reductions can be achieved for example by optimizing the plant operation. One shortcoming of state of the art plant controllers is that they only use the DNI measured at one or a few positions in the solar field. Due to the spatial variability of the DNI throughout the solar field this DNI information can be misleading. In this paper, we investigate the optimization potential of solar field control strategies with access to spatially resolved DNI information from all sky imagers (ASI). Uncertainties of the ASI system are considered by introducing additional independent spatial DNI information from a shadow camera system. The spatial and temporal DNI variability of the DNI seen by the controller is classified in distinct DNI variability classes. Two new control strategies are developed, with optimized control parameters for distinct combinations of the spatial and temporal DNI variability classes. These new variability class dependent control strategies are benchmarked in a simulation environment. A relative increase in revenue in excess of 1.9% is observed over a test period with 22 days, compared to a state of the art reference controller.

The joint Task of IEA Technological Collaboration Programmes PVPS and SolarPACES started in July 2017 and will last till 2020. The Task 16 is also collaborating with SHC. 21 countries and 57 institutions are currently part of the Task. It offers a unique platform of exchange for private and public organizations as well as data providers and science organizations. A strong emphasis on extra gain of collaboration will be promoted. Benchmarks, standardizations, state-of the art descriptions and dissemination will be in the focus. Main added values are: lower resource uncertainties and higher forecast accuracies, which will lead to lower implementation costs of PV and CSP projects. At SWC we will show the state of the work. The second year was used to exercise benchmarks and workshops. Three benchmarks were organized. One about methods to calculate regional power forecasts, a second about forecasts based on all sky imagers and a third one about site adaptation methods. Three workshops where held. The first workshop covered best practices for automatic and expert based quality control procedures and gap filling methods, the second probabilistic forecasting and the third during SWC 19 - benchmarking of site adaptation methods.

The demand for accurate solar irradiance nowcast increases together with the rapidly growing share of solar energy within our electricity grids. Intra-hour variabilities, mainly caused by clouds, have a significant impact on solar power plant dispatch and thus on electricity grids. All sky imager (ASI) based nowcasting systems, with a high temporal and spatial resolution, can provide irradiance nowcasts that can help to optimize CSP plant operation, solar power plant dispatch and grid operation. The radiative effect of clouds is highly variable and depends on micro- and macrophysical cloud properties. Frequently, nowcasting systems have to measure/estimate the radiative effect during complex multi-layer conditions with strong variations of the optical properties between individual clouds. We present a novel approach determining cloud transmittance from measurements or from correlations of transmittance with cloud height information. The cloud transmittance is measured by a pyrheliometer when shaded, as the ratio of shaded direct normal irradiance (DNI) and clear sky DNI. However, for most clouds, direct transmittance measurements are not available, as these clouds are not shading the used pyrheliometers. These clouds receive an estimated transmittance value based on (1) their height, (2) results of a probability analysis with historical cloud height and transmittance measurements as well as (3) recent transmittance measurements and their corresponding cloud height. Cloud heights are measured by a stereoscopic approach utilizing two ASIs. We discuss site dependencies of the presented transmittance estimation method and the potential integration of automatic cloud classification approaches. We validated the cloud transmittance estimation over two years (2016 and 2017) and compare the probabilistic cloud transmittance estimation approach with four simple approaches. The overall mean-absolute deviation (MAD) and root-mean-square deviation (RMSD) are 0.11 and 0.16 respectively for transmittance. The deviations are significantly lower for optically thick or thin clouds and larger for clouds with moderate transmittance between 0.18 and 0.585. Furthermore we validated the overall DNI forecast quality of the entire nowcasting system, using this transmittance estimation method, over the same data set with three spatially distributed pyrheliometers. Overall deviations of 13% and 21% are reached for the relative MAD and RMSD with a lead time of 10 min. The effects of the chosen data set on the validation results are demonstrated by means of the skill score.

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.

Cloud height information is crucial for various applications. This includes solar nowcasting systems. Multiple methods to obtain the altitudes of clouds are available. In this paper, cloud base heights derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) and three low-cost and low-maintenance ground based systems are presented and compared against ceilometer measurements on 59 days with variable cloud conditions in southern Spain. All three ground based systems derive cloud speeds in absolute units of [m/s] from which cloud heights are determined using angular cloud speeds derived from an all-sky imager. The cloud speed in [m/s] is obtained from (1) a cloud shadow speed sensor (CSS), (2) a shadow camera (SC) or (3) derived from two all-sky imagers. Compared to 10-min median ceilometer measurements for cloud heights below 5000 m, the CSS-based system shows root-mean squared deviations (RMSD) of 996 m (45%), mean absolute deviations (MAD) of 626 m (29%) and a bias of-142 m (- 6%). The SC-based system has an RMSD of 1193 m (54%), a MAD of 593 m (27%) and a bias of 238 m (11%). The two all-sky imagers based system show deviations of RMSD 826 m (38%), MAD of 432 m (20%) and a bias of 202 m (9%). The ECMWF derived cloud heights deviate from the ceilometer measurements with an RMSD 1206 m (55%), MAD of 814 m (37%) and a bias of- 533 m (- 24%). Due to the multi-layer nature of clouds and systematic differences between the considered approaches, benchmarking cloud heights is an extremely difficult task. The limitations of such comparisons are discussed. This study aims at determining the best approach to derive cloud heights for camera based solar nowcasting systems. The approach based on two all-sky imagers is found to be the most promising, having the overall best accuracy and the most obtained measurements.

With ramp rate regulations for photovoltaic plants being discussed in many countries, the speed of clouds has gained significant importance lately. Besides, measuring cloud velocities and directions is of interest for validations of numerical weather predictions and solar nowcasting systems. Recently, the Cloud Shadow Speed Sensor (CSS) was developed and validated in San Diego for low cumulus clouds. In this publication, the CSS is studied under different weather and cloud conditions in the desert of Tabemas in southem Spain. Furthermore, a novel shadow camera based low-cost, low-maintenance approach to determine cloud shadow motion vectors is presented and used as a reference to benchmark the CSS. In comparison, the absolute velocities derived from the CSS and the shadow camera on 59 days for +/- 5 min temporal medians show deviations of RMSD 2.1 m/s (28.0%), MAD 1.2 m/s (15.7%) and a bias of -0.2 m/s (2.8%). Deviations of the cloud shadow direction are RMSD 47.9 degrees (26.6%), MAD 25.3 degrees (14.0%) and bias 3.7 degrees (2.0%). An adaption of the CSS software yields 91% more measurements on 59 days in comparison to the previously used algorithms at the expense of reduced accuracies, both for the measured velocities and for the measured directions. The CSS and the novel shadow camera based reference system enable long-time, low-maintenance ground measurements of cloud shadow speeds, which were previously not available. The distinct advantages and limitations of the two systems are discussed. In addition to the comparisons between the shadow camera system and the CSS on 59 days, the detection rates of the CSS are classified and measured on 223 days by analyzing CSS radiometer signals. Depending on the shading strength and shading durations, detection rates vary between 3.7% and 21.6%. Furthermore, the basic assumption as well as possible correction approaches of the linear cloud edge - curve fitting method are studied. The CSS was found to be a robust tool with great potential. However, optically thin clouds with diffuse edges pose a challenge and the detection rate leaves room for improvements. The newly developed shadow camera system provides more measurements which scatter less but needs certain geographical requirements. The shadow camera is found to be a feasible validation tool for cloud (shadow) motion vectors.

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.

In solar tower plants, radiation losses between the heliostat field and the receiver occur due to atmospheric extinction which varies with site and time. Currently, atmospheric extinction is usually approximated using a few constant standard atmospheric conditions in ray-tracing and plant optimization tools. Some tools allow the input of time dependent extinction data, but such site specific data sets are generally not available for prospective concentrated solar power (CSP) sites. In this paper, the most applied model equations which are implemented in different ray-tracing tools are summarized and compared. Several developed approaches to determine atmospheric extinction are presented. Furthermore, different studies about the effect of atmospheric extinction on the tower plant yield are summarized. It can be concluded that project developers should consider atmospheric extinction and its temporal variation as site specific data sets in power plant optimization, plant yield forecast and plant operation. The effect of atmospheric extinction can account for a reduction of the annual plant yield of up to several percent points and is dependent on the heliostat field size, the operation strategy and the on-site atmospheric conditions. Different approaches to determine atmospheric extinction for solar tower plants at a future CSP site have been developed and validated in the past and can be applied dependent on the prevailing atmospheric conditions. The costs of a power plant can be lowered by reducing the simulation uncertainty since it implies in turn a reduction of risk margins in plant yield forecasts. (C) 2017 Elsevier Ltd. All rights reserved.

This chapter presents the principles of CSP (concentrating solar power) performance assessment. First, the measurement parameters for performance assessment are introduced, before we provide an overview of solar resource assessment for CSP. Then, standardization activities for CSP performance assessment are explained. The first section focusses on parameters and measurement approaches required for performance assessment of CSP systems by means of energy or enthalpy balancing. This includes the characterization of the system under testing in terms of focusing state and cleanliness as well as typical approaches for performance comparison and rating. In the section on solar resource assessment the meteorological parameters relevant for CSP and their determination with ground measurements, satellites, and numerical weather prediction models are explained. In the last section, the situation of the CSP activities in the international standardization committees is explained, and a list of the standards, published and in draft, is given.

The planning, Engineering, and financing of concentrating solar power (CSP) plants requires solar resource Information based on long-term historical databases. The Operation of CSP plants requires knowledge on a wider range of the upcoming meteorlogical conditions. Obviosly, there is the Need for forecasting direct normal Irradiation (DNI), but in Addition to this, other meteorological Parameters as air temperature, humidity, wind Speed, and wind gust Speed are needed. After defining meteorological terminology in Chapter 8.1.1. we focus on forecast and verification strategies in Chapter 8.1.2. Chapters 8.2 and 8.3 deal with the forecasting and nowcasting of irradiances, while Chapter 8.4 discusses development ti be expected in the future

Highly spatially and temporally resolved solar irradiance maps are of special interest for predicting ramp rates and for optimizing operations in solar power plants. Irradiance maps with lead times between 0 and up to 30 min can be generated using all-sky imager based nowcasting systems or with shadow camera systems. Shadow cameras provide photos of the ground taken from an elevated position below the clouds. In this publication, we present a shadow camera system, which provides spatially resolved Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI) and Global Tilted Irradiance (GTI) maps. To the best of our knowledge, this is the first time a shadow camera system is achieved. Its generated irradiance maps have two purposes: (1) The shadow camera system is already used to derive spatial averages to benchmark all-sky imager based nowcasting systems. (2) Shadow camera systems can potentially provide spatial irradiance maps for plant operations and may act as nowcasting systems. The presented shadow camera system consists of six cameras taking photos from the top of an 87 m tower and is located at the Plataforma Solar de Almeria in southern Spain. Out of six photos, an orthonormalized image (orthoimage) is calculated. The orthoimage under evaluation is compared with two reference orthoimages. Out of the three orthoimages and one additional pyranometer and pyrheliometer, spatially resolved irradiance maps (DNI, GHI, GTI) are derived. In contrast to satellites, the shadow camera system uses shadows to obtain irradiance maps and achieves higher spatial and temporal resolutions. The preliminary validation of the shadow camera system, conducted in detail on two example days (2015-09-18, 2015-09-19) with 911 one-minute averages, shows deviations between 4.2% and 16.7% root mean squared errors (RMSE), 1.6% and 7.5% mean absolute errors (MAE) and standard deviations between 4.2% and 15.4% for DNI maps calculated with the derived approach. The GHI maps show deviations below 10% RMSE, between 2.1% and 7.1% MAE and standard deviations between 3.2% and 7.9%. Three more days (2016-05-11, 2016-09-01, 2016-12-09) are evaluated, briefly presented and show similar deviations. These deviations are similar or below all-sky imager based nowcasts for lead time zero minutes. The deviations are small for photometrically uncalibrated, low-cost and off-the-shelf surveillance cameras, which is achieved by a segmentation approach. (C) 2017 The Author(s). Published by Elsevier Ltd.

At any site, the bankability of a projected solar power plant largely depends on the accuracy and general quality of the solar radiation data generated during the solar resource assessment phase. The term "site adaptation" has recently started to be used in the framework of solar energy projects to refer to the improvement that can be achieved in satellite-derived solar irradiance and model data when short-term local ground measurements are used to correct systematic errors and bias in the original dataset. This contribution presents a preliminary survey of different possible techniques that can improve long-term satellite-derived and model-derived solar radiation data through the use of short-term on-site ground measurements. The possible approaches that are reported here may be applied in different ways, depending on the origin and characteristics of the uncertainties in the modeled data. This work, which is the first step of a forthcoming in-depth assessment of methodologies for site adaptation, has been done within the framework of the International Energy Agency Solar Heating and Cooling Programme Task 46 "Solar Resource Assessment and Forecasting". (C) 2016 Elsevier Ltd. All rights reserved.

For solar resource assessment of solar power plants and adjustment of satellite data, high accuracy measurement data of irradiance and ancillary meteorological data is needed. For the MENA region (Middle East and Northern Africa), which is of high importance for concentrating solar power applications, so far merely 2 publicly available ground measurement stations existed (BSRN network). This gap has been filled by ten stations in Morocco, Algeria, Tunisia, Egypt and Jordan. In this publication the data quality is analyzed by evaluating data completeness and the cleanliness of irradiance sensors in comparison for all of the stations. The pyrheliometers have an average cleanliness of 99.2 % for week-daily cleaning. This is a 5 times higher effort than for Rotating Shadowband Irradiometer (RSI) stations which even have a slightly higher average cleanliness of 99.3 % for weekly cleaning. Furthermore, RSI stations show a data completeness of 99.4 % compared to 93.6 % at the stations equipped with thermal sensors. The results of this analysis are used to derive conclusions concerning instrument choice and are hence also applicable to other solar radiation measurements outside the enerMENA network. It turns out that RSIs are the more reliable and robust choice in cases of high soiling, rare station visits for cleaning and maintenance, as usual in desert sites. Furthermore, annual direct normal and global horizontal irradiation as well as average meteorological parameters are calculated for all of the stations.

Solar radiation reflected by concentrating mirrors is attenuated due to atmospheric extinction as it travels to the receiver of a solar tower plant. The lack of information on the magnitude of extinction increases the uncertainties in yield analysis and tower plant design. In-situ measurements of atmospheric extinction as well as measurement correction methods have been recently performed and developed (Hanrieder et al., 2012, 2015), but specific information is unavailable for individual plant projects. It is well known though that the extinction varies significantly with site and time.To overcome this absence of information a model to derive the attenuation loss between heliostat and receiver from common direct normal irradiance (DNI) measurements was developed by Sengupta and Wagner (2011) (SW2011 model). We present an updated version of that model and a comparison between the performance of the models using extinction measurements. In the new approach presented here, different precipitable water vapor (PWV) amounts are considered and the model is adjusted to the elevation of the investigated site.The strongest assumption in this approach is the assumption about the aerosol extinction height profile. Three different height profiles are tested for the Plataforma Solar de Almeria (PSA) resulting in three different new transmittance models.The SW2011 as well as the three new models are evaluated with one year of corrected extinction data derived with the ABC (absorption and broadband correction) method of Hanrieder et al. (2015) and a Vaisala FS11 scatterometer at PSA.The new models show a mean difference to the reference data set of 0.01, 0.05 and 0.03 and a root mean square error (RMSE) of 0.052, 0.056 and 0.049 (compared to a mean bias of -0.08 and RMSE of 0.095 for the SW2011 model for transmittances through a 1 km slant range). These results indicate the importance of adequate assumptions for the aerosol height profile. Testing the developed TM with the LIVAS height profile (Amiridis et al., 2015) for PSA shows satisfying results and this motivates testing the approach for other sites. By applying an additional correction for the Linke turbidity (TL) derived as in Ineichen and Perez (2002) the mean bias can be further lowered.An uncertainty analysis shows that the absolute uncertainty coincide with the RMSE levels of the evaluation. Performing the additional TL correction promises an improvement of the overall performance of the model.The new models outperform the SW2011 model due to the PWV and elevation adjustments. The approach can be applied for different sites and incorporated in already existing ray-tracing or plant optimization tools. It is expected to be valuable for reducing uncertainty in power tower design and operations. (C) 2016 Elsevier Ltd. All rights reserved.