Classifying direct normal irradiance 1-minute temporal variability from spatial characteristics of geostationary satellite-based cloud observations

t Variability of solar surface irradiances in the 1-minute range is of interest especially for solar energy applications. Eight variability classes were previously defined for the 1 min resolved direct normal irradiance (DNI) variability inside an hour. In this study spatial structural parameters derived fromsatellite-based cloud observations are used as classifiers in order to detect the associated direct normal irradiance (DNI) variability class in a supervised classification scheme. A neighbourhood of 3×3 to 29×29 satellite pixels is evaluated to derive classifiers describing the actual cloud field better than just using a single satellite pixel at the location of the irradiance observation. These classifiers include cloud fraction in a window around the location of interest, number of cloud/cloud free changes in a binary cloud mask in this window, number of clouds, and a fractal box dimension of the cloud mask within the window. Furthermore, cloud physical parameters as cloud phase, cloud optical depth, and cloud top temperature are used as pixel-wise classifiers. A classification scheme is set up to search for the DNI variability class with a best agreement between these classifiers and the pre-existing knowledge on the characteristics of the cloud field within each variability class from the reference data base. Up to 55 % of all DNI variability class members are identified in the same class as in the reference data base. And up to 92 % cases are identified correctly if the neighbouring class is counted as success as well – the latter is a common approach in classifying natural structures showing no clear distinction between classes as in our case of temporal variability. Such a DNI variability classification method allows comparisons of different project sites in a statistical and automatic manner e.g. to quantify short-term variability impacts on solar power production. This approach is based on satellite-based cloud observations only and does not require any ground observations of the location of interest

Scale Matters: Attribution Meets the Wavelet Domain to Explain Model Sensitivity to Image Corruptions

Neural networks have shown remarkable performance in computer vision, but their deployment in real-world scenarios is challenging due to their sensitivity to image corruptions. Existing attribution methods are uninformative for explaining the sensitivity to image corruptions, while the literature on robustness only provides model-based explanations. However, the ability to scrutinize models' behavior under image corruptions is crucial to increase the user's trust. Towards this end, we introduce the Wavelet sCale Attribution Method (WCAM), a generalization of attribution from the pixel domain to the space-scale domain. Attribution in the space-scale domain reveals where and on what scales the model focuses. We show that the WCAM explains models' failures under image corruptions, identifies sufficient information for prediction, and explains how zoom-in increases accuracy.Comment: main: 9 pages, appendix 19 pages, 32 figures, 5 table

Analysis of the uncertainty in the estimates of regional PV power generation evaluated with the upscaling method

International audienceThe estimation of the regional photovoltaic (PV) power generation is an important step prior to the forecast of the PV power generation and its integration into the energy supply system. The large majority of PV plants not being measured in Germany, the total PV power generated in a region is commonly estimated by upscaling the power production of a set of reference PV plants to the entireness of the plants installed in the considered area. A given uncertainty can be expected in the estimation of the power generation of a PV plant with the upscaling method when the reference plants used have different configurations or weather conditions. To gain better insight into the performance of the upscaling method, its error has been analysed using power measurements of a set of 366 PV plants. The analysis allows an understanding of the mechanisms underlying the uncertainty of the upscaling method and quantifies its error for the test study considered. In the case study analysed, it could be shown that the quarter hourly RMSE 1 value decreases with an increasing number of reference plants and a decreasing number of un-metered plants. It could also be shown that even for a large number of reference plants, a variation of the RMSE between 0.01 and 0.025 kW/kWp can be observed, depending on the choice of the reference plants. It is shown that the average distance between a reference and unknown plant constitutes a good indicator of the performance of a set of reference plants, but that the match between the characteristics of the reference and unknown plant also plays an important role, which could not be quantified with the available dataset

Evaluating the spatial and temporal variations of the performance of CAMS Radiation Service and HelioClim-3 databases of surface irradiation in Germany

International audienceSatellite-derived databases of the surface solar irradiance (SSI) have become an essential source of information for various applications in solar energy. Assessing the accuracy of these data by comparison with reference in-situ measurements is therefore ever gaining in importance. Several authors have reported that performances of a given database differ from one site to another depending on the geographical region, topography, orography, climate, viewing angle from the satellite.. . A good understanding of the spatial and temporal variation of the SSI estimation error is key to allow end-user to have an appropriate level of expectation on the accuracy of this data. This knowledge can also be very important for the further developments of the algorithms. The present work contributes to this objective by extending the validation works carried out in the last years for numerous regions (Europe, Brazil, Egypt, Arabic Peninsula, Morocco and The Netherlands) to Germany. We consider two databases: the CAMS Radiation Service version 3 (abbreviated as CAMS-Rad) and the HelioClim-3 version 5 (abbreviated as HC3v5) that are widely used by academics and practitioners. The present communication focuses on several stations located in Germany operated by the Deutscher Wetterdienst (DWD). They are spread over the country, thus allowing the study of the spatial consistency of the performance of each database. Measurements of 10 min means of global irradiance made by pyranometers (CM11 and CM21) and SCAPP set publicly available by the DWD for the period 2010-2018 (9 years) have been used for the validation. Measurements were quality-checked using the method described by Roesch et al. (2011). Satellite-derived SSI estimates were collected from the SoDa web site (www.soda-pro.com) for the same locations and same instants of measurements for both databases. CAMS-Rad uses the Heliosat-4 method with different inputs: the clear-sky radiation is evaluated using Copernicus Atmosphere Monitoring Service (CAMS) information on the aerosol, ozone and water vapour contained in the atmosphere, the cloud attenuation is considered using cloud optical properties retrieved every 15 min from Meteosat imagery using APPOLO. The second database is the HelioClim-3v5 that is derived from Meteosat images using the Heliosat-2 method, McClear and CAMS products. For each database, standard error metrics are computed at each station. A particular attention is paid in the presentation of the validation results to evaluate the effects of different parameters such as e.g. the solar elevation and the clearness index on the error. A focus of this work is laid on the consistency of the errors with space and time

PyPVRoof: a Python package for extracting the characteristics of rooftop PV installations using remote sensing data

Photovoltaic (PV) energy grows at an unprecedented pace, which makes it difficult to maintain up-to-date and accurate PV registries, which are critical for many applications such as PV power generation estimation. This lack of qualitative data is especially true in the case of rooftop PV installations. As a result, extensive efforts are put into the constitution of PV inventories. However, although valuable, these registries cannot be directly used for monitoring the deployment of PV or estimating the PV power generation, as these tasks usually require PV systems {\it characteristics}. To seamlessly extract these characteristics from the global inventories, we introduce {\tt PyPVRoof}. {\tt PyPVRoof} is a Python package to extract essential PV installation characteristics. These characteristics are tilt angle, azimuth, surface, localization, and installed capacity. {\tt PyPVRoof} is designed to cover all use cases regarding data availability and user needs and is based on a benchmark of the best existing methods. Data for replicating our accuracy benchmarks are available on our Zenodo repository \cite{tremenbert2023pypvroof}, and the package code is accessible at this URL: \url{https://github.com/gabrielkasmi/pypvroof}.Comment: 22 pages, 9 figures, 5 table

Climate proofing the renewable electricity deployment in Europe - Introducing climate variability in large energy systems models

Climate and weather conditions influence energy demand. as well as electricity generation, especially due to the strong development of renewable energy. The changes of the European energy mix, together with ongoing climate change, raise a number of questions on impact on the electricity sector. In this paper we present results for the whole of the European power sector regarding on how considering current and future climate variability affects the results of a TIMES energy system model for the whole European power sector (eTIMES-EU) up to 2050. For each member-state we consider six climate projections to generate future capacity factors for wind, solar and hydro power generation. as well as temperature impact on electricity demand for heating and cooling. These are input into the eTIMES-EU model to assess how climate affects the optimal operation of the power system and if current EU-wide RES and emissions target deployment may be affected. Results show that although at EU-wide level there are no substantial changes, there are significant differences in countries RES deployment (especially wind and solar) and in electricity trade.info:eu-repo/semantics/publishedVersio

Variability class dependent evaluation of the CAMS Radiation Service

The Copernicus Atmospheric Monitoring Service (CAMS) offers Solar radiation services (CRS) providing information on surface solar irradiance (SSI). The service is currently derived from Meteosat Second Generation (MSG) and the service evolution includes its extension to other parts of the globe. CRS provides clear and all sky time series combining satellite data products with numerical model output from CAMS on aerosols, water vapour and ozone. These products are available from 2004 until yesterday. A regular quality control of input parameters, quarterly benchmarking against ground measurements and automatic consistency checks ensure the service quality. Variability of solar surface irradiances in the 1-minute range is of interest especially for solar energy applications. The variability classes can be defined based on ground as well as satellite-based measurements. This study will present the evaluation of the CAMS CRS based on the eight variability classes derived from ground observations of direct normal irradiation (DNI) (Schroedter-Homscheidt et al., 2018). Such an analysis will help assess the impact of recent improvements in the derivation of all sky irradiance under different cloudy conditions. References: Schroedter-Homscheidt, M., S. Jung, M. Kosmale, 2018: Classifying ground-measured 1 minute temporal variability within hourly intervals for direct normal irradiances. – Meteorol. Z. 27, 2, 160–179. DOI:10.1127/metz/2018/0875

Leitstudie 2010

Strategien zu erarbeiten, die aufzeigen, wie das langfristige Klimaschutzziel 2050 in Deutschland erreicht werden kann, ist das oberste Ziel von Studien, die seit gut einem Jahrzehnt vom DLR-ITT, Abteilung Systemanalyse und Technikbewertung mit wechselnden Projektpartnern für das BMU und das UBA durchgeführt werden. In der Leitstudie 2010 entstanden auf der Basis differenzierter und aktualisierter Potenzialabschätzungen, die technische, strukturelle und ökologische Kriterien berücksichtigen, und detaillierten Technik- und Kostenanalysen zu den Einzeltechnologien der Erneuerbaren verschiedene Szenarien ihres möglichen langfristigen Ausbaus in Wechselwirkung mit den übrigen Teilen der Energieversorgung in Deutschland. Für die Leitstudie 2010 haben die Projektpartner DLR, Stuttgart und Fraunhofer-IWES, Kassel erstmals mittels geeigneter Modelle eine vollständige dynamische und teilweise räumlich aufgegliederte Simulation der Stromversorgung durchgeführt. Außerdem wird der Untersuchungsraum für diese Simulation auf ganz Europa (einschließlich einiger nordafrikanischer Länder) ausgedehnt, um die Wechselwirkungen eines nationalen Umbaus der Energieversorgung mit der Entwicklung in Nachbarregionen erfassen zu können

Improving the satellite retrieval of surface solar irradiance during an eclipse

Solar eclipse causes high magnitude fluctuations in the Surface Solar Irradiance (SSI) for a short duration and consequently reduces the output of solar PV systems. Grid operators try to estimate the impending loss in PV power generation prior to the occurrence of an eclipse in order to schedule conventional generators for compensating the loss. The worldwide installed capacity of grid connected solar PV systems is expected to steeply rise in the coming decade as a result of the various policy initiatives aimed to tackle the climate change. In future electric supply networks with a high penetration of solar PV systems, such large ramps in generation could impact the stability of the network. Although a solar eclipse is a purely deterministic phenomenon, it’s impact on the satellite retrieval of Surface Solar Irradiance (SSI) is complicated due to the possibility of cloud presence in the regions affected by the eclipse. The extraterrestrial solar irradiance is reduced by the moon during an eclipse. On the one hand this causes clouds to appear darker and they get assigned lower reflectance values than they should have in reality. This leads to predicting higher values for the solar irradiance under these clouds than expected. On the other hand, the eclipse also reduces the clear sky irradiance reaching the earth surface. We developed a method to make corrections for both of these effects on the High Resolution Visible (HRV) channel images from Meteosat-11 The results are validated against ground measurements of irradiance provided by BSRN, IEA-PVPS, DTN and the National Weather Services networks. The validation is performed for sites with locations across Europe and for the last two eclipses

A parametric model for wind turbine power curves incorporating environmental conditions

A wind turbine's power curve relates its power production to the wind speed it experiences. The typical shape of a power curve is well known and has been studied extensively. However, power curves of individual turbine models can vary widely from one another. This is due to both the technical features of the turbine (power density, cut-in and cut-out speeds, limits on rotational speed and aerodynamic efficiency), and environmental factors (turbulence intensity, air density, wind shear and wind veer). Data on individual power curves are often proprietary and only available through commercial databases. We therefore develop an open-source model for pitch regulated horizontal axis wind turbine which can generate the power curve of any turbine, adapted to the specific conditions of any site. This can employ one of six parametric models advanced in the literature, and accounts for the eleven variables mentioned above. The model is described, the impact of each technical and environmental feature is examined, and it is then validated against the manufacturer power curves of 91 turbine models. Versions of the model are made available in MATLAB, R and Python code for the community