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Photovoltaic (PV) power data are a valuable but as yet under-utilised resource that could be used to characterise global irradiance with unprecedented spatio-temporal resolution. The resulting knowledge of atmospheric conditions can then be fed back into weather models and will ultimately serve to improve forecasts of PV power itself. This provides a data-driven alternative to statistical methods that use post-processing to overcome inconsistencies between ground-based irradiance measurements and the corresponding predictions of regional weather models (see for instance Frank et al., 2018). This work reports first results from an algorithm developed to infer global horizontal irradiance as well as atmospheric optical properties such as aerosol or cloud optical depth from PV power measurements.
The temperature of photovoltaic modules is modelled as a dynamic function of ambient temperature, shortwave and longwave irradiance and wind speed, in order to allow for a more accurate characterisation of their efficiency. A simple dynamic thermal model is developed by extending an existing parametric steady-state model using an exponential smoothing kernel to include the effect of the heat capacity of the system. The four parameters of the model are fitted to measured data from three photovoltaic systems in the Allgäu region in Germany using non-linear optimisation. The dynamic model reduces the root-mean-square error between measured and modelled module temperature to 1.58 K on average, compared to 3.03 K for the steady-state model, whereas the maximum instantaneous error is reduced from 20.02 to 6.58 K.
This dataset contains data from two measurement campaigns in autumn 2018 and summer 2019 that were part of the BMWi project "MetPVNet", and serve as a supplement to the paper "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions", published in the special edition of "Advances in Science and Research", the proceedings of the 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019.
Data are resampled to one minute, and include:
PV module temperature
Ambient temperature
Plane-of-array irradiance
Windspeed
Atmospheric thermal emission
The data were used for the dynamic temperature model, as presented in the paper
Incoming solar radiation is an important driver of our climate and weather. Several studies (see for instance Frank et al. 2018) have revealed discrepancies between ground-based irradiance measurements and the predictions of regional weather models. In the realm of electricity generation, accurate forecasts of solar photovoltaic (PV)energy yield are becoming indispensable for cost-effective grid operation: in Germany there are 1.6 million PVsystems installed, with a nominal power of 46 GW (Bundesverband Solarwirtschaft 2019). The proliferation of PV systems provides a unique opportunity to characterise global irradiance with unprecedented spatiotemporalresolution, which in turn will allow for highly resolved PV power forecasts.
In view of the rapid growth of solar power installations worldwide, accurate forecasts of photovoltaic (PV) power generation are becoming increasingly indispensable for the overall stability of the electricity grid. In the context of household energy storage systems, PV power forecasts contribute towards intelligent energy management and control of PV-battery systems, in particular so that self-sufficiency and battery lifetime are maximised. Typical battery control algorithms require day-ahead forecasts of PV power generation, and in most cases a combination of statistical methods and numerical weather prediction (NWP) models are employed. The latter are however often inaccurate, both due to deficiencies in model physics as well as an insufficient description of irradiance variability.
The rapid increase in solar photovoltaic (PV) installations worldwide has resulted in the electricity grid becoming increasingly dependent on atmospheric conditions, thus requiring more accurate forecasts of incoming solar irradiance. In this context, measured data from PV systems are a valuable source of information about the optical properties of the atmosphere, in particular the cloud optical depth (COD). This work reports first results from an inversion algorithm developed to infer global, direct and diffuse irradiance as well as atmospheric optical properties from PV power measurements, with the goal of assimilating this information into numerical weather prediction (NWP) models.
The electricity grid of the future will be built on renewable energy sources, which are highly variable and dependent on atmospheric conditions. In power grids with an increasingly high penetration of solar photovoltaics (PV), an accurate knowledge of the incoming solar irradiance is indispensable for grid operation and planning, and reliable irradiance forecasts are thus invaluable for energy system operators. In order to better characterise shortwave solar radiation in time and space, data from PV systems themselves can be used, since the measured power provides information about both irradiance and the optical properties of the atmosphere, in particular the cloud optical depth (COD). Indeed, in the European context with highly variable cloud cover, the cloud fraction and COD are important parameters in determining the irradiance, whereas aerosol effects are only of secondary importance.
Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m−2 (7.35 W m−2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m−2 under clear and cloudy skies, respectively.
During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.
Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. Specifically, the aerosol (cloud) optical depth is inferred during clear sky (completely overcast) conditions. The method is tested on data from two measurement campaigns that took place in Allgäu, Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 minute resolution, the hourly global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 11.45 W m−2, averaged over the two campaigns, whereas for the retrieval using coarser 15 minute power data the mean bias error is 16.39 W m−2.
During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a one-dimensional radiative transfer simulation, and the results are compared to both satellite retrievals as well as data from the COSMO weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and are properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work.
Reliable and regional differentiated power forecasts are required to guarantee an efficient and economic energy transition towards renewable energies. Amongst other renewable energy technologies, e.g. wind mills, photovoltaic systems are an essential component of this transition being cost-efficient and simply to install. Reliable power forecasts are however required for a grid integration of photovoltaic systems, which among other data requires high-resolution spatio-temporal global irradiance data. Hence the generation of robust reviewed global irradiance data is an essential contribution for the energy transition.
This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications can benefit from forecasts with a high temporal resolution to address sudden fluctuations of PV-power. However, cloud formation processes and movements are subject to ongoing research. For now-casting applications, all-sky-imagers (ASI) are used to offer an appropriate forecasting for aforementioned application. Recent research aims to achieve these forecasts via deep learning approaches, either as an image segmentation task to generate a DNI forecast through a cloud vectoring approach to translate the DNI to a GHI with ground-based measurement (Fabel et al., 2022; Nouri et al., 2021), or as an end-to-end regression task to generate a GHI forecast directly from the images (Paletta et al., 2021; Yang et al., 2021). While end-to-end regression might be the more attractive approach for off-grid scenarios, literature reports increased performance compared to smart-persistence but do not show satisfactory forecasting patterns (Paletta et al., 2021). This work takes a step back and investigates the possibility to translate ASI-images to current GHI to deploy the neural network as a feature extractor. An ImageNet pre-trained deep learning model is used to achieve such translation on an openly available dataset by the University of California San Diego (Pedro et al., 2019). The images and measurements were collected in Folsom, California. Results show that the neural network can successfully translate ASI-images to GHI for a variety of cloud situations without the need of any external variables. Extending the neural network to a forecasting task also shows promising forecasting patterns, which shows that the neural network extracts both temporal and momentarily features within the images to generate GHI forecasts.
In den Atmosphärenwissenschaften spielt die Strahlungsbilanz der Erde eine wichtige Rolle für unser Verständnis des Klimasystems. Hier liefern ausgereifte Satellitenprodukte dekadische Klimazeitreihen mit einer so hohen Genauigkeit, dass z.B. Änderungen im Zusammenhang mit dem Klimawandel detektiert werden können. Dies gilt insbesondere auch für die solaren Strahlungsflüsse an der Erdoberfläche. Beim Vergleich dieser Satellitenprodukte mit instantanen Beobachtungen der Strahlung am Erdboden sind jedoch oft erhebliche Abweichungen feststellbar, die hauptsächlich durch kleinskalige Variabilität in der räumlichen Struktur von Wolken und ihrer Strahlungswirkung verursacht werden. Hier ist auch zu bedenken, dass Bodenbeobachtungen fast einer Punktmessung entsprechen, während Satellitenpixel eine Fläche in der Größenordnung von Quadratkilometern abtasten.
Renewable energies play an increasingly important role for energy production in Europe. Unlike coal or gas powerplants, solar energy production is highly variable in space and time. This is due to the strong variability of cloudsand their influence on the surface solar irradiance. Especially in regions with large contribution from photovoltaicpower production, the intermittent energy feed-in to the power grid can be a risk for grid stability. Therefore goodforecasts of temporal and spatial variability of surface irradiance are necessary to be able to properly regulate thepower supply.
Due to the policy goals for sustainable energy production, renewable energy plants such as photovoltaics are increasingly in use. The energy production from solar radiation depends strongly on atmospheric conditions. As the weather mostly changes, electrical power generation fluctuates, making technical planning and control of power grids to a complex problem.
For the winter 1999/2000 transport of air masses out of the vortex to mid-latitudes and ozone destruction inside and outside the northern polar vortex is studied to quantify the impact of earlier winter (before March) polar ozone destruction on mid-latitude ozone.
Nearly 112 000 trajectories are started on 1 December 1999 on 6 different potential temperature levels between 500–600 K and for a subset of these trajectories photo-chemical box-model calculations are performed. We linked a decline of −0.9% of mid-latitude ozone in this layer occurring in January and February 2000 to ozone destruction inside the vortex and successive transport of these air masses to mid-latitudes.
Further, the impact of denitrification, PSC-occurrence and anthropogenic chlorine loading on future stratospheric ozone is determined by applying various scenarios. Lower stratospheric temperatures and denitrification were found to play the most important role in the future evolution of polar ozone depletion.
Nitric acid partitioning in cirrus clouds: a synopsis based on field, laboratory and model studies
(2003)
From a synopsis of field, laboratory and model studies at T>205 K as well as from the field experiments POLSTAR at T<205 K we derive a general picture of the partitioning of nitric acid (HNO3) in cirrus clouds and a new hypothesis on the uptake of HNO3 on ice particles:
A substantial part of nitric acid remains in the gas phase under cirrus cloud conditions. The HNO3 removed from the gas phase is distributed between interstitial aerosol and ice particles in dependence on the temperature and ice surface, respectively. In cold cirrus clouds with small ice surface areas (T <205 K) the partitioning is strongly in favour of interstitial ternary solution particles while in warmer cirrus clouds with large ice surface areas the uptake on ice dominates. Consequently, denitrification via sedimenting ice particles may occur only in the -more frequently occurring- warm cirrus clouds
The HNO3 coverage on ice is found to be different for ice particles and ice films. On ice films the coverage can increase with decreasing temperature from about 0.1 to 0.8 monolayer, while that on ice particles is found to decrease with temperature and PHNO3 from 0.1 to 0.001 monolayer. An HNO3 uptake behaviour following dissociative Langmuir isotherms where the coverage decreases for descending temperatures may explain the observations for ice particles
From a comparison of the HNO3 measurements with model calculations it is found that (i) the global model of Lawrence and Crutzen (1998) overestimates the HNO3 partitioning in favour of the ice particles (ii) the Langmuir surface chemistry model of Tabazadeh et al. (1999) overestimates HNO3 coverages for temperatures ≤210 K More appropriate coverages are calculated when implementing in that model a temperature dependent function for the adsorption free energy (ΔGads (T)), which is empirically derived from the coverage measurements.