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.

Hydrogen as a versatile, greenhouse gas-free energy carrier will play an important role in our future economy. Yet sustainable, competitive production and distribution of hydrogen remains a challenge. Highly integrated solar water splitting systems aim to combine solar energy harvesting and electrolysis in a single device, similar to a photovoltaic module.[1] Such a system can produce hydrogen locally without the requirement to be connected to the electricity grid. Unlike large electrolysis that draws power from the grid, the power density of such a device is reduced so far that it does not require active cooling, but its operating temperature will closely follow outdoor conditions.Here, we present our work on high-efficiency integrated solar water splitting devices based on multi-junction solar absorbers. The light-absorbing component is sensitive to the shape of the solar spectrum and generally becomes more efficient at lower temperatures. Catalysis, on the other hand, benefits from higher temperatures. These conflicting trends wih respect to the temperature impact the design of the solar hydrogen production system. We analyse how the local climate affects production efficiency[2] and show in a lab study that adequate system design allows efficient operation at temperatures as low as -20°C.[3] These insights can help to design small-scale distributed solar hydrogen production for both temperate regions, but also more extreme climatic conditions.[1] M.M. May et al., Nature Communications 6 (2015), 8286.[2] M. Kölbach et al, Sustainable Energy & Fuels 6 (2022), 4062.[3] M. Kölbach, K. Rehfeld, M.M. May, Energy & Environmental Sciences 14 (2021), 4410-4417.

West Africa has a great potential for the application of solar energy systems, as it combines high levels of solar irradiance with a lack of energy production. Southern West Africa is a region with a very high aerosol load. Urbanization, uncontrolled fires, traffic as well as power plants and oil rigs lead to increasing anthropogenic emissions. The naturally circulating north winds bring mineral dust from the Sahel and Sahara and monsoons - sea salt and other oceanic compounds from the south. The EU-funded Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) project (2014–2018), dlivered the most complete dataset of the atmosphere over the region to date. In our study, we use in-situ measured optical properties of aerosols from the airborne campaign over the Gulf of Guinea and inland, and from ground measurements in coastal cities. Based on an analysis of the aerosol optical properties form the DACCIWA measurement campaign, the impact of aerosol on PV power is investigated for polycrystalline silicon and amorphous silicon technology using a spectrally resolved model chain. The model considers both spectral effects on global irradiance due to different aerosol properties as well as the spectral response of different PV technologies. First, the contribution of various aerosol types (mineral dust, biomass burning and anthropogenic pollution) derived from a post-project classification is studied. Subsequently, differences between these imaginary aerosol scenarios and a real case during a biomass burning outbreak on July 13, 2016 in Benin are presented. The results show that aerosol emissions due to the biomass outbreak on the day of the case study in Cotonou lead to solar flux losses of up to 55% and photovoltaic power reduction of up to 81% for the polycrystalline cell and 78% for the amorphous cell. The relative impact of aerosols differs depending on aerosol type and concentration, being larger for low solar zenith angles than at noon. For the situation studied in Cotonou, Benin, we are able to show that the inclusion of spectral aspects leads to a significant effect when calculating the PV power. Comparing the effects of aerosols on the photovoltaic power of the two technologies, we find that the amorphous cell suffers a greater reduction in power during the morning and evening hours - when there is more diffuse irradiance - of 36% than the polycrystalline cell (27%). Conversely, in the middle of the day, we observe greater PV power reduction of the polycrystalline cell of 12% compared to the amorphous cell (8%). Acknowledgements: Funding was provided by the German BMWi under contract 0350009A and BMBF under contract 03SF0567A-.

Hydrogen is a versatile energy carrier. When produced with renewable energy by water splitting, it is a carbon neutral alternative to fossil fuels. The industrialization process of this technology is currently dominated by electrolyzers powered by solar or wind energy. For small scale applications, however, more integrated device designs for water splitting using solar energy might optimize hydrogen production due to lower balance of system costs and a smarter thermal management. Such devices offer the opportunity to thermally couple the solar cell and the electrochemical compartment. In this way, heat losses in the absorber can be turned into an efficiency boost for the device via simultaneously enhancing the catalytic performance of the water splitting reactions, cooling the absorber, and decreasing the ohmic losses.[1,2] However,integrated devices (sometimes also referred to as “artificial leaves”), currently suffer from a lower technology readiness level (TRL) than the completely decoupled approach. Here, we describe our progress in designing integrated solar water splitting devices to power research stations in Antarctica as a first potentially economic competitive implementation of this technology.[3] In such remote world regions, local and small-scale hydrogen production can become both economically and environmentally favorable, since the logistics for fossil fuels are expensive and environmentally hazardous. One reason for the low TRL of integrated devices is the complex and poorly understood influence of different weather/climate conditions and changes in the solar spectra on their efficiency.[4] Therefore, we introduce an open-source Python-based model that combines solar cell physics, optical simulations, electrochemistry, as well as atmospheric and climate data as part of the “YaSoFo” environment.[5] We model and analyze the climatic response of a device based on state-of-the-art AlGaAs/Si dual-junction photoabsorbers in Antarctica. Furthermore, we present a first prototype demonstrating solar water splitting at temperatures as low as -20°C. [3] Our work gives important insights into the chances and challenges for thermally coupled solar water splitting and lays the foundation for our goal of using these devices in remote world regions with cold climates. [1] R. van de Krol, B. Parkinson, MRS Energy Sustain., 2017, 4(e13), 1-11 [2] S. Tembhurne, F. Nandjou, S. Haussener, Nat. Energy, 2019, 4, 399–407 [3] M. Kölbach, K. Rehfeld, M.M. May, Energy Environ. Sci., 2021,14, 4410-4417 [4] M. Reuß, J. Reul, T. Grube, M. Langemann, S. Calnan, M. Robinius, R. Schlatmann, U. Rau, D. Stolten, Sustain. Energy Fuels, 2019, 3, 801-813 [5] M. M. May, D. Lackner, J. Ohlmann, F. Dimroth, R. van de Krol, T. Hannappel, K. Schwarzburg, Sustain. Energy Fuels, 2017, 1, 492-503

The American Board of Emergency Medicine gathers extensive background information on the Accreditation Council of Graduate Medical Education-accredited emergency medicine residency and fellowship programs, as well as the residents and fellows training in those programs. We present the 2022 annual report on the status of physicians training in Accreditation Council of Graduate Medical Education-accredited emergency medicine training programs in the United States.

Integrated solar water splitting devices that produce hydrogen without the use of power inverters operate outdoors and are hence exposed to varying weather conditions. As a result, they might sometimes work at non- optimal operation points below or above the maximum power point of the photovoltaic component, which would directly translate into efficiency losses. Up until now, however, no common parameter describing and quantifying this and other real-life operating related losses (e.g. spectral mismatch) exists in the community. Therefore, the annual- hydrogen-yield-climatic-response (AHYCR) ratio is introduced as a figure of merit to evaluate the outdoor performance of integrated solar water splitting devices. This value is defined as the ratio between the real annual hydrogen yield and the theoretical yield assuming the solar-to-hydrogen device efficiency at standard conditions. This parameter is derived for an exemplary system based on state-of-the-art AlGaAs//Si dual-junction solar cells and an anion exchange membrane electrolyzer using hourly resolved climate data from a location in southern California and from reanalysis data of Antarctica. This work will help to evaluate, compare and optimize the climatic response of solar water splitting devices in different climate zones.

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.High resolution measurements from both PV systems and pyranometers were collected as part of the BMWi-funded MetPVNet project, in the Allgäu region during autumn 2018 and summer 2019. These data were then used together with a PV model and both the DISORT and MYSTIC radiative transfer schemes within libRadtran (Emde et al., 2016; Mayer and Kylling, 2005)⁠ to infer cloud optical depth as well as direct, diffuse and global irradiance under highly variable atmospheric conditions. Hourly averages of each of the retrieved quantities were compared with the corresponding predictions of the COSMO weather model as well as data from satellite retrievals, and periods with differing degrees of variability and different cloud types were analysed. The DISORT-based algorithm is able to accurately retrieve COD, direct and diffuse irradiance components as long as the cloud fraction is high enough, whereas under broken cloud conditions the presence of 3D effects can lead to large errors. In that case the global horizontal irradiance is derived from tilted irradiance measurements and/or PV data using a lookup table based on the MYSTIC 3D Monte Carlo radiative transfer solver (Mayer, 2009)⁠. This work will provide the basis for future investigations using a larger number of PV systems to evaluate the improvements to irradiance and power forecasts that could be achieved by the assimilation of inferred irradiance into an NWP model.ReferencesEmde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T. and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9(5), 1647–1672, doi:10.5194/gmd-9-1647-2016, 2016.Mayer, B.: Radiative transfer in the cloudy atmosphere, EPJ Web Conf., 1, 75–99, doi:10.1140/epjconf/e2009-00912-1, 2009.Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use, Atmos. Chem. Phys., 5(7), 1855–1877, doi:10.5194/acp-5-1855-2005, 2005.

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. Building on previous work (Buchmann, 2018), an improved forward model of PV power as a function of atmospheric conditions was developed. As part of the BMWi-funded project MetPVNet, PV power data from twenty systems in the Allgäu region were made available, and the corresponding irradiance, temperature and wind speed were measured during two measurement campaigns in autumn 2018 and summer 2019. System calibration was performed using all available clear sky days; the corresponding irradiance was simulated using libRadtran (Emde et al., 2016). Particular attention was paid to describing the dynamic variations in PV module temperature in order to correctly take into account the heat capacity of the solar panels. PV power data from the calibrated systems were then used together with both the DISORT and MYSTIC radiative transfer codes (Emde et al., 2016) to infer aerosol optical depth, cloud optical depth and irradiance under all sky conditions. The results were compared to predictions from the COSMO weather model, and the accuracy of the inverted quantities was compared using both a simple and more complex forward model. The potential of the method to extract irradiance data over a larger area as well as the increase in information from combining neighbouring PV systems will be explored in future work. References Buchmann, T., 2018: Potenzial von Photovoltaikanlagen zur Ableitung raum-zeitlich hoch aufgelöster Globalstrahlungsdaten. Heidelberg University, http://archiv.ub.uni-heidelberg.de/volltextserver/24687/. Emde, C., and Coauthors, 2016: The libRadtran software package for radiative transfer calculations (version 2.0.1). Geosci. Model Dev., 9, 1647–1672, doi:10.5194/gmd-9-1647-2016. https://www.geosci-model-dev.net/9/1647/2016/. Frank, C. W., S. Wahl, J. D. Keller, B. Pospichal, A. Hense, and S. Crewell, 2018: Bias correction of a novel European reanalysis data set for solar energy applications. Sol. Energy, 164, 12–24, doi:10.1016/j.solener.2018.02.012. https://doi.org/10.1016/j.solener.2018.02.012.

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. A promising approach to improving irradiance forecasts is to use the measured PV data themselves. In this work a novel algorithm was employed in order to infer global horizontal irradiance from measured PV data of household energy storage systems, with the goal of better characterising global horizontal irradiance (GHI) and ultimately improving irradiance and power forecasts. The inversion methods developed as part of the BMWi-funded MetPVNet project were applied to five PV-battery systems in different locations across Germany, in a pilot project sponsored by the local government of North Rhine-Westphalia (MWIDE NRW). High resolution measurements of PV power and current were used together with two different PV models in order to extract the plane-of-array irradiance. These data were then used together with both the DISORT and MYSTIC radiative transfer codes (Emde et al., 2016) to infer aerosol optical depth, cloud optical depth and irradiance under all sky conditions. The transposition of tilted to horizontal irradiance was performed with a new lookup table based on 3D radiative transfer simulations in MYSTIC. The PV-battery systems were all equipped with irradiance sensors to provide independent measurements of both global tilted irradiance (GTI) and GHI, in order to validate the proposed inversion and transposition methods. Comparisons were also made with the irradiance predictions of the ICON-D2 weather model, the irradiance and cloud optical properties from satellite retrievals and the aerosol optical depth from the relevant AERONET stations. This work can provide the basis for future investigations using a larger number of PV-battery systems to evaluate the improvements to irradiance forecasts by the assimilation of inferred irradiance into a NWP model. In addition, the results could be used to improve the intelligent control of the storage systems in the field. References Emde, C., and Coauthors, 2016: The libRadtran software package for radiative transfer calculations (version 2.0.1). Geosci. Model Dev., 9, 1647–1672, doi:10.5194/gmd-9-1647-2016. https://www.geosci-model-dev.net/9/1647/2016/.

English In the research project "MetPVNet", both, the forecast-based operation management in distribution grids and as well as the forecasts of the feed-in of PV-power from decentralized plants could be improved on the basis of satellite data and numerical weather forecasts. Based on a detailed network analyses for a real medium-voltage grid area, it was shown that both – the integration of forecast data based on satellite and weather data and the improvement of subsequent day forecasts based on numerical weather models – have a significant added value for forecast-based congestion management or redispatch and reactive power management in the distribution grid. Furthermore, forecast improvements for the forecast model of the German Weather Service were achieved by assimilating visible satellite imagery, and cloud and radiation products from satellites were improved, thus improving the database for short-term forecasting as well as for assimilation. In addition, several methods have been developed that will enable forecast improvement in the future, especially for weather situations with high cloud induced variability and high forecast errors. This article summarizes the most important project results.

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.

Purpose: To investigate age-related differences in outcomes of critically ill patients with sepsis around the world. Methods: We performed a secondary analysis of data from the prospective ICON audit, in which all adult ( >16 years ) patients admitted to participating ICUs between May 8 and 18, 2012, were included, except admissions for routine postoperative observation. For this sub-analysis, the 10,012 patients with completed age data were included. They were divided into five age groups - <= 50, 51-60, 61-70, 71-80, >80 years. Sepsis was defined as infection plus at least one organ failure. Results: A total of 2963 patients had sepsis, with similar proportions across the age groups (<= 50 = 25.2%: 51-60 = 30.3%; 61-70 = 32.8%; 71-80 = 30.7%; >80 = 30.9%). Hospital mortality increased with age and in patients >80 years was almost twice that of patients <= 50 years (493% vs 25.2%, p < .05). The maximum rate of increase in mortality was about 0.75% per year, occurring between the ages of 71 and 77 years. In multilevel analysis, age > 70 years was independently associated with increased risk of dying. Conclusions: The odds for death in ICU patients with sepsis increased with age with the maximal rate of increase occurring between the ages of 71 and 77 years. (C) 2019 Elsevier Inc. All rights reserved.

Soiling of photovoltaic (PV) systems compromises their performance causing a significant power loss and demanding periodical cleaning actions. This phenomenon raises great concerns in the solar energy field, thus leading to notable research efforts over the last decades. Soiling is caused by a dual action of dust deposition and biofouling. However, surprisingly, the microbiological contribution to PV soiling is often overlooked or underestimated. In this study, a variety of qPCR-based methods have been developed to quantify the microbial load of fungi, bacteria and phototrophs on PV panels. These protocols were evaluated by comparison with culture dependent methods, and were implemented with real solar plants for two years. The results show that the developed molecular methods are highly sensitive and reliable to monitor the microbial component of the soiling. Fungal biomass was clearly dominant in all analysed PV modules, while bacteria and phototrophs showed much lower abundance. Light microscopy and qPCR results revealed that melanised microcolonial fungi and phototrophs are the main biofilm-forming microorganisms on the studied solar panels. In particular, the fungal qPCR protocol is proposed as a useful tool for monitoring of PV soiling, and investigating the microbial contribution to specific soiling cases.

Well-designed and intelligently controlled battery storage systems are crucial for the successful integration of solar photovoltaic systems. The power variations in densely clustered systems such as those in residential areas could either be mitigated by small batteries in each household or by a district-scale storage solution. In this context it is useful to understand the correlation between individual array power outputs as a function of the distance between them. The study of correlation length and storage system response time provides a basis for optimal system design, depending on the context. Although it has already been shown that for a typical household storage system in Germany a dead time of 5 seconds in the control loop would lead to an small but non-negligible economic effect, in off-grid systems with 100% renewable energy supply the effects would be more dramatic, since the voltage and resulting power quality depends on the rapid response of the storage system to variations in power input. In order to characterise these effects the fluctuations in power output from 68 spatially separated photovoltaic arrays (each with 10 kWp) installed on the north campus of Karlsruhe Institute of Technology were analysed in detail with high-frequency data. This analysis was used to create virtual clusters of photovoltaic systems with different separation distances that were then used as inputs to a 32 kWh storage system on site. The effect of response time is studied by employing two sensors of different sampling rates. This allows one to quantify the resulting power smoothing due to both correlation effects and battery control algorithms, thus providing a benchmark for storage system integration in the local context.

An increase in electricity prices along with a decrease in the price of storage systems has led to rapid expansion of the PV-battery home storage system market in Germany. In order to be economically viable PV-storage systems must fulfill certain performance criteria, and in this context the system control strategy has a large impact on the overall system performance. In a nutshell, the control software should regulate the system in such a way as to maximize the self-supply ratio as well as the battery lifetime. An intelligent control strategy also has added benefits for the grid operator. At KIT 20 commercially available PV-battery systems have been analyzed with respect to specific performance criteria: of those the following were used to quantify the level of intelligence of the storage system control software: (a) whether the software contains a predictive module at all, and if so whether it depends on external weather data; (b) the success of delayed charging in reducing the time spent at high stage of charge (SOC) levels and (c) whether prediction errors lead to the battery not being fully charged so that the user's self-sufficiency is unnecessarily reduced. Since the different effects are not independent the goal is to quantify the effects on system performance and profitability in each case. This shows the effect of software on the overall economics of energy storage systems and complements other studies based on simulation or those that look at different aspects like cell aging in an isolated context. Roughly one quarter of the systems tested have an intelligent algorithm that controls the battery charging in such a way as to minimize calendar aging. In addition, differences of up to two years in battery lifetime are shown using voltage measurements with realistic household profiles and measured PV data. In this way, the present work outlines how control software can influence the performance and in particular the calendar aging of PV storage systems. (C) 2017 The Authors. Published by Elsevier Ltd.