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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.
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.
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.
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.
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.
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.
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.
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.