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Anhand detaillierter Netzanalysen für ein reales Mittelspannungsnetzgebiet konnte gezeigt werden, dass sowohl die Einbindung von Prognosedaten auf Basis von Satelliten und Wetterdaten, als auch die Verbesserung von Folgetagsprognosen auf der Basis numerischer Wettermodelle einen deutlichen Mehrwert für ein prognosebasiertes Engpassmanagement bzw. Redispatch und Blindleistungsmanagement im Verteilnetz aufweisen. Auch Kurzfristprognosen auf der Basis von Satellitendaten haben einen positiven Effekt. Ein weiterer wichtiger Mehrwert des Projektes ist auch die Rückmeldung der kritischen Prognosesituationen aus Sicht der Anwendungsfälle, so dass wie bereits im Projekt gezeigt und darüber hinaus, Prognosen zielgerichteter auf die Anwendung im Verteilnetzbetrieb ausgelegt und optimiert werden können.
Weiterhin konnten Prognoseverbesserungen für das Vorhersagemodell des Deutschen Wetterdienstes durch die Assimilation von sichtbaren Satellitenbildern erreicht werden. Darüber hinaus wurden Wolken- und Strahlungsprodukte aus Satelliten verbessert und somit die Datenbasis für die Kurzfristprognose als auch für die Assimilation.
Darüber hinaus wurden verschiedene Methoden entwickelt, die zukünftig zu einer weiteren Prognoseverbesserung, insbesondere für Wettersituationen mit hohen Prognosefehlern, führen könnten. Solche Situationen wurden aus Sicht des Netzbetriebs und mithilfe von satellitenbasierten Analysen der Gesamtwetterlage für die Perioden der MetPVNet Messkampagnen identifiziert. Hierbei handelte es sich insbesondere um Situationen mit starker oder stark wechselhafter Bewölkung.
Für die MetPVNet Messkampagnen wurde auf der Basis eines Trainingsdatensatzes und in Abhängigkeit der Variabilitätsklasse die Abweichung der bodennahen Einstrahlung von Satellitendaten oder von Strahlungsprognosen quantifiziert. Diese Art der Informationen bietet zukünftig die Möglichkeit zur Bewertung der Prognosegüte.
The clear-sky radiative effect of aerosol–radiation interactions is of relevance for our understanding of the climate system. The influence of aerosol on the surface energy budget is of high interest for the renewable energy sector. In this study, the radiative effect is investigated in particular with respect to seasonal and regional variations for the region of Germany and the year 2015 at the surface and top of atmosphere using two complementary approaches.
First, an ensemble of clear-sky models which explicitly consider aerosols is utilized to retrieve the aerosol optical depth and the surface direct radiative effect of aerosols by means of a clear-sky fitting technique. For this, short-wave broadband irradiance measurements in the absence of clouds are used as a basis. A clear-sky detection algorithm is used to identify cloud-free observations. Considered are measurements of the short-wave broadband global and diffuse horizontal irradiance with shaded and unshaded pyranometers at 25 stations across Germany within the observational network of the German Weather Service (DWD). The clear-sky models used are the Modified MAC model (MMAC), the Meteorological Radiation Model (MRM) v6.1, the Meteorological–Statistical solar radiation model (METSTAT), the European Solar Radiation Atlas (ESRA), Heliosat-1, the Center for Environment and Man solar radiation model (CEM), and the simplified Solis model. The definition of aerosol and atmospheric characteristics of the models are examined in detail for their suitability for this approach.
Second, the radiative effect is estimated using explicit radiative transfer simulations with inputs on the meteorological state of the atmosphere, trace gases and aerosol from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. The aerosol optical properties (aerosol optical depth, Ångström exponent, single scattering albedo and asymmetry parameter) are first evaluated with AERONET direct sun and inversion products. The largest inconsistency is found for the aerosol absorption, which is overestimated by about 0.03 or about 30 % by the CAMS reanalysis. Compared to the DWD observational network, the simulated global, direct and diffuse irradiances show reasonable agreement within the measurement uncertainty. The radiative kernel method is used to estimate the resulting uncertainty and bias of the simulated direct radiative effect. The uncertainty is estimated to −1.5 ± 7.7 and 0.6 ± 3.5 W m−2 at the surface and top of atmosphere, respectively, while the annual-mean biases at the surface, top of atmosphere and total atmosphere are −10.6, −6.5 and 4.1 W m−2, respectively.
The retrieval of the aerosol radiative effect with the clear-sky models shows a high level of agreement with the radiative transfer simulations, with an RMSE of 5.8 W m−2 and a correlation of 0.75. The annual mean of the REari at the surface for the 25 DWD stations shows a value of −12.8 ± 5 W m−2 as the average over the clear-sky models, compared to −11 W m−2 from the radiative transfer simulations. Since all models assume a fixed aerosol characterization, the annual cycle of the aerosol radiation effect cannot be reproduced. Out of this set of clear-sky models, the largest level of agreement is shown by the ESRA and MRM v6.1 models.
Background & Objective: 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. Due to used materials (semiconductors e.g. silicon, gallium arsenide, cadmium telluride) the photovoltaic cells are spectrally selective. It means that only radiation of certain wavelengths converts into electrical energy. A material property called spectral response characterizes a certain degree of conversion of solar radiation into the electric current for each wavelength of solar light.
Motivation: With the increasing penetration of distributed energy resources (DER) in power systems, the need for forecasts at the distribution level increases. Forecasts of distributed generation (DG) units and power flow forecasts of relevant distribution nodes are already required for capacity planning at the transmission level. In Germany, the upcoming redispatch 2.0 procedure will require forecasting at the distribution level. This paper evaluates the benefits of using advanced PV forecasts in predictive congestion management.
Messkampagnen im Projekt METPVNET zur Verbesserung der PV- Erzeugungsprognose auf Verteilnetzebene
(2018)
In contrast to the German power supply, the energy supply in many West African countries is very unstable. Frequent power outages are not uncommon. Especially for critical infrastructures, such as hospitals, a stable power supply is vital. To compensate for the power outages, diesel generators are often used. In the future, these systems will increasingly be supplemented by PV systems and storage, so that the generator will have to be used less or not at all when needed. For the design and operation of such systems, it is necessary to better understand the atmospheric variability of PV power generation. For example, there are large variations between rainy and dry seasons, between days with high and low dust levels - caused by sandstorms (harmattan) or urban air pollution.
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 clear-sky radiative effect of aerosol-radiation interactions is of relevance for our understanding of the climate system. The influence of aerosol on the surface energy budget is of high interest for the renewable energy sector. In this study, the radiative effect is investigated in particular with respect to seasonal and regional variations for the region of Germany and the year 2015 at the surface and top of atmosphere using two complementary approaches.
First, an ensemble of clear-sky models which explicitly consider aerosols is utilized to retrieve the aerosol optical depth and the surface direct radiative effect of aerosols by means of a clear sky fitting technique. For this, short-wave broadband irradiance measurements in the absence of clouds are used as a basis. A clear sky detection algorithm is used to identify cloud free observations. Considered are measurements of the shortwave broadband global and diffuse horizontal irradiance with shaded and unshaded pyranometers at 25 stations across Germany within the observational network of the German Weather Service (DWD). Clear sky models used are MMAC, MRMv6.1, METSTAT, ESRA, Heliosat-1, CEM and the simplified Solis model. The definition of aerosol and atmospheric characteristics of the models are examined in detail for their suitability for this approach.
Second, the radiative effect is estimated using explicit radiative transfer simulations with inputs on the meteorological state of the atmosphere, trace-gases and aerosol from CAMS reanalysis. The aerosol optical properties (aerosol optical depth, Ångström exponent, single scattering albedo and assymetrie parameter) are first evaluated with AERONET direct sun and inversion products. The largest inconsistency is found for the aerosol absorption, which is overestimated by about 0.03 or about 30 % by the CAMS reanalysis. Compared to the DWD observational network, the simulated global, direct and diffuse irradiances show reasonable agreement within the measurement uncertainty. The radiative kernel method is used to estimate the resulting uncertainty and bias of the simulated direct radiative effect. The uncertainty is estimated to −1.5 ± 7.7 and 0.6 ± 3.5 W m−2 at the surface and top of atmosphere, respectively, while the annual-mean biases at the surface, top of atmosphere and total atmosphere are −10.6, −6.5 and 4.1 W m−2, respectively.
The retrieval of the aerosol radiative effect with the clear sky models shows a high level of agreement with the radiative transfer simulations, with an RMSE of 5.8 W m−2 and a correlation of 0.75. The annual mean of the REari at the surface for the 25 DWD stations shows a value of −12.8 ± 5 W m−2 as average over the clear sky models, compared to −11 W m−2 from the radiative transfer simulations. Since all models assume a fixed aerosol characterisation, the annual cycle of the aerosol radiation effect cannot be reproduced. Out of this set of clear sky models, the largest level of agreement is shown by the ESRA and MRMv6.1 models.
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.
For a sustainable development the electricity sector needs to be decarbonized. In 2017 only 54% of the West African households had access to the electrical grid. Thus, renewable sources should play a major role for the development of the power sector in West Africa. Above all, solar power shows highest potential of renewable energy sources. However, it is highly variable, depending on the atmospheric conditions. This study addresses the challenges for a solar based power system in West Africa by analyzing the atmospheric variability of solar power. For this purpose, two aspects are investigated. In the first part, the daily power reduction due to atmospheric aerosols is quantified for different solar power technologies. Meteorological data at six ground-based stations is used to model photovoltaic and parabolic trough power during all mostly clear-sky days in 2006. A radiative transfer model is combined with solar power model. The results show, that the reduction due to aerosols can be up to 79% for photovoltaic and up to 100% for parabolic trough power plants during a major dust outbreak. Frequent dust outbreaks occurring in West Africa would cause frequent blackouts if sufficient storage capacities are not available. On average, aerosols reduce the daily power yields by 13% to 22% for photovoltaic and by 22% to 37% for parabolic troughs. For the second part, long-term atmospheric variability and trends of solar irradiance are analyzed and their impact on photovoltaic yields is examined for West Africa. Based on a 35-year satellite data record (1983 - 2017) the temporal and spatial variability and general trend are depicted for global and direct horizontal irradiances. Furthermore, photovoltaic yields are calculated on a daily basis. They show a strong meridional gradient with highest values of 5 kWh/kWp in the Sahara and Sahel zone and lowest values in southern West Africa (around 4 kWh/kWp). Thereby, the temporal variability is highest in southern West Africa (up to around 18%) and lowest in the Sahara (around 4.5%). This implies the need of a North-South grid development, to feed the increasing demand on the highly populated coast by solar power from the northern parts of West Africa. Additionally, global irradiances show a long-term positive trend (up to +5 W/m²/decade) in the Sahara and a negative trend (up to -5 W/m²/decade) in southern West Africa. If this trend is continuing, the spatial differences in solar power potential will increase in the future. This thesis provides a better understanding of the impact of atmospheric variability on solar power in a challenging environment like West Africa, characterized by the strong influence of the African monsoon. Thereby, the importance of aerosols is pointed out. Furthermore, long-term changes of irradiance are characterized concerning their implications for photovoltaic power.
This paper addresses long-term historical changes in solar irradiance in West Africa (3 to 20° N and 20° W to 16° E) and the implications for photovoltaic systems. Here, we use satellite irradiance (Surface Solar Radiation Data Set – Heliosat, Edition 2.1 – SARAH-2.1) and temperature data from a reanalysis (ERA5) to derive photovoltaic yields. Based on 35 years of data (1983–2017), the temporal and regional variability as well as long-term trends in global and direct horizontal irradiance are analyzed. Furthermore, a detailed time series analysis is undertaken at four locations. According to the high spatial resolution SARAH-2.1 data record (0.05°×0.05°), solar irradiance is largest (up to a 300 W m−2 daily average) in the Sahara and the Sahel zone with a positive trend (up to 5 W m−2 per decade) and a lower temporal variability (<75 W m−2 between 1983 and 2017 for daily averages). In contrast, the solar irradiance is lower in southern West Africa (between 200 W m−2 and 250 W m−2) with a negative trend (up to −5 W m−2 per decade) and a higher temporal variability (up to 150 W m−2). The positive trend in the north is mostly connected to the dry season, whereas the negative trend in the south occurs during the wet season. Both trends show 95 % significance. Photovoltaic (PV) yields show a strong meridional gradient with the lowest values of around 4 kWh kWp−1 in southern West Africa and values of more than 5.5 kWh kWp−1 in the Sahara and Sahel zone.
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.
Long-term variability of solar irradiance and its implications for photovoltaic power in West Africa
(2020)
West Africa is one of the least developed regions in the world regarding the energy availability and energy security. Located close to the equator West Africa receives high amounts of global horizontal irradiance (GHI). Thus, solar power and especially photovoltaic (PV) systems seem to be a promising solution to provide electricity with low environmental impact. To plan and to dimension a PV power system climatological data for global horizontal irradiance (GHI) and its variability need to be taken into account. However, ground based measurements of irradiances are not available continuously and cover only a few discrete locations.
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.
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.
Long-term variability of solar irradiance and its implications for photovoltaic power in West Africa
(2020)
This paper addresses long-term changes in solar irradiance for West Africa (3° N to 20° N and 20° W to 16° E) and its implications for photovoltaic power systems. Here we use satellite irradiance (Surface Solar Radiation Data Set-Heliosat, Edition 2.1, SARAH-2.1) to derive photovoltaic yields. Based on 35 years of data (1983–2017) the temporal and regional variability as well as long-term trends of global and direct horizontal irradiance are analyzed. Furthermore, at four locations a detailed time series analysis is undertaken. The dry and the wet season are considered separately.
Atmospheric aerosols affect the power production of solar energy systems. Their impact depends on both the atmospheric conditions and the solar technology employed. By being a region with a lack in power production and prone to high solar insolation, West Africa shows high potential for the application of solar power systems. However, dust outbreaks, containing high aerosol loads, occur especially in the Sahel, located between the Saharan desert in the north and the Sudanian Savanna in the south. They might affect the whole region for several days with significant effects on power generation. This study investigates the impact of atmospheric aerosols on solar energy production for the example year 2006 making use of six well instrumented sites in West Africa. Two different solar power technologies, a photovoltaic (PV) and a parabolic through (PT) power plant, are considered. The daily reduction of solar power due to aerosols is determined over mostly clear-sky days in 2006 with a model chain combining radiative transfer and technology specific power generation. For mostly clear days the local daily reduction of PV power (at alternating current) (PVAC) and PT power (PTP) due to the presence of aerosols lies between 13 % and 22 % and between 22 % and 37 %, respectively. In March 2006 a major dust outbreak occurred, which serves as an example to investigate the impact of an aerosol extreme event on solar power. During the dust outbreak, daily reduction of PVAC and PTP of up to 79 % and 100 % occur with a mean reduction of 20 % to 40 % for PVAC and of 32 % to 71 % for PTP during the 12 days of the event.
This study deals with the in-situ detection of volume fractions of melt in labradorite and basalt at 0.3 GPa pressure and temperatures ranging from 400–1500 °C. Methods used were frequency dependent electrical conductivity (EC) and energy dispersive X-ray diffraction (EDX). These techniques allowed melt fraction determination under in-situ pressure and temperature conditions, while optical analysis (SEM) was performed on quenched samples. EC allowed detecting melt frac- tions as low as 0.03 due to changes in dielectric properties. Increasing melt fractions caused the formerly isolated melt bubbles to interconnect along grain boundaries, thus increasing the bulk conductivity. Electrical conductivity thus provides a measure for both, the formation of melt (dielectric property) and the degree of interconnection of melt (bulk conductivity). Energy dispersive X-ray diffraction experiments (EDX) provided an additional measure for the volume fraction of melt. EDX diffraction data were used to calculate the volume fraction of melt on the basis of the peak to background ratio. In a final step the experimental data (SEM, EC, EDX) were compared with geometric models of melt distribution, namely the Archie-, cube-, tube-, Hashin-Shtrikman HS + and HS - model. The electrical "polarisability" data closely fit the HS + model, while bulk conductivity data were found to be less sensitive for melt fraction detection.
