Chairpersons: Yves-Marie Saint-Drenan, Ekaterina Batchvarova
A method to combine a highly resolved All sky imager (ASI) network forecast with a satellite based forecast has been developed. The ASI network forecast input is based on the data from the DLR’s Eye2Sky network. This network is installed in North West Germany and includes 29 ASIs, ten Rotating Shadowband Irradiometers (RSIs) and two reference meteorological stations (based on thermal irradiometers) in an extent of 100 km². This network forecast was developed by our colleges from DLR-SF (publication in preparation). It has a forecast horizon of 30 minutes and a step of 1 min with an update of 30 seconds on a domain of 40 km². The second input is based on our operational satellite forecast at DLR-VE and has a horizon of 6 hours with a step and update of 15 minutes. The satellite domain is reduced to the same 40 km² area.
The method consists of three blocks, forecasts homogenization, regression and prediction. In the homogenization block the satellite forecast is interpolated in space and time to the resolutions of the ASI network forecast. We applied linear interpolation for both resolutions as first test case. In the second block, a linear regression is applied to find the optimal weights of the linear combination of the forecast inputs, including a bias term. The regression is based on timeseries extracted from the historical forecasts (features) where the reference is taken from the historical timeseries of ground measurements (samples). Historical data is used in order to indirectly characterize the mean actual local weather conditions on the domain. It is important to note that the regression is performed independently for every lead time. In the third block, we use the optimized weights and biases along with the present (not historical) forecasts to produce the hybrid forecasts. The hybrid forecast resolutions are the same as the ASI based forecast. The output product can be given as maps or timeseries.
For the test case, we are limited from the ASI network side to a dataset of two full months of forecasts (July and August 2020). The highly resolved hybrid forecast was validated against the individual input sources and satellite persistence. We found that this newly developed forecast outperforms the RMSE of persistence and the individual input forecasts for all calculated lead times. It shows an improvement on RMSE of 5.1% to 14.0% with respect to satellite forecasts and 7.6% to 15.1% with respect to the ASI network forecast on lead times going from 5 to 30 minutes. It also shows a lower RMSE under high variability conditions.
How to cite: Lezaca, J., Hammer, A., and Lünsdorf, O.: High resolution hybrid forecast based on the combination of satellite and an All Sky Imager (ASI) network forecasts, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-486, https://doi.org/10.5194/ems2022-486, 2022.
The sustainable electric grid of the future will rely on comprehensive measurements and forecasts of its millions of components. For PV, such high-resolution forecasts will benefit from the near real-time data and detail provided by satellite observations. Nowcasting or forecasting into the future from detailed satellite images based on e.g. root mean squared error optimization, (e.g. machine learning), however inevitably promotes smoothing and removes detail from the forecast, calling into question the definition of forecast quality. Probabilistic forecasting in the form of ensemble solutions offers an answer, allowing for the detail from satellite images without the expectation of deterministic pixel point accuracy. (Ensemble numerical weather predictions exist on high-resolution grids, but also present smoothed predictions of clouds).
This study creates ensemble forecasts using a new version of the optical-flow-based nowcasting system presented in past sessions that solves for the global cloud motion using Taylor-approximated streamlines. The optimized flow field is physically constrained through a combination of mass and angular momentum conservation. The errors for the motion of different structures in the image are discerned as secondary objectives to the overall optimization. The optical flow algorithm uses ant-colony, multi-objective optimization, following many solutions before arriving at a Pareto optimum.
The experiments test the viability of using other cloud motion solutions in the Pareto front to generate ensemble forecasts of the cloud cover and subsequently of irradiance maps and regional PV power. Where regions are fully cloudy or clear, the ensemble solutions should be uniform, while moving regions of variable cloudiness aim for realistic ensemble distributions. The reliability and potential of such forecasts are evaluated and compared to numerical ensemble weather predictions using continual ranked probability scores (CRPS).
How to cite: Good, G.: Pareto optical flow solutions for ensemble, satellite-based forecasts of irradiance and PV , EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-175, https://doi.org/10.5194/ems2022-175, 2022.
Cloud forecasting is an enormous challenge in numerical weather prediction (NWP) models because of the complex physical processes, high variability in spatial and temporal scales, and lack of observations for model evaluation. The capability to represent cloud fields in the NWP models directly impacts the accuracy in predicting surface solar irradiance. Verifying cloud forecasts from NWP models is essential to investigate the source of uncertainty and error stemming from predicting various types of clouds. However, it requires high-quality observations containing various information of clouds and in-depth analysis over a wide range of regions to assess the cloud fields. In this study, we produce day-ahead cloud forecast over the contiguous United States (CONUS) for 2018 using the Weather Research and Forecasting-Solar Ensemble Prediction System (WRF-Solar EPS) which is a state-of-the-art ensemble NWP model specialized for solar applications. The strengths and limitations of WRF-Solar EPS in reproducing cloud fields is diagnosed using satellite observations from the National Solar Radiation Database (NSRDB). The frequency of clouds and various cloud detection metrics including the probability of detection (POD), the false alarm rate (FAR), the hit rate (HR), Kuiper’s skill scores (KSS), and mismatched cloud frequency (MCF) are calculated to assess the performance of WRF-Solar EPS. In the first part of this study, we focus on monthly analysis using the detection metrics to account for seasonal performance of WRF-Solar EPS. In the second part, the MCF classified by cloud top height and cloud optical depth is analyzed to investigate the model’s capability to predict nine different types of clouds. The study exhibits that the WRF-Solar EPS has difficulty predicting optically thin clouds; overall MCFs show 46% (cumulus), 34% (stratocumulus), 19% (stratus), 33% (altocumulus), 23% (altostratus), 16% (nimbostratus), 27% (cirrus), 13% (cirrostratus), and 8% (deep convective) for the nine cloud types.
How to cite: Sengupta, M., Yang, J., Xie, Y., Jimenez, P., and Kim, J.-H.: Using Satellite Information to Evaluate Cloud Forecast from WRF-Solar EPS , EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-609, https://doi.org/10.5194/ems2022-609, 2022.
Photovoltaic production is highly variable with a limited predictability due to stochastic variation of cloud attenuation of surface solar irradiance. Forecasting this irradiance helps to manage the safe and stable operation of the grid and thus enables to increase solar electricity penetration for a lower carbonated energy mix. In particular, intraday horizon forecasts (typically up to 6 h) are more and more required for the grid power reserve management, the increase of intra-day auctions in the electricity market and the current worldwide development of micro-grids.
Forecast irradiance using images from geostationary meteorological satellite is particularly appropriate for intraday horizon. It gives better performance than NWP models, does not require instrumentation, or costly computing resource. However, the accuracy of such methods is very sensitive to the cloud cover state and its short-term evolution. For instance, methods based on the temporal extrapolation of cloud motion present better results for passing cloud events than for sudden cloud appearance or disappearance. In several studies, reliability predictors have been identified for satellite-based irradiance forecast. They showed clear signals when uncertainty is computed as a function of season, solar zenith angle, cloud albedo and more recently synoptic weather regimes. This anticipation of error range would help grid managers to prepare the sizing of storage capacity or ancillary electricity resource.
A pertinent predictor must have a significant influence on forecast error. It also must be easy to obtain in operational forecast conditions. In this work, we propose to exploit the only observation source required in satellite-based forecast: the satellite image itself. Using 5 years (2017-2021) of image-derived forecasts at 15 min time step over Palaiseau (France), we computed the forecast uncertainties as a function of multiple parameters derived from the HRV channel of Meteosat Second Generation satellite. We highlighted the influence of cloud albedo spatial variance and cloud motion vector field spatial-temporal features in the forecast uncertainties. In addition to the error range provided to users, this work can help forecasters to better characterize their sources of error and to select new predictors for machine-learning approaches.
How to cite: Cros, S., Badosa, J., Szantaï, A., and Haeffelin, M.: On hand available predictors for operational satellite-based forecast, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-572, https://doi.org/10.5194/ems2022-572, 2022.
With the increase of solar power use, the need for solar irradiance forecasts is also increasing. Solar power is a naturally fluctuating energy source, which makes solar irradiance forecasts necessary for e.g. grid integration and management. Both the use of solar energy and the need for forecasts are present also in the Nordic countries, where the sub-Arctic latitude brings its own challenges.
Various methods exist for forecasting solar irradiance. Numerical Weather Prediction (NWP) models have been found superior for forecasting for the following days, while satellite-based models are found suitable for the first few hours of the forecast. The satellite-based model Solis-Heliosat has been found to perform well also in the high latitudes. The current operational NWP models in Finland, however, have not been yet extensively validated for this purpose.
To determine the suitability of the operational NWP models for forecasting solar irradiance in the Nordic countries, we have comparatively validated the MetCoOp Ensemble Model (MEPS), and the MetCoOp Nowcasting Model (MNWC) against in situ irradiance measurements at several stations in Finland and Sweden. We have also included the Solis-Heliosat model in the study, to improve our understanding on the differences and the relative accuracy between the NWP and satellite-based models. As a benchmark, two persistence models are included. The comparison is made for one summer, including all model runs and all MEPS ensemble members, with both hourly and 15 minute output depending on the model.
The results show all models to somewhat under predict irradiance. MEPS shows very good performance in the full length of the forecast, while Solis-Heliosat is better in the first 2-3 hours of the forecast. Solis-Heliosat has some difficulty with the forecasts starting in the morning, whereas MNWC slightly struggles in the afternoon.
Overall we find the NWP models very suitable for forecasting solar irradiance in Finland and Sweden, particularly with the full forecast horizon of MEPS, and the 15-minute time step of MNWC. Nevertheless, Solis-Heliosat brings further value to the beginning of the forecast.
Kallio‐Myers, V., Riihelä, A., Schoenach, D., Gregow, E., Carlund, T., & Lindfors, A. V. (2022). Comparison of irradiance forecasts from operational NWP model and satellite‐based estimates over Fennoscandia. Meteorological Applications, 29(2), e2051.
How to cite: Kallio-Myers, V., Riihelä, A., Schoenach, D., Gregow, E., Carlund, T., and Lindfors, A.: Comparison of irradiance forecasts from operational NWP model and satellite-based estimates over Fennoscandia, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-203, https://doi.org/10.5194/ems2022-203, 2022.
As Europe strives for a decarbonization of electricity supply, many countries proclaim ambitious targets for capacity additions of wind and photovoltaic (PV) power. By increasing renewable capacities, countries seek to reduce their dependency on fossil fired electricity generation. For instance, Germany recently announced new capacity targets that mean a triplication of wind and PV power capacities while at the same time aiming for a full coal exit until 2030. However, the potential of renewable energies is not equally distributed over the European continent. Due to higher irradiance levels with decreasing latitude, PV power achieves higher full load hours and thus higher generation per installed capacity in southern Europe. Wind power reaches higher capacity factors close to shorelines, where near-surface wind speeds are higher.
In light of the unequal distribution of PV and wind power generation potentials, national capacity targets may not lead to optimal utilization of capacities, i.e. maximum renewable generation. We analyse the effects of such national capacity targets for the case of PV generation. To do so, we compare two scenarios: A scenario with PV generation capacity distributed over Europe according to national political targets and a scenario with a distribution of newly installed capacities chosen such that it optimizes the renewable power potentials. Next to PV generation potentials, our assessment considers the European power grid, because line capacities limit the transmission and hence the trading between different countries. Line limits can have a negative impact on the overall power generation since renewable generation has to be curtailed if power can neither be locally used nor transmitted to another region. We employ a linear optimization-based electricity market model with inter-European transmission constraints and hourly resolution weather data for 35 historic weather years (1982-2016). We analyse the scenarios if terms of differences in power generation and curtailment, greenhouse gas emissions, and system operational cost. We pay special attention to extreme weather situations and the effects of a sub-optimal PV power allocation on security of supply.
How to cite: Czock, B., Keutz, J., and Fiedler, S.: Follow the sun? The effects of national solar capacity targets on renewable generation and security of supply, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-198, https://doi.org/10.5194/ems2022-198, 2022.
Chairpersons: Marion Schroedter-Homscheidt, Sven-Erik Gryning
Modelled irradiance data based on satellite products is frequently used in solar energy applications and atmospheric sciences. This kind of data is offered by many different institutional or commercial providers, and currently it is not possible for users to independently identify the best provider for their specific application and location. This work presents a benchmark of satellite-derived global horizontal irradiance (GHI) as well as direct normal irradiance (DNI) at 129 ground-based radiation measurement stations distributed globally. High temporal resolution data (1 min) from these stations from between 2015 and 2020 has been quality controlled by a team of experts using a comprehensive set of best practices and newly implemented quality control procedures. The ground stations provide measurements of GHI and DNI and/or diffuse horizontal irradiance (DIF) from 25 different providers or networks. The 129 stations are spread out worldwide with 31 stations in Africa, 31 in Asia, 27 in North America, 20 in Europe, 13 in Australia, 5 in South America and 2 in Antarctica. GHI and DNI data from eleven different commercial or open-access radiation models is compared against these stations’ high-quality ground data. Additionally, one common measurement year is used to perform a site adaptation of the model datasets. The site adaptation is based on the empirical quantile mapping method and is the same for all test data sets. The comparison of the raw model data as well as the site adapted data is conducted at both 60-min and 15-min temporal resolutions. The performance of the raw and site-adapted data is analysed with respect to different regions and climate zones. Users can rely on the results of this work to make an informed decision about which surface radiation model(s) and data providers are most suited for their application.
How to cite: Forstinger, A., Wilbert, S., Jensen, A. R., Kraas, B., Fernández Peruchena, C., Gueymard, C. A., Ronzio, D., Yang, D., Collino, E., Polo Martinez, J., Ruiz-Arias, J. A., Hanrieder, N., Blanc, P., and Saint-Drenan, Y.-M.: Worldwide solar radiation benchmark of modelled surface irradiance, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-547, https://doi.org/10.5194/ems2022-547, 2022.
Energy system models rely on accurate weather information to capture spatio-temporal characteristics of renewable energy generation. Whereas energy system models are often solved with high abstraction of the actual energy system, meteorological data from reanalysis or satellites provides rich gridded information of the weather. The mapping from meteorological data to renewable energy generation usually relies on major assumptions as for solar photovoltaic energy the photovoltaic module parameters. In this study, we show that these assumptions lead to large deviations between reported and estimated energy as shown in case of photovoltaic energy feed-ins in Germany. To decrease these deviations, we propose a novel gradient-based end-to-end framework which is able to learn local representative photovoltaic capacity factors from aggregated reported transmission system operator feed-ins. As part of the end-to-end framework, we compare physical and neural network model formulations to obtain a functional mapping from meteorological data to photovoltaic capacity factors. We show that all developed methods have better performance than commonly used reference methods. The neural network shows remarkable success to predict the aggregated Transmission System Operator photovoltaic energy feed-ins leading to an accurate, unbiased prediction model. However, choosing the neural network model is not always the strictly preferred choice as it depends on the use case: Operational use cases may decide for the neural network implementation due to its higher accuracy whereas academic settings prefer the physical model due to its high interpretability and transferability. In this talk, we discuss the development of the end-to-end framework which we believe is highly relevant due to its energy-meteorological and methodological contributions.
How to cite: Zech, M. and von Bremen, L.: End-to-end learning of representative PV capacity factors from aggregated PV feed-ins, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-661, https://doi.org/10.5194/ems2022-661, 2022.
The estimation of solar surface irradiance at high spatio-temporal resolution from geo-stationary satellite images is a well-established technique, for example by using the Heliosat method. The method has widely reduced the need for expensive ground measurements, especially in remote regions. However, the location of cloud shadows at the ground is difficult to determine and thus a significant source of errors when either the distance from the sub-satellite point or the cloud top height (CTH) increases. Although several methods have been proposed in the literature to reduce these errors, it is still an issue. We present a novel approach to correct the cloud shadow location based on the satellite-cloud-sun geometry using the CTH maps from the EUMETSAT data archive. It uses satellite viewing angles and solar position angles to determine the correct cloud shadow location for each cloudy pixel. The method is tested on cloud index (CI) maps for the months of July, August and September 2018 derived by applying the Heliosat method on the 0.6 um visible channel images from Meteosat-8 located at 41.5°E. Convective clouds with large CTHs are frequently observed over the Indian subcontinent in these three months due to the Indian summer monsoon. The global horizontal solar irradiance (GHI) obtained from the corrected CI image is validated at two BSRN stations. The normalized root mean square error (nRMSE) is reduced from 23.2% to 20.9% for the Gurgaon station and from 15.4% to 13.9% at Tiruvallur. In general, correcting the cloud shadow location on CI map improved the accuracy of the estimated GHI. Nonetheless, the method is sensitive to the accuracy of the CTH dataset and individual cases were found for which the correction reduced the accuracy.
How to cite: Roy, A., Hammer, A., Heinemann, D., and Lünsdorf, O.: Cloud Shadows in Satellite-Based Solar irradiance Estimation: Improved Correction using EUMETSAT’s Cloud Top Height Data, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-504, https://doi.org/10.5194/ems2022-504, 2022.
Accurate intraday forecasts of surface solar radiation (SSR) are essential for utility companies and electricity grid operators. However, satellite SSR estimates can suffer from surprisingly low accuracies on short observation time scales and from significant spatial and temporal biases when compared to ground-based SSR measurements.
We present a bias assessment of two high-resolution satellite SSR products for intra-day application time scales. The satellite SSRs are retrieved with the Heliosat SARAH-2 and the HelioMont algorithms (Müller et al., 2015; Stöckli, 2013). We investigate intra-day and intra-hour estimates for altitudes from 200 to 3570 m a.s.l. We make use of 133 ground stations of the high-precision monitoring network SwissMetNet for the bias analysis. For solar zenith angle (SZA) lower than 90 degrees, we find that Heliosat SARAH-2 underestimates SSR at high altitudes (over 1000m) with an instantaneous root mean squared deviation (RMSD) of 179 W/m2and a mean bias deviation (MBD) of -68 W/m2 in the winter half year. The bias magnitude is approximately the double w.r.t. low altitude stations, due to difficulties in distinguishing snow-covered surfaces from clouds.
We also present an intra-hour bias correction approach: a deep neural network exploiting time-encoding features to model the bias in the time dimension. Our model achieves up to 30% RMSE reduction in the case of Heliosat SARAH-2, especially in mountainous regions. Moreover, we highlight the importance of the clear-sky index for bias correcting the Heliosat SSR estimates. Including a clear-sky index as a regressor in the bias correction improves the bias correction on average from 15.5% to 21.9% RMSE reduction. We also discuss the relevance of bias correcting satellite-derived SSR maps for short-term forecasting applications of SSR.
References
How to cite: Carpentieri, A., Wild, M., Folini, D., and Meyer, A.: Characterizing and correcting Heliosat Surface Solar Radiation bias on intra-day time scales with deep neural networks, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-366, https://doi.org/10.5194/ems2022-366, 2022.
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.
Within the BMWK-funded MetPVNet project (Meilinger et al., 2021), inversion algorithms were developed in order 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. In this work, both the DISORT 1D and MYSTIC 3D radiative transfer schemes within libRadtran are used to extract cloud properties and irradiance from pyranometer and PV power data, from two measurement campaigns in Allgäu, Germany and under different weather conditions. 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. Horizontal photon transport results in radiation overshoots at the edges of clouds, and here these deviations are quantified using simulated cloud fields with known cloud microphysical properties, for different degrees of irradiance variability. In addition, global horizontal irradiance is derived directly from tilted irradiance measurements and/or PV data using a lookup table based on these same cloud fields and MYSTIC 3D simulations. This work will provide the basis for future investigations using a larger number of PV systems and/or irradiance sensors to evaluate the improvements to irradiance and power forecasts that could be achieved by the assimilation of inferred irradiance into an NWP model.
References:
Meilinger, S., Herman-Czezuch, A., Kimiaie, N., Schirrmeister, C., Yousif, R., Geiss, S., Scheck, L., Weissmann, M., Gödde, F., Mayer, B., Zinner, T., Barry, J., Pfeilsticker, K., Kraiczy, M., Winter, K., Altayara, A., Reise, C., Rivera, M., Deneke, H., Witthuhn, J., Betcke, J., Schroedter-Homscheidt, M., Hofbauer, P. and Rindt, B.: Development of innovative satellite-based methods for improved PV yield prediction on different time scales for distribution grid level applications (MetPVNet)., IZNE Working Paper Series, doi:10.18418/978-3-96043-094-0, 2021.
How to cite: Barry, J., Meilinger, S., Pfeilsticker, K., Gödde, F., Mayer, B., Deneke, H., Witthuhn, J., Scheck, L., Schroedter-Homscheidt, M., Hofbauer, P., and Struck, M.: Irradiance and cloud optical properties from photovoltaic power data under variable atmospheric conditions, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-713, https://doi.org/10.5194/ems2022-713, 2022.
Dramatically reduced costs, increased electrification and a growing resistance to onshore wind power, makes solar energy, and photovoltaics increasingly relevant in Norway. However, there is a lack of accurate knowledge on the available solar resource. Of key importance is solar irradiance, and the accuracy of this is paramount for the accuracy of energy production estimates. Global horizontal irradiation (GHI) for a location can be found in several different databases, but they are less reliable for high latitudes such as Norway. In our study of the solar resource in Norway we assess surface measurements, satellite data, re-analysis and weather prediction model data. Surface measurements are available for about 100 locations in Norway, but the quality of the data is often not well documented, and quality assurance is necessary. Due to the lack of coverage of geostationary satellite data over the Northernmost part of Norway polar orbiting satellites are preferred, but the passages of the polar satellites are temporally irregular. Model GHI data has regular temporal resolution and availability, but the accuracy is uncertain and needs further exploitation. In this work we present a quality assurance of surface GHI measurements including visual inspection tools, and a comparison of the quality assured surface measurements with satellite and model derived GHI. A discussion of the three data sets is given and a preliminary solar resource map for Norway is also presented. Our study shows that locations with the highest annual GHI of ca. 1000-1100 kWh/m2 are encountered in high mountain areas with GHI peaking in late spring early simmer when the mountains still are covered with snow. Slightly lower GHI values of about 1000 kWh/m2 are found at southern coastal sites reaching the peak GHI in mid-summer. Lower values of typically 700-800 kWh/m2 are seen at the west coast and 600-700 kWh/m2 are in northern Norway.
How to cite: Dobler, A., Berge, E., Eastwood, S., Førland, J. R., Hygen, H. O., and Sætra, M. L.: Solar resource mapping in Norway, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-404, https://doi.org/10.5194/ems2022-404, 2022.
West Africa has great potential for the use of solar energy systems, as it has both a high solar radiation rate and a lack of energy production. West Africa is a very aerosol-rich region, whose effects on photovoltaic (PV) use are due to both atmospheric conditions and existing solar technology. This study reports the variability of aerosol optical properties in the city of Koforidua, Ghana over the period 2016 to 2020, and their impact on the radiation intensity and efficiency of a PV cell. The study used AERONET ground (Giles et al., 2019) and satellite data produced by CAMS (Gschwind, et al., 2019), which both provide aerosol optical depth (AOD) and metrological parameters used for radiative transfer calculations with libRadtran (Emde, et al., 2016). A spectrally resolved PV model (Herman-Czezuch et al., 2022) is then used to calculate the PV yield of two PV technologies: polycrystalline and amorphous silicon. It is observed that for both data sets, the aerosol is mainly composed of dust and organic matter, with a very increased AOD load during the harmattan period (December-February), also due to the fires observed during this period.
We compared CAMS satellite data with AERONET ground data. A good annual correlation (correlation coefficient, R2 ~0.8) was observed between the CAMS and AERONET AOD data. However, CAMS satellites tend to underestimate the high AOD measured on the ground by AERONET photometers; but they also overestimate the low AOD compared to AERONET. Both datasets also show differences in the average assumed optical properties of the aerosol encountered. A detailed analysis of the year 2020 shows a daily reduction in PV yield for the polycrystalline cell of up to 90% with the AERONET data and of up to 32% with the CAMS data. For the amorphous cell, the daily reduction in PV yield is up to 71% with the AERONET data and 34% with the CAMS data. These strong differences are due to the seasonal dispersion of the measured AODs but also to the variability of water vapor and ozone concentrations provided by CAMS and AERONET over Koforidua.
Acknowledgements: Funding was provided by the German BMBF under contract 03SF0567A-.
References
How to cite: Meilinger, S. and Zemo Mekeng, A.: Influence of aerosols on photovoltaic power in Ghana: Case study from Koforidua, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-286, https://doi.org/10.5194/ems2022-286, 2022.
Fostering solar power as a sustainable, as fossil fuel free as possible, energy source demands accurate, location-optimized, and highly resolved forecasts of power production. For this study, we consider the issue of production offsets due to Sahara dust events in large areas of Central Europe as well as data coverage and inconsistency issues with evolving solar power sites. In the presented case study we investigate subhourly nowcasts using machine learning for (i) specific solar power plants in Central Europe (ii) the suitability of synthetically generated production using NWP grid points in the studied areas.
Deep learning enables us to consider complex timeseries from historic data to model highly variable (spatial, temporal) diurnal and seasonal changes in the expected power production. In particular, we investigate how to exploit the spatio-temporal relationships in highly resolved forecasts by a sequence-to-sequence encoder-decoder inspired LSTM (long short-term memory) artificial neural network. We optimize the performance of our deep learning approach by tuning hyper-parameters, network weighting, and loss as well as addressing the input feature selection accordingly. Our preprocessing steps transform available data into a suitable representation for learning efficiently from a combination of multiple, very heterogeneous data sources with varying temporal availability and spatial resolution. For instance, we utilize 3D-fields from other weather prediction models, satellite data and remote sensing products, and observation time-series as well as their generated climatologies. A key objective is to properly process the differing temporal and spatial resolution while still generating nowcasts efficiently. To extend the historic training data set of complex models, we generate synthetic solar production data using machine learning and consider climatological driven data transformation. We investigate transfer learning as a further option in our deep learning setup.
Results obtained by the developed method generally yield high forecast-skills, where the best model setups are shown in our analysis. We compare the forecast results of up to 6 hours ahead obtained through this machine learning approach to available forecast methods, e.g., forecasts generated with python pvlib driven with AROME.
How to cite: Papazek, P. and Schicker, I.: Solar Power Nowcasting in the Presence of Sahara dust: Can Deep Learning based on Satellite and Synthetic Production Data Recognize the Production-Offsets?, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-151, https://doi.org/10.5194/ems2022-151, 2022.
Chairpersons: Ekaterina Batchvarova, Jana Fischereit
Over the last years, climate monitoring and operational weather forecasts have become an important topic for the renewable energy sector. An effective operation of national, and in the case of EU international, power generation aims to find the right balance between the minimization of CO2 emission and reduction of energy costs. In Germany, a considerable part of the electricity generation comes from wind. Therefore, an accurate forecast of low-level wind is essential to predict the generation of electrical power produced by wind parks. This enables timely adjustments of the conventional power plants. Currently, short-term low-level wind forecasts have considerable uncertainties. One of the cost-effective solutions to improve low-level wind forecasts is the assimilation of new observations into numerical weather prediction models. Even though in the last decade, the number of remote-sensing sites has been continuously growing, the coverage is far from being optimal to achieve significant improvement in the short-term wind forecast. However, before building new large networks of ground-based instruments it is important to estimate in advance which instruments to install, what effect to expect, and what spatial density of the distributed instruments should be.
Ground-based instruments that can provide valuable information for low-level wind forecasts are Doppler lidars. In this study, we focus on the estimation of the potential impact of Doppler lidars for short-term low-level wind forecasts essential for sustainable energy applications. The potential impact is analyzed using the ensemble sensitivity analysis (ESA) [1]. ESA is based on the Ensemble Transform Kalman Filter and allows us to investigate how the assimilation of hypothetical Doppler lidars can reduce the wind forecast variance. The impact of a Doppler lidar network was analyzed with respect to surface measurements operationally assimilated by national weather services. We investigated the sensitivity of the obtained results to the number of Doppler lidars in the network, the number of altitude layers observed by Doppler lidars, and forecast lead time. Our analysis is based on a 1000-member ensemble simulation for the urban and highly populated Rhein-Ruhr area and surrounding regions [2]. The simulation uses a full-physics non-hydrostatic regional model (SCALE-RM) and covers a two-week time period in May/June 2016.
This work has been conducted in the framework of the Hans-Ertel-Centre for Weather Research funded by the German Federal Ministry for Transportation and Digital Infrastructure (grant number BMVI/DWD 4818DWDP5A). This online publication is based upon work within the COST Action CA18235 supported by COST (European Cooperation in Science and Technology). We acknowledge RIKEN for providing the SCALE-RM model data.
References
[1] Ancell, B., and G. J. Hakim, 2007: Comparing adjoint-and ensemble-sensitivity analysis with applications to observation targeting, MWR., doi.org/10.1175/2007MWR1904.1.
[2] Necker, T., et al, 2020: A convective-scale 1000-member ensemble simulation and potential applications. QJRMS, doi.org/10.1002/qj.3744.
How to cite: Nomokonova, T., Griewank, P., Löhnert, U., Miyoshi, T., Necker, T., and Weissmann, M.: Benefits of Doppler wind lidars to improve short-term low-level wind forecasts, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-216, https://doi.org/10.5194/ems2022-216, 2022.
The increasing amount of electricity generated from wind power leads to a stronger variability in electricity supply to the national electricity grids. This makes wind power forecasts a necessary component in the wind energy market. The forecasts are used to estimate the amount of energy that will be generated and prevent potential imbalances between production and consumption in electricity grids. Energy price in Finland is set every day at the Nordic energy trading stock Nordpool. Unaccounted shortage or surplus of power in the grid needs to be compensated. This may result in costs required to balance the grid.
To account for the uncertainties in wind power calculations, a probabilistic wind power forecasting tool has been developed. It is based on the MetCoOp Ensemble Prediction System (MEPS), in which the HARMONIE Numerical Weather Prediction model is run with 2.5 km horizontal resolution and 65 vertical levels. The system is operational through joint efforts of a group of Nordic countries participating in the MetCoOp cooperation.
The skills of wind power calculations, however, depend not only on the skills of the MEPS system, but also on the information on the wind farms. Detailed information on the locations of wind turbines (WT), their hub heights and technical specifications is important in adequate calculations of power production and power losses. Most essential WT’s parameters are so-called power and thrust coefficient (CT) curves. Power curve is necessary to convert wind speed to corresponding power output. CT curve is needed to calculate wind flow retardation by WT, also called wake effect, resulting in power losses downwind in a wind farm. Power and CT curves, as well as other WT technical parameters, are turbine-specific and typically provided by the manufacturer under commercial terms and conditions. In power calculations over a country with a multitude of wind farms, gathering such information may be challenging.
Nowadays power curves of many WT models can be found in open sources, such as thewindpower.net and wind-turbine-models.com. However, they are still missing for some and especially new WT models. CT curves, on the other hand, are practically not available in open sources. A statistical solution was developed to approximate both power and CT curves using as input commonly available WT specifications. The uncertainty in power calculations associated with approximated power and CT curves was found to be smaller than that associated with the uncertainty in the predicted wind speed.
Wind to power conversion is done for essentially all WTs installed in Finland, to produce an aggregated probabilistic wind power forecast. Wind variability is accounted by applying Gaussian smoothening to power (and CT) curves. Wake-related losses were calculated applying Katic-Jensen wake model, with missing CT obtained applying the above method.
How to cite: Atlaskin, E., Lindfors, A., Kallio-Myers, V., and Suomi, I.: Probabilistic forecasting of the aggregated Finnish wind energy based on the MetCoOp ensemble prediction system (MEPS), EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-219, https://doi.org/10.5194/ems2022-219, 2022.
The increasing penetration of the power grid with renewable energy (RE) sources comes with an increase of distributed generation units (DGUs), which exhibit a very heterogeneous structure. Grid operators often face challenges when employing RE due to its large volatility. Especially in exceptional situations, e.g. power blackouts or frequency events, conventional generation units are often preferred to stabilize the grid. Making use of highly volatile power sources requires very reliable knowledge about the near future of the available active power (AAP) signal, hence giving rise to the necessity of accurate and robust RE generation forecasting in short time ranges (nowcasting).
In order to maximize the availability of forecast data to the grid operators the forecasting process can be deployed in a distributed fashion. The presented method makes use of several machine learning (ML) algorithms and provides a probabilistic short-term forecast with little requirements to computational resources on-site. The local availability of forecasts improves the AAP signal and hence enables ancillary services from wind farms such as the provision of a frequency containment or restoration reserve. Also it provides robustness against communication failure between grid operation and forecast provider.
The suggested forecasting procedure is two-fold: On the one hand a data-driven nowcasting (from 0 to 6 hours) approach is pursued within decentralized forecast units, that is designed to be deployable on-site of the respective DGUs. This approach employs local sensor data, i.e. active power, wind measurements from the nacelle anemometer and temperature, as well as the azimuth angle of the nacelle. The various ML models in use are adaptively trained on the latest sensor data and produce ensemble forecasts, from which both the minimally available power and the forecast uncertainty can be deduced. The individual model outputs are then combined by an adaptive genetic algorithm.
This purely data-based nowcast is then enriched with a physical forecast based on numerical weather prediction (NWP) model runs with a time horizon of up to 10 days. It considers actual production data from the wind farm as well as turbine specific control behaviour, which leads to excellent forecast quality. This NWP-based forecast is generated at a central computing centre and can be distributed to the forecasting units at the DGUs. On-site the data-based nowcast and the physics-based forecast are combined to provide good reliability for both time horizons.
In our paper we present the method in detail and describe the infrastructure, for which it was developed. We further conduct a performance assessment of the forecasting procedure and provide performance indicators of expected forecast errors and reliability.
Parts of the presented research have been carried out in the joint research project SysAnDUk (FKZ 03EI4004A) funded by the German Federal Ministry for Economic Affairs and Energy.
How to cite: Holicki, L., Dröse, M., Schürmann, G., and Letzel, M.: Decentralized forecasting of wind energy generation with an adaptive machine learning approach to support ancillary grid services, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-428, https://doi.org/10.5194/ems2022-428, 2022.
Offshore wind turbines induce atmospheric wakes downstream of typically tens of kilometers length while converting wind energy into electrical power. In the recent years, wakes behind single wind farms have been extensively studied. The growing number of offshore wind farms (OWFs) in the German Bight increases the occurrence of interferences between close neighboring OWFs. Previous studies showed that the proximity of neighboring OWFs impacts the performance of downwind turbines and reduces their capacity factor. However, the interactions of wakes from different wind farms is not well understood, in particular concerning the resulting wind speed deficits, turbulence intensities and superimposed wake lengths.
The interaction of wakes from two OWFs generally leads to longer wakes. Also, it was observed frequently that wakes resulting from 2 OWFs reach a third wind farm. Several conditions can entail interactions of wakes from several OWFs, such as the size of OWF (geometry and density of turbines), atmospheric stability and wind speed and directions. The study focuses on the investigation of the interactions of wakes from two and three OWFs using Synthetic Aperture Radar system (SAR), which is an interesting instrument to observe large spatial areas at resolution of 20 m. The statistical analysis of a 5-year period of SAR data acquisitions by the Sentinel-1A and Sentinel1-B satellites revealed that the occurrence of interactions of wakes between OWFs exceeds 75% of all the cases for which wakes were observed. The interaction between OWFs is clearly correlated with a certain wind direction range. Additionally, the geometry of OWFs plays a role in the two-dimensional structure of the wakes and the potential for impacting neighboring wind farms. Obviously, wind directions parallel to the alignment of several OWFs are likely to induce strong interactions of wakes.
SAR data are combined with stability information from atmospheric models and mast measurements to analyse the respective impacts on key parameters of superimposed wakes (e.g. deficit, wake length). Results are compared with simplified empirical models, which make assumptions about the linearity of the wake superposition process.
How to cite: Djath, B. and Schulz-Stellenfleth, J.: Study of the interaction of atmospheric wakes from several offshore wind farms as observed by Synthetic Aperture Radar (SAR) system, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-465, https://doi.org/10.5194/ems2022-465, 2022.
Unique airborne in-situ measurements were evaluated to investigate the influence of offshore wind farms on the latent heat flux in the marine boundary layer. 21 of the total 42 measurement flights carried out in the framework of the WIPAFF project over the German Bight in the year 2016 and 2017 enabled such an evaluation under different atmospheric conditions. The airborne data was collected with the research aircraft Dornier 128 belonging to the Technische Universität Braunschweig, Germany. The aircraft is equipped among others with high-resolution sensors for water vapor, three-dimensional wind vector, temperature and pressure. The measurements of 15 flights showed a significant increase of the vertical upward latent heat flux over the offshore wind farm clusters Amrumbank West, Nordsee Ost, Meerwind Süd/Ost or the wind farm cluster Godewind. For thermally stable conditions, all nine measurement flights except one showed an enhanced latent heat flux downstream of the wind farms at hub height, with an increase of up to +70 W m-2compared to the undisturbed flow. For flights during unstable thermal conditions, 8 out of 13 cases showed an increase, with the largest difference with respect to undisturbed flow of +400 W m-2 above the wind farm. The results also suggest that not only the thermal stratification but also the moisture gradient plays a decisive role in whether the influence of the wind farm is visible in the latent heat flux.
How to cite: Platis, A., Büchau, Y., and Bange, J.: Influence of offshore wind farms on the latent heat flux in the marine boundary layer, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-174, https://doi.org/10.5194/ems2022-174, 2022.
Atmospheric stability is a measure of atmospheric status which determines whether thermodynamically perturbed air will rise, sink, or be neutral. Atmospheric stability has a major impact on the evolution of wind turbine wakes and thus on the yield and performance of offshore wind parks. For estimations of wind park power output and for improving analyses of offshore wind park wakes, a crucial parameter was found to be profiles of atmospheric temperature and stability metrics. Atmospheric temperature profiles can be measured in-situ by balloon-borne sensors, but also estimated from the ground using remote sensing observations.
Ground-based microwave radiometer (MWR) units operating in the 22-30 GHz and 50-60 GHz bands are commonly used to estimate atmospheric temperature and humidity profiles. A handful of MWR profiling types are nowadays available as off-the-shelf commercial products, and a MWR network is currently being established in the framework of EUMETNET E-PROFILE programme (Rüfenacht et al., 2021). This presentation reviews the stability metrics useful for monitoring wind park performances and provides a quantitative assessment of the value of MWR observations to estimate these stability metrics from near surface, either over land or ocean. Results from three different MWR instruments, representing the most common available on the market, will be presented, as obtained during at least three field experiments, both onshore and offshore.
This contribution presents the main outcomes of the Radiometry and Atmospheric Profiling (RAP) scoping study, carried in the framework of the COST Action PROBE (https://www.probe-cost.eu/) and funded by Carbon Trust and the partner companies of the Off-shore Wind Accelerator (OWA) program: (in alphabetical order) EnBW, Equinor, Orsted, RWE, Scottish Power Renewables, Shell, SSE Renewables, Total Energies, Vattenfall.
How to cite: Cimini, D., Gandoin, R., Fiedler, S., Wilson, H., Pospichal, B., Martinet, P., Balotti, A., Gentile, S., and Romano, F.: Assessment of atmospheric stability measurements from microwave radiometer observations for wind energy applications, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-631, https://doi.org/10.5194/ems2022-631, 2022.
Chairpersons: Sven-Erik Gryning, Jana Fischereit
It is scientific consensus that the Paris Agreement (COP21) can only be achieved by drastically reducing the emissions of anthropogenic greenhouse gases, in particular those related to burning fossil energy sources. Succesfully providing the required energy with renewable energy sources, such as wind and solar power, is dependant on the weather conditions. Dunkelflauten, periods during which only little energy is available by wind and solar power, can put strain on the energy system based on high shares of variable renewables. Particularly, cold Dunkelflauten with coinciding low renewable energy generation and high heat demand are critical. To mitigate the risk of such critical conditions, energy storage systems are an important component in the future energy system. The appropriate dimensioning of energy storage requires knowledge about the typical duration and frequency of Dunkelflauten.
This study presents a climatological analysis of Dunkelflauten that would have occurred in Germany from 1979 until 2018, with the assumption of a 100% renewable energy scenario. On average four Dunkelflauten are detected per year, with durations of up to nine days. To forecast the chances of a Dunkelflauten event several weeks ahead of time, it is helpful to unveil the long-lasting weather conditions, called weather regimes, during which Dunkelflauten occur. We show that Dunkelflauten are more likely to occur during blocked regimes than during cyclonic regimes. The particularly critical cold Dunkelflaute occurs predominantly during the Greenland blocking. Accordingly, accurate predictions of weather regimes are key to manage energy storage systems and to eventually avoid temporary shortages in energy supply.
How to cite: Mockert, F., Grams, C. M., Brown, T., Neumann, F., and Fallon, J.: Dunkelflauten in Germany: Climatology and Relation to Weather Regimes, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-294, https://doi.org/10.5194/ems2022-294, 2022.
In electricity grids with high shares of solar and wind, storage technologies, such as electric batteries, are suitable to complement renewable generation. Renewable generation is unequally distributed both temporally and spatially. Storage can offset the temporal volatility of wind and photovoltaic (PV) by shifting generation or load if it they do not coincide temporally. In Germany, the power system is additionally affected by a spatial mismatch between load and renewable generation. While wind power generation is predominantly located in the North of Germany, demand is centred in the more densely populated West and South. Due to insufficient transmission capacity, North-South transmission lines are frequently overloaded in situations with high wind generation. As a result, wind generation is curtailed while fossil fired generation is used to supply loads south of the transmission bottleneck, thus jeopardizing German efforts to increase renewable generation and reducing greenhouse gases. Installing storage capacities can address this spatial mismatch to achieve a more balanced transmission line utilization, thus increasing overall transmission grid utilization, decreasing curtailment and reducing the use of fossil-fuel combustion. To do so, the allocation of storage within the electricity grid is critical. To investigate the relationship between storage, renewable generation, and the transmission grid, we employ a linear optimization model that allows to analyse optimal allocation of storage and generation technologies within Germany. Our model simulations use high resolution reanalysis meteorological data from the COSMO-REA6 data set developed by the Hans-Ertel-Centre for Weather Research, Climate Monitoring and Diagnostics, and a detailed depiction of electricity grid constraints of Germany. We find that an allocation of storage close to grid bottlenecks is optimal to facilitate wind generation replacing fossil fired generation, thus making efficient use of renewable generation. If grid bottlenecks are not accounted for in the storage allocation, storage cannot be used to relieve grid congestion. Consequently, we find an increase of up to 10% in the overall system operational costs and higher curtailment rates for wind and solar power compared to having storage installed at optimal locations.
How to cite: Czock, B., Sitzmann, A., Zinke, J., and Fiedler, S.: The place beyond the lines - Efficient storage allocation in a spatially unbalanced power system with a high share of renewables, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-196, https://doi.org/10.5194/ems2022-196, 2022.
The use of marine energies from waves and tides have been discussed since long, however sustained application is rare. Nevertheless, in recent years systems harnessing tidal streams in the MW scale have started commercial operation after a year-long test (OES 2019).
Tidal stream flows have – as compared to other renewable energy flows - a remarkably high predictability. However, this predictable flow is characterized by distinct variabilities on various time scales that have to be negotiated when designing their application in energy systems, see e.g. Robins et al. 2015, Lewis at al. 2019, Guillou et al 2020, for discussions on the the handling of fluctuations from hourly to weekly scales. The presence of multi-year fluctuations (variations in long term mean generation) gives the task of a reasonable sizing of storage and generation capacity for integrating the tidal generation into energy supply systems.
This will be analysed for the case of the isolated supply system of the Faroe Islands, for which the integration of tidal power is foreseen for a 2030 carbon neutral scheme (see Trondheim 2021). For this, a 25-year set of predicted tidal stream data using the IOS Tidal Package (IOS 2020) on a 2 month long measured timeseries of the current from the strait of Skopunarfjørður (Niclasen and Simonsen, 2009) is applied.
Under inspection are the storage capacities necessary to assure stable generation for given generation capacities.
References
IOS 2020, https://www.dfo-mpo.gc.ca/science/data-donnees/tidal-marees/index-eng.html, as of 20.04.2020
Guillou et al 2020, Guillou N. , Neill S.P., Thiébot J., Spatio-temporal variability of tidal-stream energy in north-western Europe, Phil. Trans. R. Soc. A 378: 20190493 , 2020 http://dx.doi.org/10.1098/rsta.2019.0493
Lewis at al. 2019, Lewis M, McNaughton J. , Marquez-Dominguez C., Todeschini G.,
Togneri M., Masters I., Allmark M., Stallard Y., Neill S.,
Goward-Brown A., Robins P., Power variability of tidal-stream energy and implications for electricity supply, Energy 1832019
Niclasen B. A., and Simonsen K., Current measurements in Skopunarfjørður, 2009, Technical Report, NVDRit 2009:12, University of the Faroe Islands, 2009.
OES 2019, https://www.ocean-energy-systems.org/publications/oes-brochures/document/tidal-current-energy-developments-highlights/ as of 20,04.2020
Robins et al. 2015, Robins P.E., Neill S.P., Lewis MJ., Ward S.L. , Characterising the spatial and temporal variability of the tidal-stream energy resource over the northwest European shelf seas, Applied Energy, 147, 20
Trondheim 2021, Tróndheim H.M.,Niclasen B., Nielsen T., Faria da Silva F. Leth Bak C, 100% Sustainable Electricity in the Faroe Islands: Expansion Planning Through Economic Optimization, IEEE open access Journal Power and Energy 8, 2021
How to cite: Beyer, H. G. and Simonsen, K.: Sizing of generation and storage capacities for tidal stream energy systems in view of the temporal pattern of tidal stream power , EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-91, https://doi.org/10.5194/ems2022-91, 2022.
Wind-atlases are vital for planning of the massive transition towards renewable energy that is required over the next decades. Reanalysis products like ERA5 are frequently used to obtain winds at hub heights, but coarse reanalyses products often lack crucial details that are needed to represent the microscale flow. To circumvent this aspect, wind-atlases use a procedure of generalizing and downscaling mesoscale model outputs to predict the flow down to the microscale (~250 m grid spacing). The methods to do this are based on the geostrophic drag law and the logarithmic wind profile, which both need information from the mesoscale model to represent for example the wind profile in each wind-direction sector. However, one also needs high-resolution roughness and elevation maps to represent microscale speed-up effects. Combining these large-scale and microscale effects is notoriously difficult
Due to lack of measurements it has been difficult to validate the accuracy of these wind atlases at the hub heights of modern turbines. In this presentation we briefly discuss recent improvements in the model chain related to stability and surface roughness modelling, but mostly focus on a validation of these new wind atlases at more than 60 tall masts around the world. Three python packages have made it much easier to generate and validate these wind atlasses. The first package contains wind-related data structures including geospatial information that make your data self-explanatory. A second python package to make wind validations easier to perform is also introduced. It contains a work flow to validate data and create simple reports to analyze them. Finally, flow modelling is done using PyWAsP, which contains submodules for roughness, stability and wake-modelling.
How to cite: Floors, R., Olsen, B. T., and Davis, N.: Creating and validating high-resolution wind atlases, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-678, https://doi.org/10.5194/ems2022-678, 2022.
The share of solar/wind energy in the electricity systems of many countries in the world will reach unprecedented values in the coming decades, fostered by the mitigation of climate change and also by the economic competitiveness of these energies. Accurate regional models of wind/solar power generation are crucial to making a smooth transition to these new energy systems.
In this work, regional solar PV and wind power models for Spain, based on the Random Forest machine learning model, were built and evaluated. Models were obtained for each of the 50 Spanish regions using as input the installed wind/solar capacity of the corresponding region and a set of meteorological variables. Regional installed capacities values were elaborated based on information from wind farms at 506 locations and photovoltaic solar plants at 3854 locations collected from public databases. The study is carried out for the year 2018 at hourly resolution; the model estimates were evaluated based on the actual power generation values provided by the Spanish TSO. This study is part of the Spanish MET4LOWCAR project, that aims at demonstrating the benefits of design low carbon power systems that accounts for the regional climatic patterns of both solar and wind renewable resources, using the Spanish territory as a testbed.
Different studies were undertaken. Firstly, two different modeling approaches were evaluated. In the first one, the meteorological inputs were derived by simple averaging the values at all the grid cells of the corresponding region. In the second approach, meteorological inputs were computed as a weighted average at the solar/wind farms locations using as weights the corresponding installed capacity. Secondly, two different meteorological databases were evaluated: the ERA-5 reanalysis and specific database derived from an ad-hoc integration conducted with the WRF NWP model. This late database has a spatial resolution of 5 km and temporal resolution of 10 minutes. Derived from these databases, up to 11 meteorological variables were used as inputs for the models The relevance of these variables in the models performance was assessed based on the feature importance provided by the Random Forest modeling procedure.
Results show, firstly, that the two modeling approaches perform similarly, being the overall power estimates relative errors of about 20% for Solar PV and 30% for the Wind power. Secondly, that the models performance using the two meteorological databases are also similar, although the WRF databases provide slightly more accurate estimates of the power variability. Thirdly, the most important meteorological input variables for the solar models were the GHI, DNI and temperature, while for the wind models were the wind at 10 and 100 meters above the ground and the wind at the 850 hPa level. The sources of the uncertainty of the power estimates are discussed.
How to cite: Aler-Mur, R., Sánchez-Hernández, G., Jiménez-Garrote, A., López-Cuesta, M., Galván-León, I., and Pozo-Vazquez, D.: Evaluation of machine-learning-based solar PV and wind power regional models for Spain, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-168, https://doi.org/10.5194/ems2022-168, 2022.
For an efficient integration of photovoltaic (PV) energy into the power grids, more accurate forecasts of the expected PV-power production are needed. However, most operational numerical weather prediction models rely on an aerosol climatology and ignore the spatio-temporal variability of the atmospheric aerosol. For specific weather conditions like during mineral dust outbreaks or major wildfire events, the negligence of prognostic aerosol often leads to significant deficiencies in the operational forecasts, however.
At Deutscher Wetterdienst (DWD) and Karlsruhe Institute of Technology (KIT) the project “PermaStrom” aims at the operational prediction of various natural aerosol species to improve radiation forecasts. Emission, transport and deposition of mineral dust, black carbon from vegetation fires, and sea salt are thus explicitly simulated in the ICON-ART model system. In the model, direct aerosol effects on radiation are considered using state-of-the-art optical properties. Microphysical effects of aerosol acting as cloud condensation nuclei (CCN) and ice nucleating particles (INPs) are investigated in a high-resolution regional model with the long-term goal to improve the parameterization of aerosol-cloud effects in global models.
Aerosol-cloud-radiation effects are studied in a regional ICON-ART model with 2 km grid spacing with an aerosol-aware two-moment bulk microphysics scheme. In addition, first steps are made towards a global ensemble system for aerosol forecasts using ICON-ART. This will allow to quantify the uncertainty of the forecasts. A multi-fidelity ensemble, which combines ICON-ART and ICON simulations to optimally sample the aerosol- and flow-dependent variability, is used to keep the computational processing manageable. The ICON-ART simulations are validated with aerosol and radiation measurements at surface stations as well as cloud, aerosol and radiation products from satellites and ceilometers.
We will give an overview of the ICON-ART configuration of the pre-operational real-time global aerosol prediction system at DWD. This includes aspects like mineral dust, sea salt, and wildfire emissions. For the latter, a machine learning emulator of the plume rise model is currently being developed.
How to cite: Seifert, A., Förstner, J., Porz, N., Hoshyaripour, A., Filipitsch, F., Wagner, A., Doppler, L., Vogel, H., Bachmann, V., Rohde, A., and Hanisch, T.: Predicting the direct and indirect effects of atmospheric aerosol on photovoltaic power generation, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-677, https://doi.org/10.5194/ems2022-677, 2022.
In the last years there is an increasing interest for developing enhanced solar nowcasting methods, fostered by the massive deployment of small-scale / residential PV systems. Reference nowcasting methods are based on the use of All-Sky-Imagers (ASI). Although ASI-based nowcasting methods has received a notable attention in the last decade, their reliability is relatively low, because the processes involved in deriving the nowcasts are prone to many uncertainties. In this work, we propose and evaluate new methods aimed at increasing this reliability. Notably, we propose the use of an automatic cloud-type recognition system in the nowcasting procedure, allowing the use of cloud-specific transmittance values for the different cloud types. The study was carried out at a location of southern Spain, using a total of 1901 samples representing all the cloud types. Each sample is composed by one-minute time resolution ASI images, GHI and DNI measurements as well as cloud base height values derived from a ceilometer. Up to 30 minutes ahead one-minute time resolution forecasts were obtained and benchmarked against reference methods that not uses cloud-specific transmittances.
In the first part of the study, a statistical analysis was conducted to determine the GHI and DNI transmittance of 11 different cloud types; to this end, a Gaussian Mixture Model (GMM) was used. For some of the cloud types, the cloud base height was also used as a parameter of the model. In a second part of the study, these cloud-specific transmittances were incorporated in the nowcasting procedure using an operational automatic cloud type recognition method. This new nowcasting method was evaluated as an operational forecasting procedure.
Results of the first study reveals, firstly, that DNI total transmittances are almost negligible for cumuliform clouds. In contrast, stratiform clouds show a wide range of transmittances. Notably, while stratus and altostratus clouds behaves as cumuliform clouds, transmittances for cirrostratus and cirrus are, respectively, 30% and 80%, approximately. For the GHI, the total transmittance values ranges from 80% (cirrus) to 30% (stratus).
Results from the second study show that the proposed method provide slightly enhanced GHI nowcasts associated with cirrus, altocumulus and cirrocumulus clouds skies. For these cloud-types skies, the reduction achieved in the rRMSE values ranges from 2% to 6%. On the other hand, the proposed method provide a clear superior performance for the DNI nowcasting, with an overall reduction of rRMSE values of around 10% for the entire dataset. The reduction in rRMSE values also show a dependence of the cloud types, ranging from 6% (cirrostratus/cirrocumulus) to 20% (cirrus clouds).
How to cite: López-Cuesta, M., Jiménez-Garrote, A., Aler-Mur, R., Galván-León, I., Tovar-Pescador, J., and Pozo-Vazquez, D.: Improving ASI-based solar radiation nowcasting by using automatic cloud type recognition methods, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-166, https://doi.org/10.5194/ems2022-166, 2022.
The share of solar energy in the electricity systems of many countries in the world will reach unprecedented values in the coming decades, fostered by the mitigation of climate change and also by the economic competitiveness of this energy. Accurate solar radiation forecasting models are critical for the integration of the increasing solar energy in power systems.
In this work, the benefits obtained by blending seven models: four All-sky imagers (ASI)-based, two satellite images based (one using low resolution and other using high-resolution images) and a data-driven model, were analyzed. The use of two blending models (linear and Random Forest (RF)) and two blending approaches (General and Horizon) were explored. The horizon approach constructs a different blending model for each forecast horizon, while the general approach trains a single model valid for all horizons. The study is conducted in southern Spain and blending models provide one-minute resolution 90-minutes ahead GHI and DNI forecasts. Results show the General approach and the RF blending model to perform superior and to provide enhanced forecasts. The relative improvement in rRMSE obtained by model blending was up to 30% for GHI (40% for DNI), being maximum at lead times between 15 and 30 minutes and negligible at lead times greater than 50 minutes. Results also show that blending of just the data-driven model and the two satellite models (low and high resolution), without including the ASI-based models, performs similarly to those blending models that used as input the ASI-based models. Results then indicate that, for point nowcasting, the use of ASI-based forecasting systems can be avoided by using a suitable blending of data-driven, high resolution and low resolution satellite-images-based forecasting models.
How to cite: López-Cuesta, M., Aler-Mur, R., Galvan-León, I., Rodríguez-Benítez, J., and Pozo-Vázquez, D.: Enhanced solar radiation nowcasting by machine-learning-based blending of data-driven, satellite-images and all-sky-imagers based models, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-86, https://doi.org/10.5194/ems2022-86, 2022.
The assessment of Direct Normal Irradiance (DNI) is essential for the solar power industry. Satellite estimations offer many advantages over ground-based measurements with immediate and affordable installation (no hardware installation), inexistant operation and maintenance of sensors, and efficient detection of any malfunction by comparing the production measured on the meter with the satellite estimation. In this work, we compare two estimations models of DNI from geostationary meteorological satellites in Germany.
The first model is provided by Copernicus Atmosphere Monitoring Service (CAMS) solar radiation services. In its latest v4 update, this model uses the APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme that is a probabilistic interpretation of the original APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) method to infer the Cloud Optical Depth (COD).
The second model is based on COD of the “Satellite Application Facility on support to NoWCasting and very short range forecasting” (SAFNWC) with the version 2018.1. The algorithm is based on a multispectral threshold technique applied to each pixel of the satellite image and uses Numerical Weather Prediction (NWP) input data for surface temperature and total atmospheric water vapour content.
Both models use a Cloud Mask (CMA) product to delineate all cloud-free pixels in a satellite scene with a high confidence, then DNI is forced to the value obtained by the McClear clear sky model when CMA indicates "cloud free". Otherwise, the COD is combined with the clear sky model in order to compute the effective DNI.
Moreover, we take into account the circumsolar radiation to avoid underestimation of the forward scattered radiation in DNI when compared to ground observations.
The model outputs are compared to 10-minute solar radiation measurements from Deutscher Wetterdienst (DWD) stations located in Germany over the period 2021-04-01 and 2022-03-31. This network measures the Global Horizontal Irradiance (GHI) and Diffuse Horizontal Irradiance (DHI). DNI is computed out of the quality-checked measurements of GHI and DHI. The results are expressed for all available measurement data in terms of relative Root Mean Scare Error (RMSE), RMSE Skill Score, Mean Absolute Error (MAE), MAE Skill Score, and mean bias error.
We conclude that the two models offer similar performances with a MAE between 25% and 35% for Germany.
How to cite: Turpin, M., Kurzrock, F., and Schmutz, N.: Assessment of direct normal irradiance assessment from cloud optical depth of geostationary meteorological satellites in Germany, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-338, https://doi.org/10.5194/ems2022-338, 2022.
The great increase of penetration rate in the photovoltaic (PV) system into electricity grid requires renewable energy management system (REMS) which is usually made by the power forecast at the various time scales; 15 minutes ahead, 3 hours ahead, same day and 24 hours ahead. cloud moving vector which is derived by two consecutive satellite imagery in general. In this study, however, horizontal wind fields are taken from the Unified Model Local Data Assimilation and Prediction System (UM – LDAPS) that is operated by Korea Meteorological Administration. The horizontal resolution of UM – LDAPS is 1.5 km and forecast horizon is 48 hours per each initialization. Wind speed at the level where the relative humidity, averaged over the domain, is largest is incorporated into the advection module that produces the cloud or clear sky index displacement. Solar irradiance from the satellite imagery is derived by the University of Arizona Solar Irradiance Based on Satellite – Korea Institute of Energy Research model (Kim et al., 2017). The temporal and spatial resolution of satellite imagery are 15 minutes and 1 km, respectively. Finally, solar irradiance at a cloudy pixel displaced after the forecasted time, will be recalculated where the clear sky index and solar zenith angle. The smart persistent model is employed for the reference model at the evaluation. The investigation period is from January to December, 2018. Forecasts by cloud moving vector are produced every hour but forecast at 09 LST is only employed here to compare with persistent model. Below is the example from the 1 hour ahead forecasted and observed GHI. Cloud clusters are similar between forecast and observation but small clouds over the Korean Peninsula disappeared in the observed imagery. The solar irradiance forecast based on cloud moving vector assumes that the clouds do not form nor dissipated for the forecasting horizon. That is why the discrepancy between forecast and observation exists. The relative root mean square error (rRMSE) between observed and forecasted GHI during the investigation period. In the intra-hour ahead forecast, the persistent model is better than the forecast by cloud moving vector. However, the rRMSE does not show that cloud moving vector forecast performs better than the persistent model for the forecasting horizon exceeding 2 – 3 hours. That is due to the microphysical characteristics of clouds that will be affected to cloud moving vector and solar irradiance itself. We will show the major results in detail in the upcoming conference.
Acknowledgement
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 2019371010006B).
How to cite: Kim, C., Kim, H.-G., Oh, M., and Kang, Y.-H.: Short-Term Forecast of Satellite-Derived Solar Irradiance in corporation with Numerical Weather Prediction over the Korean Peninsula, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-47, https://doi.org/10.5194/ems2022-47, 2022.
Renewable energies are becoming increasingly important in energy production due to proceeding climate change. This makes the energy sector one of the fastest growing and changing sectors worldwide. Solar energy is expected to account for large parts of the world’s future energy supply.
Solar radiation forecast will become more important with the future expansion of existing energy supply structures with solar energy resources. Due to the natural fluctuation of solar radiation, a reliable forecast is required for an efficient use of solar energy. This study deals with a solar radiation verification of the global numerical weather prediction models NEMSGLOBAL, GFS, ICON, ARPEGE-World, as well as the reanalysis model ERA5. The aim is to find out which models performs the best and how multi-model approaches can lower the forecast error.
For the verification 81 global solar radiation measurements from the Basic Surface Radiation Network (BSRN) and the World Radiation Data Center (WRDC) were used. Hourly forecast data are compared to quality controlled and aggregated measurement data for the years 2018 until 2020. Statistical analysis is conducted for each measurement location separately to evaluate and compare the performance of each raw or multi-model by using the error metrics like mean absolute error (MAE) and mean bias error (MBE).
Among all models, the reanalysis model ERA5 performed the best with a MAE of 43 Wm-2 and a MBE of 8 Wm-2. For the weather forecast models, ICON showed the lowest error with a MAE of 48 Wm-2 followed by GFS, NEMSGLOBAL and ARPEGE-World with MAEs of 51 Wm-2, 61 Wm-2 and 67 Wm-2. Unlike the other forecast models, NEMSGLOBAL and ARPEGE-World tend to underestimate solar radiation. Within the best performing multi-models, ICON is usually weighted the highest with up to 50 %. Implementing a multi-model approach for a solar radiation forecast, the MAE can be reduced as the number of models increases. Including two models in the multi-model the MAE can already be reduced by 4.6 Wm-2, while three models reducing the MAE by 7 Wm-2, and four models by 8.8 Wm-2.
Results indicate that the multi-model approach can improve solar radiation forecast. That shows the potential of further investigation of forecast models and their combination.
How to cite: Reiß, A., Bader, N., Bührer, M., and Schlögl, S.: Global verification of numerical solar radiation forecast , EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-373, https://doi.org/10.5194/ems2022-373, 2022.
The effect of local shadows cast by e.g. infrastructure or vegetation is of paramount importance for accurately estimating the local solar energy potential. Given the high spatial and temporal resolution that is required to provide accurate estimates of solar radiation components at very fine scales, deriving a solar cadaster across large areas is a computationally demanding undertaking. Here we present the development of a solar cadaster for the Austrian federal state of Salzburg, covering an area of more than 7.150 km² at a spatial resolution of 0.5 meters and a temporal resolution of 1 hour.
The dataset is based on the radiation model STRAHLGRID, which calculates near-surface direct and diffuse solar radiation on horizontal, real and arbitrarily inclined surfaces in the spectral range 0.3-3 μm, their component sum (global radiation), and sunshine duration. Results are provided at a spatial resolution of 100 m and a temporal resolution of 15 minutes across the national territory of Austria in near real-time. The model takes atmospheric turbidity, cloudiness, terrain shading, multiple / terrain reflections and ground albedo feedbacks into account. It integrates temporal changes of atmospheric turbidity by including precipitable water (water vapor transmittance) and visibility fields (aerosol transmittance) as obtained from the nowcasting model INCA as well as a cloud raster based on measured sunshine fraction and satellite data. The underlying digital elevation model (DEM) used as input in STRAHLGRID and INCA has a spatial resolution of 100 m.
The downscaling procedure used for providing a consistent solar radiation cadaster is based on combining the 100 m radiation data with a very high resolution digital surface model (VHR-DSM) obtained from airborne laser scanning. First, a test reference year is computed by aggregating the 15 min data to one hour and averaging across all years between 2006 and 2021 on an hourly basis. Second, diffuse and direct radiation on the horizontal surface are upsampled using bilinear interpolation. Third, radiation datasets are modified based on the VHR-DSM. Diffuse radiation is corrected using an updated version of the sky view factor based on the original DEM and the VHR-DSM. Direct radiation is corrected using shade maps from the VHR-DSM derived from the horizon angle and the solar position, computed using azimuth angle steps of 1 degree and time steps of 4 minutes. In addition to applying the shadow mask, direct radiation on the real surface is obtained using a correction term for the tilted surface based on slope, aspect and solar position. Finally, global radiation is computed as the sum of diffuse and direct radiation, which is of high relevance for spatial energy planning and solar energy applications at diverse scales.
How to cite: Göbel, M., Schlögl, M., Lehner, S., Krautgasser, F., and Olefs, M.: Development of a very high resolution solar radiation cadaster for estimating solar energy potential across the entire federal state of Salzburg, Austria, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-396, https://doi.org/10.5194/ems2022-396, 2022.
Photovoltaic production of a given solar power plant is mainly correlated with solar irradiance reaching the solar panel and to a lesser extent with air temperature and wind speed. Therefore, available energy in a building with roof-top photovoltaic (PV) panels in self-consumption can be highly variable due to cloud cover stochastic behaviour. Accurate irradiance forecast within the next hours are useful to help the energy management system of the building to cope with this variability and thus to maximize the consumption of the locally generated electricity at the expense of grid energy, then reducing financial and environmental costs of the overall building energy consumption.
Any solar energy forecast solution presents several sources of uncertainty at each main steps of the process: cloud forecast, radiative transfer of cloud and aerosols, irradiance conversion into power. Reducing the uncertainty of PV power modelled from irradiance forecast is a specific issue because it depends on certain conditions of the forecast application. PV performance models convert irradiance into PV power if PV cells characteristics are known and correctly specified by manufacturer, which is not always the case. Moreover, unexpected shadowing, panel surface soiling or aging of materials cannot be easily taken into account. If a consistent historical record of PV power data is available, a model output statistics (MOS) helps to decrease these induced uncertainties. If only real-time data are available, adaptive calibration using Kalman filter can improve the accuracy. If both historical and real-time data access are available, various machine learning approaches can set up more accurate MOS.
The start-up incubator of Institut Polytechnique de Paris is a building equipped with a roof-top PV farm with a total capacity of 17 kWp, made up with 53 using 8 different technologies with several tilted angle and some panels are equipped with reflectors. Minute PV power data have been continuously recorded from July 2020 onwards, collocated with the irradiance measurements of the Palaiseau BSRN station. This exceptional testbed enabled us to benchmark the several approaches for PV power modelling. We implement reference algorithms (linear regression, Kalman filter and autoregression) simulating respectively the availability data conditions (historical, real-time, both) and compared them with more complex machine-learning approaches. Results according to data availability, season, solar zenith angle, air temperature and irradiance variability are discussed. One of the outcome is that availability of real-time power measurements improves significantly forecasting results in most weather situations.
How to cite: Cros, S., Briand, S., and Badosa, J.: Benchmarking different approaches to convert surface solar irradiance into PV power production : a case study with an operational forecast system for a roof-top PV farm, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-576, https://doi.org/10.5194/ems2022-576, 2022.
The feasibility of national and supra-national low carbon power systems (LCPSs) is challenged under high penetration rates of renewables because the inherent variability of renewable sources increases the system’s operation and transmission costs. Optimizing the operation of the systems to make them efficient and profitable requires adapting their design to the regional solar and wind patterns.
The MET4LOWCAR Spanish project aims at demonstrating the benefits of a synergistic design of the generation and transmission power system that accounts for the regional climatic patterns of both solar and wind renewable resources, using the Spanish territory as a testbed. To that aim, a 30-yr weather integration is being performed with the Weather Research and Forecasting (WRF) model over the Iberian Peninsula and the Balearic Islands throughout a 5-km spatial grid and a 10-min temporal grid. The ultimate goal is to compound a climatic data base of solar- and wind-related variables to simulate the performance of a high-penetration LCPS in multiple scenarios, from long runs to extreme weather events.
Here, we present an original debiasing approach of the WRF global horizontal solar irradiance (GHI) that guarantees a reliable and more realistic representation of the solar power generation in the LCPS. First, the GHI simulated by WRF is compared against radiometric observations from the Spanish National Radiometric Network to demonstrate that it is indeed affected by a seasonal bias, related to a misrepresentation of convective clouds in the WRF model. Then, the spatial and temporal GHI grid is corrected cell by cell and day by day using the monthly GHI SARAH satellite product as a reference. The method is purposedly designed to interfere only at monthly scale (thus using monthly GHI SARAH as a reference) in order to preserve the original fine-scale spatial and temporal structure of GHI. However, it propagates the debiasing to daily steps using a tailored interpolation approach that prevents any spikes and data artifacts in the bounds between consecutive months.
The method will be described in detail, and some preliminary results will be shown at least for the period 2010–2020, using ground observations and satellite-based GHI data as a reference to assess the debiasing performance.
How to cite: Ruiz, J., Jiménez-Garrote, A., and Ruiz-Arias, J. A.: A fine-scale-preserving bias correction approach for global solar irradiance simulated with a numerical weather prediction model over a high-resolution spatial and temporal grid, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-173, https://doi.org/10.5194/ems2022-173, 2022.
State-of-the-art high resolution, convection resolving NWP models and reanalysis typically operate on a horizontal resolution of 1-5 km. These models require specific data assimilation schemes with frequent analysis (every 1-6 h) and corresponding dense and frequent observations to define the detailed initial conditions. Key variables needed for convection-resolving data assimilation are, among others, the 3-dimensional fields of temperature and humidity. Both variables are not adequately (vertically, horizontally and temporally) measured by current observing systems. The vertical resolution of atmospheric profiles provided by satellite sensors is poor, especially in the atmospheric boundary layer, where convection resolving models have many layers close to the surface to better describe the surface-atmosphere exchange processes. To the necessary information, a new generation of observations through the lowest few kilometers of the atmosphere is required. A network of ground-based remote sensing sensors (e.g., microwave radiometer, MWR, or water vapor DIAL) has the potential to provide real time profile observations to forecasting centers. Maintaining an operational observing network is a difficult and expensive task. Therefore, it is essential to evaluate the impact of different components of the current observing system and to assess the potential contribution of a new observing components to the analysis of the atmospheric state.
In our study we perform an Observing System Simulation Experiment (OSSE) to show the potential benefit of ground-based MWR for improving the initial thermodynamic state of the atmosphere. The Nature Run (NR), representing the “true” atmosphere, is performed using the ICON-LES model for a 150x150 km domain in the western part of Germany with 500 m horizontal resolution for summer convective cases. The MWR observations, dependent on cloudiness, temperature and humidity profiles, are simulated with the radiative transfer model RTTOV-gb and assimilated into the convection resolving ICON model (2km horizontal resolution). In this contribution, we will present first impact studies of assimilating synthetic observations of single MWR instruments on the initial temperature and humidity fields and extend the approach for evaluating the effect of a network of instruments with respect to other variables relevant for solar power applications, such as precipitation and solar radiation.
How to cite: Toporov, M., Löhnert, U., Schemann, V., Schomburg, A., and Vural, J.: Assimilation of ground-based microwave radiometer observations into convection resolving ICON model using an observing system simulation experiment, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-523, https://doi.org/10.5194/ems2022-523, 2022.
Proposal of a poster for the EMS2022
Intention:
Within the research project EnerSHelF (Energy-Self-Sufficiency for Health Facilities in Ghana), i. a. energy-meteorological and load-related measurement data are collected, for which an overview of the availability is to be presented on a poster.
Context:
In Ghana, the total electricity consumed has almost doubled between 2008 and 2018 according to the Energy Commission of Ghana. This goes along with an unstable power grid, resulting in power outages whenever electricity consumption peaks. The blackouts called "dumsor" in Ghana, pose a severe burden to the healthcare sector. Innovative solutions are needed to reduce greenhouse gas emissions and improve energy and health access.
The aim of the project is therefore to develop PV-based energy solutions for healthcare facilities and to improve the reliability and integrability of such systems in the local electricity grid.
The work is based on a measurement campaign that has been running since 2020 at three hospitals spread across the country. The variables measured include:
Global tilted irradiance (GTI)
Soiling ratio and temperature of the PV panels
All-sky camera recordings
Load measurement aggregate (grid node)
Load measurement sub-distribution (departments and devices)
In addition, weather stations are operated at the sites to improve weather forecasts.
These datasets can be used to follow different approaches to managing the harsh conditions caused by dry and rainy seasons, and to design and control PV hybrid systems appropriately.
According to the World Bank (2017) only 3% of the population in West Africa and the Sahel can currently access PV power through off-grid systems. As an important catalyst for sustainable development, access to a reliable source of clean energy is vital for inclusive economic development, improved human health, wellbeing and security. As such, EnerSHelF can contribute to Sustainable Development Goals (SDG) of health (SDG 3), energy (SDG 7) and partnerships (SDG 17).
How to cite: Yousif, R., Kimiaie, N., Meilinger, S., Bender, K., Abagale, F. K., Ramde, E., Schneiders, T., Kunstmann, H., Diallo, B., Salack, S., Denk, S., Bliefernicht, J., Sawadogo, W., Guug, S., Rummeny, S., Bohn, P., Chaaraoui, S., Schiffer, S., Abass, M., and Amekah, E.: Measurement data availability within EnerSHelF, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-530, https://doi.org/10.5194/ems2022-530, 2022.
For over 20 years, the National Solar Radiation Database (NSRDB), covering most of the western hemisphere, has been a source of public data for many solar energy applications. Recent improvements in satellite technology and machine-learning-based remote sensing methods have added tremendous value to the NSRDB in terms of both the quantity and quality of the data.
For example, the historical NSRDB data that is available from 1998 to present with one year lag is processed on a nominal 4x4 km grid spacing at a 30min frequency. Beginning in 2018, the NSRDB has additional datasets at 2x2 km 5min resolution available for the Continental United States, Hawaii, Mexico, and the Caribbean Islands, and at a 2x2 km 10min resolution available for North and South America from +60 to -60 degrees latitude. The improved spatiotemporal resolution should be a great asset to our stakeholders, especially for the analysis of utility scale solar installations which typically desire a higher resolution than the previously available 4x4 km 30min data.
Moreover, we have developed new methods for the prediction of cloud properties from satellite data using physics-guided machine learning. These methods were originally developed to compensate for the limitations of traditional cloud property retrieval algorithms, but they have proven to be generally more accurate than the traditional algorithms. The results demonstrate higher accuracy in the modeled irradiance that is expected to be helpful for a wide variety of solar energy applications.
In summary, the goal of the NSRDB is to provide the public with the highest-quality freely-available solar irradiance data possible. In this context, the NSRDB continues to evolve and push the envelope of what a public solar dataset can be. We think these recent advancements are important contributions to the solar energy community, and we hope that they will be fully taken advantage of.
How to cite: Sengupta, M., Habte, A., Xie, Y., Buster, G., Benton, B., and Bannister, M.: Recent Updates to the National Solar Radiation Database (NSRDB) , EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-611, https://doi.org/10.5194/ems2022-611, 2022.
The 'Network of Experts' (BMDV-Expertennetzwerk) of the German Ministry for Digital and Transport (BMDV) is a network of German government agencies. Their main topic is the future-oriented transition of the transportation infrastructure in Germany. The main task of the topic area 5 (“renewable energies”) is to assess the potential of renewable energies along the transportation infrastructure in Germany. Germany’s national meteorological service DWD coordinates the topic area and is responsible for provision of meteorological data that is used in the assessments of the potential energy generation. Further partners in this activity are esp. the Federal Highway Research Institute (Bundesanstalt für Straßenwesen, BASt) and the the Federal Railway Authority (Eisenbahn Bundesamt (EBA) / Deutsche Zentrum für Straßenverkehrsforschung (DZSF)).
One focus in the first phase of the activity (2017 – 2019) was a pilot study in a small-scale area in Germany. It was evaluated to what extend the potential energy production with photovoltaics alongside the infrastructure fits to the temporal profile of energy needs for pumping requirements of the waterways. Estimating the potential energy production with photovoltaics does not only require radiation data, but also additional meteorological parameters, as the efficiency of the PV modules also depend on the ambient temperature. Therefore, high-resolution data from satellites (CMSAF SARAH-2) in combination with data from the regional reanalysis COSMO-REA6 have been used. Both datasets are produced by DWD and are publicly available. Quality assessments of the datasets have been performed.
In the current phase of the activity (2020 – 2023) the assessment is extended to to cover all of Germany. Currently, the main focus is the assessment of the potential of photovoltaics integrated in noise barriers along the railways and highways. A similar assessment for small wind turbines along the infrastructure is in preparation.
Reference:
Auerbach, M., Ebner von Eschenbach, A-D., Eichler, D., Gersdorf, F., Kaspar, F., Majewski, D., Niermann, D., Schima, B., Streek, P.: Einsatzpotenziale erneuerbarer Energien für Verkehr und Infrastruktur verstärkt erschließen: Ergebnisbericht des Themenfeldes 5 im BMVI-Expertennetzwerk für die Forschungsphase 2016 - 2019, Bundesministerium für Verkehr und digitale Infrastruktur (BMVI), Berlin, https://www.bmdv-expertennetzwerk.bund.de/DE/Publikationen/TFSPTBerichte/TF5_3Auflage.html
How to cite: Bär, F., Kaspar, F., Streek, P., Rieck, D., and Auerbach, M.: Potential of renewable energy alongside the transportation infrastructure in Germany, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-686, https://doi.org/10.5194/ems2022-686, 2022.
The viability of low carbon power systems (LCPS) is becoming more realistic. However, such energy systems present serious challenges in becoming a reality. One of the most important problems is related to the inherent variability of renewable sources, which represent a risk for these systems. Although these systems may include methods to minimize these risks (backup plants, improved interconnection, etc.), these solutions may not be sufficient in extreme weather conditions, in particular, in the so-called compound extreme events. These events are characterized by the simultaneous occurrence of several hazards, generally associated with complex interactions between a wide range of meteorological processes acting on different temporal and spatial scales. In the context of LCPS analysis, compound events can be associated with periods with below normal solar and/or wind power generation and above normal demand. The occurrence of such events may overwhelm the capacity of LCOP systems in the future, thus causing socially significant impacts. In this work, the occurrence of compound extreme weather events related to wind and solar power generation in Spain are identified and the meteorological conditions that cause them described. The study is part of the MET4LOWCAR Spanish project, that aims at demonstrating the benefits of the design of the LCPSs that accounts for the regional weather and climatic patterns of both solar and wind renewable resources, using the Spanish territory as a testbed.
The study is carried out based on real data on daily demand, wind and solar generation provided by the Spanish TSO and corresponding to the 2008-2020 period. Three different types of events were analyzed: compound solar/wind events (below normal solar/wind and above normal demand) and compound solar and wind events (below normal wind and solar and above normal demand). Specific threshold were used to identify these events. The study is conducted using normalized time series, at different periods (1, 5 and 15 days) and separately for each season of the year. The weather patterns associated with the compound events were analyzed using composite analysis by means of ERA-5 reanalysis data. Results showed firstly, that while compound events are relative frequent for short periods (1 and 5 days), very long events (15 days) are much rarer. In addition, a marked seasonality of the events occurrence is observed, with a peak during the winter season. It was also found that compound event related to wind power generation anomalies are more frequent and intense. Many events were found associated with the presence of negative/positive centers of geopotential heights anomalies roughly located over Portugal/Great Britain, although the intensity and location of the centers varies along the year. Finally, some conclusion regarding the optimal design of a reliable LCPS for Spain are derived and discussed.
How to cite: Pozo-Vázquez, D., Gálvez-Romero, J., Jiménez-Garrote, A., Sánchez-Hernánez, G., Rodríguez-Esteban, V., Cuesta-López, M., and Tovar-Pescador, J.: Analysis of compound extreme weather events related to wind and solar power generation in Spain, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-170, https://doi.org/10.5194/ems2022-170, 2022.
Photovoltaic power production strongly depends on meteorological conditions. Influencing factors are the air temperature and wind speed at the photovoltaic module, as well as changes in irradiance caused by humidity, clouds and aerosols. Here we analyse the global photovoltaic (PV) power potential and compare the changes therefore between a historical time period (1985 to 2014) and possible futures (SSP1-2.6, SSP2-4.5 and SSP5-8.5) at the end of the 21st century (2071-2100). To that end, we use an ensemble of more than 12 CMIP6 models with interactive aerosol schemes and estimate the potential photovoltaic power generation following Crook et al. (2011). We calculate the change in the PV power potential associated with surface irradiance, temperature, and wind speed between the future and the past, and divide the change in PV power generation into contributions from changes in temperature, cloud cover, humidity, aerosols and wind speed.
Our results point to a global decrease in future PV power potentials due to the rising air temperatures and the associated increase of humidity by -1.2% to more than -3.5% depending on the future scenario. The contribution from changes in cloud cover and aerosols have heterogeneous spatial patterns, with typically stronger influences in SSP5-8.5 compared to SSP1-2.6, e.g. for the clouds. The contribution by clouds is mostly positive, but has a negative effect for PV power potential in the pole regions, over India, and equatorial oceans. The models also show a large spread in their contributions from clouds, especially over Europe and North America. The future change in the contributions from aerosols has beneficial effects for PV power production over Europe and China in all scenarios assesses, but over South-America, Australia and Africa, the contributions from aerosols are positive in SSP1-2.6 but negative in SSP5-8.5 owing to the different behaviour in anthropogenic aerosols and of natural desert-dust emissions. Our study highlights the impacts of CMIP6 model uncertainty in the future development of aerosol burden, the magnitude of future warming, and the unclear response of clouds to warming on estimating future PV power potentials.
How to cite: Scheele, R. and Fiedler, S.: Future change of global photovoltaic power potential in a warming world, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-89, https://doi.org/10.5194/ems2022-89, 2022.
Wind and photovoltaic (PV) power production vary across time and space. We aim at a first systematic assessment of anomalies in PV and wind power production associated with different synoptic-scale weather patterns with a kilometre-scale resolution for all of Europe. To that end, we have developed the University of Cologne’s Renewable Energy Model (UoC-REM). UoC-REM simulates the hourly PV and wind power production using the reanalysis data set COSMO-REA6 with a horizontal resolution of 6 km. The installed capacities of PV and wind power for 2050 are taken from gridded scenario data. The output of UoC-REM is paired with data from a classification of 29 synoptic weather patterns identified and provided by the German Weather Service. Our results reveal substantial spatio-temporal differences in PV and wind power production depending on the weather pattern. We group the PV and wind power production from individual weather patterns into three groups to facilitate a composite analysis underlining similarities for the PV and wind power production across the weather patterns. These are (1) the group with anomalously high wind power production that almost always produces above average total production, primarily associated with patterns related to westerly winds, (2) the group of moderate PV plus wind power production with weather patterns causing mostly mild anomalies in both PV and wind power, but also ‘dark doldrums’ with simultaneously low wind and PV power production during blocking high pressure systems, e.g., South-Shifted Westerly, and (3) the group with high PV power production paired with below average wind power that is mostly associated with anti-cyclonic weather patterns. We identify that the lowest 10-day production event of PV plus wind power occurs during the pattern Anticyclonic South-Easterly from Group 3, with a reduction by 41% compared to the average hourly total production for Europe. On the contrary, the highest 10-day production event occurs during the pattern Cyclonic North-Westerly from Group 1, with an increase by 18%. Our results suggest that identifying the weather pattern can be used as a quick estimate of the overall production anomaly in PV and wind power production. It would allow to monitor and issue warnings of weather conditions that pose a risk to a future energy system that relies on more weather-dependent renewable sources, without the need to perform operational simulations of the expected power production. Future work will focus on an in-depth analysis of extreme events in renewable power production considering the full spatial and temporal resolution of the new dataset of UoC-REM.
How to cite: Ho-Tran, L. and Fiedler, S.: Extremes in European wind and photovoltaic power production in 2050 identifiable by weather patterns, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-261, https://doi.org/10.5194/ems2022-261, 2022.
In complex terrain, wind power production depends not only on wind velocity but on wind direction too. Estimating such volatile variables is a difficult but essential task for the installation of a Wind Turbine (WT) and the operation of Wind Farm (WF). Our goal is to create a short term (5-15min) forecasting physical model when terrain complexity excludes measurements using long-range scanning lidars. In this study we will try to extrapolate the wind field over a wind farm with the use of steady state CFD simulations. Steady state RANS CFD simulations are extremely faster than time dependent ones (LES), which makes them fast enough for operational use, as well as for estimating wind flow over a wanted location for many different scenarios.
The computer used for the simulations has 24-cores and 128gb ram installed. The software used for the simulations is COMSOL Multiphysics. In this study, each steady state simulation represents 10 min averages and the computational time needed is below 2 min. The simulations were run for an area 2.5x2x0.6 km3 large with a non-uniform mesh and the distance between computational nodes ranging from 22,5 to 75.5m in the area of interest.
Wind measurements from a vertical Wind Profile LIDAR installed at CRES WF located at Lavrio (Greece), were used for the validation of the model. The terrain location is complex with a RIX index of 10%. Lidar measurements at 6 different heights (54 m, 78 m, 100 m, 120 m, 140 m, 160 m) were used as inlet conditions after being vertically extrapolated and interpolated, acknowledging the factors that govern lidar accuracy in complex. The inlet plane was positioned 1.3 km away from the LIDAR location. Horizontal homogeneity was assumed at inlet plane. The simulations were run at an expanded area to capture the effects of the terrain on wind movement, because of lack of more measurement locations.
Results from 291 simulations that were corrected with linear regression, were compared with data from the three WTs and the LIDAR. Extreme errors were detected when sudden changes in wind speed or direction were observed. The maximum mean absolute error (MAE) of wind velocity with respect to LIDAR measurements at all available heights is 0.28 m/s and the minimum is 0.17 m/s. In addition, absolute errors are smaller than 0.7 m/s and maximum absolute percentage error is 11% for 95% of the estimations. The accuracy of the model increases with height because of the terrain anomalies and turbulence effects.
How to cite: Michos, D., Kazantzidis, A., Catthoor, F., and Foussekis, D.: Preliminary results of a physical model for extrapolating the wind field over complex terrain, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-296, https://doi.org/10.5194/ems2022-296, 2022.
The correct representation of wind speeds at hub height (100m above ground) is becoming more and more important with respect to the expansion of renewable energies. Since there are only a few long-term measurements at hub heights available in Europe, we rely on wind speed estimates from reanalyses. Reanalyses provide a physically consistent state of the atmospheric dynamics over long periods, but are not able to represent local effects due to their limited horizontal resolution. We perform a post-processing of the wind speeds from the regional reanalysis COSMO-REA6 in Central Europe based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid spacing by a factor of eight compared to COSMO-REA6 (to approx. 800m) and considers different vertical atmospheric stabilities. While the downscaled wind fields might be better in line with the orography, the data still has inherent uncertainties (e.g., fit of the COSMO-REA6 input to the orography, errors in COSMO-REA6, assumptions in the wind model) and thus may still deviate considerably from the observations.
Therefore, in a second step, a statistical correction based on various reanalysis parameters as predictors. These corrections are performed using a neural network approach as well as a generalized linear model as reference. Although only few measurements by masts or lidars are available at hub heights, a reduction of wind speed RMSE of up to 30% can be achieved depending on location. A comparison with radiosonde observations also confirms the added value of combining the physical and statistical approach in wind speed post-processing.
How to cite: Brune, S. and Keller, J. D.: Statistical post-processing of COSMO-REA6's wind speeds at hub heights using a diagnostic wind model and neural networks, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-410, https://doi.org/10.5194/ems2022-410, 2022.
The feasibility low carbon power systems (LCPSs) is challenged under high penetration rates of renewables because the inherent variability of renewable sources increases the system’s operation and transmission costs. Optimizing the operation of the systems to make them efficient and profitable requires adapting their design to the regional solar and wind patterns. The MET4LOWCAR Spanish project aims at demonstrating the benefits of the design of the LCPSs that accounts for the regional climatic patterns of both solar and wind renewable resources, using the Spanish territory as a testbed. To that aim, a 30-yr weather integration is being performed with the Weather Research and Forecasting (WRF) model over the Iberian Peninsula and the Balearic Islands throughout a 5-km spatial grid and a 10-min temporal grid (Regional reanalysis). The ultimate goal is to compound a climatic data base of solar- and wind-related variables to simulate the performance of a high-penetration LCPS in multiple scenarios, from long runs to extreme weather events.
In this work, the results of the assessment of the wind speed and wind power estimates derived from this regional reanalysis are presented and discussed. Different validation studies were conducted. Firstly, the wind estimates at 10 and 40 m a.g.l. were evaluated based on wind measurement collected at 44 surface meteorological stations and two masts located at wind farms. This study was conducted for years 2019 and 2020. In a second study, regional wind power models for 42 continental Spanish provinces were built and evaluated. Models were obtained using reference power curve and the regional installed capacities, elaborated based on information from wind farms at 506 locations collected from public databases. This study was carried out for the year 2020 at hourly resolution and the model estimates were evaluated based on actual power generation values provided by the Spanish TSO. Finally, wind and wind power estimates derived from the ERA5 reanalysis were also obtained and used for benchmarking purposes.
Results of the wind speed validation show a superior performance of the regional reanalysis for complex topography locations (where most of the Spanish wind farms are located). For instance, differences in the wind speed RMSE values greater than 2 m/s (4.27 vs 2.14 m/s) were found for one mast. Nevertheless, the overall performance of both reanalysis are similar (1.6 m/s mean RMSE value for surface stations). Results of the wind power modeling show a similar overall performance of both reanalysis. Nevertheless, wind power estimates derived from the regional reanalysis were able to better reproduce the wind power intra-day variability.
How to cite: Jiménez-Garrote, A., Santos-Alamillos, F., López-Cuesta, M., Ruiz-Arias, J. A., Sánchez-Hernández, G., and Pozo-Vazquez, D.: Validation of a Spanish regional high-resolution database for wind energy applications , EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-169, https://doi.org/10.5194/ems2022-169, 2022.
The Offshore Wind Energy Act (Windenergie-auf-See-Gesetz - WindSeeG) legally requires annual tender procedures for sites in the North Sea in the Exclusive Economic Zone of Germany for the construction of wind turbines. The Federal Maritime and Hydrographic Agency (Bundesamt für Seeschifffahrt und Hydrographie - BSH) conducts so called preliminary investigation of sites (PIS), pursuant to §10 WindSeeG. These include investigations of the wind conditions at the site, to give the interested firms information for calculating a bid. The preliminary investigation of the wind conditions is conducted together with the German Weather Service (Deutscher Wetterdienst) and is based on three elements: a measurement campaign at the site for one year, an evaluation of reanalysis data by the German Weather Service, and a report which brings together all available information about the wind conditions. These reports are published together with the measurement and model data and the results of all other preliminary investigations via the PIS-Data Hub https://pinta.bsh.de as part of the tendering process.
The sites that are dedicated for the construction of wind turbines are defined in the Site Development Plan (Flächenentwicklungsplan), together with the year of the intended tendering process. The first PIS have been successfully completed for two sites in the southeastern North Sea and for one site in the Baltic Sea in March 2021. In the years 2022 and 2023, a call for tender for sites N-3.5, N-3.6 and N-7.2 will be published. These sites are located in the southwestern part of the German Exclusive Economic Zone in the North Sea. At or near these sites, meteorological measurements have been conducted, using a floating lidar which was installed on a buoy (site N-7.2) as well as scanning lidars installed on the transition pieces of wind turbines (N-3.5, N-3.6). These measurements resulted in time series of vertical profiles of wind direction and speed, covering one year each, and giving information about the wind conditions at the sites at relevant heights of future wind turbines. In addition, temperature, relative humidity, air pressure, and sea surface temperature were measured.
How to cite: Brast, M., Gehrke, M., Hahn, J., Hüttl-Kabus, S., Möller, T., Outzen, O., and Spangehl, T.: Investigating the wind conditions in the North Sea for wind turbines at sites N-3.5, N-3.6 and N-7.2, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-182, https://doi.org/10.5194/ems2022-182, 2022.
The construction of offshore wind farms in Germany's Exclusive Economic Zone (EEZ) is an important component for the successful implementation of the energy transition. In 2021 the Federal Network Agency started to launch yearly tenders for sites in the North Sea and Baltic Sea which are conducted in cooperation with the Federal Maritime and Hydrographic Agency (BSH) according to the Wind Energy Act (WindSeeG). The German Meteorological Service (DWD) supports the BSH in compiling the required wind information. This information is made publicly available by the BSH to bidders via the PINTA portal (https://pinta.bsh.de) as part of the tendering procedures.
Detailed information on the wind conditions on the sites is crucial for the bidders' bid calculations. This information is compiled in collaboration by the BSH and the DWD with the participation of external contractors. In detail, these are in-situ measurements on the research platforms in the North Sea and Baltic Sea, FINO1, FINO2 and FINO3 (https://www.fino-offshore.de/de/index.html). Furthermore, this comprises one-year LiDAR measurements are carried out by external contractors on behalf of the BSH at the sites to be tendered, as well as data and evaluation results of the COSMO-REA6 and ERA5 reanalyses are provided by the DWD. These data sets are the basis for the preparation of summarised overall reports on the wind conditions on the sites.
The reanalysis and measurement data provided allow a detailed investigation of the seasonal variability as well as an in-depth assessment of the current as well as the historical wind field on each site. The focus of the measurements is on the heights relevant for future wind turbine types, i.e. in the range of 40 to 200 metres. The evaluation of the reanalyses is carried out for the grid points closest to the areas as well as the surrounding grid points and is validated using the existing measurement data. Previous evaluations show a very good correlation, which gives the reanalyses a high significance to determine the wind conditions on the sites. In addition, information on interannual variability is required. Long-term time series of geostrophic wind derived from air pressure data from coastal stations enable an assessment of multi-decadal variations.
How to cite: Möller, T., Spangehl, T., Brast, M., Andersson, A., and Tinz, B.: Provision of wind information for site tenders for offshore wind farms according to WindSeeG in the German North and Baltic Seas, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-60, https://doi.org/10.5194/ems2022-60, 2022.
Operational forecasting models of Renewable Energy Sources (RES) are usually developed for specific time frames, using a reduced set of data sources as a function of the forecasting horizon. This results in discontinuities in predictions between the different time frames from the next minutes to the next days, which is detrimental to decision-making in power systems. Although advanced forecasting model may lower forecasting error, an alternative and maybe more impactful option consists in expanding the set of potential sources for RES forecasting, going beyond recent power measurements, classical Numerical Weather Prediction and information from satellites. It is therefore crucial for next-generation RES forecasting to propose continuous, seamless predictions that harvest the full potential of heterogeneous data sources. This presentation highlights the different solutions developed in the Horizon2020 project Smart4RES for seamless RES forecasting based on the combination of multiple data sources, including high-resolution weather measurements and forecasts.
A step towards high-resolution RES forecasting, i.e. temporal resolutions below 5 min and spatial resolutions at the scale of 100 m, is achieved by the integration of high-resolution into RES forecasting models. The use of lidar measurements for the minute-ahead power forecasting of wind turbines improves RMSE against persistence. The production of wind speed forecasts at a 100m-30s resolution thanks to Large Eddy Simulation (LES) at different wind farms of an isolated power systems enables to predict the total variability of wind power production on 10-min rolling intervals, with higher reliability than an approach based on traditional NWP that have a lower spatio-temporal resolution.
The combination of multiple data sources has proven to be efficient for the improvement of RES forecasting, especially at intraday horizons. Smart4RES proposes optimal combinations for both weather and RES forecasting. The combination of high-resolution irradiance maps derived from a network of All-Sky-Imagers (ASI) with satellite images outperforms the predictions of the ASI network only. Similarly, the combination of filter-based models for Wind and PV forecasting improves the forecasting scores of the individual models.
How to cite: Camal, S., Van Der Meer, D., and Kariniotakis, G.: Towards seamless and high-resolution renewable energy forecasting by the combination of approaches and data sources, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-534, https://doi.org/10.5194/ems2022-534, 2022.
Climate change observed in recent decades is manifested by an increasing occurrence of extreme meteorological phenomena, which have impact significantly various sectors of the economy and society. One of the most vulnerable sectors is energy production and distribution.
Over the past 20 years, we have observed a steady increase in the demand for electricity in Poland. It increases both in individual months and for each season of the year. However, it can be seen that the changes are not identical for winter and summer. In the scale of the whole country, for January the increase in energy consumption between the first and the second decade of the 21st century is below 10%, while for July it is almost 15%. During the hottest months, energy consumption is already similar to the winter months.
One of the factors analyzed by the authors responsible for such a large change are the more frequent, longer, and more intense heat waves. One of such waves, occurred in 2015, led to limitations in energy supplies to industrial recipients. The aim of the presented study is to estimate the impact of heat waves on the demand for electricity in Poland on a time scale: from hourly and daily to monthly demand. The 20-year time series of hourly electricity consumption in Poland provided by the national operator of the energy grid (Polskie Sieci Elektroenergetyczne) will be compared with a similar time series of meteorological data such as the maximum, minimum, and average daily air temperature and hourly temperature values. Energy consumption during 5 selected heat waves from 2002 to 2021 will also be analyzed. This will allow to assess whether the limitations in energy supplies may repeat in the future and what are the meteorological factors behind them.
Acknowledgements: This research has been funded by the National Science Center of Poland under the grant number: 2018/31/B/HS4/03223
How to cite: Graczyk, D., Pińskwar, I., and Choryński, A.: Too hot for air conditioning. Increased energy demand during heat waves in Poland. , EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-483, https://doi.org/10.5194/ems2022-483, 2022.
