ERE2.1

Energy Meteorology

Wind and solar power are the predominant new sources of electrical power in recent years. Several countries or regions regularly exceed 100% of variable renewable energy in their grids. By their very nature, wind and solar power, as well as hydro, tidal, wave and other renewable forms of generation are dependent on weather and climate. Modelling and measurement for resource assessment, site selection, long-term and short term variability analysis and operational forecasting for horizons ranging from minutes to decades are of paramount importance.

The success of wind power means that wind turbines are increasingly put in sites with complex terrain or forests, with towers extending beyond the strict logarithmic profile, and in offshore regions that are difficult to model and measure. Major challenges for solar power are notably accurate measurements and the short-term prediction of the spatiotemporal evolution of the effects of cloud field and aerosols. Planning and meteorology challenges in Smart Cities are common for both.

For both solar and wind power, the integration of large amounts of renewable energy into the grid is another critical research problem due to the uncertainties linked to their forecast and to patterns of their spatio-temporal variabilities.

We invite contributions on all aspects of weather dependent renewable power generation, e.g.:

• Wind conditions (both resources and loads) on short and long time scales for wind power development, especially in complex environments (e.g. mountains, forests, coastal or urban).
• Inter-annual variability of solar and wind resource.
• Typical Meteorological Year and probability of exceedance for wind and solar power development.
• Wind and solar resource and atlases.
• Wake effect models and measurements, especially for large wind farms and offshore.
• Performance and uncertainties of forecasts of renewable power at different time horizons and in different external conditions.
• Forecast of extreme wind events and wind ramps.
• Local to global impacts of renewable energy power plants or of large-scale integration.
• Dedicated wind measurement techniques (SODARS, LIDARS, UAVs etc.).
• Dedicated solar measurement techniques (pyranometric sensors, sun-photometer, ceilometer, fish-eye cameras, etc.) from ground-based and space-borne remote sensing.
• Tools for urban area renewable energy supply strategic planning and control.
Other related topics will be considered by the conveners.

Co-organized by AS1
Convener: Xiaoli Larsén | Co-conveners: Gregor Giebel, Somnath Baidya Roy, Philippe Blanc
Presentations
| Wed, 25 May, 13:20–18:30 (CEST)
 
Room 1.85/86

Session assets

Session materials

Presentations: Wed, 25 May | Room 1.85/86

Chairpersons: Xiaoli Larsén, Gregor Giebel
13:20–13:25
Wind Modelling
13:25–13:35
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EGU22-12936
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solicited
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Highlight
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On-site presentation
Andrea Hahmann

The Working Group III contribution to the Sixth Intergovernmental Panel on Climate Change (IPCC) Assessment Report-- mitigation of climate change-- will be publically released on 4 April 2022. The sections on "Mitigation Options: Energy Sources and Energy Conversion", "Climate Change Impacts on the Energy System", and a BOX on "Energy Resilience" are highly relevant to the Energy Meteorology community. As a Chapter Lead Author, I will summarise the findings of Chapter 6, Energy System, and emphasise their relevance to Energy Meteorology and Climate. I will also discuss my experience as a lead author and the challenges of communication between such different research communities. 

How to cite: Hahmann, A.: The GWIII contribution to the IPCC AR6 and its relevance to Energy Meteorology and Climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12936, https://doi.org/10.5194/egusphere-egu22-12936, 2022.

13:35–13:42
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EGU22-2287
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Highlight
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Virtual presentation
Anthony Kettle

January 2007 was a remarkably stormy period in Europe with impacts on societal infrastructure and implications for energy meteorology.  A series of cyclones tracked across the North Atlantic and into Europe during the two week period 8-22 January 2007.  For many parts of Europe, Storm Kyrill on 18 January 2007 was the most important of these for the infrastructure damage that it caused.  It had the highest European storm-related insurance losses in recent history.  The storm spawned a high intensity derecho that started in western Germany and travelled into eastern Europe. It was associated with severe convection, lightning, several tornadoes, and strong wind gusts.  The storm caused over 50 fatalities, widespread disruption of transport and power networks, and a lot of forest damage.   Storm Hanno on the 14 January 2007 was the second most severe storm of the period with serious impacts in Norway and southern Sweden.  Wind gusts reached the level of the 20-50 year event.  There were 6 fatalities in southern Sweden, some building damage, power cuts, and forest damage.  Storm Franz on 12 January 2007 caused the highest surge in the southern North Sea for January.  However, its flooding impact was reduced because the monthly cycle of spring and neap tides was near a minimum.  By contrast, astronomical tides were highest near the end of the period on 20-22 January 2007.  The highest absolute water levels for the month for many tide gauge stations were registered during Storm Kyrill on 18 January 2007 and also during Storm Lancelot on 20 January 2007.  This contribution takes a closer look at the North Sea surge of two important storms of the period: Storm Franz and Storm Kyrill.  An analysis is presented of tide gauge data to elucidate the storm surge and wave field around the North Sea and to assess possible links with shipping accidents and offshore incidents.  An unusually large wave sequence had been registered at the FINO1 offshore wind energy research platform only a couple of months previously on 1 November 2006.  The water level data is analyzed to ascertain if there may have been a repeat of the wave event during the storm sequence on 8-22 January 2007.

How to cite: Kettle, A.: Kyrill, Franz, and the Societal Impacts of the Storms of January 2007, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2287, https://doi.org/10.5194/egusphere-egu22-2287, 2022.

13:42–13:49
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EGU22-10440
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ECS
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On-site presentation
Clara Lambin, Xavier Fettweis, Damien Ernst, and Christoph Kittel

Fighting global warming implies replacing fossil fuels by renewable energy sources. Wind has the benefit to be an easily accessible and infinitely renewable resource but is not evenly distributed in space and time. A solution to prevent energy scarcity in a decarbonised world would be the building of a global interconnected grid that provide populated regions with electricity generated in remote but resourceful areas. In this context, it has appeared in previous studies that Greenland and Europe have complementary wind regimes. In particular, the southern tip of Greenland, Cape Farewell, has gained increasing interest for wind farm development as it is one of the windiest places on Earth. In order to gain new insights about future wind speed variations over South Greenland, the Modèle Atmosphérique Régional (MAR), validated against in situ observations over the ice-free area where wind turbines are most likely to be installed, is used to built climate projections under the emission scenario SSP 5-8.5 by downscaling an ensemble of CMIP6 earth system models (ESMs). These projections enable to assess the long term wind speed and maximum wind power change between 1981 and 2100 over Cape Farewell, quantified with the help of a linear regression. It appears from this analysis that, during this period over the ice-free area, the annual wind speed is expected to decrease of ~-0.8 m/s at 100m a.g.l. This decrease is particularly marked in winter while in summer, wind speed acceleration occurs along the ice sheet margins. An analysis of two-dimensional wind speed change at different vertical levels indicates that this decrease is likely due to synoptic circulation change, while in summer, the katabatic winds gowing down the ice sheet are expected to increase due to an enhaced temperature contrast between the ice sheet and the surroundings. As for the mean annual maximum wind power a turbine can yield, a decrease of ~-300.5 W is to be expected over the ice-free area of Cape Farewell between 1981 and 2100 at 100m a.g.l. Again, the decrease is especially marked in winter. Considering the very high winter wind speeds occuring in South Greenland which can cut off wind turbines if too intense, the projected wind speed decrease might be beneficial for the establishment of wind farms near Cape Farewell.

How to cite: Lambin, C., Fettweis, X., Ernst, D., and Kittel, C.: Long term wind speed and wind power change analysis over South Greenland using the regional climate model MAR., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10440, https://doi.org/10.5194/egusphere-egu22-10440, 2022.

13:49–13:56
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EGU22-5232
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On-site presentation
Andrea N. Hahmann, Alfredo Peña, Oscar García-Santiago, and Sara C. Pryor

In 2020, the North Sea already had 19.8 GW or 79% of the European offshore wind installations. The size and number of wind farms in this region are expected to increase substantially to reach climate mitigation targets, with forecasts of offshore wind commitments across Europe adding up to 111 GW of offshore wind by 2030. However, governments base their climate mitigation plans on past historical wind resources data. Still, there is a probable threat that these will change in the future due to climate change during the lifetime of a wind farm. 

The study of future changes in wind resources is not a new subject. A systematic literature search with the keywords "Wind Resources" and "Climate Change" returned over 80 peer-reviewed articles that assessed future wind resources at the global, regional and local scale. Most of these studies used the 10-m wind speed output from the climate or regional model to directly estimate a wind turbine's power production, using the power law and sometimes an idealised power curve. As far as we know, only two studies explored the possible implications of changes in wind direction. 

In this presentation, we explore the implications of the various assumptions. We use the example of the North Sea and Northern Europe and the CMIP6 climate model archive to demonstrate that some assumptions can exaggerate future wind resource changes. We also consider the consequences of the changes in boundary layer stability, wind direction and vegetation changes to the future wind resources in Northern Europe. 

How to cite: Hahmann, A. N., Peña, A., García-Santiago, O., and Pryor, S. C.: Evaluation of the assumptions used in the assessment of future wind resources - A case for CMIP6 in Northern Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5232, https://doi.org/10.5194/egusphere-egu22-5232, 2022.

13:56–14:03
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EGU22-2373
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ECS
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Virtual presentation
Jana Fischereit, Bjarke Tobias Olsen, Marc Imberger, Henrik Vedel, Xiaoli Guo Larsén, Andrea Hahmann, Gregor Giebel, and Eigil Kaas

Wind farms extract kinetic energy from the flow to generate electricity. Thereby, they modify the wind and turbulence fields upwind, at the side and especially downwind of the farm. Due to the induced enhanced mixing, other meteorological variables such as temperature and humidity are also affected by the presence of wind farms. With the massive growth of installed capacity both on- and offshore, these wind farm effects play an increasing role in numerical weather forecasts. This study will investigate the impact of currently installed wind farms in Europe on the weather forecast accuracy.

We performed forecasts for central and northern Europe with and without wind farm parameterizations. We used the operational mesoscale model HARMONIE-AROME equipped with the wind farm parameterization (WFP) by Fitch et al. (2012) as implemented by van Stratum et al. (2021). We added another WFP, the explicit wake parameterization (EWP, Volker et al. 2015). We created a European wind turbine data set by combining different data sets and using a machine learning gap-filling approach. This data set includes turbine locations and their characteristics. Different scenarios were tested using this data set: (A) including only offshore turbines in the German Bight and surrounding Denmark, (B) including all on- and offshore turbines present in the European wind turbine data set.

The simulation results from HARMONIE-AROME indicate that wind farms affect near-surface wind speed, temperature and humidity. The magnitude of these differences decreases with increasing distance from the farm, but still amounts to ±0.5 m/s in 10-m-wind or ±0.25 K in 2-m-temperature at a non-negliable number of locations in Denmark for an investigated exemplary summer day compared to the scenario without wind farms. The impact of onshore turbines is generally smaller than that of offshore turbines. However, the response to scenarios (A) and (B) differ, indicating that it is necessary to include both on- and offshore turbines to capture the full effect of wind farms in Europe. The wind farm effect also depends on the chosen wind farm parameterization, and both schemes provide plausible results. Future studies are necessary to better evaluate the two parameterizations and derive possible fine-tuning or combinations of the schemes. Overall, an ensemble consisting of both wind farm parameterizations could give a more reliable forecast in the future.

 

A. C. Fitch, J. B. Olson, J. K. Lundquist, J. Dudhia, A. K. Gupta, J. Michalakes, and I. Barstad. Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model. Mon Weather Rev, 140(9):3017–3038, sep 2012. ISSN 00270644. doi:10.1175/MWR-D-11-00352.1

B. van Stratum, N. E. Theeuwes, J. Barkmeijer, B. van Ulft, and I. Wijnant. A year-long evaluation of a wind-farm parameterisation in HARMONIE-AROME. Earth and Space Science Open Archive, page 29, 2021. doi: 10.1002/essoar.10509415.1. URL doi:10.1002/essoar.10509415.1

P. J. H. Volker, J. Badger, A. N. Hahmann, and S. Ott. The Explicit Wake Parametrisation V1.0: a wind farm parametrisation in the mesoscale model WRF. Geoscientific Model Development, 8(11):3715–3731, 2015. ISSN 1991-959X. doi:10.5194/gmd-8-3715-2015.

How to cite: Fischereit, J., Olsen, B. T., Imberger, M., Vedel, H., Guo Larsén, X., Hahmann, A., Giebel, G., and Kaas, E.: Wind farm effects on weather forecast using the operational model HARMONIE-AROME, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2373, https://doi.org/10.5194/egusphere-egu22-2373, 2022.

14:03–14:10
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EGU22-6872
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Virtual presentation
Mathilde Lepetit, Frederik Kurzrock, Pierre Aillaud, Nicolas Sebastien, and Nicolas Schmutz

Historically, electricity was provided by dispatchable sources that are able to adjust to demand variations. Adding an irregular source to the network is a challenge for the grid stability. Especially, wind turbine production varies depending on meteorological conditions. At a regional or country scale, Transmission System Operators (TSOs) are responsible to maintain supply demand equilibrium in their network. In this context, wind power production forecast is one of the tools needed to manage the network. Traditionally, physical models are used to predict power production based on turbine characteristics and numerical weather prediction models. Indeed, wind power production is strongly correlated to wind speed at turbine hub height and other meteorological parameters. One limit of those physical approaches is that they require precise knowledge on turbines characteristics and locations, in particular at a regional scale.

To overpass this limit, a statistical approach such as deep learning can be used but needs to be qualified in terms of performances. In this study, a supervised deep learning model is explored. This model does not require information on turbine location or characteristics but does require historical samples of weather parameters and associated production.

Our work focuses on day-ahead forecasts (horizons 24 to 48 hours) for a German TSO (region-scale). One physical model was selected as a reference and the goal was to combine deep learning and physical predictions to obtain the best possible forecast. Both the physical and the deep learning models use spatiotemporal meteorological inputs from the IFS (ECMWF) and GFS (NCEP) models. A convolutional neural network (CNN) was used to exploit the spatial information of maps of features. A LSTM was added to capture information from the time series evolution. Finally, several deep learning predictions were combined with the physical model prediction using a multi-layer perceptron. With this method, the MAE-based skill score of our final model, combining the physical one and deep learning ones, reaches more than 6% over a validation period of one year.

How to cite: Lepetit, M., Kurzrock, F., Aillaud, P., Sebastien, N., and Schmutz, N.: Regional-scale day-ahead wind power forecasting using deep learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6872, https://doi.org/10.5194/egusphere-egu22-6872, 2022.

14:10–14:17
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EGU22-13481
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ECS
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Virtual presentation
Christoffer Hallgren, Stefan Ivanell, Heiner Körnich, Ville Vakkari, and Erik Sahlée

Accurately forecasting short-term wind power production is a challenging task. As the share of wind power in the electrical system is rapidly growing, this task is becoming increasingly important not only for power production companies but also for transmission system operators. By applying post-processing methods to forecasts of wind speed from numerical weather prediction (NWP) models, power production forecasts can be improved. In this study, we used two years of lidar measurements of the wind speed from a coastal site in the Baltic Sea to calculate a theoretical power production and evaluated forecasts from the NWP model HARMONIE-AROME. Six post-processing methods of varying degree of complexity were implemented and tested in order to mimic how they could be used operationally. The performance of the methods in different weather situations was analysed in terms of the mean absolute error (MAE) skill score. For the test period it was found that, in general, the simple method of temporally smoothing the wind speed forecast by applying a low-pass filter (moving average) with a window of ±1 h outperformed the other methods tested. The main reason for this being a reduced risk of double penalty due to small time shifts in wind speed variations in the forecast compared to the observations. However, under weak synoptic forcing the best skill score was achieved using a mix of the forecast from the previous and the current day. Additionally, when low-level jets were forecasted, the best result was achieved using the machine learning random forest algorithm.

How to cite: Hallgren, C., Ivanell, S., Körnich, H., Vakkari, V., and Sahlée, E.: Improving 0-24 h offshore wind power forecasts over the Baltic Sea: comparing post-processing methods of varying complexity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13481, https://doi.org/10.5194/egusphere-egu22-13481, 2022.

14:17–14:24
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EGU22-800
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On-site presentation
Alfredo Peña and Jeffrey Mirocha

We present an intercomparison of a one year of atmospheric simulations performed with a numerical atmospheric model system based on the WRF model with tall mast observations. We employ the nestinga capabilities of the WRF model to run up to high resolution large-eddy simulations (WRF-LES). The simulations  aim at describing the wind climatology and the turbulence characteristics at Østeild, Denmark. There, DTU established the National Test Site for Wind Turbines, where some of the largest wind turbines prototypes are under testing. We evaluate the goodness of the simulations using the WRF-LES system by comparison with high-quality mean wind and turbulence observations from a 250-m meteorological mast. The main objective of the work is to demostraste that the WRF model does not only provide long-term time series of wind speed and direction but also turbulence characteristics and parameters, which are needed for the evaluation of the site conditions, and turbine design and peformance.

The WRF-LES based simulations are performed using four nested telescopic domains centered at the Østerild mast position. The outermost and largest domain has a horizontal resolution of 6250 m, whereas the innermost and smallest domain a horizontal resolution of 50 m. By modeling at these scales, we intend to resolve most of the turbulent scales.  We run the two outermost domains in a traditional mesoscale fashion, which means we use a commmonly used planetary boundary layer (PBL) scheme, whereas the two innermost domains are run in large-eddy simulation mode, i.e., without a PBL scheme. A complete year is simulated through parallel ten day long simulations. The output for the innermost domain is produced at the model grid point closest to Østerild every 12 s, whereas that of the other domains is produced every 10-min.

After computing the 10-min statistics for the full year on the model output of the innermost domain output and the 1-Hz data of the cup anemometers at Østerild that cover the range of the mast, we find very good agreement between the observed and simulated wind climatology. Turbulence estimates from both observations and simulations are also in good agreement, even though from the observations the site shows a wide variety of atmospheric stability and turbulence conditions. The turbulence intensity changes with wind speed in a similar way both in the simulations and the measurements. Our work shows that numerical models can be used as a tool to describe turbulent site conditions required, among others, for the efficient siting of wind turbines.

How to cite: Peña, A. and Mirocha, J.: Intercomparing WRF-LES based turbulence simulations with measurements from a 250-m tall meteorological mast, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-800, https://doi.org/10.5194/egusphere-egu22-800, 2022.

14:24–14:31
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EGU22-10583
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ECS
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On-site presentation
Ángel García Gago, Auguste Gires, Paul Veers, Daniel Schertzer, and Ioulia Tchiguirinskaia

Wind fields are known to be extremely variable in space and time over a wide range of scales. Universal Multifractals (UM) are a common tool used to model and simulate such features. This parsimonious framework is based only on 3 parameters (α , C1 and H) with physical interpretation, while the 4th, the power a of a conservative flux, is absorbed by the empirical estimation of the mean singularity over a non-conservative field. Obviously, in the context of wind power production, these properties are transferred to wind turbine torque and ultimately power.

Here, we investigate this transfer through modelling of wind turbine torque. For this purpose, 2 different modelling chains have been developed. The first one takes into account the spatial distribution of the wind velocities and the rotation of the blade considering an integral torque along the blades, this is in contrast with traditional approach which uses the average wind speed at hub height and blade radius to compute the torque. The second one is based on TurbSim for wind input computation, and OpenFAST for torque computation, which are tools developed by National Renewable Energy Laboratory (NREL). The main challenge is to input a space-time varying wind because, although it is possible to know the wind data at isolated points, where high resolution anemometers can be located, obtaining the wind speed in all points over a given area is rather complex. Using uniform wind fields in space creates too strong artificial correlations.

In this work, we suggest the reconstruction of wind fields from a point measurement by relying on well established scaling laws. More precisely, the wind field at any location is obtained by adding to the data at the available anemometer point, the product of a prefactor, a random UM field and distance increment raised to power av and Hv respectively. The exponents are obtained in the literature using purely dimensional arguments. Data from 2 high resolution 3D sonic anemometers located on a meteorological mast in a wind farm situated approximately 110 km south-east of Paris, with approx. 33 m vertical distance, are used to tune parameters of UM field and the prefactor according to the event. Data is collected in the framework of the RW-Turb measurement campaign (https://hmco.enpc.fr/portfolio-archive/rw-turb/); which is supported by the French National Research Agency (ANR-19-CE05-0022)

UM analysis over numerous events (more than 1-year data is available) was carried out to confirm good agreement between UM parameters retrieved on anemometer data and simulation data. A comparison between torque obtained with the traditional approach and both modelling chains using simulated fields and UM analysis of the outputs was also performed to observe the differences focusing on the small scales.

How to cite: García Gago, Á., Gires, A., Veers, P., Schertzer, D., and Tchiguirinskaia, I.: Transfer of small scales space-time fluctuations of wind fields to wind turbines torque computation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10583, https://doi.org/10.5194/egusphere-egu22-10583, 2022.

14:31–14:38
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EGU22-4572
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On-site presentation
Xiaoli Larsén, Søren Larsen, Erik Petersen, and Torben Mikkelsen

As today’s wind farm clusters can be as large as thousands of kilometers squared and individual turbines hundreds of meters tall, we are challenged when applying classical wind and turbulence models for corresponding wind energy-related calculations.  Typical boundary-layer turbulence models are applicable to time scales smaller than ~1 h, or as denoted by Högström et al. (2002) the Kolmogoroff inertial subrange, the shear production range, and for ranges within the spectral gap region. 

This study revisits some key characteristics of the atmospheric boundary-layer turbulence, covering frequencies from 1/year, over the energy-containing range, to synoptic- and mesoscales, to the gap region and to the 3D turbulence range. This study aims at investigating the following fundamental questions: How to characterize the full-scale spectral behaviors and what are the mechanisms behind them? To which extent is the condition of stationarity fulfilled? What are the 2D-isotropy characteristics? How are numerical modeling abilities in capturing these characteristics? We also show how these findings have been used in wind energy applications, e.g. for generating time series of wind speed including meso-scale variability, for investigating meandering, for extreme wind calculation and for improving turbulence intensity calculation in the presence of organized atmospheric phenomena.

The study includes literatures as well as a series of our studies in recent years (e.g. Larsén et al. 2013, 2016, 2019, 2021).  We combined measurements and modeling in the analysis. The primary datasets are from several met stations over Denmark and the North Sea region, including both 10-min and sonic measurements from about 10 m up to 240 m. The investigations include both statistical and numerical modeling.

 

References:

Högström U, Hunt J, Smedman AS (2002) Theory and measurements for turbulence spectra and variances in the atmospheric neutral surface layer. Boundary-Layer Meteorol 103:101–124

Larsén, X. G., Larsen, S. E., Petersen, E. L., & Mikkelsen, T. K. (2021). A Model for the Spectrum of the Lateral Velocity Component from Mesoscale to Microscale and Its Application to Wind-Direction Variation. Boundary-Layer Meteorology, 178, 415-434. https://doi.org/10.1007/s10546-020-00575-0

Larsén X., Larsen S., Petersen E. and Mikkelsen T. 2019: Turbulence Characteristics of Wind-Speed Fluctuations in the Presence of Open Cells: A Case Study. Boundary-Layer Meteorology, https://doi.org/10.1007/s10546-019-00425-8, (171), 191 – 212.

Larsén X. Larsen S. and Petersen E. (2016): Full-scale spectrum of the boundary layer wind. Boundary-Layer Meteorology, Vol 159, p 349-371

Larsén X., Vincent C. and Larsen S.E. (2013): Spectral structure of mesoscale winds over the water, Q. J. R. Meteorol. Soc., DOI:10.1002/qj.2003, 139, 685-700.  

 

How to cite: Larsén, X., Larsen, S., Petersen, E., and Mikkelsen, T.: Full-scale turbulence structure of wind and applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4572, https://doi.org/10.5194/egusphere-egu22-4572, 2022.

14:38–14:44
Wind Measurements
Coffee break
Chairpersons: Andrea Hahmann, Xiaoli Larsén
15:10–15:17
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EGU22-3177
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On-site presentation
Eduardo Weide Luiz and Stephanie Fiedler

Nocturnal Low-Level Jets (NLLJ) are maxima in vertical profiles of the horizontal wind speed in the lowest hundreds of meters of the troposphere. NLLJs therefore influence the winds at typical rotor heights. However, due to rare measurements with a sufficient precision and resolution, the occurrence frequency and spatio-temporal characteristics of NLLJs on the mesoscale are still poorly understood. The present work uses new measurements of wind profiles for June to August 2020 from Doppler wind lidars that were installed as a part of the Field Experiment on Submesoscale Spatio-Temporal Variability (FESTVaL) campaign, in Lindenberg and Falkenberg (Germany), at about 6 km of distance from each other. The aim of our NLLJ assessment is to characterize their mesoscale properties and evaluate their potential impacts on wind power production. The vertical profiles of the 10-minute mean winds from the lidar measurements were statistically analysed using automated detection tools for NLLJs. These allowed the determination of the frequency of occurrence, height and wind speed in the core of NLLJs as well as the vertical wind shear with a high temporal resolution. First, we intercompared the results from the two sites in order to analyse the temporal and spatial variability of NLLJs on the mesoscale. Our automatic detection identified NLLJs in about 64% to 74% of the summer nights in 2020, showing that they were a common phenomenon during that summer. About half of the NLLJ events were longer than 3 hours, with Lindenberg having more often shorter events of less than 1 hour. If very long NLLJ events (> 6 hours) occurred, they typically affected both places simultaneously, an indicative of their mesoscale character. Our results further suggest that very long NLLJ events are generated by the classical inertial oscillations, influenced by a large-scale horizontal pressure gradient and intermittent turbulent mixing, while shorter NLLJ events are more strongly dependent on local conditions or are driven by shorter-living density currents. Regarding their potential impact on wind turbines, we simulated wind power production for two different turbine types of different height and capacity. Both simulations indicate that NLLJs clearly increase the power production compared to nights without NLLJs. The quantitative NLLJ impacts on power production strongly depend on the height of the wind turbines: during NLLJ events the average wind production was 80% higher for a hub height of 135 m and only 53% higher for 94 m. At the same time, NLLJs increased the wind shear across the rotor layer. Extreme shear in the rotor layer was often associated with NLLJs, with 37% of all NLLJs leading to extreme shear and 48% of all extreme shear cases being caused by NLLJs. We infer from our assessment that particularly long NLLJ events strong influence wind power production, while shorter NLLJs can increase the temporal and spatial variability in power production, causing power ramps.

How to cite: Weide Luiz, E. and Fiedler, S.: Assessment of nocturnal low-level jets and their implication for wind power from FESST@home measurements , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3177, https://doi.org/10.5194/egusphere-egu22-3177, 2022.

15:17–15:24
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EGU22-5200
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Virtual presentation
Provision of wind information for site tenders for offshore wind farms according to WindSeeG in the German North and Baltic Seas
(withdrawn)
Thomas Möller, Thomas Spangehl, Maren Brast, Axel Andersson, and Birger Tinz
15:24–15:31
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EGU22-7048
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Virtual presentation
Investigating the wind conditions in the North Sea for wind turbines at sites N-3.5, N-3.6 and N-7.2
(withdrawn)
Maren Brast, Michael Gehrke, Johannes Hahn, Sabine Hüttl-Kabus, Thomas Möller, Olaf Outzen, and Thomas Spangehl
15:31–15:38
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EGU22-8241
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ECS
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Virtual presentation
Marcos Paulo Araujo da Silva, Francesc Rocadenbosch, Joan Farré-Guarné, Andreu Salcedo-Bosch, Daniel González-Marco, and Alfredo Peña

In this work, we revisit the 2D parametric-solver algorithm [1] to estimate the Obukhov length, and hence, determine atmospheric stability from floating Doppler wind lidar (FDWL) wind profiles. The algorithm fits the wind-profile model derived from Monin-Obukhov similarity theory to the FDWL-measured wind profile by means of a constrained non-linear least squares optimisation. Observational data were gathered at the IJmuiden test site in the North Sea (52.848 N, 3.436 E) between March and June of 2015. The reference Obukhov length was obtained via bulk Richardson number, which was estimated from IJmuiden-mast observations. Comparisons with the reference stability are performed by using a simplified atmospheric stability classification consisting of only three types, namely stable, neutral and unstable. Fairly similar results were obtained from the 2D-estimated and the mast-derived reference stability classifications for the stability behaviour during the time of day as well as for horizontal-wind-speed dependence on the stability type.

This research is part of the projects PGC2018-094132-B-I00 and MDM-2016-0600 (“CommSensLab” Excellence Unit) funded by Ministerio de Ciencia e Investigación (MCIN)/ Agencia Estatal de Investigación (AEI)/ 10.13039/501100011033/ FEDER “Una manera de hacer Europa”. The work of M.P Araujo da Silva was supported under Grant PRE2018-086054 funded by MCIN/AEI/ 10.13039/501100011033 and FSE “El FSE invierte en tu futuro. The work of A. Salcedo-Bosch was supported under grant 2020 FISDU 00455 funded by Generalitat de Catalunya—AGAUR. The European Commission collaborated under projects H2020 ACTRIS-IMP (GA-871115) and H2020 ATMO-ACCESS (GA-101008004).

[1] M. P. Araujo da Silva, F. Rocadenbosch, J. Farré-Guarné, A. Salcedo-Bosch, D. González-Marco, and A. Peña, “Assessing obukhov length and friction velocity from floating lidar observations: A data screening and sensitivity computation approach,” Remote Sensing, 2022, submitted.

How to cite: Araujo da Silva, M. P., Rocadenbosch, F., Farré-Guarné, J., Salcedo-Bosch, A., González-Marco, D., and Peña, A.: Offshore atmospheric stability estimation from floating lidar wind profiles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8241, https://doi.org/10.5194/egusphere-egu22-8241, 2022.

15:38–15:45
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EGU22-9818
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ECS
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Virtual presentation
Andreu Salcedo-Bosch, Francesc Rocadenbosch, and Joaquim Sospedra

A study on the floating Doppler wind lidar (FDWL) motion-correction performance by means of the Unscented Kalman Filter (UKF) method as a function of the lidar measurement heights is presented. The study is carried out by simulating one, three, and five lidar measurement heights by means of time-series down-sampling techniques. The performance is tested over experimental data measured by a fixed and a FDWL sited 50 m appart in the context of Pont del Petroli measurement campaign.

The motion-correction UKF [1] relies on FDWL dynamics as formulated by Kelberlau et al. [2] as well as on the lidar internal wind-vector estimation algorithm to recursively estimate the clean (i.e., motion-free) wind vector. To carry out the correction, the filter uses the FDWL-measured wind vector and 6 Degrees of Freedom buoy motion measurements by the Inertial Measurement Units installed on the FDWL buoy.

Continuous-wave focusing DWLs measure the wind at multiple heights sequentially and, therefore, they sound a particular height every n scans (≈1 scan/s), with n the number of measurement heights. When a lidar is configured to measure at multiple heights, this is equivalent to down-sampling the wind-vector time-series by a factor n.

To study the UKF motion-correction performance, the turbulence intensity (TI) measured by the FDWL, with and without correction, were compared (at 10-minute resolution) against the TI measured by the reference fixed DWL considering three measurement-height configurations (emulated as downsampled time-series): single-height sounding, and 3, and 5 sounding heights.

The experimental results showed that the filter successfully takes the sea motion out of the wind speed measurements, hence it virtually removes the apparent turbulence induced by wave motion for all three measurement-height configurations. However, the poorer one-to-one-point correspondence found when increasing measurement height numbers (equivalently, lower sampling rates in the simulation) stated that less wind information was retained in the 10-min time-series. Thus, the coefficient of determination reduced from R2=0.94 (1 height) to 0.81 (5 heights), and the RMSE increased from 0.74 % (1 height) to 1.34 % (5 heights).

Future work plans to validate the quantitative statistical indicators retrieved by the UKF simulator with reference to experimental wind-speed data measured under real conditions.

Acknowledgements

This research was funded by the Spanish Government and EU Regional Development Funds, ARS project PGC2018-094132-B-I00, H2020 ACTRIS-IMP project GA-871115 and H2020 ATMO-ACCESS project GA-101008004. The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (call FP7) supported the

measurement campaigns. The Generalitat de Catalunya—AGAUR funded doctoral grant 2020 FISDU 00455 by A. Salcedo-Bosch. CommSensLab-UPC is an Excellence Unit (MDM-2016-0600) funded by the Agencia Estatal de Investigación, Spain.

 

 

References

[1] Andreu Salcedo-Bosch, Francesc Rocadenbosch, and Joaquim Sospedra, “A robust adaptive unscented kalman filter for floating doppler wind-lidar motion correction,” Remote Sens., vol. 13, no. 20, 2021.

[2] Felix Kelberlau, Vegar Neshaug, Lasse Lønseth, Tania Bracchi, and Jakob Mann, “Taking the motion out of floating lidar: Turbulence intensity estimates with a continuous-wave wind lidar,” Remote Sens., vol. 12, no. 5, 2020.

How to cite: Salcedo-Bosch, A., Rocadenbosch, F., and Sospedra, J.: Influence of the number of lidar sounding heights on Adaptive Unscented Kalman Filtering for Floating Doppler wind-lidar motion correction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9818, https://doi.org/10.5194/egusphere-egu22-9818, 2022.

15:45–15:52
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EGU22-2602
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ECS
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Virtual presentation
Andreas Platis, Yann Büchau, and Jens Bange

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 frame work of the WIPAFF project over the German Bight in the years 2016 and 2017  enabled such an evaluation under different atmospheric conditions. 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 Godewind. Under thermally stable conditions, all except one of the measurement flights showed an increase of latent heat flux over or in the wake of the wind farms, with an heat flux up to 17 times higher compared to the undisturbed flow. During flights under unstable thermal stratification, the phenomenon was observed in 8 out of 13 cases. The results also suggest that not only thermal stratification but also moisture stratification plays a decisive role in whether the influence of the wind farm becomes noticeable in the latent heat flux.  Considering the absolute amount of the increase of the upward latent heat flux, a maximum increase of +400 W/m² was measured in unstable conditions. 

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, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2602, https://doi.org/10.5194/egusphere-egu22-2602, 2022.

15:52–15:59
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EGU22-11858
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ECS
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Virtual presentation
Ines Weber, Andreas Platis, Kjell zum Berge, Martin Schön, Jakob Boventer, Bughsin Djath, Johannes Schulz-Stellenfleth, and Jens Bange

The Multipurpose Airborne Sensor Carrier (MASC) is a fixed-wing unmanned aircraft system (UAS) that has been continuously developed and used for in-situ, high-resolution flight measurements of atmospheric variables such as wind, temperature, humidity as well as trace gas and particle concentrations by the Environmental Physics group at University of Tübingen. The most recent innovation in the MASC-series is the MASC-V type vertical takeoff and landing UAS. It has been designed in cooperation with ElevonX d.o.o.. Compared to its predecessor, MASC-3, it can automatically takeoff and land on small patches of land while carrying an identical atmospheric measurement payload. This capability, complemented by an enhanced safety and operational concept, allows for deployment in offshore applications. Particularily, MASC-V has demonstrated safe operation beyond visual line of sight (BVLOS) from the remote safety pilot in offshore applications within the EUs new legal framework introduced in 2020.

Before its first offshore mission, MASC-V underwent a full system validation against a meteorological tower at the German Weather Service (DWD) Observatory site at Falkenberg, Germany. Offshore measurements were conducted from the German offshore island Heligoland at the Testfield for Maritime Technologies in cooperation with the Fraunhofer Institute for Applied Material Science in September 2021. The goal of the Heligoland campaign was to validate the remote sensing of sea surface wind measurements by Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 formation at low flight altitudes (20 m - 30 m). SAR satellites can deliver detailed wind data over large areas such as the German Bight including for example wind farm wake effects. Direct validation of these results is difficult with other in-situ techniques. Buoys and measurement towers or platforms can provide stationary data. Aerial measurements with manned aircraft are only possible at higher altitudes. The new UAS data provide the first aerial in-situ SAR validation measurement at low altitude. Additionally, we have demonstrated the capabilities of VTOL fixed-wing UAS for vertical profiling as well as to operate tens of kilometers away from ground personell over open water.

How to cite: Weber, I., Platis, A., zum Berge, K., Schön, M., Boventer, J., Djath, B., Schulz-Stellenfleth, J., and Bange, J.: An unmanned aircraft system for (offshore) wind energy research, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11858, https://doi.org/10.5194/egusphere-egu22-11858, 2022.

Renewables
15:59–16:06
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EGU22-7926
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Highlight
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Virtual presentation
Gregor Giebel, Caroline Draxl, Helmut Frank, John Zack, Jethro Browell, Corinna Möhrlen, George Kariniotakis, and Ricardo Bessa

The last 6 years, the International Energy Agency (IEA) Wind Task 36 “Forecasting for Wind Energy” has provided forecasting stakeholders (weather institutes, forecast service providers, end users and academics) a platform to discuss challenges and benefits of forecasting for wind power. These discussions have led to a number of activities and initiatives to overcome challenges and to broadcast the benefits of forecasting. Among the major outputs are an information portal with links to free data, a collection of use cases for probabilistic forecasts, and the IEA Recommended Practice on Forecast Solution Selection, including 4 chapters dealing with the (1) solution selection process, (2) benchmarks and trials, (3) verification process and  use of online measurements from wind farms for real-time forecasting applications.

 In the future, we will no longer “integrate” wind and solar into existing power systems, but instead are wind and solar going to be the backbone of our power systems.

To address those challenges in an integrated fashion, the IEA Task for Forecasting under the IEA Wind Technology Collaboration Programme (TCP) relaunched with a new Task number (51) and a new work program. The work packages (WPs) are still structured according to stakeholder topics: WP1 deals with weather forecasting, and mainly addresses meteorologists, WP2 deals with the conversion of the weather feeds to the application specific variables such as wind power and addresses forecast vendors, and WP3 deals with the applications and how to get most value out of the forecasts, and therefore addresses the forecast users, including recent advances in data science and digitalisation. However, many of the topics the new Task takes up are cross-cutting, and are therefore now  Work Streams (WS):

  • Atmospheric physics and modelling (lead by WP1)
  • Airborne Wind Energy Systems (WP1)
  • Seasonal forecasting (WP1)
  • State of the Art for energy system forecasting (WP2)
  • Forecasting for underserved areas (WP2)
  • Minute scale forecasting (WP2)
  • Uncertainty / probabilistic forecasting (WP3)
  • Decision making under uncertainty (WP3)
  • Extreme power system events (WP3)
  • Data science and artificial intelligence (WP3)
  • Privacy, data markets and sharing (WP3)
  • Value of forecasting (WP3)
  • Forecasting in the design phase (WP3)

Most of these work streams require collaboration, and therefore have dedicated partners in other IEA Wind Tasks, or in IEA Tasks outside of the Wind TCP. Task 51 will therefore collaborate with IEA Wind Tasks 32, 44, 48 and 50,  IEA PVPS Task 16, IEA Hydro, the IEA Hydrogen TCP, IEA Bioenergy Task 44  and WMO. 

A major activity of Task 51 will be four public workshops in the next four summers, starting with a workshop on the State of the Art and Research Gaps in 2022, on seasonal forecasting with a special emphasis on hydro power and storage in 2023,  on minute scale forecasting in 2024, and on extreme power system events in 2025.  We keep the community updated on events, new publications and other relevant information on our website ieawindforecasting.dk and via LinkedIn and Research gate.

How to cite: Giebel, G., Draxl, C., Frank, H., Zack, J., Browell, J., Möhrlen, C., Kariniotakis, G., and Bessa, R.: Forecasting for the Weather Driven Energy System – The New IEA Wind Task 51, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7926, https://doi.org/10.5194/egusphere-egu22-7926, 2022.

16:06–16:13
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EGU22-12923
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Presentation form not yet defined
Georges Kariniotakis and Simon Camal and the Smart4RES team

In this presentation we detail highlight results obtained from the research work within the European Horizon 2020 project Smart4RES (http://www.smart4res.eu). The project, which started in 2019 and runs until 2023, aims at a better modelling and forecasting of weather variables necessary to optimise the integration of weather-dependent renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to the pathway towards energy systems with very high RES penetrations by 2030 and beyond, through thematic objectives including:

  • Improvement of weather and RES forecasting,
  • Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models;
  • New data-driven optimization and decision-aid tools for market and grid management applications;
  • Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). 

In this presentation we will focus on our results on models that permit to improve forecasting of weather variables with focus on extreme situations and also through innovative measuring settings (i.e. a network of sky cameras). Also results will be presented on the development of seamless approach able to couple outputs from different ensemble numerical weather prediction (NWP) models with different temporal resolutions. Advances on the contribution of ultra-high resolution NWPs based on Large Eddy Simulation will be presented with evaluation results on real case studies like the Rhodes island in Greece.

When it comes to forecasting the power output of RES plants, mainly wind and solar, the focus is on improving predictability using multiple sources of data. The proposed modelling approaches aim to efficiently combine highly dimensionally input (various types of satellite images, numerical weather predictions, spatially distributed measurements etc.). A priority has been to propose models that permit to generate probabilistic forecasts for multiple time frames in a seamless way. Thus, the objective is not only to improve accuracy and uncertainty estimations, but also to simplify complex forecasting modelling chains for applications that use forecasts at different time frames (i.e. a virtual power plant - VPP- with or without storage that participates in multiple markets). Our results show that the proposed seamless models permit to reach these performance objectives. Results will be presented also on how these approaches can be extended to aggregations of RES plants which is relevant for forecasting VPP production.

How to cite: Kariniotakis, G. and Camal, S. and the Smart4RES team: Highlight results of the Smart4RES project on weather modelling and forecasting dedicated to renewable energy applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12923, https://doi.org/10.5194/egusphere-egu22-12923, 2022.

16:13–16:20
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EGU22-12965
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ECS
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Virtual presentation
Simon Camal, Dennis Van Der Meer, and George Kariniotakis

High temporal resolution intra-day and day-ahead renewable energy source (RES) power forecasts are important to maximize the value of RES systems because they give stakeholders the opportunity to participate in both the energy and ancillary services markets. In the realm of electricity markets, day-ahead electricity markets often require bids at hourly temporal resolution. However, the requirements for temporal resolution on intra-day markets are more demanding and may require a temporal resolution of 5 minutes in the near future.

Moreover, high resolution forecasts offer the possibility to employ advanced control strategies to mitigate severe frequency fluctuations in, for instance, island grids. More specifically, battery integration can improve power system management in isolated grids with high RES power penetration. However, battery control requires high temporal resolution forecasts.

Since the temporal dependence structure between time steps is highly relevant in control problems, there is a need to efficiently generate trajectory forecasts that can be used in stochastic optimisation problems. This study proposes an efficient method to generate trajectory forecasts of RES power production that is based on pattern matching. Consequently, we do not need to forecast all forecast horizons separately and estimate a covariance matrix that represents the dependence structure between the forecast horizons. To compare our method against the state-of-the-art, we use quantile regression forests in combination with a Gaussian copula and show that our method performs similar in terms of relevant scores but is approximately 98% faster and simplifies the modelling chain considerably.

The proposed method is evaluated on real data from operating renewable sites in an isolated power system. Considering its fast computation and its applicability to diverse situations (different energy sources, individual sites or aggregated production), the method has the potential to be integrated into the decision-making process of forecasting end-users such as operators of power systems under high renewable penetration.

How to cite: Camal, S., Van Der Meer, D., and Kariniotakis, G.: Short-term forecasting of renewable production trajectories at high-temporal resolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12965, https://doi.org/10.5194/egusphere-egu22-12965, 2022.

16:20–16:27
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EGU22-6339
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ECS
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On-site presentation
Shuying Chen, Stefan Poll, Heidi Heinrichs, Harrie-Jan Hendricks-Franssen, and Klaus Görgen

The largest part of the global population without reliable access to electricity lives in Africa. Here, renewable energy is a sustainable, cost efficient, and climate-friendly solution, especially given the large untapped renewable energy potential existing over the African continent. However, most renewable energy-related studies over Africa typically use input datasets at relatively coarse spatial resolutions (e.g., ERA5 at about 30km). Our objective is to produce a prototypical high-resolution dataset over southern Africa from dedicated atmospheric simulations. The data will be used with renewable energy assessment models, to eventually evaluate the renewables potentials. The hypothesis is that the high-resolution datasets provide more realistic and accurate renewable energy potential estimates. The ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON-NWP) model is run as the operational forecast model at the German Weather Service (DWD); and we employ the same model in its Limited Area Mode (ICON-LAM) in this project. The study domain over southern Africa is chosen due to its high solar and wind energy potential. ICON-LAM dynamically downscales the global deterministic ICON-NWP forecasts dataset from a spatial grid spacing of 13km to a convection-permitting resolution of 3.3km, without convection parameterization. This southern Africa ICON-LAM implementation is novel and has not been run before. Simulations cover the time span from 2017 to 2019 with contrasting meteorological conditions. The high-resolution triangulated grid cells of the 3.3km domain are exactly inscribed in the 13km global grid cells, following the sub-triangle generation rule of the ICON model mesh. To keep the ICON-LAM close to the observed atmospheric state the model atmosphere is reinitialized every 5 days, with one day spinup. The land surface and subsurface are run transient. In a very initial evaluation step, simulated 10m wind speed, global solar radiation, 2m air temperature, and precipitation from the coarser driving model, the ERA5 reanalysis as well as our ICON-LAM setup are validated using satellite data and in situ observations from the two local meteorological networks (SASSCAL and TAHMO). Initial results point to an added value of the convection-permitting simulations.

How to cite: Chen, S., Poll, S., Heinrichs, H., Hendricks-Franssen, H.-J., and Görgen, K.: Convection-permitting ICON-LAM simulations as input to evaluate renewable energy potentials over southern Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6339, https://doi.org/10.5194/egusphere-egu22-6339, 2022.

16:27–16:34
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EGU22-380
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Presentation form not yet defined
Abderrahmane Mendyl, Arun Gandhi, Peter K Musyimi, Balázs Székely, and Tamás Weidinger

Wind and solar energy have emerged as the one of the most popular and successful sources of renewable energy in combating environmental degradation and climate change. Countries around the world are developing policy mechanisms for increasing the share of renewable energy technologies for fulfilling their energy demands. Both wind and solar have proved their potential as clean and efficient sources of energy generation. Therefore, transitioning into a sustainable future requires a shift from fossil fuels to renewable energy technologies. The main goal of this study is to compare wind and solar energy potential for different climate regions of Morocco, India and Kenya using standard methodologies.

In this study we have used the wind profile power law relationship for estimating the wind speed and power at 100 m level. We are analysing long term synoptic datasets from 2 to 4 synop stations in arid and humid regions of North India, Morocco and Kenya based on the Meteomanz standard meteorological database. Stability dependent power law profile approximations were used and comparisons made with ERA5 reanalysis data. Estimation of wind energy production for different continental wind generators were also provided. Using the connection between the wind speed and profile law we demonstrated how wind energy can vary using different values of power law exponents for different climatic regions.

Standard meteorological measurements (temperature, humidity and cloudiness) gave the opportunity for estimation of global irradiance which was also compared with the ERA5 dataset. Applicability of widely used direct and diffuse irradiance parameterizations for different climate regions were also investigated.

For instance, in Marrakech the six Pasquill-Gifford stability classes were determined by estimating the global solar irradiance for cloudy and clear sky conditions as well as the wind speed. Analysis of the data showed that windspeed at 10 m varied between 1.8 m/s in the early morning (UTC 06:00) to 3.5 m/s in the evening (UTC 18:00) while the windspeed at 100 m varied between 2.6 m/s and 5 m/s at the same time periods.  The estimated wind energy at 100 m level for rural areas was more than that of urban areas The wind energy at 100 m varied between 47.2 KW in the early morning (UTC 06:00) to 573 KW in the evening (UTC 18:00) for the rural areas while in urban areas the variation was between 83.8 KW to 670.5 KW during the same time periods. The annual average global solar radiation was found to be maximum during the afternoon with a value more than 970 W/m2.

How to cite: Mendyl, A., Gandhi, A., Musyimi, P. K., Székely, B., and Weidinger, T.: Comparative analysis of wind and solar energy potential from differnet  climate regions, case studies  of Morocco , India and  Kenya , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-380, https://doi.org/10.5194/egusphere-egu22-380, 2022.

16:34–16:39
Coffee break
Chairpersons: Alfredo Peña, Xiaoli Larsén, Philippe Blanc
17:00–17:07
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EGU22-1837
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ECS
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On-site presentation
Noelia Otero, Olivia Martius, Sam Allen, Hannah Bloomfield, and Bettina Schaefli

 The transition towards decarbonized power systems requires to account for the impacts of the climate variability and climate change on renewable energy sources. With the growing share of wind and solar power in the European power system and their strong weather dependence, balancing the energy demand and supply becomes a great challenge. In this study, we assess energy compound events, defined as periods of simultanous low renewable production of wind and solar power, and high electricity demand. Using a country-based logistic regression approach, we model the binary occurrence of energy compound events and we examine the effects of meterological and weather regimes. Then, we quantify the meteorological conditions resulting in the highest probability of occurrence of energy compound events. We found that the combination of extremely low temperatures (below the 5th percentile) and low wind speed (below the 10th percentile), along with moderate-high solar radiation (above the 50th percentile), lead to the highest probability of occurrence of energy compound events over most European countires. Furthermore, we show that blocked weather regimes lead to the weather conditions that can have a major risk in the European power system. In particular, the Greenland blocking and the European blocking were associated with widespread energy compound events that affected multiple countries at the same time. Our results highlight the importance of the weather regimes that result in spatially compounding energy events, which might a major impact within a potential fully interconnected European grid.

How to cite: Otero, N., Martius, O., Allen, S., Bloomfield, H., and Schaefli, B.: Quantifing the influence of meteorological and large-scale atmopsheric drivers on energy compound events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1837, https://doi.org/10.5194/egusphere-egu22-1837, 2022.

17:07–17:14
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EGU22-11781
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ECS
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On-site presentation
Bastien Cozian, Freddy Bouchet, and Corentin Herbert

In the coming decades, the European energy system will undergo major transformations, like widespread electrification and fast development of variable renewable energy, in order to reach carbon neutrality and comply with the Paris Agreement. As a consequence, energy production will increasingly depend on weather variability, and the future European energy system needs to be designed to cushion this variability, at all time scales. In particular, rare events leading to extreme fluctuations in energy production or demand can be expected to play a major part in this design. For instance, one of the main challenging events is a combination of low renewable energy production with high demand for a long time period. To know how much flexibility will be needed (seasonal storage such as green gas reserves, number of auxiliary thermal power plants, needs in terms of demand-side management, etc.) and assess its cost, one needs to estimate the probability of occurrence of such events. However, observations of renewable energy production or climate variables are too short to quantitatively study these critical events. Therefore we need to rely on climate and energy models, and to develop dedicated tools to study extreme events of energy production and demand.

Here, we study the extreme imbalance between renewable generation and demand in Europe, at the sub-seasonal to seasonal scale. Based on a state-of-the-art climate model (CESM1.2.2) with extremely long simulations, we couple models of wind, solar PV, and demand with climate variables to obtain very long time series of energy production and demand. We consider 9 scenarios of renewable installed capacities to assess the probability of occurrence of extreme residual loads (demand minus renewable production). We study the statistics of extremely rare events that last for several weeks to several months.

The results show that extremely high residual loads are dominated by extremely low wind energy production events in winter, that are not visible in historical data. Leveraging our very long time series, we compute return time curves for extreme wind energy fluctuations. These curves tell us how frequent energy shortfalls of a given amplitude are. We find a renormalization such that return time curves depend weakly on the scenario. The estimation of such return times relies crucially on the available amount of data. We show that good approximations can be obtained from simple stochastic processes. 

How to cite: Cozian, B., Bouchet, F., and Herbert, C.: Assessing the probability of extremely rare renewable production and residual load in Europe at sub-seasonal to seasonal time scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11781, https://doi.org/10.5194/egusphere-egu22-11781, 2022.

17:14–17:21
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EGU22-12664
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ECS
|
On-site presentation
Nimal Sudhan Saravana Prabahar, Sam Fredriksson, Göran Broström, and Björn Bergqvist

Tidal turbines harnessing power from tidal currents, have the prospective to become an important source for renewable energy production. The tidal power plant studied here, the Deep Green, rather than being fixed like conventional horizontally mounted axial tidal turbines, uses a ‘flying’ kite with a turbine attached to it. The kite, which is tethered to the bottom, converses in a lemniscate trajectory (Ꝏ) perpendicular to the direction of the tidal current. In the trajectory, the apparent flow velocity experienced by the turbine is several times the tidal flow, thereby allowing utilization of sites with lower tidal current velocities than most traditional tidal power plants. To study the operation of single power plants and for designing efficient arrays of tidal power plants Computational Fluid Dynamics (CFD) are used.

Through previous studies, the Deep Green is modelled using Large Eddy Simulations (LES) and the Actuator Line Model (ALM). While using ALM, the Deep Green wing and its turbine are represented as a momentum source that moves in a prescribed trajectory (lemniscate). Using the numerical simulations, the impact of the Deep Green on the tidal flow is studied by analysing the changed velocity field and turbulence characteristics downstream of the power plant. Before conducting large-scale numerical studies on the design of arrays, the numerical model needs to be validated against observations.

The measurements used for this study were performed by Minesto AB in the site Holyhead deep using a vessel mounted ADCP (Acoustic Doppler Current Profiler) downstream of the kite. A domain and boundary conditions similar to the measurements are set up in the numerical simulation. The velocity downstream of the power plant is compared with the measured velocity data, and the preliminary study shows good agreement between ADCP observations and output from the CFD model. The results of the validation will be helpful to strengthen the methods used in numerical modelling in order to conduct sound tidal power array analysis.

How to cite: Saravana Prabahar, N. S., Fredriksson, S., Broström, G., and Bergqvist, B.: Experimental Validation of Numerical Simulation of Tidal Power Plants (Deep Green) using ADCP Measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12664, https://doi.org/10.5194/egusphere-egu22-12664, 2022.

17:21–17:22
17:22–17:29
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EGU22-2164
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ECS
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On-site presentation
Wouter Mol, Bert Heusinkveld, Wouter Knap, and Chiel van Heerwaarden

Surface irradiance variability is present on many spatio-temporal scales, but most strongly on the scale of minutes to seconds due to low broken clouds. Fast and large fluctuations, or spatial heterogeneity, of irradiance affects solar energy production. In idealised settings, let alone in operational forecasts, the modelling of realistic fields of surface irradiance in the presence of clouds is challenging. It relies on realistic cloud fields, is computationally demanding due to the nature of 3-d radiative transfer models, and ultimately requires observations for validation. Dense spatial observation of irradiance on the scale of cloud shadows or solar energy parks are rare, however. Even 1-d time series are often not available at high enough resolution. 

In ongoing work, we provide those missing observations. I will present our gathering and analyses of new and detailed observations of surface irradiance to address knowledge gaps in our physical understanding and provide validation datasets for models. In 2021, we deployed a dense network of custom, low-cost radiometers at two field campaigns, FESSTVaL (Germany) and LIAISE (Spain), to observe spatial patterns of irradiance driven by clouds. The instruments are able to closely match expensive conventional instruments, and combined with skyview imagery, the spatial observations are directly linked to observed clouds. To complement these short term spatial data, long-term statistics of irradiance variability are derived from a 10-year 1 Hz resolution data from the Baseline Surface Radiation Network station in Cabauw, the Netherlands. Distributions and typical spatio-temporal scales of cloud shadows and irradiance peaks can be related to cloud type and meteorological conditions. The gathering and study of these datasets will lead to a better understanding of the physics, help validate models, and ultimately improve our ability to accurately forecast irradiance variability at the small scales.

How to cite: Mol, W., Heusinkveld, B., Knap, W., and van Heerwaarden, C.: Climatology and Spatial Patterns of Cloud Shadows and Irradiance Peaks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2164, https://doi.org/10.5194/egusphere-egu22-2164, 2022.

17:29–17:36
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EGU22-3641
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ECS
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On-site presentation
Alberto Carpentieri, Martin Wild, Doris Folini, and Angela Meyer

Accurate maps of surface incoming solar (SIS) radiation are a crucial prerequisite for producing precise solar radiation and photovoltaic power (PV) nowcasts useful to utility companies, grid operators and energy traders. 

We present a new bias correction approach for satellite-retrieved SIS measurements using deep neural networks with time encoding features, achieving significantly reduced biases on high time resolution data. Moreover, we demonstrate the necessity and the benefits of automated bias correction prior to performing surface radiation and PV nowcasts.

We make use of SIS retrieved from the SEVIRI spectrometer onboard the geostationary Meteosat MSG satellite with the HelioMont (Stöckli, 2013) and HelioSat (Müller et al., 2015) algorithms by the CM SAF team. HelioMont comes at a spatial resolution of 0.02x0.02 degrees, while HelioSat provides a resolution of 0.05x0.05 degrees. For the bias correction, we employ high-quality long-term pyranometer measurements from 113 ground stations of one of the densest meteorological networks around the world, the SwissMetNet.  The SIS radiations are retrieved at 30-minutes, 15-minutes and 10-minutes resolutions (HelioSat, HelioMont, and SwissMetNet, respectively) for the entire year 2018. We use 46 weeks as training set and 6 weeks as test set, wherein the latter consists of the 3 sunniest and 3 cloudiest weeks of 2018.

Our approach involves a multilayer perceptron (MLP) trained to correct the satellite SIS bias by exploiting the predictor variables (time encoding, location features and satellite SIS) and fitting them to predict the ground station SIS. By doing so, we demonstrate that our novel bias correction method can reduce the SIS mean absolute bias (MAB) of both HelioMont and HelioSat by more than 10%. Comparing our results with a standard linear regression (LR) model, we find that the MLP outperforms the LR approach on 112 and 111 SwissMetNet stations for HelioMont and HelioSat, respectively. 

Moreover, we found that the bias magnitude is significantly correlated with the altitude of the considered location and with the time of year. The biases are largest in mountainous regions that tend to have a higher albedo due snow and ice. In fact, the Pearson correlation between the altitude and the average MAB is 0.76 and 0.80 for HelioMont and HelioSat, respectively.

 

References

  • R. Stöckli (2013). The HelioMont Surface Solar Radiation Processing. Scientific Report 93, MeteoSwiss, 122 pp.
  • Müller, R., U. Pfeifroth, C. Träger-Chatterjee, J. Trentmann, and R. Cremer (2015), Digging the METEOSAT Treasure-3 Decades of Solar Surface Radiation, Remote Sensing, 7(6), 8067-8101, doi:10.3390/rs70608067.

How to cite: Carpentieri, A., Wild, M., Folini, D., and Meyer, A.: Deep learning for improved bias correction of satellite-derived SIS maps , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3641, https://doi.org/10.5194/egusphere-egu22-3641, 2022.

17:36–17:43
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EGU22-5888
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On-site presentation
Darragh Kenny and Stephanie Fiedler

Accurate irradiance data is necessary for model estimates of expected photovoltaic (PV) power production. Such data is freely available from reanalysis and satellite products with a high temporal and spatial resolution, including locations without ground-based measurements. Gridded irradiance data is therefore used for the characterization of solar resources at specific locations and larger areas, e.g. by power system modellers. Past assessments of irradiance data for PV modelling often relied on the evaluation of global horizontal irradiance (QGHI). However, the direct and diffuse irradiance components as well as differences in seasonal characteristics can strongly affect the PV capacity factors (C) potentially leading to larger biases in C than for QGHI. We therefore systematically assess differences in QGHI, direct and diffuse horizontal irradiance (Qdir  and Qdif) and quantify the subsequent bias propagation from individual radiation components to C in a contemporary PV power model. Our PV model simulations use seven different gridded irradiance data sets, namely ERA5, COSMO-REA6, COSMO-REA6pp, COSMO-REA2, CAMS radiation service, SARAH-2 and CERES Syn1Deg. All data sets provide Qdir and Qdif as separate time series spanning seven to 43 years and with a temporal resolution of 15 minutes to one hour. The results are compared against seven years of simulations based on reference measurements from 30 weather stations of the German Weather Service. We compute metrics characterizing biases in seasonal and annual spatial means, day-to-day variability and extremes in C, considering single stations and a simulated PV fleet. Our results highlight biases of -1.4 % (COSMO-REA6) to +8.2 % (ERA5) in annual and spatial means of C at single stations across Germany, while the bias in QGHI is -3 % for COSMO-REA6 to +3.6 % for ERA5. We also show the bias on days of very low PV production, relevant for extreme event analysis: The days within the lowest ten percent of daily PV production in a PV fleet show a bias of +70.2 % in ERA5, while it is only +4 % in the post-processed COSMO-REA6 data (COSMO-REA6pp). SARAH-2 and COSMO-REA6pp outperform the other products for many metrics, but also cause some biases in C. For instance, SARAH-2 yields good results in summer, but overestimates C in winter by 16 % averaged across all stations. COSMO-REA6pp represents day-to-day variability in C of a simulated PV fleet very well and has a relatively small bias of +0.5 % in the annual spatial means, but this is partly due to compensating biases from individual stations. Our results suggest that gridded irradiance data should be used with caution for site assessments and should ideally be complemented by local measurements. For power system modellers, our results may provide guidance for the quantification of uncertainties caused by gridded irradiance data.

 

Reference:

Kenny, D., and Fiedler, S., in press, Which gridded irradiance data is best for modelling photovoltaic power production in Germany?, Solar Energy.

How to cite: Kenny, D. and Fiedler, S.: Inter-comparison of PV power simulations from seven gridded irradiance data sets , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5888, https://doi.org/10.5194/egusphere-egu22-5888, 2022.

17:43–17:50
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EGU22-6831
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ECS
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On-site presentation
Chao Tang, Pauline Mialhe, Benjamin Pohl, Béatrice Morel, Shunya Koseki, Babatunde Abiodun, and Miloud Bessafi

The impacts of large scale climate variabilities on the solar energy resource are widely investigated around the world, however their effects are not yet clear for Mascarene Islands (southwest Indian Ocean, SWIO) and needs to be addressed. In this study, surface solar radiation (SSR) classification and anomalies at the diurnal scale from SARAH-E satellite product over Reunion Island are linked to the large scale climate variabilities in SWIO region. These climate variabilities include Tropical Cyclones (TCs) and the Tropical Temperate Troughs (TTTs) at the synoptic scale, the Madden–Julian Oscillation (MJO) at the intraseasonal scale, and the Indian Ocean Dipole (IOD), the Subtropical Indian Ocean Dipole (SIOD) and the El Niño–Southern Oscillation (ENSO) at the interannual scale.
We identified the variability of SSR at various time scales where both local processes and the large scale convective variabilities play important roles. At the synoptic and intraseasonal timescales, the local variability of SSR over Reunion shows a significant association with TCs, TTTs and the MJO. The sign and amplitude of SSR diurnal anomaly are found to be correlated with the enhanced- / depressed- convective phase and amplitude of these events. The SSR anomaly is strongly altered with the presence of nearby TCs, with a value of up to about 30% of climatology, although at low occurrence; TTTs and MJO have relatively weaker impact, with a value of about 13% and 5% respectively. At the interannual timescale, IOD, SIOD and ENSO have relatively much less importance on local SSR variability. The daily total solar energy density has been calculated for all these variabilities to provide useful information for energy applications. 

How to cite: Tang, C., Mialhe, P., Pohl, B., Morel, B., Koseki, S., Abiodun, B., and Bessafi, M.: Variability of the Surface Solar Radiation over Reunion island and its interaction with the synoptic, intraseasonal and interannual convective variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6831, https://doi.org/10.5194/egusphere-egu22-6831, 2022.

17:50–17:57
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EGU22-9393
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ECS
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On-site presentation
Miguel A. Prósper, Ian Sosa-Tinoco, and Gonzalo Miguez-Macho

Regional meteorological models are becoming a generalized tool for solar energy production forecasting,  due to their capacity to simulate different types of cloud formations and their interaction with solar radiation. The greater demand for reliable forecasting tools in the energy industry is the motivation for the development of an integrated system that combines the Weather Research and Forecasting atmospheric model package designed to fulfill the needs of solar energy applications (WRF-Solar), with the solaR power plant model. This study focuses on the use and validation of this coupled tool in forecasting the energy production for two real solar plants, one in Spain and another in India. A period of one year for the Spanish emplacement and nine months for the Indian site are simulated with a daily operational forecasting set-up. Aerosol data from the Copernicus Atmosphere Monitoring Service (CAMS) are considered in the calculations, a new capability in WRF-Solar. Power predictions are obtained and compared with real data from the inverters of both plants provided by the operating company.

The results show that WRF-Solar obtains accurate forecasts of global, direct, and diffuse radiation and of the ambient temperature that solaR uses as input to predict the energy production of the solar plants. The normalized Mean Annual Errors (NMAE) is 5.18% in the Spanish and 5.59% in the Indian plant for the first day of predictions, demonstrating a reliable performance of the forecasting system in different climate locations. The skill scores for the second day of prediction are also promising, with practically the same errors as the previous day (5.19% and 6.17 for Spain and India respectively). By comparing the model predictions, with and without AOD input during the dustiest days in the Spanish site, the importance of the aerosol effect inclusion is demonstrated with an improvement up to 10% in the energy forecast. These results demonstrate the system’s potential both for solar plant operation and energy market applications.

How to cite: Prósper, M. A., Sosa-Tinoco, I., and Miguez-Macho, G.: Development of a solar energy forecasting system for two real solar plants based on WRF Solar with aerosol input and a solar plant model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9393, https://doi.org/10.5194/egusphere-egu22-9393, 2022.

17:57–18:04
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EGU22-9709
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ECS
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Virtual presentation
Petrina Papazek, Irene Schicker, and Rosmarie de Wit

With the rapidly increasing use of solar power accurate predictions of the site-specific power production are needed to ensure grid stability, energy trading, (re)scheduling of maintenance, and energy transfer. Particularly in systems relying on many factors such as solar energy, extreme events can be a threat to the power grid stability and accurate nowcasts. Thus, warnings within a reasonable amount of time ahead for preparation are essential. In the MEDEA project, funded by the Austrian Climate Research Program, we aim at improving the definition and detection of extreme events relevant for renewable energies and using these findings to improve both weather and climate predictions of such extreme events.

In the presented case study, we investigate selected (extremes) cases of Sahara dust events in 2021 where various weather prediction models were unable to properly reproduce the amount of aerosols in Central Europe resulting in a discrepancy between actual solar power production compared to predictions being off by more than 5 GW.  Here, several solar production forecasts gave impaired results based on raw NWP model output. To tackle such events and improve the predictability, a deep learning framework including an LSTM (long short-term memory; type of an artificial neural network) and random forest will be adopted for nowcasting with multiple heterogeneous data sources available. Relevant features include 3D-fields from different NWP models (AROME, WRF), satellite data and products (CAMS), point-interpolated radiation time series from remote sensing, and observation time-series (site observations, close meteorological sites). We investigate up to 6 hours ahead nowcasts at several Austrian solar power farms with an update frequency of 15 minutes.

Results obtained by the developed method yield, in general, high forecast-skills, where we elaborate on interesting cases studies from a meteorological point of view. Different combinations of inputs and processing-steps are part of the analysis. We compare obtained forecast results to available forecast methods, e.g., an analogs-based method, pvlib forecasts driven with AROME and AROME RUC. 

How to cite: Papazek, P., Schicker, I., and de Wit, R.: Nowcasting of Solar Power Production by a Deep Learning  Methodology: Improving Forecasts for Solar Energy Sites during Sahara Dust Events using Highly Resoved Historic Time Series, Remote Sensing and Numeric Weather Prediction Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9709, https://doi.org/10.5194/egusphere-egu22-9709, 2022.

18:04–18:11
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EGU22-13480
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ECS
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Virtual presentation
Yves-Marie Saint-Drenan, Rodrigo Amaro e Silva, Hartwig Deneke, and Philippe Blanc

At small time scales, the spatio-temporal variability of downwelling surface solar radiation can be considered in a first approximation as resulting from the advection of clouds. It is common in the solar energy and atmospheric science communities to use a quantity called Cloud Motion Vector (CMV) to quantify this displacement.
Intuitively, cloud advection is expected to be a direct result of wind patterns at cloud-height levels. This idea is reflected, for example, in various works where wind information at cloud height is used as a driver of solar variability in solar forecasting applications [1]–[6]. However, relating spatio-temporal characteristics of the solar radiation to the wind speed is not obvious. In some meteorological situations, such as orographic clouds, the wind speed and the apparent cloud movement can be decoupled. More generally, the coexistence of different layers of clouds advecting in different directions question the validity of the use of wind information at a single level.
Other inference techniques can be used to estimate the cloud motion vectors such as the calculation of block matching or optical flow algorithms applied to sequences of satellite images [7], [8] or the cross-correlation analysis of high-resolution measurements of dense networks of sensors [9], [10], like the ones from the HOPE [11] or Oahu [12] campaigns. Yet, these alternative methods have their own weaknesses: the conclusion of cross-correlation analysis can be hampered when the characteristics of the clouds are not appropriate to track their motion (e.g., absence of texture, edges), while satellite-based CMV may miss local advection due to limited spatial and temporal resolutions.
To better understand cloud advection dynamics and understand the strength and weaknesses of the different estimation methods, a benchmark has been done using the HOPE measurement campaign [11] as a reference, with 99 pyranometers with time step of 0.1 s and inter-sensor distances ranging from 100 m to 10 km. CMV timeseries have been inferred from three different approaches:
• Applying an optical-flow method to sequences of images of surface solar irradiance from the HelioClim-3 database [13], [14], derived from Meteosat Second Generation satellite.
• Evaluating the lagged cross-correlation between different pairs of ground sensors.
• Extracting vertically-resolved wind estimates from the ERA5 reanalysis [15].
The evaluation has been conducted in two steps. Firstly, a global evaluation was conducted to assess and rank the performance of CMV-based solar forecasting from the different sources/methods using as reference quality-checked measurements from the HOPE campaign. In a second step, a comprehensive compilation of relevant and typical situations, selected from a systematic visual inspection of time series, is proposed to explain the difference between the CMV sources/methods using illustrative examples.

 

How to cite: Saint-Drenan, Y.-M., Amaro e Silva, R., Deneke, H., and Blanc, P.: Estimation of cloud motion vectors: exploring different approaches using a dense network of solar radiation sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13480, https://doi.org/10.5194/egusphere-egu22-13480, 2022.

18:11–18:18
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EGU22-2875
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On-site presentation
Frank Kreuwel and Chiel van Heerwaarden

Forecasting day-ahead 1-minute irradiance variability from NWP output

 

Accurate forecasts of solar irradiance are required for the large scale integration of solar photovoltaic (PV) systems. Fluctuations of energy generation in the order of minutes can lead to issues on the electricity grid, therefore accurate forecasts of minute-to-minute irradiance variability are required. However, state of the art numerical weather predictions (NWP) deliver forecasts at a much coarser temporal resolution, e.g. hourly averages.

In this work we present a methodology to forecast a quantification of minute-to-minute irradiance variability as well as the probability distribution function (pdf), by applying statistical postprocessing and machine learning on hourly NWP ourput. In total, 10 target parameters related to the irradiance variability are forecasted. The algorithm is tested using the NWP HARMONIE-AROME (HA) mesoscale model as input, with 1-minute irradiance observations for 18 locations throughout the Netherlands used as ground truth.

Results show that the proposed algorithm is capable of forecasting the 1-minute irradiance PDF with reasonable resemblence to the observed PDF. Moreover, we show that inaccuracies of the postprocessed result are to a large extent due toerrors in the radiation forecast of the NWP used as input, reducing the average R2 score from 0.75 to .57 for the most relevant targets. The generalizability of the proposed algorithm is demonstrated by training the model on data of a single site and testing the performance on all 18 sites. Surprisingly, we find for 14 sites the model achieves higher accuracy than at the site it was trained on. Including data of all sites in the train set improves the accuracy on 3 of the 6 relevant target parameters while decreasing the accuracy on 1. Finally, we compare this work on post-processing to the next generation weather models based on high resolution Large Eddy Simulation (LES). A case study spanning four days is performed on four days well-captured by the NWP model and results are compared to results from the post-processing algorithm. While LES underestimates values of high irradiance due to lack of 3D radiative effects, it enables detailed analysis of the dynamics at high spatial and temporal resolution unreachable by statistical postprocessing.

The algorithm presented in this work is able to predict intra-hour irradiance variability based on day-ahead NWP output. Thereby moving forward significantly towards improving grid operation, planning, and resilience in relation to large-scale solar PV generation.

How to cite: Kreuwel, F. and van Heerwaarden, C.: Forecasting day-ahead 1-minute irradiance variability from NWP output , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2875, https://doi.org/10.5194/egusphere-egu22-2875, 2022.

18:18–18:25
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EGU22-13264
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On-site presentation
Rodrigo Amaro e Silva and Hadrien Verbois

The solar forecasting literature is rich and diverse; to navigate it, practitioners can rely on review papers, or on recent papers’ introduction sections. In a considerable share of the literature, however, the focus is put almost exclusively on model design and statistical assessment aspects. The economic value of solar forecasting research, on the other hand, is seldom discussed. Looking, for example, at two prominent review works [1,2], only 5-10% of their references address this issue. However, it is important not to assume from this that there is a lack of research done on this topic.
The present work aims to share the preliminary results of the analysis of an abundant and diverse amount of literature addressing the economic value of solar forecasts for energy-related applications. The goal is to better understand how that value depends on the accuracy of a given forecasting model, and how much it varies from one application to another. It is also of relevance to discuss how researchers infer such value.


References
1. Notton, G.; Nivet, M.L.; Voyant, C.; Paoli, C.; Darras, C.; Motte, F.; Fouilloy, A. Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting. Renew. Sustain. Energy Rev. 2018, 87, 96–105, doi:10.1016/j.rser.2018.02.007.
2. Antonanzas, J.; Osorio, N.; Escobar, R.; Urraca, R.; Martinez-de-pison, F.J.; Antonanzas-torres, F. Review of photovoltaic power forecasting. Sol. Energy 2016, 136, 78–111, doi:10.1016/j.solener.2016.06.069.

How to cite: Amaro e Silva, R. and Verbois, H.: The value of solar forecasting for energy-related applications: a treasure box of literature yet to be opened, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13264, https://doi.org/10.5194/egusphere-egu22-13264, 2022.

18:25–18:30