ERE2.1

Photovoltaic (PV) power data are a valuable but as yet under-utilised resource that could be used to characterise global irradiance with unprecedented spatio-temporal resolution. The resulting knowledge of atmospheric conditions can then be fed back into weather models and will ultimately serve to improve forecasts of PV power itself. This provides a data-driven alternative to statistical methods that use post-processing to overcome inconsistencies between ground-based irradiance measurements and the corresponding predictions of regional weather models (see for instance Frank et al., 2018). This work reports first results from an algorithm developed to infer global horizontal irradiance as well as atmospheric optical properties such as aerosol or cloud optical depth from PV power measurements.

Building on previous work (Buchmann, 2018), an improved forward model of PV power as a function of atmospheric conditions was developed. As part of the BMWi-funded project MetPVNet, PV power data from twenty systems in the Allgäu region were made available, and the corresponding irradiance, temperature and wind speed were measured during two measurement campaigns in autumn 2018 and summer 2019. System calibration was performed using all available clear sky days; the corresponding irradiance was simulated using libRadtran (Emde et al., 2016). Particular attention was paid to describing the dynamic variations in PV module temperature in order to correctly take into account the heat capacity of the solar panels.

PV power data from the calibrated systems were then used together with both the DISORT and MYSTIC radiative transfer codes (Emde et al., 2016) to infer aerosol optical depth, cloud optical depth and irradiance under all sky conditions.  The results were compared to predictions from the COSMO weather model, and the accuracy of the inverted quantities was compared using both a simple and more complex forward model. The potential of the method to extract irradiance data over a larger area as well as the increase in information from combining neighbouring PV systems will be explored in future work.

References
 
Buchmann, T., 2018: Potenzial von Photovoltaikanlagen zur Ableitung raum-zeitlich hoch aufgelöster Globalstrahlungsdaten. Heidelberg University, http://archiv.ub.uni-heidelberg.de/volltextserver/24687/.
Emde, C., and Coauthors, 2016: The libRadtran software package for radiative transfer calculations (version 2.0.1). Geosci. Model Dev., 9, 1647–1672, doi:10.5194/gmd-9-1647-2016. https://www.geosci-model-dev.net/9/1647/2016/.
Frank, C. W., S. Wahl, J. D. Keller, B. Pospichal, A. Hense, and S. Crewell, 2018: Bias correction of a novel European reanalysis data set for solar energy applications. Sol. Energy, 164, 12–24, doi:10.1016/j.solener.2018.02.012. https://doi.org/10.1016/j.solener.2018.02.012.

How to cite: Barry, J., Böttcher, D., Grabenstein, J., Pfeilsticker, K., Herman-Czezuch, A., Kimiaie, N., Meilinger, S., Schirrmeister, C., Gödde, F., Mayer, B., Deneke, H., Witthuhn, J., Hofbauer, P., and Struck, M.: Irradiance and atmospheric optical properties from photovoltaic power data: model improvements and first results, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7581, https://doi.org/10.5194/egusphere-egu21-7581, 2021.

Energy output from photovoltaics (PV) strongly depends on the respective weather situation. To ensure continuous energy availability in power grids with large PV contribution, flexibly manageable power plants have to compensate for variations in PV power production. Within the project NETFLEX, an intra-hour irradiance now-casting algorithm is developed as a basis for a PV power forecast used for management of a combined PV / biogas power plant.

The now-casting algorithm is designed around a cloud representation in a simplistic 2D advection model, which is updated with currently measured data and which projects cloud situations up to 15 minutes into the future. Main input to the model are images captured by two CMS Schreder all-sky imagers (ASI) installed at the PV plant in locations separated by about 530m. Captured images are processed to extract cloud masks, cloud base heights and cloud movement. To obtain cloud masks, ratios of red and blue channels as well as saturation and brightness are compared to reference data from a clearsky library. This library is composed from synthetic clearsky data computed by the radiative transfer model libRadtran (Mayer and Kylling, 2005), which are processed to resemble imager geometry and optics. The creation of synthetic references allows for any desired sun position and aerosol condition. Simultaneously captured images of both cameras are evaluated and corresponding pixels are matched. Exact calibration of the imager geometry then allows for cloud base height derivation using the method of miss-pointing vectors (Kölling et al., 2019). Consecutive images are evaluated for each ASI to estimate horizontal cloud motion by matching corresponding pixels. All cloud information computed from ASI images is assimilated into the 2D model as a base for cloud field predictions with information about cloud position, base height and velocity. The model-centered approach allows for flexible integration of additional data sources, e.g. satellite imagery and numerical weather prediction data.

Validation of image evaluation methods and now-casting model is done using synthetic all-sky images of LES cloud fields. Additionally, cloud base height from a ceilometer as well as global and direct integrated solar irradiance were measured on site of the PV power plant. This also allows for validation on real world cases.

Literature:

Kölling, T., Zinner, T., and Mayer, B.: Aircraft-based stereographic reconstruction of 3-D cloud geometry, Atmos. Meas. Tech., 12, 1155–1166, 2019.

Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, 2005.

How to cite: Gregor, P., Zinner, T., Mayer, B., and Schreder, J.: Deriving cloud information from all-sky images for intra-hour PV nowcasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14726, https://doi.org/10.5194/egusphere-egu21-14726, 2021.

Phenomena in the atmospheric boundary layer are investigated in the Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL, www.fesstval.de). Our aim is the retrieval of wind gusts from measurements of a Doppler wind lidar (DWL). DWLs allow the determination of wind vector profiles with high vertical resolution (∼30 m) and represent an alternative to classical meteorological tower observations. They can receive signals from altitudes higher than towers and are flexible in positioning. However, the retrieval of wind gusts from DWL measurements is not trivial because a monostatic lidar provides only one radial velocity, i.e., only one component of a three-dimensional vector, and measurements in three linearly independent directions are necessary to derive the wind vector. These have to be performed sequentially which limits the achievable time resolution, while wind gusts are short-lived phenomena. Therefore, we have developed a new wind retrieval that is applicable to different scanning configurations and various requested time resolutions. We tested several DWL configurations in autumn 2019 using DWL systems ’StreamLine’ from Halo Photonics and evaluated gust peaks and the 10min mean wind at 90 m height against data from a sonic anemometer at the meteorological tower. The most useful configuration for retrieving wind gusts is a fast continuous scan mode (CSM) that completes a full circulation cone within 3.4s. During this time interval, about eleven radial velocity measurements are completed. This fast CSM configuration was again successfully operated over a three-months period in summer 2020. We found that CSM paired with our new retrieval technique provides gusts which compare well to classical anemometer measurements from a meteorological tower. Future work includes the application of the new retrieval to DWL data during the FESSTVaL campaign in 2021 when DWL measurements are planned at different sites in order to study the sub-mesoscale variability of wind gusts.

How to cite: Steinheuer, J., Detring, C., Beyrich, F., Löhnert, U., and Fiedler, S.: New flexible retrieval for gusts and mean winds from Doppler wind lidars, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2867, https://doi.org/10.5194/egusphere-egu21-2867, 2021.

Simulations of turbulent wind flows in complex, mountainous terrain prove to be challenging tasks for today’s numerical simulation models. However, knowing about these wind flow patterns and speeds would be beneficial to assess potential environmental risks for various stakeholders – aviation, wind farms, ski resorts, cable cars or others. With the PALM model system, a state-of-the-art turbulence resolving meteorological model for atmospheric boundary layer flows is available, that can be used to assess these types of questions. By treating topography on a cartesian grid, complex terrain can be accurately represented in simulations.

In this study, the complex local flow patterns in mountainous terrain were analyzed by means of high-resolution large eddy simulations with the PALM model system. This was conducted for the Rhine valley region focusing on a small peculiar topographic feature upstream of Balzers in the area of the border between the Principality of Liechtenstein and Switzerland, were flow splitting is known to occur. There, Foehn events lead to pronounced local wind maxima and pose a damaging risk to the upwind part of the village. The model results were compared with data from measurement masts equipped with sonic and cup-anemometers at the position of assumed wind speed maxima. As well, measurements of a continuous-wave LIDAR system located at the outflow of the side valley were integrated in our study. The validation measurements for the Foehn events in Balzers were taken in December 2020, during which two pronounced Foehn events took place.

In PALM, a nested simulation approach was chosen, with the smallest domain having a resolution of only a few meters. The simulation was forced by COSMO-1 model results in order to factor in the synoptic weather conditions of the respective days. We show model results of the flow patterns that occur in this complex topography, analyze the wind maxima present in the valley and compare the results with local measurement data. This study demonstrates how large eddy simulation tools like PALM can produce insights into complex flow structures in mountainous terrain, and how these insights can be used to make more informed decisions to protect residents from damaging outcomes of environmental risks.

How to cite: Spirig, C. T., Fluck, S., Vogt, K., and Anet, J. G.: Comparing large eddy simulations with sonic anemometer and LIDAR measurement data during Foehn events in complex terrain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10881, https://doi.org/10.5194/egusphere-egu21-10881, 2021.

Extreme wind speeds are a multifaceted environmental risk. They may cause considerable damage to infrastructure (e.g., bridges, private property), they can jeopardize maritime and aviation activities, and sometimes even human safety. Furthermore, the design of wind turbines for on and off-shore wind farms requires a study of the return periods of extreme wind speeds in combination with the lifespan of the wind turbines. Windstorms also result in major economic losses and cause up to 80 % of the natural hazards' long term insurance loss in Europe. The scope of this work is to identify location-specific extreme wind speed thresholds and obtain accurate estimates of exceedances for multiple future horizons. In this context, the Extreme Value Analysis framework is used for providing the return periods and the respective return levels of extreme wind speeds. The Peaks Over Threshold method is utilized for the 10 m wind speed for a domain centered over Greece, in Southeastern Mediterranean. Wind speed data at 10 m are extracted from the ERA5 reanalysis dataset that provides hourly estimates of surface wind speed with a horizontal resolution of 0.25°x0.25°, from 1979/01/01 up to 2019/12/31 (i.e., 41 years). The thresholds are selected using the Mean Residual Life plots, which is the most reliable method for identifying accurate threshold values. The seasonal analysis of the exceedances is discussed in terms of the physical mechanisms in the region. The exceedances are modelled using the Generalized Pareto Distribution, whose shape and scale parameters (ξ and σ, respectively) are estimated using the Maximum Likelihood Estimation method. The return levels and their confidence intervals are estimated for return periods up to 100 years. Geographic Information Systems are used for mapping future projections of extreme wind speeds and the corresponding confidence intervals. The results are discussed in terms of identifying high-risk areas and the findings could assist in informed decision-making in the wind energy industry. The proposed methodological framework could be extended to other areas characterized by particularly high wind speeds and the results can contribute towards sustainable investments and support adaptation mechanisms.

How to cite: Blougouras, G., Tzanis, C. G., and Philippopoulos, K.: Extreme wind speed climatology over Greece, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3117, https://doi.org/10.5194/egusphere-egu21-3117, 2021.

Wind turbines in a wind farm extract energy from the atmospheric flow and convert it into electricity, resulting in a localized momentum deficit in the wake that reduces energy availability for downwind turbines. Atmospheric momentum convergence from above, below, and sides into the wakes replenish the lost momentum, at least partially, so that turbines deep inside a wind farm can continue to function. In this study, we explore recovery processes in hypothetical offshore wind farms with particular emphasis on comparing the spatial patterns and magnitudes of horizontal and vertical recovery processes and understanding the role of mesoscale phenomena like sea breezes in momentum recovery in wind farms.

For this study, we use the Weather Research and Forecasting (WRF) model, a state-of-the-art mesoscale model equipped with a wind turbine parameterization, to simulate deep offshore and coastal wind farms with different wind turbine spacings under realistic initial and boundary conditions. The wind farms consist of 10000 turbines rated 3 MW spread over a 50 km x 50 km area. We conduct experiments with various background conditions, including low wind, high wind, and sea breeze cases identified using Borne’s method.

Results show that for deep offshore wind farms, power generation peaks at the upwind edge and monotonically decreases downwind into the interior due to cumulative wake effects of multiple rows of turbines. Vertical turbulent transport of momentum from aloft is the main contributor to recovery except in cases with strong background winds and high inter-turbine spacing where horizontal advective momentum transport can also contribute equally. Coastal wind farms behave similarly in the absence of sea-breezes.  However, under sea breeze conditions, the power production is high at the upwind edge and decreases thereafter but starts to increase again towards the downwind edge of the wind farm because of the sea breeze. The results further show that the contribution of horizontal (vertical) recovery in case of sea breeze conditions increases (decreases) to around 14% (86%) as compared to the non-sea breeze conditions where the horizontal (vertical) recovery contributes 9% (90%) to the momentum recovery in the wind farms. Vertical recovery shows a systematic dependence on wind farm density and wind speed. This relationship can be quantified using low-order empirical equations that can perhaps be used to develop parameterizations for replenishment in linear wake models. Overall, this study is likely to significantly advance our understanding of recovery processes in wind farms and wind farm-ABL interactions.

How to cite: Gupta, T. and Baidya Roy, S.: Recovery processes in coastal wind farms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12303, https://doi.org/10.5194/egusphere-egu21-12303, 2021.

TRAIN²WIND is a PhD TRAINing school analysing enTRAINment in offshore WIND farms with computer models and experiments. By its very nature, a wind turbine extracts energy from the wind, which is replenished from the wind field on the sides and above due to the ambient turbulence. However, offshore the turbulence is lower, and wind farms are typically larger than onshore, therefore the wind can only be replenished from above in a process called entrainment. Train²Wind will investigate the entrainment process using advanced high-resolution computer modelling and wind tunnel models together with measurements of the wind field above, in and downstream of large wind farms, using lidars, radars, satellites and Unmanned Aerial Systems.

Besides the natural science package, one humanities PhD student at the University of Copenhagen will investigate the collaboration between the researchers from a social science and collaboration tools perspective.

The main work is done during the education of 18 fellows, where 13 embark on a PhD, while the other ones are employed for one year. The students will work with high-fidelity numerical simulations, lidars, unmanned aerial systems, wind tunnels and satellite data in order to understand entrainment of new momentum in very large wind farms. This changes the atmospheric boundary layer over a very extended wind farm, which becomes a wind turbine array boundary layer. The resulting change in wind resource is the main object of interest. The main planned activity is an experimental campaign at a major cluster of wind farms, probably in the North Sea. Another activity revolves around vertical axis turbines and their significantly different wake pattern, a potential mitigation measure.

So far we recruited the fellows and started with the simulations and the development of the hardware. We intend to employ a vertical take-off and landing model plane with a wing span of about 2m, which would allow to start and land from a helicopter pad offshore, and after the vertical start enjoy the advantage of a winged plane and its much larger range and endurance. Another instrument is a hexacopter mounted with a sonic anemometer, which allows to sample in a single point much akin a conventional met mast, but at any given point in or above a large wind farm. Lidar usage and development is part of the project as well, with a floating lidar in Bergen University and long-range lidars at DTU.

There are three numerical codes used in Train²Wind: Ellipsys3D, a Large Eddy Simulation (LES) high-fidelity code from DTU, WIRE-LES, another LES code from EPFL, and the Weather Research and Forecasting model run at DTU.

The outcome of the project is more knowledge of the entrainment process, and a guidance on how close to position clusters of wind farms in order not to exhaust the wind resource. The talk will give an overview of the project, highlighting the challenges.

How to cite: Giebel, G., Göçmen, T., Mann, J., Sempreviva, A. M., Lund, H., Reuder, J., Bange, J., and Porte-Agel, F.: Train2Wind, or how large is an infinite wind farm?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15816, https://doi.org/10.5194/egusphere-egu21-15816, 2021.

We present the evolution of the methods used to create and validate the various numerical wind atlases during the past ten years of the Wind Atlas for South Africa (WASA) project. In WASA 3, we improved on the previous numerical wind atlases by:

• Creating an ensemble of 2-year simulations to find the optimal set of parameterisations and surface conditions for the wind climate of South Africa.
• Using a new method of generalisation and downscaling of the WRF-derived wind climate using the PyWAsP engine.
• Producing the most extensive to date wind climatology for South Africa, 30 years (1990–2019) simulation covering all South Africa at 3.33 km × 3.33 km spatial resolution and 30 minutes time output.

We will discuss these three areas and their improvements to the wind atlas' quality. The WASA 3 wind atlas' final error statistics show that the new WRF + PyWAsP method has a MAPE of 11.8% and 3.5% for the long-term mean power density and mean wind speed, respectively. These statistics are improved from those in WASA 1 and WASA 2.

When disregarding the two masts (WM09 and WM11) located in highly complex terrain, where the methodology was never designed, the use of the WRF and WRF + PyWAsP downscaling narrows the error distributions for both long-term wind speed and power density compared to the global reanalysis, ERA5.

The validated numerical wind atlas has further been used to model the wind resources of the entire land area of South Africa using the microscale WAsP model. Raster data exist with a horizontal resolution of 250 meters and three levels of 50, 100 and 150 meters a.g.l. of mean wind speed, power density, air density, Weibull A and k parameters, and ruggedness index.  These data sets and the WRF dataset will be made available in the public domain at the end of the project. Data sets for other heights above the ground and offshore can easily be added later.

How to cite: Hahmann, A., Lennard, C., Floors, R., Cavar, D., Mortensen, N. G., Olsen, B. T., Prinsloo, L., Bechmann, A., Davis, N., and Hansen, J. C.: Ten years of the Wind Atlas of South Africa: Final results from WASA 3, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6020, https://doi.org/10.5194/egusphere-egu21-6020, 2021.

An atlas of the 50-year gust wind at a resolution of 3 s is calculated over South Africa, at a spatial resolution of 3.3 km at several heights, including 10 m and 60 m where measurements are available.

In developing the atlas, first, 30-year wind climate (1990 - 2019) is simulated using the Weather Research and Forecasting (WRF) model. The WRF model was initialized and forced with the ERA5 data, with three nested domains and the innermost one, covering the whole country, has a spatial resolution of 3.3 km. The model outputs include the wind time series at several heights (50 m, 100 m and 200 m) every 30 minutes. The 50-year 30-min winds at several heights are then obtained by application of a suitable extreme value distribution. Afterwards, the Kaimal turbulence model is applied, in connection with an assumption of Gaussian process for the time series in the time scale 30 min to 3 s, to obtain the corresponding 3 s gust value to the 30-min values of the 50-year winds.

There is a prevalence of a variety of strong wind events in South Africa, including mid-latitude cyclones, fronts and thunderstorms. The different physical mechanisms have different levels of challenges to the simple modeling approaches applied above. For more than 100 measurement stations, the 50-year gust values have also been calculated, mostly at 10 m, some at 60 m. They are used to validate the modeled values and identify regions and areas where our meso-to-turbulence modeling needs improvement or adjustment to eventually produce a verified extreme gust atlas.

How to cite: Larsén, X., Kruger, A., Floors, R., Cavar, D., and Hahmann, A.: Extreme gust atlas over South Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5829, https://doi.org/10.5194/egusphere-egu21-5829, 2021.

Power curves for a substantial number of wind turbine generators (WTG) became available in a number of public sources during the recent years. They can be used to estimate the power production of a wind farm fleet with uncertainty determined by the accuracy and consistency of the power curve data. However, in order to estimate power losses inside a wind farm due to wind speed reduction caused by the wake effect, information on the thrust force, or widely used thrust coefficient (Ct), is required. Unlike power curves, Ct curves for the whole range of operating wind speeds of a WTG are still scarcely available in open sources. Typically, power and Ct curves are requested from a WTG manufacturer or wind farm owner under a non-disclosure agreement. However, in a research study or in calculations over a multitude of wind farms with a variety of wind turbine models, collecting this information from owners may be hardly possible. This study represents a simple method to define Ct curve statistically using power curve and general specifications of WTGs available in open sources. Preliminary results demonstrate reasonable correspondence between simulated and given data. The estimations are done in the context of aggregated wind power calculations based on reanalysis or forecast data, so that the uncertainty of wake wind speed caused by the uncertainty of predicted Ct is comparable, or do not exceed, the uncertainty of given wind speed. Although the method may not provide accurate fits at low wind speeds, it represents an essential alternative to using physical Computational Fluid Dynamics (CFD) models that are both more demanding to computer resources and require detailed information on the geometry of the rotor blades and physical properties of the rotor, which are even more unavailable in open sources than power curves.

How to cite: Atlaskin, E., Suomi, I., and Lindfors, A.: Statistical calculation of thrust curve of a wind turbine based on available power curve and general specifications data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14402, https://doi.org/10.5194/egusphere-egu21-14402, 2021.

Solar Photovoltaic (PV) has the potential to fulfill a considerable amount of growing electricity demands worldwide.  In addition, being neat and clean, it can help to keep the greenhouse gases emission within safe limits. This resource needs a substantial amount of area for its sitting to supply the required amount of electricity. Such an area mainly depends on the available solar resource which is mainly the function of the local environment where PV is installed. Although some previous studies exist at the global scale, however, they have not comprehensively considered environmental (e.g., temperature, dust deposition, and snow) limiting factors that affect the actual solar PV yield. This study addresses such shortcomings and deals with all limiting factors simultaneously to provide a reliable assessment of potential PV performance at a global scale. PV cell efficiency is reduced due to an increase in resistance between cells at a temperature above a certain limit. Meanwhile, the accumulation of soil (dust) and snow on PV modules are also proven to limit the solar PV resources as it tends to block the incoming solar radiation. Lastly, the geomorphological parameter, which is an arrangement of a PV module to face the sun, is also shown to change its power output.

PV cell efficiency corrections for temperature changes, soil, and snow covers are applied using the biased corrected data from Global Soil Wetness Project 3 (GWSP3), CanSISE Observation-Based Ensemble of Northern Hemisphere Terrestrial Snow Water Equivalent, Version 2 from National Snow and Ice Data Center (nsidc), and TERRA/MODIS Aerosol Optical Thickness data available from NASA Earth Observations (NEO). The daily mean solar climatological values near the Earth’s surface for the last 14 years (2001–2014) with global coverage of 0.5º x0.5º are used in the analysis. The results have demonstrated that PV performance is affected by temperature increase, soil, snow, and varying tilt-angles. An annual maximum reduction of 5.7% in the total solar PV resource is seen in the Middle East due to the temperature changes. Likewise, a maximum loss of 6.45% in the total solar PV resource is witnessed for soil deposition for Sub-Saharan Africa. A higher reduction (~20%) is shown by snow covers for Russia and Canada in the upper Northern Hemisphere. In addition, a decline of 5–7% is observed for variation in the solar PV tilt-angles in comparison to optimum ones. As a whole, a maximum reduction of 19.45% in the total solar PV resource is found, which leads to a higher coefficient of determination (R²= 0.78) than uncorrected estimation (R²=0.67). This study will be helpful for household as well as large scale solar schemes and may contribute particularly to achieving the UN SDG No. 07 — Affordable and Clean Energy — and No. 13 — Climate Action — quantitatively.

How to cite: Ali, M. and Kim, H.: Global assessment for reduction of solar photovoltaic potential due to meteorological and geomorphological limiting factors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5773, https://doi.org/10.5194/egusphere-egu21-5773, 2021.

This work considers photovoltaic solar energy as an alternative to promote the diversification of the energy matrix and contribute to improving access to the citizens of Medellín (MMA) Metropolitan Area,  Colombia. The objective is a more sustainable and resilient energy use.  To achieve this, we assess how much of the energy demand can be generated within the city, integrated into the urban morphology at the roofs of existing buildings. We use meteorological information and power measurements from three experimental solar panels. We analyze the photovoltaic energy potential in these Representative Urban Areas (RUA) and provide information relevant to the whole Valley's context to guide sustainable and resilient energy planning.  One particular result is about the energy reduction factor due to cloudiness, which quantifies how energy would vary under the region's cloud conditions.

How to cite: Correa Sánchez, N., Mesa Sánchez, O. J., and Hoyos Ortíz, C. D.: Estimation of photovoltaic energy generated in urban environments, case: Medellín Metropolitan Area (MMA) (Colombia), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15214, https://doi.org/10.5194/egusphere-egu21-15214, 2021.

Monitoring and forecasting cloud coverage is crucial for nowcasting and forecasting of solar irradiance reaching the earth surface, and it’s a powerful tool for solar energy exploitation systems.

In this study, we focused on the assessment of a newly developed short-term (up to 3h) forecasting system of Downwelling Surface Solar Irradiation (DSSI) in a large spatial scale (Europe and North Africa). This system forecasts the future cloud position by calculating Cloud Motion Vectors (CMV) using Cloud Optical Thickness (COT) data derived from multispectral images from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite and an optical flow motion estimation technique from the computer vision community. Using as input consecutive COT images, CMVs are calculated and cloud propagation is performed by applying them to the latest COT image. Using the predicted COT images, forecasted DSSI is calculated using Fast Radiative Transfer Models (FRTM) in high spatial (5 km over nadir) and temporal resolution (15 min time intervals intervals).

A first evaluation of predicted COT has been conducted, by comparing the predicted cloud parameter of COT with real observed values derived by the MSG/SEVIRI. Here, the DSSI is validated against ground-based measurements from three Baseline Surface Radiation Network (BSRN) stations, for the year 2017. Also, a sensitivity analysis of the effect on DSSI for different cloud and aerosol conditions is performed, to ensure reliability under different sky and climatological conditions.

The DSSI short-term forecasting system proposed, complements the existing short-term forecasting techniques and it is suitable for operational deployment of solar energy related systems

Acknowledgements

This study was funded by the EuroGEO e-shape (grant agreement No 820852).

How to cite: Papachristopoulou, K., Fountoulakis, I., Kosmopoulos, P., Kouroutsidis, D., Raptis, P. I., Kontoes, C., Hatzaki, M., and Kazadzis, S.: Assessment of a newly developed short-term forecasting system (nextSENSE) of Downwelling Surface Solar Irradiance (DSSI) and validation with ground-based measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7218, https://doi.org/10.5194/egusphere-egu21-7218, 2021.

How to cite: Bellinguer, K., Girard, R., Bontron, G., and Kariniotakis, G.: Assessment of Alternative Ways to Integrate Weather Predictions in Photovoltaic Generation Forecasting., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16091, https://doi.org/10.5194/egusphere-egu21-16091, 2021.

Due to global warming and the worldwide depletion of fossil fuel resources, there is a growing need to transform the energy system toward greater use of renewable sources. In Poland, poor air quality constitutes an additional argument for the necessity of such transition. High levels of pollutants concentrations in many locations, especially in urban and suburban areas are caused by emissions from individual heating systems running on fossil fuels.

Data from recent years show that renewable generation forms the largest share of the total generation mix in Europe. Regarding new installation, solar and wind energy dominate renewable capacity expansion, jointly accounting for example in 2019 for 90% of all net renewable additions. However, along with the increase in the penetration of these energy sources also increases the sensitivity of the power system to weather and climatic conditions.

The study presents the impact of climate change up to the year 2100 on the photovoltaic power generation potential (Pvpot) in Poland. For determination of Pvpot index a set of high-resolution climate models projections, made available within the EURO-CORDEX initiative was used. Maps showing spatial distribution of absolute values of Pvpot in future climate (30-year average for 2071-2100) and relative changes with respect to current climate (30-year average for 2006-2035) are presented, separately for RCP4.5 and RCP8.5 scenario. The influence of meteorological conditions (temperature, wind and solar radiation) on PV module performance is taken into account by applying two different formula (Ciulla et. al, 2014 and Davy and Troccoli, 2012). Furthermore, two options for module orientation are considered: horizontal and inclined at an optimal angle.

How to cite: Zdunek, M.: Climate change impact on photovoltaic potential in Poland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13850, https://doi.org/10.5194/egusphere-egu21-13850, 2021.

With the increasing usage of renewable energy systems to meet the climate agreement aims accurate predictions of the possible amount of energy production stemming from renewable energy systems are needed. The need for such predictions and their uncertainty is manifold: to estimate the load on the power grid, to take measures in case of too much/not enough renewable energy with reduced nuclear energy availability, rescheduling/adjusting of energy production,  maintenance, trading, and more. Furthermore, TSOs and energy providers need the information as finegrained, spatially and temporarily, as possible, on third level hub or even on solar farm / wind turbine level for a comparatively large area.

These needs pose a challenge to numerical weather prediction (NWP) post-processing methods. Typically, one uses selected NWP fields aswell as observations, if available, as input in post-processing methods. Here, we combine two post-processing methods namely a neural network and random forest approach with the Flex_extract algorithm. Flex_extract is the pre-processing algorithm for the langrangian particle dispersion model FLEXPART and the trajectory model FLEXTRA. Flex_extract uses the three-dimensional wind fields of the NWP model and calculates additionally the instantaneous surfaces fluxes. Thus, coupling Flex_extract with a machine learning post-processing algorithm enables the usage of native NWP fields with a higher vertical accuracy than pressure levels. To generate an ensmeble in post-processing from deterministic sources different tools are available. Here, we will apply the Schaake Shuffle. 

In this study a neural network and random forest approach for probabilistic forecasting with a high horizontal grid resolution (1 km ) as well as a high temporal forecasting frequency of wind speed and global horizontal irradiance for Austria will be presented. Evaluation will be carried out against gridded analysis fields and observations.

How to cite: Schicker, I., Papazek, P., Perrone, E., and Arnold, D.: Spatio-temporal ensemble predictions for wind and solar energy combining dispersion modelling methods and machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15646, https://doi.org/10.5194/egusphere-egu21-15646, 2021.

The International Energy Agency (IEA) Wind Task 36 on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, ...), forecast vendors and forecast users to facilitate scientific exchange to be prepared for future challenges.

The talk discusses the general setup of the Task, and the latest developments. Among those are decision making under uncertainty. To this aim, a series of forecasting experiments are being developed and one initial experiment was tested by a wide audience. The forecasting experiments took the form of a game, during which the participants could experience the benefit of probabilistic information on their decisions to trade.
Other results include an information portal for meteorological data, and the IEA Recommended Practice for Forecast Solution Selection which is divided into 3 parts:  (1) "Forecast Solution Selection Process", (2) "Designing and Executing Forecasting Benchmarks and Trials", and  (3) "Evaluation of Forecasts and Forecast Solutions". The Recommended Practice guideline encourages forecast users to establish a framework of metrics that help identify, whether the user's forecast performance criteria effectively incentivize the forecast provider to optimize towards the forecast target variable that has the most value for the user's application(s). For this year, we intend to update the guideline in the light of the experiences throughout the industry in its initial application, and after collecting this experience at 3 Open Space workshops.

Collaboration is open to IEA Wind member states; 12 countries are already actively collaborating.

The Task is divided in three work packages: Work Package (WP) 1 is a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction (NWP) model physics, but also widely distributed information on accessible datasets. This WP also currently organises a benchmark for NWP models, based on the Wind Forecast Improvement Project 2 (WFIP2) datasets. WP2 deals with the power conversion from the wind speed forecasts and the associated vendor issues. Amongst other things, WP2 published the IEA Recommended Practice on how to select an optimal wind power forecast solution for a specific application. The focus of WP3 is on the engagement of end users to disseminate the best practice in the use of wind power predictions, especially probabilistic forecasts and also what kind of measurements are required in real-time environments

A major activity of the Task is the organisation of workshops and special sessions at conferences, like this one. Previous workshops on e.g. forecasting on the minute scale including lidars, a workshop on the value of forecasts, or special sessions on the Wind Energy Science Conference, the Wind Integration Workshop, the ESIG Meteorology and Market Design for Grid Services workshops are still visible online from the IEAWindForecasting YouTube channel.

How to cite: Giebel, G., Shaw, W., Frank, H., Draxl, C., Zack, J., Pinson, P., Möhrlen, C., Kariniotakis, G., and Bessa, R.: IEA Wind Task 36 – International Collaboration on Forecast Improvements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13417, https://doi.org/10.5194/egusphere-egu21-13417, 2021.

Mid-latitude storms are large-scale weather patterns. They involve a large range of spatial and temporal atmospheric scales of motion. Their characteristic extreme precipitation, wind gusts and high surface winds can significantly impact wind farms (e.g. shutdowns of turbines due to exceedance of cut-off wind speed) affecting grid performance and safety. Adequate storm forecasting, which relies on high spatial model resolution, is crucial. Traditional methods usually involve the use of limited area models (LAMs). While the performance of LAMs is generally satisfactory, challenges arise when large-scale storm structures enter near the the lateral boundaries of the LAM. In this case, insufficient update intervals of the forcing data at the lateral boundaries and spatial and temporal interpolation can deteriorate the storm structure that cause insufficient storm deepening. The global Model for Prediction Across Scales (MPAS) with regional mesh refinement avoids lateral boundary conditions and allows refinement with smooth transition zones. Based on a case study of storm “Christian”, MPAS’ capabilities in simulating key storm characteristics are explored in this work. Buoy measurements of sea level pressure, reanalysis and forecast products from the Climate Forecast System (CFSv2) and simulations with the Weather Research and Forecasting (WRF) model are used to evaluate the forecast performance with respect to storm intensity, storm arrival time and storm duration. A mesh configuration with refinement from 54-km to 18-km (further referred to as variable-resolution mesh) is compared with quasi-uniform mesh configurations to examine the impact of transition zone and mesh refinement on the storm structure and forecast performance. It is found that MPAS is generally able to predict the storm intensity based on the local minimum sea level pressure, while the estimation of storm arrival time and storm duration have been negatively influenced by model drifts in MPAS and by impacts of the transition zone on the storm development in the variable-resolution configuration. An additional low pressure system emerged in the variable-resolution mesh whereby its presence is sensitive to model physics. The investigation highlights the importance of the transition zone design in MPAS and the need for additional strategies like data assimilation techniques to prevent model drifts for storm forecasting.

How to cite: Imberger, M., Larsén, X. G., and Davis, N.: Strength and Challenges of global model MPAS with regional mesh refinement for mid-latitude storm forecasting: A case study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5642, https://doi.org/10.5194/egusphere-egu21-5642, 2021.

Obtaining a quantitative measure for the uncertainty of forecasts for renewable energy has proven to be a challenging problem in the past. We present results on predicting uncertainty of a forecast conditioned on the large weather situation (Großwetterlage). As a first attempt, we use the objective weather classification by the German Meteorological Service (DWD), which sorts the weather into 40 situations based on wind direction, cyclonality and moisture in the atmosphere.

The considered forecasts concern the day-ahead production of solar power for two exemplary solar parks. To quantify the uncertainty, we define five different metrics (based on normalized absolute error and probability distribution), where each one is trained individually using machine learning. As a result, we obtain measures for over- and underprediction conditioned on the said Großwetterlage.

We consider this to be a very promising yet accessible approach to derive a quantitative measure for uncertainties based on the current, day-to-day weather situation. Future work may concern an improvement of the Großwetterlagencharacterization and a general, probabilistic formulation of the problem, e.g. using Bayesian inference.

How to cite: Denisenko, G., Abel, M., Siebert, D., Seidler, P., and Seidler, T.: Wind Energy Forecast Conditioned on Großwetterlage (large scale weather situation), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13132, https://doi.org/10.5194/egusphere-egu21-13132, 2021.

In this paper we present the research directions and innovative solutions developed in the European Horizon 2020 project Smart4RES (http://www.smart4res.eu) for better modelling and forecasting of weather variables necessary to optimise the integration of 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 reach very high RES penetrations in power grids of 2030 and beyond, through thematic objectives including:
•    Improvement of weather and RES forecasting (+10-15% in performance),
•    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). 

Smart4RES focuses both on improving forecasting models of weather (e.g. physical models, data assimilation, Large Eddy Simulation) and RES production (e.g. seamless models, highly resolved predictions), and on addressing applications in power grids. Developments in the project have been formalized in Use Cases that cover a large range of time frames, technologies and geographical scales. For example, use-cases on power grids refer to the provision of ancillary services to the upper-level grid (e.g., balancing power) and the local grid (e.g., voltage control and congestion management), where the accurate forecasts of variable generation are key for accurate decision-making. A grid state forecasting will quantify dynamically the flexibility potential of RES in distribution grids. Collaborative forecasting investigates the improvement associated to local data sharing between distributed RES plants. This data sharing paves the way to a data market where agents exchange measurements, predictions or other types of valuable data. Lastly, data-driven approaches will streamline decision-making by simplifying the model chain of bidding RES production, storage dispatch or predictive management electricity grids. They will also provide interpretable hindsight to decision-makers by integrating the decisions of experts (human-in-the-loop) and will be tested in realistic laboratory conditions (software-in-the-loop).

In this paper we focus on the work done for improving modelling and forecasting of weather variables; i.e. through innovative measuring set-ups (i.e. a network of sky cameras in Germany); through the development of seamless numerical weather prediction (NWP) approaches to be able to couple outputs of NWP models with different resolutions; through ultra high resolution NWPs based on Large Eddy Simulation. We present results using data from real world test cases considered in the project. Finally we assess how the new forecasting products may bring value to the applications. 

How to cite: Kariniotakis, G. and Camal, S. and the Smart4RES team: Smart4RES: Improved weather modelling and forecasting dedicated to renewable energy applications., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16219, https://doi.org/10.5194/egusphere-egu21-16219, 2021.

This study evaluates S2S forecasts of meteorological variables relevant for the renewable energy sector from six global coupled forecast models: ECMWF-SEAS5, DWD- GCFS 2.0, Météo-France’s System 6, NCEP-CFSv2, UKMO- GloSea5-GC2-LI, and CMCC-SPS3. The variables include 10m wind speed, incoming shortwave radiation, 2 m temperature, and relative humidity because these variables are critical for estimating the supply and demand of renewable energy. The study is conducted over seven homogenous climate regions of India for 1994-2016 April and May when energy demand peaks throughout the country. The evaluation is done by comparing the forecasts at 1, 2, 3, 4, and 5-months lead-times with ERA5 reanalysis data. In order to assess the forecast quality, deterministic metrics such as bias and correlation and probabilistic metrics such as Ranked Probability Score (RPS) and Continuous Ranked Probability Score (CRPS) are calculated by spatially averaging the forecasts and reanalyses over each region. The tercile limits for each variable are determined separately for each homogenous region from the ERA5 reanalysis using leave-one-out cross-validation. The forecasts show the highest skill at 1-month lead-time and the skill reduces with the increase in lead-time. However, deviations from this pattern are observed in some cases. For example, the 2 m temperature forecasts tend to perform better at longer lead-times over the western Himalayas perhaps because the slowly-varying snow dynamics aids in long-term predictability. The 2 m temperature and relative humidity forecasts generally show high correlations with observations over the western coast of the Indian peninsula in May at all lead-times, indicating the ability of the models to simulate presence of moisture prior to monsoon onset. Results show that the model performance depends on time-period of initialisation, better representation of surface fluxes, interaction between radiation and microphysics schemes, land-surface processes and factors governing radiative forcing such as greenhouse gases and aerosols. Overall, the SEAS5 model performs better than other models, although the Météo-France’s System 6 and UKMO- GloSea5-GC2-LI models also perform well in some regions.

How to cite: Das, A. and Baidya Roy, S.: Evaluation of S2S forecasts over India for renewable energy applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2228, https://doi.org/10.5194/egusphere-egu21-2228, 2021.

Climate change and offshore renewable energy sector are connected by a double nature link. Even though energy generation from clean marine sources is one of the strategies to reduce climate change impact within next decades, it is expected that large scale modification of circulation patterns will have in turn an impact on the spatial and temporal distribution of the wind fields. Under the WINDSURFER project of the ERA4CS initiative, we analyse the climate change impact on marine wind energy resource for the European offshore wind energy sector. Long-term changes in specific climate indicators are evaluated over the European marine domain (e.g. wind power density, extreme winds, operation hours) as well as local indicators (e.g. gross energy yield, capacity factor) at several relevant operating offshore wind farms.

Adopting an ensemble approach, we focus on the climate change greenhouse gases scenario RCP8.5 during the end of the century (2081-2100 period) and analyze the changes and uncertainty of the resulting multi-model from seven high resolution Regional Climate Models (RCM) realized within Euro-Cordex initiative (EUR-11, ~12.5km). ERA5 reanalysis and in-situ offshore measurements are the historical data used in present climate.

Results indicate a small decrease of wind energy production, testified by reduction of the climatological indicators of wind speed and wind power density, particularly in the NW part of the domain of study. The totality of the currently operating offshore windfarms is located in this area, where a decrease up to 20% in the annual energy production is expected by the end of the century, accompanied by a reduction of the operation hours between 5 and 8%. Exceptions are represented by Aegean and Baltic Sea, where these indicators are expected to slightly increase. Extreme storm winds however show a different spatial pattern of change. The wind speed associated to 50 years return period decreases within western Mediterranean Sea and Biscay Bay, while increases in the remaining part of the domain (up to 15% within Aegean and Black Sea). Finally, the estimated variations in wind direction are relevant on the Biscay Bay region.

How to cite: Susini, S. and Menendez, M.: Long-term climate change effects in the European offshore wind energy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7671, https://doi.org/10.5194/egusphere-egu21-7671, 2021.

Autonomous micro-grids based on solar photovoltaic (PV) are one of the most promising solution to bring electricity access in many off-grid regions worldwide. The sizing of these microgrids is not straightforward. It is especially highly sensitive to the multiscale variability of the solar resource, from sub-daily to seasonal times scales (cf. Plain et al. 2019). Because of this, achieving a given level of service quality requires to provision 1) storage and 2) extra PV production capacity, the main challenge being to also deliver electricity during times with no solar resource (night) and during periods with low solar resource (e.g. winter). Different storage / PV panel sizes can produce the same level of service quality. The optimal design is typically identified to minimize the levelized cost of electricity (LCOE). The cost optimization however obviously relies on a number of technical and economic hypothesis that come with large uncertainties, such as the installation and maintenance costs of both PV and batteries, the system lifetime or the temporal profile of the electricity load.

This work explores the robustness of the optimal sizing to variations of different such parameters. Using irradiance data from Heliosat SARAH2 and temperatures from ERA5 reanalysis, we simulate the hourly solar PV production of a generic array of PV panels for 200 locations in Africa over a 8-years period. We then identify the configurations (storage, PV panel surface) for which 95% of demand hours are satisfied. For different PV/storage costs’ ratios and different electrical demand profiles, we then identify the configuration with the lowest LCOE.

Our result show that the optimal configuration is highly dependent on the characteristics of the resource, and especially on its co-variability structure with the electric demand on different timescales (seasonal, day-to-day, infra-day). It is conversely very robust to changes to costs hypotheses.

These results have important practical implications. They especially allow us to propose simple design rules that are based on the only characteristics of the solar resource and electrical demand. The storage capacity can be estimated from the 20% percentile of the daily nocturnal demand and the PV surface area can be estimated from the mean daily demand and the standard deviation of the mean daily solar energy.

These rules are very robust. They allow to guess the optimal configuration for different costs’ ratios with a good precision. The normalized root mean square error is 0.17 for the PV capacity, 0.07 for storage capacity and 0.02 for LCOE.

Plain, N., Hingray, B., Mathy, S., 2019. Accounting for low solar resource days to size 100% solar microgrids power systems in Africa. Renewable Energy. https://doi.org/10.1016/j.renene.2018.07.036

How to cite: Chamarande, T., Hingray, B., Mathy, S., and Plain, N.: Optimal design and Levelized Cost of Electricity of 100% solar power microgrids in Africa: robustness and sensitivity to meteorological and economical drivers., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10141, https://doi.org/10.5194/egusphere-egu21-10141, 2021.

Spatial and temporal characteristics of simulated wind fields - stemming from the high-resolution weather model WRF with boundary conditions from ERA5 reanalysis data had been validated against the respective data stemming from measurements regarding annual characteristics as reported by [1]. As one result, the tendency of the WRF sets showed some overestimation of the coherency and underestimation of the power spectral density (PSD).

Here, this investigation is deepened to look on the capability of the modelled data to reflect the variability of the PSD and coherencies of the wind speed fluctuations on a monthly and seasonal (three- monthly) scale.

The intra annual variation of the PSD and the coherence functions are well captured by the WRF-generated wind speeds.  No seasonal dependency can be detected for the underestimation of the spectra from the modelled data. The shape can well be modelled by the approach of [Larsén et al., 2013].  Concerning the coherences, the tendency of an overestimation as detected in the analysis of annual sets, shows up in the seasonal scale in similar magnitude, reflecting a systematic shortcoming of the simulated sets to reflect the spatial inhomogeneity of the field.

[1] Poulsen, T, Beyer, H.G., Cross spectral characteristics of  modelled and measured sets of spatially distributed wind in the Faroe Islands, poster presentation  EGU 2020 (2020)

[2] Larsén, X., Vincent, C., and Larsen, S. (2013). Spectral structure of mesoscale wind over the water. Quarterly Journal of the Royal Meteorological Society, 139:685–700. (2013)

How to cite: Beyer, H. G. and Poulsen, T.: How well do ERA5/WRF generated wind fields reflect the seasonal variability of the spatio-temporal characteristics of wind field, tested for North-Atlantic conditions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7759, https://doi.org/10.5194/egusphere-egu21-7759, 2021.

Light wind conditions can be a very relevant feature from the wind power perspective. If such values are below a certain threshold (fixed or relative to some percentile), from the renewable energy production perspective, the amount of such energy is then strongly reduced or even suppressed. Frequency and intensity of such conditions is therefore of high interest, and a characterization of how these conditions can remain in time (during several hours, or days) can be even more important. From a climatic perspective, those episodes could be named as spells. This is the case of dry or wet ones, when referring to precipitation and its absence, or hot or cold ones, when focusing on temperatures. There is plenty of literature focused on that extreme conditions, for example in the set of indices to define extreme events developed by the ETCCDI (the CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices: http://etccdi.pacificclimate.org/). However, no mention is made to wind there. Here, we will explore the application of those indices for temperature and precipitation, but now applied to wind values, when they remain below normal values during a certain period of time. Several considerations will first be made to define light wind thresholds. Then, the statistical characterization of the persistence of those conditions will be inspected. ERA5 reanalysis over Europe for the last 40 years is used as the database to perform such analysis, at a resolution of 0.25 degrees for the whole region. From ERA5 time frequency, we are able to analyze hourly scales, due to the high time variability of wind, which can be also of quite relevant interest from the energetic resource perspective. We also analyze daily scales, which is more typical from a climatic focus, and see how these results can be related to mean wind conditions at each point. Time climatic variability and spatial obtained patterns are also studied. Results from this work will be useful to advance in a more systematic and rigorous climatic description of such wind conditions, that would be very important from the energy perspective, for example. In particular, we are interested in exploring the recently developed concept of energy droughts (Raynaud et al., 2018).

How to cite: Sanchez, E., Gutierrez, C., Molina, M. O., and Ortega, M.: Low wind spells characterization over Europe as seen from ERA5 reanalysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9125, https://doi.org/10.5194/egusphere-egu21-9125, 2021.

To reach its goal of net greenhouse gas neutrality by 2050, the European Union seeks to massively expand wind and solar power. Relying on weather-dependent power generation, however, poses substantial risks if climate variability is not adequately understood and accounted for in energy system design. Through informed combination of centennial reanalyses that have been tested for agreement with large scale climate phenomena, we here quantify European wind and solar generation variability over the last century. We report that wind and solar generation vary on a multidecadal scale, but wind more strongly. We identify hotspots and study dominant patterns of (co-)variability, finding that solar generation varies mostly uniformly across Europe while the leading wind variability modes reveal cross-border balancing potential. Continental scale transmission thus proves useful in balancing wind power variability also on multidecadal timescales. Evaluating local solutions to balance generation variability, we find that combined wind and solar systems optimized for minimal seasonal variability exhibit multidecadal variability of around 10% in many European countries. This amplitude can be reduced three-fold through wind shares optimised for minimal multidecadal variability. Thus, with improved spatial planning only, multiple options to mitigate long-term renewable generation variability exist.

How to cite: Wohland, J., Brayshaw, D. J., and Pfenninger, S.: A century of European wind and solar power generation variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-550, https://doi.org/10.5194/egusphere-egu21-550, 2021.

In recent years, renewable energy is gaining importance in the energy mix, increasing the dependence of the energy system on weather. Atmospheric patterns that affect wind energy production focusing on the winter months have been studied in previous works, as wind resource in Europe is higher for this season, but also because it is when there is a greater and more stable heating demand in Europe. Southern European countries, however, present summer demand increases due to the cooling needs of these countries (Spain, Portugal , Italy and Greece). These increases have been seen with real daily demand data from ENTSO-E (the European Network of Transmission System Operators for Electricity). Demand in Spain is even higher on days of heat waves in the 2015-2018 period, reaching in that case the annual maxima. The objective of this work is to study the wind patterns in these episodes of heat waves. Reduced overall summer wind power supply coupled with high energy demand under these conditions could be compromised. We will analyse means of daily wind anomalies on days of heat waves (composites) using data from the ERA5 reanalysis and the E-OBS temperature observations. The study of the wind resource in conditions of high energy demand due to extreme climate events, can help in the energy supply strategic planning and control to minimize the impact of these events on an electricity system with high penetration of renewables.

How to cite: Molina, M. O., Sánchez, E., Gutiérrez, C., and Ortega, M.: Analysis of wind resource under heat wave conditions in southern Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4692, https://doi.org/10.5194/egusphere-egu21-4692, 2021.