Solar energy plays a major role in climate change mitigation. With rising shares of solar power in the grid, short-term forecasts of surface solar irradiance (SSI) are becoming increasingly important for grid operators to enable cost-eDicient supply and demand balancing. Solar nowcast models provide estimates of SSI from minutes to hours ahead. Accurate solar nowcasts are required across spatially extensive areas as most solar power is generated by decentralised photovoltaic systems. Such regional-scale SSI estimates can be derived from geostationary satellites, like Meteosat, that monitor Earth in visible and infrared bands.
Existing regional-scale solar nowcast models are usually deterministic, lacking forecast uncertainty awareness, and require satellite Level-2 products of SSI as input obtained from radiation retrievals such as Heliosat. We present the first probabilistic regionalscale solar nowcast models, SolarSTEPS and SHADECast (Carpentieri et al., 2023, 2024), an autoregressive model and a generative diDusion model, that can be applied to regions ranging from tens to several thousand kilometers in extent. Our solar nowcast models improve forecast accuracy and reliability in all cloudiness conditions compared to existing models. SHADECast extends the forecast horizon of our state-of-the-art SolarSTEPS model by 26 minutes at lead times of 15 minutes to 2 hours. We also present a deep-learning-based emulator of Heliosat SARAH-3 (Pfeifroth et al., 2021) that estimates instantaneous SSI across Europe with similar ccuracy as SARAH-3. We demonstrate that the emulator, a convolutional residual network, can even outperform SARAH-3 in SSI accuracy when a subsequent finetuning step is added in which the emulator is retrained on pyranometer stations, resulting in more accurate SSI initialisations for solar nowcast models. The emulator estimates SSI at kilometer-scale and 15-minute intervals based on visible and infrared images of Meteosat's Spinning
Enhanced Visible and Infrared Imager. Pyranometers from BSRN, IEA-PVPS and European national weather services were employed for emulator finetuning and testing.

Carpentieri, A., S. Pulkkinen, D. Nerini, D. Folini, M. Wild, A. Meyer, 2023, Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection, Applied Energy, 351, doi:10.1016/j.apenergy.2023.121775

Carpentieri, A., D. Folini, J. Leinonen, A. Meyer, 2024, Extending intraday solar forecast horizons with deep generative models, arXiv:2312.11966, doi:10.48550/arXiv.2312.11966

Pfeifroth, U., J. Drücke, J. Trentmann, R. Hollmann, 2021, SARAH-3 - a new satellite-based Cimate Data Record for surface radiation parameters from the CM SAF, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-454, doi:10.5194/ems2021-454

How to cite: Meyer, A., Carpentieri, A., and Schuurman, K.: Probabilistic minute-scale forecasting of solar energyacross Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1147, https://doi.org/10.5194/ems2024-1147, 2024.

With the current and expected development of the installed solar capacity, solar forecasting is becoming a crucial part of the electricity supply system to allow an optimal integration of the weather dependent power production. A growing number of algorithms and forecast providers has appeared in the academic and industrial scenes to address this need.

The multitude of forecast solutions is beneficial for the industry because it ultimately results in an increase the forecast accuracy. But at the same time, it makes it difficult for forecast users to have a clear view on the strengths and weaknesses of the different solutions. This lack of clarity is reinforced by the use of different evaluation methodologies and proprietary reference solar measurements.

This work aims at contributing to the establishment of a standard for solar forecast evaluation based on backtest simulations (also known as hindcasts). The cornerstone of our proposed methodology is to base the forecast evaluation on open-data and FAIR in-situ measurements so that the evaluation is reproducible. Furthermore, we propose to share our evaluation methodology so that any deviation resulting from the data preparation, quality control, filtering or selected baseline are avoided. Finally, we propose in addition to standard error metrics (MAE, RMSE, bias…) a set of evaluation procedures aimed to inform end-users on the main characteristics of a forecast:

• Performance of the forecast in specific weather conditions (cloud free, overcast, variable conditions, regime change) following the approach suggested in Verbois et al. (2020)
• Comparison of the variability of the forecasted solar irradiance and the reference solar irradiance measurements
• Performance of ramp detections (sharpness of ramps, time lag) based on the work of Vallance et al. (2018)
• …

The numerical, visual or behavioral performance of a particular model against these procedures will be systematically compared with results obtained similarly on typical baseline forecast models (such as persistence, NWP only or CMV forecasts).

The current work is limited to deterministic forecast but an extension to probabilistic forecast is planned in the very near future.

Verbois, H., Blanc, P., Huva, R., Saint-Drenan, Y.-M., Rusydi, A., Thiery, A., Beyond quadratic error: Case-study of a multiple criteria approach to the performance assessment of numerical forecasts of solar irradiance in the tropics, Renewable and Sustainable Energy Reviews, Volume 117, 2020, 109471, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2019.109471.

Vallance, L., Charbonnier, B., Paul, N., Dubost, S., Blanc, P. Towards a standardized procedure to assess solar forecast accuracy: A new ramp and time alignment metric. Solar Energy, 2017, 150, pp.408 - 422. ⟨10.1016/j.solener.2017.04.064⟩. ⟨hal-01522453⟩

How to cite: Yezeguelian, L., Carrière, T., Pitaval, S., Rodriguez, B., Saint-Drenan, Y.-M., and Ménard, L.: Towards a transparent and reproducible evaluation framework of commercial solar forecasting solutions, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-496, https://doi.org/10.5194/ems2024-496, 2024.

This study aimed to assess the impact of climate change on solar energy production up to 2100 on the Italian peninsula by focusing on the key variables of global solar irradiance (GHI) and temperature and examining the photovoltaic (PV) energy production by means of the capacity factor (CF). Regional Climate Models (RCM) simulations from Euro-CORDEX models assimilating time-evolving aerosols were selected to ensure accurate estimations of solar radiation trends in future RCP scenarios. A bias correction was applied using the SARAH-3 solar radiation dataset for GHI and MERIDA reanalysis for 2 m temperature data to enhance the accuracy of CF estimation.

The trend analysis revealed a slight decrease in the GHI under RCP 2.6, while the other RCPs exhibited significant increases, especially over the mountain regions in central Italy. The opposite trend is foreseen in the Alpine region, particularly under RCP 8.5. Moreover, temperature is projected to increase, notably under RCP 4.5 and RCP 8.5, with potential implications for production efficiency and snow cover reduction in the Alps, with subsequent decreases in solar irradiance related to the diffuse component.

Analyzing the trend of the ensemble mean CF for the 2021–2100 span across the different RCPs, under RCP 8.5, a significant decrease is predicted, particularly in the Alps, due to the reduced GHI. Despite the general increase in the GHI, a decrease in the CF is likely for most of Italy due to rising temperatures potentially reducing solar panel efficiency. RCP 4.5 and RCP 2.6 showed less pronounced decreases in solar production, with RCP 2.6 being the scenario with the lowest magnitude of the climate signal.

Seasonal cycle analysis revealed variations primarily linked to changes in GHI throughout the year. RCP 8.5 exhibited a significant decrease in production during winter, followed by a slight increase in summer, which was likely dampened by increasing temperatures. RCP 4.5 maintained similar characteristics, with a less pronounced decrease in winter and stable production in other months. RCP 2.6 showed a slight increase in spring and generally stable production throughout the year.

In conclusion, climate change is expected to marginally influence photovoltaic power production in the Italian peninsula under different RCP scenarios, with temperatures playing a predominant role, particularly under the RCP 4.5 and RCP 8.5 scenarios, dampening the effect of increased solar radiation on PV production. The Alpine region represents an exception, with marked solar radiation decreases likely associated with reduced snow cover expected by the end of the century, leading to a significant decrease in the CF. The future expansion of photovoltaic installations should consider these findings, especially those in the Alpine region.

How to cite: Bonanno, R. and Collino, E.: Assessing the impact of climate change on solar energy production on the Italian peninsula, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-229, https://doi.org/10.5194/ems2024-229, 2024.

Renewable energy sources (RES), such as wind and solar photovoltaic (PV), already account for a large share of today’s electricity systems. This share is set to grow significantly in the near future, due to ambitious emission reduction targets. A significant proportion of energy generation in the future, therefore, will be dependent on local weather conditions, which can change significantly over short time horizons. These sudden changes in renewable generation will need to be managed by electricity grid operators, who will need to ensure sufficient reserve capacity to maintain grid stability, particularly if an increase/decrease in renewable generation is coincident with a decrease/increase in electrical demand.

RES-induced ramps are generally caused by changes in weather, which result in rapid, large changes in electricity generation, particularly as weather fronts sweep across a country with the associated winds and cloud coverage. Other events linked to large ramps are solar eclipses for PV and high-wind periods that can lead to wind farm shutdowns. RES ramps are defined as changes in generation, taking place over a number of hours, that exceed a given threshold. In this work, we explore ramping events over Ireland at farm and national scale. First, we explore ramps at individual farm level using data provided by EirGrid, the Irish transmission system operator. Next, we evaluate how ramping events at a national level compare, derived from the combination of all farms. The aim of this first part is to characterise the behaviour of current wind and PV ramps, by means of their duration, magnitude and timing, as well as their temporal and spatial patterns.

We will then extend our analysis beyond the available historical generation data, by using models driven by ERA5 reanalysis data to generate hourly, farm-level wind and PV data from 1940 to today. We will quantify the skill of our modelled data, and use it for temporal and spatial analysis of RES ramping events, allowing us to capture a broader range of extremes and return periods, and better understand the seasonality and temporal cycles linked to ramps. Finally, anticipated changes in ramp patterns under the 2030 target of 80% RES in Ireland will be explored, considering the planned developments in capacity for the different technologies, located both onshore and offshore.

How to cite: Maimo Far, A., Sweeney, C., and Flynn, D.: Wind and solar PV generation ramping events from farm to national level: the case of Ireland, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-412, https://doi.org/10.5194/ems2024-412, 2024.

Climate change will have clear impacts on wind resource that are highly dependent on regions and on greenhouse gases emission scenario. These changes will significantly affect the development and utilization of regional wind energy given the massive integration of wind power in the electrical system. In this context, we aim to assess the impact of climate change on wind resource along the French coastline in order to provide stakeholders in the offshore wind sector with appropriate indicators and trends to assess the expected evolution for the next few decades and to quantify associated uncertainties. First, we investigate several sets of wind-speed data from in-situ measurements (LIDAR and coastal meteorological stations) at the local scale on the different coastline regions of mainland France: English Channel, Bay of Biscay, and Mediterranean Sea. Then comparisons between measurements and numerical reanalyzes (ERA-5, COSMO-REA6, CERRA) are carried out for inter-annual variability, seasonal cycle, diurnal cycle and distribution of wind at different levels. The wind speed from CMIP6 Climate models is also evaluated for the historical period in comparison to reanalyzes and measurements to select the best performing models. Climate change effects on wind speed are then assessed on particular sites of interest on the different coastline regions of mainland France based on bias-corrected climate projections. The possible drivers and tendencies are identified. In addition, events of low wind speed, under cut-in velocity of offshore wind turbines, and high wind speed, above the cut-off velocity are studied to best determine future changes in electrical production.

How to cite: Patra, A., Oueslati, B., Kervella, Y., Renaud, P., and Dubus, L.: Impact of climate change on wind resource along the French coastline, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-325, https://doi.org/10.5194/ems2024-325, 2024.

Renewable energy from resources like the wind and sun comprise a gradually increasing share of the energy mix. Since these resources are fluctuating, forecasts for renewable energy sources are crucial for an efficient operation of the power system. For a large part, forecasting of renewable energy production relies on the output from numerical weather prediction (NWP) models. NWP models operate at coarse mesh sizes that cannot resolve turbulence, which is one of the key processes affecting wind and clouds alike. This leads to systematic biases, most notably an overestimation of solar radiation in the presence of low (stratus) clouds, and a severe underestimation of the wind speed during the night. Both situations are frequently occuring weather conditions.
Large-eddy simulation (LES) models apply a high spatial resolution (~10-100m) which is sufficiently fine to resolve turbulence. LES modeling is an established, yet computationally expensive technique. Recently a giant gain in the computational speed was obtained by running LES on a GPU (Graphics Processing Unit), which nowadays allows the TU Delft spin-off company Whiffle to operate it as a high-resolution weather forecast model. The GPU‐Resident Atmospheric Simulation Platform (GRASP) receives the large-scale forcing conditions from the European Weather European Centre for Medium-Range Weather Forecasts (ECWMF) model. The full potential of LES-based forecasts is however not acquired since the initial state, which is also taken from the ECMWF model, contains errors.
With REFORM, we aim to improve weather forecasts of solar radiation and wind by introducing a novel, hybrid approach which makes use of GRASP as well as observations to obtain the best possible estimate of the initial atmospheric state in terms of wind, temperature, humidity and clouds. We will capitilize on the recently initiated national observational platform Ruisdael, which includes ground-based in-situ measurements as well as advanced remote sensing retrievals. To fully exploit the capabilities of GRASP the proposed research will strongly focus on turbulent regimes such as the nocturnal stable boundary layer, the clear convective boundary layer, their transitions, and low clouds. 

How to cite: de Roode, S. R., van Soest, M., Frei, M., Jonker, H. J. J., Verzijlbergh, R. A., Russchenberg, H. W. J., MacKenzie, R., and Sourzac, M.: The Renewable Energy Forecasts from Observations and high-Resolution Modeling (REFORM) project, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-464, https://doi.org/10.5194/ems2024-464, 2024.

Convective cold pools routinely pass over the dense network of wind turbines in northern Germany, causing short-term changes in boundary-layer wind speeds (i.e., wind ramp events) and atmospheric stability. These large, rapid, and more-localized variations in the low-level kinematic and thermodynamic structure are difficult for numerical weather prediction models to forecast with sufficient spatial and temporal accuracy for utilization by wind turbine operators. As boundary-layer stability and winds strongly influence wind turbine structural loads, downstream turbulent wake behavior, and power generation, it is important to better understand how rapid changes in dynamic processes evolve within the vertical layer of wind turbine rotor blades (~50 - 150 meters altitude).

Using in-situ observations and high-resolution modeling focused on the WiValdi research wind park in Krummendeich, Germany, we examine how convective cold pool passages from 2020-2023 impact the inflow and turbulent wakes for two installed turbines with a hub height of 92 meters. Meteorological mast, Doppler wind lidar, and microwave radiometer observations provide upstream and downstream measurements of stability, vertical shear, and turbulence variations at ~1-minute resolution. While this measurement coverage adequately captures the cold pool evolution relative to each turbine, we remain somewhat limited by the fixed instrument locations for measuring upstream conditions and the three-dimensional turbulent wake structure. Therefore, we also utilize the mesoscale model WRF in large-eddy-simulation mode, with inserted generalized actuator disks acting as proxy wind turbines, to analyze far-upstream inflow conditions and three-dimensional wake characteristics during cold pool passages. The proposed work provides a foundation for future analysis which will more robustly verify WRF output using additional WiValdi observations.

How to cite: Thayer, J., Kilroy, G., Wildmann, N., and Englberger, A.: How do convective cold pools influence the stability and turbulence conditions near wind turbines in Northern Germany?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-508, https://doi.org/10.5194/ems2024-508, 2024.

In the way towards fully renewable power systems, the variability of conventional renewables represents a fundamental challenge. One of the options to tackle this issue is to exploit the complementarity between different renewable energy sources and technologies. Airborne wind energy (AWE) technology can tap the energy of winds at several hundred meters height, where winds are stronger and less variable than at the height of conventional wind turbines. The deployment of AWE systems is being hindered by their higher present costs, associated to their early development phase, but their large potential advantages point to future highly competitive costs if there are early adoption opportunities that accelerate the progress of this technology.

The aim of this study is to analyse the potential added value of AWE resources over Spain, in terms of temporal complementarity to mainstream renewables (onshore wind and photovoltaic energies) and adaptation to demand. Using data from a high resolution reanalysis (CERRA, Copernicus European Regional Reanalysis) and the AWERA tool [1] for analysing AWE resources, complementarity between AWE resources and conventional renewables is evaluated using actual PV and wind energy production data from the Spanish Transmission System Operator (Red Eléctrica Española), while demand data from the same source are used to explore adaptation to demand.

We focus on the summer season. This season is associated to distinct problems for a system based on conventional renewables, due to the strong seasonal decrease of conventional wind energy and the simultaneous increase in power demand for air cooling. Summer power demand may even exceed winter demand in the future, due to climate change. Despite its summer production peak, PV energy covers only part of the day. 

The high summer temperatures in Spain are associated with a particular seasonal low pressure system over the Iberian Peninsula, the Iberian thermal low [2]. The corresponding wind fields at heights of several hundred meters above the surface show a very interesting daily cycle over certain areas of Spain, with strong complementarity to PV generation, while the vertical wind variations reveal substantial advantages with respect to conventional wind energy. Consequently, AWE resources could make a significant contribution to the energy transition in Spain, filling some of the gaps of mainstream renewables.

References
[1] Thimm, L., Schelbergen, M., Bechtle, P., Schmehl, R.: The Airborne Wind Energy Resource Analysis Tool AWERA. 9th International Airborne Wind Energy Conference (AWEC 2021). Available at: http://resolver.tudelft.nl/uuid:ba0c7fb2-baff-4110-9a51-c27a8498663b

[2] Hoinka, K. P., Castro, M. D. (2003). The Iberian peninsula thermal low. Quarterly Journal of the Royal Meteorological Society, 129, 1491-1511.

How to cite: Gaertner, M. A., López-Franca, N., and Ortega, M.: Airborne wind energy resources: key advantages for the energy transition in Spain, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-542, https://doi.org/10.5194/ems2024-542, 2024.

The past years have shown a rapid increase in the installation of new wind farms, despite the usual regulatory slow downs and social acceptance issues. Furthermore, existing wind farms have undergone repowering processes with some being repowered to newer, more efficient, and higher wind turbines. This repowering process included sometimes even the deinstallation of a number of wind turbines within a wind farm to avoid unwanted side effects such as wake inferences.  
This, however, poses some issues for the extrapolation of wind speed to hub heights higher than approximately 130m agl and adjustments to the power law and log law approaches may be necessary.
In Austria, a new wind atlas based on a concise set of 10 m meteorological observations of wind speed and direction, for wind energy applications and climate scenario downscaling, is currently being generated. With the idea of providing wind speed at different nacelles heights and including the requests of the wind industry, wind speed analyses at heights of up to 300m agl need to be provided. This requires thorough testing and adjusting of the classical methods as well as looking into machine learning methods for extrapolation (trained on e.g. the NEWA data).
In this work, a comparison of methods, including adjusting classical methods as well as testing ML methods for Austria, is shown. The methods are evaluated against both the NEWA data sets, the DANRA reanalysis, AROME operational, and AROME RUC data, was well as the ERA5 data. Furthermore, testing against open tall tower data such as the Cabauw tower or the FINO platforms is carried out.

How to cite: Schicker, I., Lexer, A., Tilg, A.-M., and Andre, K.: Extrapolation of wind speed for wind energy - how to (?) for high nacelle heights , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-672, https://doi.org/10.5194/ems2024-672, 2024.

Agri-photovoltaics (Agri-PV) represents a new strategy for maximising land use by integrating solar energy production into the agricultural operation. Greenhouses, which are essential to modern agriculture, provide a controlled environment that promotes plant growth, extends the growing season and protects crops from extreme weather conditions. The integration of photovoltaic (PV) systems into these greenhouse structures is a forward-thinking move toward sustainable and energy-efficient agricultural practises. The REGACE project, funded by Horizon Europe, aims to develop and validate a new technology, i.e. a responsive tracking system placed inside the greenhouse driven by a PLC controller that changes the angle of the tracking system according to the needs of the plants, while enabling the continuous production of renewable energy in greenhouses throughout the year, supporting uninterrupted food production without the constraints of energy availability.

In this progressive and dynamic context, we are advancing the development and testing of a Digital Twin (DT) ecosystem. This DT ecosystem simulates the energy production of PV greenhouse systems under specific internal conditions and external meteorological forcings. This paper presents our results from the use of Deep and Shallow Neural Networks (DSNN) to model the environmental conditions in these greenhouses. These networks improve our understanding of microclimatic variables and can lead to more general control strategies that optimise both crop yield and energy consumption.

Preliminary results indicate that Deep and Shallow Neural Networks (DSNN), like traditional complex physical models, can effectively model the internal conditions of greenhouses with relatively low error and strong correlationpredictive capability, but with less computational effort. These results were derived from data collected during the summer at one of the pilot sites in Greece. The DSNN skilfully captures the variability of internal thermodynamic parameters, which is crucial for the management of external environmental influences and internal systems such as cooling and shading.

The next steps aim to integrate the modelling of plant, water and energy balances within the greenhouse environment. This extended model will be further validated in the first pilot plant in Greece as well as at five other greenhouse sites involved in the project. This step is crucial for the development of integrated and adaptive control strategies that can improve both agricultural productivity and energy efficiency under different climatic conditions.

How to cite: Petitta, M., Seri, E., Bovesecchi, G., and Cornaro, C.: The Impact of Meteorological Forcings on Agri-Photovoltaic Systems: Advances in Greenhouse Energy Modeling, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-670, https://doi.org/10.5194/ems2024-670, 2024.

Surface solar irradiance short-term forecasts (up to 6h) are crucial for solar energy-related applications, including energy trading, grid management, and energy storage solutions.

Operational current forecasting methods for this time horizon are based on meteorological satellite imagery analysis. However, some atmospheric processes, such as the early stages of convection or cloud dissipation, cannot be considered if the forecast model depends solely on this type of observation. As a result, these processes are ignored, leading to significant forecast errors.

The objective of this study is to identify the NWP output variables that most significantly influence the evolution of satellite-observed cloud albedo at few hours scale. The findings are expected to facilitate the integration of these variables as ancillary inputs in a deep-learning-based forecasting model, which could improve the performance of intraday forecasts based on satellite imagery.

In this work, we explore the relationship between cloud albedo derived from the HRV channel of the Meteosat Second Generation’s (MSG) SEVIRI instrument and meteorological variables from the ERA5 reanalysis, specifically focusing on the Paris, France, area. A preliminary exploratory data analysis is conducted to highlight the specific context of the study, using descriptive statistics and visual representations.

Subsequently, we implement an XGBoost model to forecast cloud albedo, identifying the meteorological variables that most effectively contribute to the estimation under varying cloud regimes, meteorological situations and seasonal variations.

The findings of this study reveal significant variability in the predictability of cloud albedo when using certain NWP outputs, particularly under specific cloud regimes such as low clouds. This analysis has enabled us to select a restricted group of variables, including total column water and surface pressure, which were the most influential in forecasting cloud albedo, reducing the need for costly extensive experimentation with different variable configurations in a future deep learning forecasting framework.

How to cite: Chea, N., Cros, S., Tuomiranta, A., Badosa, J., Meddahi, A., Haeffelin, M., and Guillon, S.: Exploring the relationships between satellite-observed cloud albedo and ERA5 variables to improve data driven short-term irradiance forecast, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-699, https://doi.org/10.5194/ems2024-699, 2024.

As PV technology evolves, more sophisticated methods of solar irradiance forecasting and ex-post estimation are needed for PV power forecasting and monitoring purposes, especially regarding the different components ( Direct, Global and Diffuse) and their spectral distribution. To achieve an accurate prediction across the spectrum for each component, radiative transfer models (RTMs) are often used; however, they are based on a vertical characterization of the atmosphere, and particularly of cloud thickness, which  traditionally is retrieved as a fixed thickness values for each different cloud type (under the assumption that same-type clouds have the same thickness) or from NWP models.

In this field, a relatively new and quite promising technology that helps in better characterizing the clouds is the ceilometer, a ground-based lidar instrument designed for vertical profiling offering high-resolution assessments of cloud properties. RSE S.p.A. installed one such instrument (Vaisala CL61) in Milan (Italy) in June 2023. The goal of this early-stage study is to exploit the ceilometer’s cloud base measurements and satellite’s cloud top height estimations to train a machine learning model to derive the information on the vertical thickness. We aim to use  the same model to obtain experiment-based estimates of cloud thickness over the entire Italian domain, to be used in RSE’s choice RTM (libRadtran).

The starting dataset to train the model consists of:

• Backscatter and depolarization measurements from the Milan-based ceilometer, from which it is straightforward to obtain the cloud base height. The data has a 1-minute time frequency and a 5m vertical resolution over a 10-month period.
• Meteosat Second Generation satellite data, from which cloud position, cloud type and cloud top height have been inferred. The data has a 15-minute time frequency and a 4 km spatial resolution (parallax and data acquisition time have been accounted for).

From the dataset, it is straightforward to calculate the thickness of single-layer clouds as the difference between the cloud top (satellite) and cloud base (ceilometer). The cloud thickness has therefore been used as predictand in a random forest model (RF), using the cloud type and the cloud top height as predictors. Other meteorological variables can also be added as predictors.

The method’s accuracy has been tested indirectly by comparing the irradiance components calculated using libRadtran fed with the RF derived cloud thickness with respect to the experimental ones, registered in different Italian sites.

How to cite: D'Amico, F., Perona, D., Ronzio, D., Collino, E., and Bonanno, R.: Improving Satellite Cloud Thickness Characterization by means of Ceilometer Data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-850, https://doi.org/10.5194/ems2024-850, 2024.

The aim of SolaRes is to provide precise and accurate estimates of solar resource for any location on the globe, in any meteorological and ground surface conditions, and for any solar plant technology. To suit most applications, not only the Global Horizontal Irradiance (GHI) is computed at 1-minute time resolution, but also the direct normal irradiance (DNI), the Diffuse Horizontal Irradiance (DifHI) and the components in tilted planes with any orientation (GTI, DifTI). To make comparisons with ground-based measurements, the circumsolar contribution in measured DNI is also computed.

SolaRes was validated in clear-sky conditions encountered in northern France, affected by local and transported anthropogenic pollution and irregular incursions of Saharan desert dust to Europe. For the validation, AERONET provided the input spectral AOT data. Tests were also done with CAMS-NRT as input data source, and comparisons with measurements made on the ATmospheric Observations in LiLLe (ATOLL, France, 50.61167°N, 3.141670°E) platform provided RMSD in GHI smaller than 3% at 1-minute resolution, and RMSD in DNI of 8% [Elias et al., submitted to AMT].

In this work, the performances of SolaRes are evaluated in all-sky conditions encountered in northern France and in Germany. The Copernicus Atmosphere Monitoring Service in the near real time mode (CAMS-NRT) provides the input spectral aerosol optical thickness (AOT) data, and the Nowcasting Satellite Application Facilities (NWCSAF) provide the cloud optical thickness. The SolaRes estimates of the three solar resource components GHI, DNI, and DifHI are compared with measurements acquired at ATOLL. The SolaRes estimates of GHI and GTI are compared with measurements made by the PVlive network [Lorenz et al., 2022; and Dittmann et al., 2024 for the data]. Each PVlive station is equipped of a horizontal thermopile pyranometer, and 3 tilted silicon sensors, orientated eastwards, southwards and westwards.

RMSD in GHI is found to be 18% at the PVlive station of Freiburg for one year of data (2021) at 1-hour resolution. RMSD in GTI slightly increases to reach 20% at a tilt angle of 25° orientated southwards, and 21% at the same tilt angle but orientated westwards.

The satisfying comparison scores in GTI are obtained by considering a solar spectrum restricted between 300 and 1100 nm to simulate the Silicon detector. Improvement will be performed by considering the detailed spectral response as well as the angular loss.

SolaRes in its standard mode considers horizontal homogeneous cloud field. Performances could be improved by selecting such observed situations. Moreover, DifTI could be individually tested by selecting measurements in shadows occurring for example for the eastwards instrument when the sun sets down.

How to cite: Elias, T., Ferlay, N., Moulana, M., Saint-Drenan, Y.-M., Metzger, S., Feigel, G., Chesnoiu, G., and Chiapello, I.: Comparison between SolaRes estimates of the tilted solar irradiance and measurements by the PVlive network, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-924, https://doi.org/10.5194/ems2024-924, 2024.

With the worldwide increase of photovoltaic (PV) resources in the recent years, PV power forecasting becomes widely used by system operators in many contexts e.g. energy trading, smart grids, island grid operations or industrial off-grid power systems.

Forecasts providers generally have a good understanding of how to evaluate forecast accuracy, but low understanding of how the forecasts are used and generate value. It is the opposite for the forecasts user, which results in difficulties for the provider to generate high-value forecasts, and for the user to use them effectively.

Estimating the added-value of forecasts is thus a difficult task. Attempts have been made for market-related use-cases [1], [2]. However it is difficult to extend this work to power systems operation, since they have much more physical constraints and no quantitative price signal. In this work, we present a forecasting framework for operators of an hybrid PV/fuel off-grid industrial site, combined with a simulator that estimates the overall economical added-value of the system.

Based on discussions with the forecast users and understanding of their decision-making process, the framework is based on three elements. The first is a standard day-ahead forecast algorithm based on weather forecast. The second is a hour-ahead forecast that corrects the first based on measurements and satellite observations, which also takes into account live operational conditions of the PV plant : trackers malfunction, inverters faults, or curtailment. Finally, a state-of-the-art algorithm based on sky imagers that identifies incoming clouds in the next 30 minutes and the expected PV production drop.

The simulator takes the outputs of this framework and all measurements from the PV plant and simulates the system operation. Infering the added value of the system is helpful for all parties :

• The forecast supplier can promote his solution in a quantitative way to other potential customers,
• The forecast user can estimate the financial gain that came from using forecasts,
• The community can benefit from a reference point on how much value can be expected from forecasts in this use case, and progress towards increasing this value.

[1] David, M., Boland, J., Cirocco, L., Lauret, P., & Voyant, C. (2021). Value of deterministic day-ahead forecasts of PV generation in PV+ Storage operation for the Australian electricity market. Solar Energy, 224, 672-684.

[2] Alvarenga, R., Herbaux, H., & Linguet, L. (2023). On the Added Value of State-of-the-Art Probabilistic Forecasting Methods Applied to the Optimal Scheduling of a PV Power Plant with Batteries. Energies, 16(18), 6543.

How to cite: Carriere, T., Yezeguelian, L., De Gabaï, D., Rodriguez, B., Pitaval, S., and Blanc, P.: Assessing irradiance forecasts value for an hybrid PV/fuel industrial site , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-497, https://doi.org/10.5194/ems2024-497, 2024.

How to cite: Rouges, E., Shepherd, T., and Kretschmer, M.: On the influence of weather regimes on high shortfall days during winter for European countries, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-19, https://doi.org/10.5194/ems2024-19, 2024.

Evaluating surface solar irradiance (SSI) variability is important for irradiance models’ evaluation, resource assessment and forecasting applications in the solar energy field. In this work, SSI time series are classified in different sky conditions using one scheme based on direct normal irradiance (DNI) and another based on global horizontal irradiance (GHI) measurements, both at 1-minute resolution and for hourly classification. The classification follows the model proposed by Schroedter-Homscheidt et al. (2018) with four classes associated with clear sky and thin clouds and four classes related with thick clouds. The method is based on a visual interpretation of GHI and DNI measurement patterns for the Baseline Surface Radiation Network (BSRN) station of Carpentras during one year, which forms a reference database. The proposed scheme was reviewed for improvements in the reference database, a new normalization method for the variability indices, and the usefulness of the DNI-based variability indices are investigated for extension of the method to GHI-only data. Thirteen variability indices are applied in the classification including the clear sky index (k_c); the average, maximum and standard deviation of the absolute values for the first derivative of SSI and k_c; the variability indices proposed by Stein et al. (2012) and Coimbra et al. (2013); and variability indices based on the integrals of envelopes curves obtained according to the local maxima and minima time-series. The classification model is based on a statistical comparison between the median of the variability indices from the reference database and the median of the variability indices for the data being classified. The DNI-based classification results show an accuracy of up to 85% when applying the model in the reference database, which is one improvement compared with the previous method (accuracy of 77%). Preliminary results of the GHI-based classification show an accuracy of up to 60%. Improvements in the GHI classification method are expected, which includes an evaluation of the reference database classification for GHI time-series and additional variability indices, for example, in the case of cloud enhancement phenomenon.

References:

Schroedter-Homscheidt, M. et al., Classifying direct normal irradiance 1-minute temporal variability from spatial characteristics of geostationary satellite-based cloud observations. Meteorol. Z. 27, 2, 160–179, DOI:10.1127/metz/2018/0875, 2018.

Coimbra, C.F.M. et al., Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation. Kleissl, J. (Ed.): Solar Energy Forecasting and Resource Assessment. Oxford, 171–194, 2013.

Stein, J. S. et al., The Variability Index: A New and Novel Metric for Quantifying Irradiance and Pv Output Variability. World Renewable Energy Forum, WREF 2012, Including World Renewable Energy Congress XII and Colorado Renewable Energy Society (CRES) Annual Conference 4(May): 2764–70, 2012.

How to cite: Rodrigues de Miranda, D., Azam, F., Lezaca, J., and Schroedter-Homscheidt, M.: Ground-based classification method for direct normal and global horizontal irradiance, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-863, https://doi.org/10.5194/ems2024-863, 2024.

Fog or low stratus clouds can reduce solar power production significantly. A not forecasted fog event can lead to huge power misses, even at country scale and thus lead to high imbalance prices in energy trading. This presents a financial risk to the solar park owner, who bears the balancing responsibility in the European power markets. Reducing this risk makes solar investments financially more interesting and thus can help for the achievement of a climate neutral Europe. More accurate solar forecasts can help to achieve this goal, and for this an accurate historical data of fog occurrence at the park level is important. It is not straightforward to get this information for several reasons: Fog can be a very local phenomenon, and observations only rarely exist close to solar parks. Reanalysis data such as ERA5 do not have visibility information and numerical weather prediction models (NWP) often poorly predict fog, in part because their grid is too large to model fog formation.  

In this study, we will present a machine learning model to reconstruct fog history at solar park level. To build our model we use solar parks, which have weather observation in close vicinity. The weather observation will give us information about the true fog events and thus the target data, i.e. the data we want to predict. We use visibility and low cloud measurements from observation stations from the German weather operator DWD, Deutscher Wetterdienst. Fog was defined to include fog and mist (visibility < 5km) and to low clouds, which are lower than 2 km. To train the model, we will use reanalysis data from ECMWF (ERA5) and solar production. Part of the work was to find the best features to represent fog formation. We used data such as cloud cover, temperature (at surface and gradients) and humidity. Then we trained a classifier to predict the fog history at park level. This model showed good improvements of fog detection compared to NWP fog forecast. Our model achieved an accuracy of 94% and Brier score of 0.05, while the NWP fog forecast of the closest grid point to the solar park shows respective values of 86% and 0.17. This shows that our model was able to produce accurate historical fog data at the solar park level.

How to cite: Peinke, I. and Agard, V.: Machine learning approach to reconstruct fog history for solar parks, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-921, https://doi.org/10.5194/ems2024-921, 2024.

This study demonstrates a new regional PV estimation system in Germany using historical data from a network of around 100 meteorological stations. This is compared to the satellite or numerical weather data usually used in the same, variable-resolution physical model. The PV estimation with the ground stations performs better for Germany than satellite data, despite its far lower resolution, though the satellite data maintains the advantage for small, substation regions.

The solar prediction system (SPS) used in this study uses physical models to probabilistically simulate plants of many orientations and other properties at all points of a weather data grid and works modularly with various numerical weather predictions and satellite data. It takes a somewhat opposite approach of a typical upscaling, as instead of averaging several reference input values, it simulates nonlinear effects for all possible reference locations and aggregates only probabilistically at the end.

Here, we test something new with the SPS, treating the meteorological stations of the German Weather Service (DWD) like an irregular weather data grid that is comparably sparse, with the hypothesis that it may nevertheless have positive features due to the higher quality of its observations of global and diffuse horizontal irradiance. We evaluate the results against German PV meter data as well as via correlations to vertical load time series from TSO substations.

The results question the conception that machine learning outperform physical PV modelling, so long as the meteorological inputs are truly accurate. Closing this gap has practical consequences, as physical models otherwise hold several advantages, including far superior computational efficiency, e.g. for realizing digital twin models today, as well as for distinguishing between PV production, storage, and feed-in for grid operation, which empirical models based on reference plant feed-in cannot. In addition to improving PV estimation, the results thus motivate future research to improve irradiance modelling.

How to cite: Good, G.: Regional PV estimation based on a ground station network as a meteorological grid, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-916, https://doi.org/10.5194/ems2024-916, 2024.

The National Solar Radiation Database (NSRDB) provides global solar resource data at a high temporal and spatial resolution. This data is primarily used in solar energy modeling and is updated on a regular basis. The NSRDB uses a physical approach to satellite-based solar modeling. The underlying Physical Solar Model (PSM) computes cloud-properties using satellite remote sensing and subsequently solar radiation using radiative transfer models. The retrieved cloud properties include cloud-mask, cloud-type, cloud optical depth and cloud droplet size. The radiative transfer models require additional input parameters such as aerosol optical properties (AOD), preciptable water vapor, surface albedo, temperature and pressure to accurately model solar radiation. While cloud properties are obtained directly from the geostationary satellites other inputs are obtained from additional source such as the National Aeronautical and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications version 2 (MERRA2), the Interactive Multisensor Snow and Ice Mapping System (IMS) model data from the U.S. National Ice Center and NASA’s polar orbiting satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Aqua and Terra Platform.

 In 2022 the NSRDB was updated to include improved surface albedo and gap-filling of cloud properties. Further, significant new updates have been included in 2023. This includes the use of the new FARMS DNI model under cloudy sky situations which results in a more accurate decomposition of the GHI in direct and diffuse. With the expansion of the NSRDB to provide data from the region covered by Meteosat, the coverage is fully global at this point.

While standard data from the GOES continues to be served at an hourly 4km x 4km resolution, full resolution data has also been made available to the user. The user is provided significant flexibility for downloading data depending on the amount of data required. Data can be downloaded using either the web-interface, an Application Programming Interface or directly from the cloud using Amazon Web Services. Services such as spectral data use on-demand computation and delivery.

Evaluation of the NSRDB was conducted for 18 stations and the Mean Bias Error (MBE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were computed for both GHI and DNI. The evaluation was conducted for the 1998-2023 period. Generally, the MBE lies within plus or minus ±5% for GHI and ±7% for DNI. The RMSE is less than 25% for GHI and 35% for DNI. 

There are additional plans to include cloud fraction in cloudy sky situation to improve the accuracy of the NSRDB. This presentation will provide users with the latest information about the NSRDB as well as plans for future development and updates.

How to cite: Sengupta, M., Habte, A., Benton, B., Xie, Y., Buster, G., and Foster, M.: Improving the Accuracy and Coverage of the NSRDB, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-972, https://doi.org/10.5194/ems2024-972, 2024.

Objective and Background

Ensemble weather forecasts have been promoted by meteorologists for use due to their inherent capability of quantifying forecast uncertainty. Despite this advantage over deterministic forecasts, their application is still limited since many processes to manage power systems are not ready to deal with uncertain information. The probabilistic power forecast evaluation tool ProPower has been developed at DLR to demonstrate possible applications of probabilistic forecasts in power systems. Furthermore, ProPower is used to assess the value of probabilistic forecasts for PV and wind power systems compared to the usage of deterministic forecasts, but also to compare the value of different probabilistic forecasts. This includes post-processing of ensemble forecast, e.g. calibration.

Method

Usual approaches to derive the cost-optimal power dispatch within a market zone considering power constraints (e.g. grid capacities, ramp rates) do not account for potential balancing costs arising from errors in wind and solar forecasts. Following [1] DLR has designed a stochastic market clearing model. In this model, expected balancing costs are estimated from a set of scenarios of renewables feed-in that are equivalent to ensemble members. Lately, a second market clearing based on updated forecasts of higher skills has been implemented in ProPower and is thoroughly tested. Currently, we use ECMWF ensemble forecasts [2] for the day-ahead market clearing and the intraday market clearing. However, in the research project WindRamp the benefit of shortest-term Lidar forecasts [3] of an offshore wind farm is tested in a sample power system. In this context the Lidar forecasts got calibrated with the EMOS method suggested by Thorarinsdottir, T., and T. Gneiting [2010].

Principal Findings

We found a positive impact of stochastic market clearing to reduce total power system compared to the deterministic market clearing. The use of Lidar forecasts as forecast updates in an intraday market is beneficial compared with NWP forecasts. Persistence forecasts (+15 min) can be outperformed in unstable atmospheric conditions.

Conclusion

The ProPower tool is capable to translate probabilistic forecast skill into benefits for sample power systems. ProPower has the potential to analyze which forecasts errors are most expensive to balance and how valuable skillful uncertainty information from different sources (e.g. Lidar shortest-term forecast) is.

References

[1] Morales, J.M., Zugno, M., Pineda, S., and Pinson, P. (2014): Electricity Market Clearing with Improved Scheduling of Stochastic Production, European Journal of Operational Research

[2] Leutbecher, M., and Palmer, T.N. (2007): Ensemble forecasting

[3] Theuer, F., Rott, A., Schneemann, J., von Bremen, L., and Kühn, M.: Observer-based power forecast of individual and aggregated offshore wind turbines, Wind Energy Science

[4] Thorarinsdottir, T., and T. Gneiting, 2010: Probabilistic forecasts of wind speed: Ensemble model output statistics by using heteroscedastic censored regression. J. Roy. Stat. Soc.

How to cite: von Bremen, L., Bents, H., and Schyska, B.: ProPower: A new tool to assess the value of probabilistic forecasts in power systems management, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-904, https://doi.org/10.5194/ems2024-904, 2024.

The transition from a fossil fuel-based to a renewable-based energy system has become a reality in recent years. As the pace of climate change accelerates, the need for decarbonisation has provided the necessary momentum for the expansion of the renewable energy sector, heavily driven by the growth in wind and solar energy production (IEA, 2024). At the same time, the transition towards forms of renewable energy with highly variable production entails new challenges for the energy system, potentially endangering supply security and grid stability (Johnson et al., 2020). Existing tools and datasets often overlook the impact of both climate and climate change on renewable resources, particularly on wind energy, which is highly sensitive to internal variability and extreme weather events (Pryor & Barthelmie, 2010). These knowledge gaps require new tools, including climate information from high-resolution global climate models (GCMs), which can accurately estimate spatiotemporal changes in wind resources (e.g., mean state, frequency of extreme events) under current and future climate conditions.

In this work, we aim to show how high-frequency (i.e., hourly) climate data from km-scale GCMs (Rackow et al., 2024), in contrast to state-of-the-art models (e.g., CMIP, CORDEX), can be transformed into regional and local climate information tailored towards the needs of the wind energy sector (e.g., capacity factor and energy production estimates, long-term changes in wind speed distributions, frequency of high and low wind events, heating and cooling degree days), aiding stakeholders in their decision-making process. The unprecedented volumes of data generated by these high-resolution projections pose a challenge to traditional storage methods. Data streaming offers an adequate solution to this challenge by deriving statistical summaries of climate data as the model progresses (Grayson et al., 2024). The implementation of a streaming environment allows to estimate relevant user-tailored indicators, as well as other types of climate information, without the need to permanently store the complete model output. By directly simulating wind components at turbine hub height, removing the need for vertical interpolation, and through enhanced horizontal resolution and increased temporal frequency, high-resolution GCMs represent a step forward in assisting adaptation measures against the impacts of climate change. 

IEA (2024), Renewables 2023, IEA, Paris https://www.iea.org/reports/renewables-2023, Licence: CC BY 4.0

Johnson, S. C., et al. (2020). Understanding the impact of non-synchronous wind and solar generation on grid stability and identifying mitigation pathways. Applied Energy, 262(January), 114492. https://doi.org/10.1016/j.apenergy.2020.114492

Pryor, S. C., & Barthelmie, R. J. (2010). Climate change impacts on wind energy: A review. Renewable and Sustainable Energy Reviews, 14(1), 430–437. https://doi.org/10.1016/j.rser.2009.07.028

Rackow, T., et al.: Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5/NEMOv3.4, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-913 , 2024.

Grayson, K., et al.: Statistical summaries for streamed data from climate simulations. Geoscientific Model Development (submitted), 2024

How to cite: Lacima-Nadolnik, A., Grayson, K., Versteeg, G., Roura-Adserias, F., Soret, A., and Doblas-Reyes, F. J.: Streamed climate information from high-resolution global climate models for the renewable energy sector, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-563, https://doi.org/10.5194/ems2024-563, 2024.

Solar power/PV installations pose challenges to traditional and AI forecasting technologies. Major difficulties in forecasting PV – particularly aiming to simulate production peaks in high spatial and temporal resolution – include being presented with poorly resolved or accumulated data (e.g.: grid-level data only, no exact location referenced), missing specifications within installations, rapidly changing environments or installation setup, and a high diversity among sites that feed into the power grid. Non-shareable, not continuously recorded or fragmented data-sets can be another obstacle, leading to reduced data-sets. Particularly, AI powered forecasts rely on sufficient, consistent historic data and may fail or underperform under these circumstances. To still provide reliable PV location forecasts tailored approaches are needed. Within this study, we work on tailored machine learning based solutions addressing challenged reduced data locations. We evaluate the effect of impaired data on AI based approaches to investigate the benefit of a data driven approach within an Austrian case study. We follow a multi-step machine learning approach, including the generation of semi-synthetic data, data transformation, and feature selection by exploiting a set of spatial and temporal strongly associated inputs from non-reduced auxiliary data. Data sources used are for instance, satellite data products (e.g.: CAMS by Copernicus) and reanalysis fields (e.g.: ERA-5 reanalysis by ECMWF), PV production records, and high-resolution numerical models (e.g.: AROME). We show the added value of combining different data sources within a post-processing model. Further considered inputs are, for instance, climatology of satellite data and reanalysis, pvlib’s estimation, AROME surface parameter simulations, and inhouse nowcasting models (e.g.: IrradPhyD-Net). This way, we are able to address PV forecasts of trafo level only data and challenged locations.  

How to cite: Papazek, P., Gfäller, P., and Schicker, I.: Reduced Data PV Forecasting Challenges: An Austrian Casestudy  , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-588, https://doi.org/10.5194/ems2024-588, 2024.

Accurate precipitation estimation is crucial for various meteorological and climatological applications, including renewable energy generation. The NASA Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product provides precipitation data derived from microwave and infrared satellite sensors. In this study, we evaluate the performance of two successive versions, V6 and V7, of the IMERG Final Precipitation Satellite Product. 

The evaluation of IMERG V6 and V7 products involves comparing rainfall time series of 6 years (2015-2020) against rain gauge measurements from 28 weather stations in complex terrain in Navarra, Spain. We assess various statistical metrics such as correlation coefficient, bias, root mean square error, and probability of detection. 

We hypothesize that the V7 product displays improvements from the V6, particularly in terms of accuracy. The objective is to assess if the V7 product demonstrates enhanced performance in capturing precipitation events in a region with complex terrain, such as Navarra. We aim to provide evaluation results and valuable insights for users relying on IMERG precipitation data for hydrological, meteorological, and climate studies.  

We also investigate the utility of IMERG precipitation products in the context of wind energy applications. Precipitation, including rain and hail, can impact the structural integrity and performance of wind turbines over time. Surface erosion of wind turbine blades is caused by heavy precipitation and strong winds and represents a major challenge in the wind energy industry. Therefore, we focus on predicting the lifetime of wind turbine blades in the 28 stations by integrating IMERG precipitation in an erosion onset prediction model along with New European Wind Atlas (NEWA) wind speeds. Previous studies that have used IMERG V6 in a blade lifetime prediction model have shown that IMERG V6 is sufficient to predict erosion onset time in blades in selected European sites, but it requires further calibration and adjustments since it tends to overestimate orographic rainfall. We hereby explore any improvements in the prediction of erosion onset time by incorporating the newly published IMERG V7 product. Finally, we aim to highlight the usefulness of satellite data in monitoring leading-edge erosion in wind turbine blades.

The proposed approach holds promise for improving the reliability and efficiency of wind turbines. The knowledge of erosion onset time in blades can optimize maintenance schedules, reduce downtime, and enhance the overall operational performance of wind farms. Our findings may offer valuable implications for the renewable energy sector and precipitation monitoring.

This work is supported by the AIRE project, which has received funding from the European Union under the grant agreement 101083716.

How to cite: Dimitriadou, K., Hannesdóttir, Á., Cantero Nouqueret, E., and Hasager, C. B.: Evaluation of IMERG V6 and V7 satellite precipitation and their application on lifetime prediction of wind turbine blades , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-524, https://doi.org/10.5194/ems2024-524, 2024.

As part of the EU-funded CAMS (Copernicus Atmospheric Monitoring Services) project, the CAMS Radiation Service (CRS) provides estimates of direct and global downwelling solar irradiance (SSI). The SSI estimate uses ozone, water vapor and aerosols from CAMS and cloud properties assessed with the APOLLO_NG algorithm. In parallel to the continuous improvement of the SSI estimation method, the regular evaluation of solar radiation estimates is an important component of the CRS activities.

CRS evaluation is generally carried out using high-quality three-component (GHI, DHI, DNI) measurements as a reference, such as the ones provided by the stations of the Baseline Surface Radiation Network (BSRN). The advantage of such stations is that the maintenance, the availability and the strict quality control (QC) procedures of the three redundant components allow reaching a high level of confidence in the data. However, such stations are sparsely spread, which limits our understanding of the spatiotemporal error structure of CRS.

In this work, we tested the use of a dense meteorological network of pyranometers as a complement to above-mentioned pyranometric stations. The potential of 1-minute GHI measurements from 250 meteorological stations operated by Météo-France and 40 stations from the German PV-Live network for assessing CRS was tested based on an overall 8-year timespan of GHI measurements.

Because of the large number of stations, pyranometer maintenance is not as systematic as recommended by e.g. the BSRN. This means that QC must be carefully and specifically carried out to check the plausibility of the measurements before they are used in the spatio-temporal evaluation. Unfortunately, only GHI is measured, so it is not possible to apply quality control tests involving several redundant components, with consistency checks. To overcome this limitation, we propose several tests to verify, for example, radiometric calibration, time reference and instrument leveling.

Due to the limited QC based on GHI, the possibility of faulty measurements must be considered in the evaluation. Here, the uncertainty over data quality can be partially compensated by the high density of stations, with statistical consistency check in the domain of the spatio-temporal variability. If we consider that the measurements are independent, we consider that information on CRS is plausible if there is a consensus between the different stations located in a close vicinity. On the other hand, erroneous measurements appear as outliers in relation to the other nearby stations, if applicable, depending on the local distribution of the stations and the local orography. Using this strategy, we were able to provide, to some extent, an initial indication of a spatial structure in the CRS error. These results are presented, along with their potential sources (clear-sky modeling, cloud modification factors, cloud coverage, solar zenith angle, parallax, etc.) and the potential CRS improvements identified to address them.

How to cite: Saint-Drenan, Y.-M., Lezaca, J., Schroedter-Homscheidt, M., Blanc, P., and Elias, T.: Spatial evaluation of CAMS Radiation service using dense pyranometric networks , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-840, https://doi.org/10.5194/ems2024-840, 2024.

Germany is to be climate-neutral by 2045, with important interim targets set for 2030. This includes a 65% reduction in greenhouse gas emissions compared to 1990 and a significant increase in the proportion of renewable energies in the electricity mix to at least 80% by 2030. This share currently stands at around 56%. A key element of this strategy is the Renewable Energy Sources Act (EEG 2023), which provides for an accelerated expansion of renewable energies. This includes wind and solar energy, with particularly ambitious targets for offshore wind energy: at least 30 gigawatts by 2030, 40 gigawatts by 2035 and 70 gigawatts by 2045. 

This strong expansion of offshore wind energy brings with it several challenges. The biggest is certainly the space required in the German Bight and in the Baltic Sea. Studies are already showing long wake lengths of wind farms and wind farm clusters, which can reach wind farms located downstream and thus reduce their energy production. With the increasing expansion of wind energy in the German Bight, the influence of wind farms on each other will increase and lead to greater power losses. This influence must be considered in future planning. The models often used by industry are so-called engineering models. They have low computational costs due to their fast calculations but are made for power yield calculations within a wind farm and not for modelling the wake in the far field. An approach was taken to use different setups of this model to evaluate the results against measurement flights of a research aircraft in the lee of large wind farm clusters in the German Bight. As part of the X-Wakes project, the wakes of wind farm clusters were measured and evaluated under different meteorological conditions using the D-IBUF research aircraft operated by the Technische University of Braunschweig. This data was then used to evaluate the performance of the Weather Research and Forecasting Model with Wind Farm Parameterization (WRF-WF) and the engineering model "FOXES" (Farm Optimization and eXtended yield Evaluation Software) with four different setups.
 

How to cite: zum Berge, K., Centurelli, G., Dörenkämper, M., Bange, J., and Platis, A.: Assessment of Engineering Models for Large-Scale Cluster Wakes Using In Situ Airborne Measurements, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-844, https://doi.org/10.5194/ems2024-844, 2024.

With the completion of the first Belgian offshore wind energy zone in 2020, for an installed capacity of 2.26 GW, a significant amount of wind energy is now available in the Belgian part of the North Sea. Due to the relative lack of space in this area, all wind farms lie close together in a narrow band, and each wind farm has a high density, in terms of number of turbines, and/or installed power per area. There are thus considerable wake losses in the Belgian offshore zone. Moreover, in case of a major storm, many wind farms might experience a so called cut-out event, with automatic shut-down of the turbines due to high mean wind speed, at practically the same time. Since this can lead to large imbalance risks on the electricity grid, the Royal Meteorological Institute of Belgium (RMI) has developed a dedicated storm forecast tool for Elia, the Belgian transmission system operator for high-voltage electricity. This storm forecast tool, which has been operational since November 2018, consists of 15 minute wind and power forecasts per wind farm, together with cut-out probabilities and uncertainty quantification, by combining our high-resolution (4km) ALARO model with the ENS ensemble forecasts of the European Centre for Medium Range Weather Forecasting (ECMWF). We report on several approaches to improve the offshore wind power forecasts, as part of the BeFORECAST project (Nov 2022 - Oct 2025), funded by the Energy Transition Fund of the Belgian federal government. In particular, to take into account wake losses, the Fitch et al. wind farm parameterization (WFP) was implemented in our ALARO model, based on an earlier implementation by KNMI into HARMONIE-AROME. Both these models are being developed in the ACCORD consortium, and use the same dynamical core to some extent, with IFS/ARPEGE global codes as basis, but differ greatly in the different physics parameterizations used, and the physics-dynamics coupling (tendencies vs fluxes). For instance, unlike AROME and HARMONIE-AROME, the ALARO model uses an explicit deep convection scheme (3MT) and turbulence is based on the TOUCANS framework. Verification of the improved wind and power forecasts is based on several lidars at different locations, and power data per wind farm from Elia, possibly supplemented with SCADA data from wind farms where available. Other approaches we study are multivariate statistical postprocessing based on historical wind speed observations to generate corrected wind speed scenarios, and postprocessing of forecasts using wake models (since we cannot implement a WFP in the ENS ensemble). Finally, an alternative power forecasting method, using an artificial neural network trained on power observations and NWP forecasts is also looked at. Special consideration is given to wind storms and fast ramping events.

How to cite: Smet, G., Van den Bleeken, D., Van den Bergh, J., Dehmous, I., Degrauwe, D., Van Ginderachter, M., and Deckmyn, A.: Improving wind power forecasts in the Belgian North Sea, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-872, https://doi.org/10.5194/ems2024-872, 2024.

Accurately accounting for Low-level jets (LLJs) in wind resource assessment is increasingly important as the height of wind turbines continues to grow. During LLJ events, wind speeds increase, leading to a general increase in power output. However, the vertical wind shear and veer associated with LLJs also impact the performance and reliability of wind turbines. Atmospheric conditions, conductive of the LLJs may also modify the wake dissipation properties in large offshore wind farms, depending on the LLJ height relative to the height of the wind farm's rotors. This study aims to optimize the configuration of the Weather Research and Forecasting (WRF) model to represent LLJs around the North and Baltic Seas at heights relevant to wind energy production. Using the optimal WRF model configuration, we derive a detailed long-term LLJ climatology focusing on wind energy implications.

We utilize wind measurements from LiDARs and a mast for five sites to assess the quality of the WRF model simulations for LLJ characterization. We also investigate the benefits of WRF simulations compared to the widely used ERA5 re-analysis. In the WRF model simulations, we vary the grid spacing, vertical resolution, and the planetary boundary layer scheme and land surface models, parameters we deemed most likely to have a substantial impact. The model’s performance was evaluated based on its ability to replicate observed distributions of LLJs and relevant associated characteristics, such as the shear and veer across the rotor-plane of typical large offshore wind turbines (30-300 meters). 

Our results show a strong dependency of the LLJ representation and the associated wind profiles on WRF model configuration and that relying on ERA5 for LLJ characterization is insufficient. For example, the LLJ rate-of-occurrence varied by up to a factor of 3 and more between some WRF model runs. The optimized model more accurately reflects the frequency, intensity, and vertical extension of LLJs, as confirmed by LiDAR data. Subsequent application of this configuration to a multi-year climatology provides new insights into the region's temporal patterns and potential wind energy impacts of LLJs.

How to cite: Olsen, B. T., Alonso-de-Linaje, N. G., Hahmann, A. N., and Žagar, M.: Simulated Low-Level Jets in the North and Baltic Seas: Sensitivity Analysis and Climatology, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-937, https://doi.org/10.5194/ems2024-937, 2024.

The crucial role of wind power in the future energy generation demands to investigate the influence of climate change on wind speed and the associated consequences for wind power. Especially in areas with complex terrain, there are still open research needs for wind energy in future climate (Clifton et al., 2022). To assess future wind climate, one requirement is the availability of a climatological dataset of wind speed with high spatial and temporal resolution to downscale wind speed from global and regional climate models (GCMs, RCMs) to a local scale. So far, no such dataset has been available for Austria. To fill this gap, a wind speed atlas for ideally the past 30 years (1991 – 2020) is compiled for the diverse landscape of Austria, having flat and mountainous terrain, using a set of artificial intelligence (AI) methods.

A comparison of gridded wind speed data considering three baseline methodologies will be presented: The ‘Integrated Nowcasting through Comprehensive Analysis’ (INCA) model (Haiden et al., 2011) combining ERA5 and station observations, a generalized additive regression model (GAM) and a deep neural network (DNN) model with adapted loss function. All approaches are used to create a wind speed analysis for the years 1991 to 2020 with a spatial resolution of 1 km to 1 km for the territory of Austria. Furthermore, they consider the same station-observation dataset of 10-m wind speed measurements of the national weather service in Austria. Last year’s shown first results of the GAM and DNN baseline were promising. The uncertainty in interpolation given through the methodologies is, so far, within the expected range. This year, preliminary final results of the gridding and validation of the different methods used will be presented.

The availability of a wind atlas for Austria allows downscaling of climate projections and thereby the investigation of the climate change impact on future wind speeds and future wind power potential in Austria.

References:

Clifton, A., Barber, S., Stökl, A., Frank, H., and Karlsson, T.: Research challenges and needs for the deployment of wind energy in hilly and mountainous regions, Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, 2022.

Haiden T, Kann A, Wittmann C, Pistotnik G, Bica B, Gruber C. 2011. The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region. Weather and Forecasting, 26/2, 166-183, doi: 10.1175/2010WAF2222451.1

How to cite: Tilg, A.-M., Schicker, I., Lexer, A., and Andre, K.: Wind speed maps for Austria: An artificial-intelligence approach part 2, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-747, https://doi.org/10.5194/ems2024-747, 2024.

To support evidence-based decision-making, energy system modelers in research, administration and industry rely on the best-available meteorological and/or climatological information on a variety of scales, from the local distribution grid level to the European transmission system, and from short-term forecasts for the operation to climate scenarios for making investment decisions. However, there is a lack of knowledge among energy system modellers about available meteorological data sets, their characteristics and the implications of using different data sets for grid planning and adequacy assessment activities. Standardised tools and methods to add the analysis of climate change and/or climate uncertainty to user workflows or to conduct tailored bias correction, validation or downscaling tasks rarely exist. The energy systems modelling and climate modelling communities can be considered at least partially disconnected. This observed disconnect hinders energy system modellers in fully making use of the available meteorological information, prevents them from tapping the full potential of the data and, consequently, potentially leads to sub-optimal or inefficient decisions.

In this presentation, we introduce approaches for deriving relevant information for grid planning and adequacy assessment from climate simulations to support overcoming the current disconnect between the two disciplines. In particular, we show how results obtained from energy system simulations can be linked to certain characteristics of the meteorological input, e.g. the occurrence of extreme events or prevailing weather regimes, through running and evaluating a semi-operational energy system modeling workflow for a great number of weather scenarios. Furthermore, using insights gained from this analysis we derive a criticality measure for certain weather situations and show how criticality can be used to group weather years limiting thereby the  weather scenarios required for adequacy assessment and/or grid planning purposes. For this study, we use meteorological data from the new Pan-European Climatic Data Base (PECD version 4.1) and, as one of the first studies, climate projections and historical simulations from the Destination Earth Digital Twin for Climate Adaptation.

How to cite: Schyska, B., von Bremen, L., Witte, F., Zech, M., Medjroubi, W., and Schroedter-Homscheidt, M.: Tools and data for planning and operating a climate-proof European power system, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-971, https://doi.org/10.5194/ems2024-971, 2024.

Due to various factors such as urban construction development and excessive energy usage, the urban heat island effect causes the air temperature in urban centers to be higher than in surrounding suburban areas. The causes of the urban heat island effect are numerous, including the thermal environmental impact caused by heat emissions from air conditioning energy, which is a concern for countries around the world. This study focuses on the built-up areas of Taipei City and New Taipei City in Taiwan and proposes solutions based on previous research literature. The approach involves using machine learning methods to identify high-energy usage areas caused by air conditioning and their corresponding temperature differences to formulate strategies for mitigation.

The study uses the National Science and Technology Center for Disaster Reduction and the High Resolution Atmospheric Model (HiRAM) to conduct global climate simulation under the RCP8.5 warming scenario. Through dynamic downscaling using WRF, the data was scaled down to a 5km resolution for the Taiwan region. The climate data produced was then used in Energy Plus to simulate residential air conditioning cooling power consumption and estimate Energy Usage Intensity (EUI). Additionally, cooling demand was controlled in areas with higher EUI, and a neural network prediction model was utilized to estimate air conditioning cooling demand in various heat zones.

The study aims to implement control strategies using the public sector's Building Energy Management System, such as controlling air conditioning usage during peak periods through load shedding to keep air temperature rise within a set threshold. This approach not only reduces energy consumption but also effectively reduces air conditioning heat emissions, thereby enhancing thermal comfort in urban environments. Based on the research framework, the results show that under the HiRAM scenario of a 2°C temperature rise, building air conditioning EUI is expected to increase by 15-20%. The accumulated air conditioning heat emissions become one of the major factors in air temperature rise. Furthermore, simulation verification using neural networks and the implementation of energy strategies demonstrate that effective control of EUI thresholds can lower regional air temperatures by approximately 0.2 to 0.5°C.

Keywords: Urban heat island, High resolution atmospheric model, Energy plus, Demand control, Machine learning

How to cite: Wang, S. Y., Cheng, C.-Y., and Lin, T.-P.: Climate Change Energy Data Simulation Scenarios: Urban Thermal Environment Improvement through Demand Control Strategy, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-393, https://doi.org/10.5194/ems2024-393, 2024.

Atmospheric large-eddy simulation (LES), a computational fluid-dynamics technique that resolves turbulence in the atmospheric boundary layer, is increasingly used for wind resource assessment (WRA), by including wind turbine parametrizations and using external weather data as initial- and boundary conditions. The large computational costs of doing such a 'real-weather' LES, however, limits length of the simulation to < 1 year; whereas long-term, multi-year, mean power production values are of high interest to many parties in the wind energy sector. To address this need, this work presents several methods to estimate long-term mean power production/annual energy production and wind from a < 1 year LES run, by applying Bayes' theorem on short-term LES output and long-term ERA5 reanalysis data.
A 10 year LES run of a hypothetical large offshore wind farm is performed in order to validate these 'long-term correction' methods, in three scenarios of increasing complexity. First, long-term correction of 365 consecutive days gives estimates of long-term mean power with a mean absolute error of 0.35 %, and 95th percentile of the absolute error within 0.8 % of the long-term mean, reducing the uncertainty by an order or magnitude. Second, in the scenario when the simulation period is not fixed, using several simple day selection techniques to select the simulation period can reduce the error further. Then, only around 200 days are needed to arrive at the same error values. The results indicate that long-term correction is insensitive to the particulars of the day selection methods, but that including a diverse set of days from different years and seasons is essential. Third, a method to also include wind observations in the long-term correction is presented and tested. This requires an additional 'free stream' LES run without active turbines, and gives estimates of long-term power and wind that are corrected for a potential LES bias. Although validation of this final approach is difficult in the employed modelling strategy, it gives valuable insights, and fits within the common WRA practice of combining models and observations.
The presented techniques are based on physical arguments, computationally cheap, and simple to implement; and as such could be a useful extension to the diverse set of modelling, observational, and statistical techniques used in WRA.

How to cite: Postema, B., Verzijlbergh, R., van Dorp, P., Baas, P., and Jonker, H.: Estimating Long-Term Annual Energy Production of a Large Offshore Wind Farm from Short Large-Eddy Simulations: Methods and Validation with a 10-year LES Run, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-430, https://doi.org/10.5194/ems2024-430, 2024.

Scanning lidars are a powerful tool to accurately sense wind conditions over large distances up to about 10 kilometers. In offshore wind energy applications this ensures comprehensive coverage of atmospheric dynamics in and around wind farms. This contribution presents measurement results of a dual-Doppler scanning lidar campaign conducted from March to November 2023 in and around an operating wind farm in the German Bight and compares them to engineering wake models.

Two scanning lidars were positioned on transition pieces of two different turbines and were used for measuring the undisturbed inflow as well as a spatially highly resolved grid for the wakes behind the wind farm at hub height. Central to the campaign’s success was an in-house developed scanning lidar software, enabling customized scan patterns and a spatially and temporally synchronized steering of the laser beams. An innovative feature of this software is the adaptive campaign steering, which adjusts the measurement layout automatically based on the prevailing wind direction. This ensured that the wake could be consistently measured behind the wind farm, utilizing two synchronized scanning lidar devices in dual-Doppler mode to capture the spatiotemporal evolution of wake characteristics. Inside the park dual-Doppler PPI-scans showed to be an effective tool in capturing the spatiotemporal characteristics of wakes within the wind farm. Additionally, a detailed uncertainty assessment regarding the movement of the scanning lidars on the transition pieces through wind and wave induced turbine tilt was performed and a correction method was developed.

Regarding the wake extent at westerly winds, the mean wind speed deficits show the expected tendencies to decrease with distance from the windfarm and to increase with wind turbine density per area, when multiple turbines being positioned behind each other in flow direction. In the comparative analysis of wake models conducted with PyWake, utilizing the TurbOPark model a consistent overestimation of wake deficits at the grid points measured by the dual-Doppler scanning lidar is visible, while the Jensen model underestimates the deficits at those locations. This discrepancy highlights the need for further refinement of wake models to better match the lidar-measured data.

The findings offer promising insights into the optimization of offshore wind farm operation and showcase the potential of dual-Doppler scanning lidar systems in offshore wind energy applications.

How to cite: Hildebrand, T., Krasnov, A., Jäger, F., Callies, D., Schmitt, C., Riechert, J., and Pauscher, L.: Dual-Doppler scanning lidar measurements of wakes in an offshore wind farm, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1021, https://doi.org/10.5194/ems2024-1021, 2024.