• Wind and turbulence profiles with respect to wind energy applications (measurements and theory) including wakes within a wind farm;
• Clouds and aerosol properties with respect to solar energy applications (measurements and theory);
• Marine renewable energy (wind, wave, tidal, marine current, osmotic, thermal);
• Meteorology and biomass for energy;
• Impact of wind and solar energy farms and biomass crops on local, regional and global meteorology;
• The use of numerical models and remote sensing (ground based and from satellites) for renewable energy assessment studies;
• Research on nowcasting, short term forecasts (minutes to day) and ensemble forecasts and its application in the energy sector;
• Quantification of the variability of renewable resources in space and time and its integration into power systems;
• Impacts of long term climate change and variability on power systems (e.g., changes in renewable resources or demand characteristics);
• Practical experience using meteorological information in energy related applications.
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 forms a major pillar of climate change mitigation. Yet, the inherent volatility of solar irradiance as a primary renewable energy source poses significant challenges. The stochastic dynamics of clouds induce rapid fluctuations in photovoltaic energy production by modulating incoming solar irradiance, which is a challenge for grid operators. More accurate solar irradiance forecasts encompassing the grid area and associated photovoltaic plants will help address this challenge.
We will provide an overview of regional forecasting methods for surface solar irradiance (SSI) using satellite observations for forecast lead times of minutes to hours. Existing solar forecast models are based on Level-2 products that comprise satellite-derived estimates of SSI or clear-sky index (e.g., Carpentieri et al., 2023) such as the third edition of the Surface Solar Radiation Data Set - Heliosat (SARAH-3; Pfeifroth et al., 2021).
We present a probabilistic regional solar forecast model for lead times of up to several hours that has been trained directly on visible and infrared satellite radiances. We utilise the multispectral Level-1.5 images of the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) imager aboard the Meteosat Second Generation geostationary satellite to this end. Our solar forecast model is based on the architecture of the generative SHADECast model (Carpentieri et al., 2024). Leveraging multiple years of SEVIRI scans, we demonstrate accurate probabilistic forecast of spatially extended surface solar irradiance fields and forecast model runtimes reduced to the order of seconds to minutes. We chacterise the model performance including benchmarks with existing solar forecast models and discuss the Meteosat channel importance for solar forecasting.
Carpentieri, A., S. Pulkkinen, D. Nerini, D. Folini, M. Wild, A. Meyer, Intraday probabilistic forecasts of surface solar radiation with cloud scale-dependent autoregressive advection, Applied Energy, 351, 2023. 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: Carpentieri, A. and Meyer, A.: Forecasting global radiation directly from Meteosat SEVIRI spectral observations , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-294, https://doi.org/10.5194/ems2024-294, 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.
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.
We provide here insight into the time variability of the surface solar irradiance (SSI) and of its characteristics obtained from an analysis of ground-based measurements from a western Europe location with both a high cloudiness and a highly variable load in aerosols. We detail here the application of automatic filtering methods on 1 min resolution SSI measurements that lead to the distinction of clear-sky, clear-sun-with-cloud, and cloudy-sun situations. Coincident photometric measurements of aerosol properties and radiative transfer simulations provide the mean to conduct a multivariate analysis of some SSI observed trends and year-to-year evolutions, and to estimate aerosol and cloud forcings under clear-sun conditions. The analysis focuses in particular on clear-sun-with-cloud situations that are often associated with positive cloud enhancement effects. On monthly-average 13 % more global horizontal irradiance and 10 % additional diffuse proportion are encountered in clear-sun-with-cloud situations compared with clear-sky situations, setting the amount of solar irradiance at the remarkable level of pristine (aerosol and cloud free) conditions but with a proportion of diffuse component multiplied by 2.5. With a synergy of observations (pyrheliometer and lidar) that identify cloud covers, we analyze the statistical characteristics of the surface solar irradiance per cloud cover category and how they vary with the width of a temporal averaging window. Results show that positive cloud radiative forcing are more frequent and higher for Cumulus situations compared with Cirrus one, but attenuates faster with time. Our results address the capacity of atmospheric modelling and of satellite-based surface radiation data set to generally not yet represent these important details in SSI variability’s features.
How to cite: Ferlay, N., Chesnoiu, G., Chiapello, I., and Elias, T.: Aspects of time variability of the surface solar irradiance as measured and analysed from ground-based measurements with a distinction of cloudyness , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-962, https://doi.org/10.5194/ems2024-962, 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 (kc); the average, maximum and standard deviation of the absolute values for the first derivative of SSI and kc; 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.
Solar forecasting is a critical tool for mitigating the variability of solar power availability within the electric grid, notably influenced by atmospheric conditions. These models often fail to adapt to the diverse climatic and cloud conditions across different regions [1], complicating efforts for PV project developers to estimate forecast accuracy without historical dataset of in-situ and hindcast.
Among a great number of solar forecasting applications and approach, we focus on intra-day solar forecasting based on geostationary satellite. We examine the sensitivity of solar irradiance forecasting accuracy to varying geographic locations and cloud conditions across Europe. Our goal is to assess how these models perform under different settings and their generalizability across diverse European regions and weather situations.
To evaluate our methodology, we employed a cloud motion vector (CMV) algorithm through the high-resolution visible (HRV) channel of the Meteosat Second Generation (MSG) SEVIRI instrument [2]. The scope of our study was confined to Western and Central Europe to ensure comparable satellite spatial resolutions, thereby eliminating spatial resolution as a significant source of variability in performance comparison. We selected six BSRN stations [3], which underwent advanced quality checks [4], with half located in Cfb (temperate oceanic) climate zones and the other half in Dfb (humid continental) zones. We analyzed a complete year's dataset from these stations to evaluate the forecasting model’s performance across the different seasons, cloud coverage, meteorological regimes and spatial locations.
Preliminary analysis using the persistence method has identified three distinct performance clusters of geographical locations, each represented by one meteorological station from both Dfb and Cfb climatic zones. To enhance this analysis, the CMV algorithm will be employed as it quantifies advection movements of clouds, thereby improving our understanding of performance variability across diverse sites.
[1] Liu, Bai, et al. "Predictability and forecast skill of solar irradiance over the contiguous United States." Renewable and Sustainable Energy Reviews 182 (2023): 113359.
[2] Cros, Sylvain, et al. "Reliability predictors for solar irradiance satellite-based forecast." Energies 13.21 (2020): 5566.
[3] Driemel, Amelie, et al. "Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017)." Earth System Science Data 10.3 (2018): 1491-1501.
[4] Blanc, Philippe, et al. "Data sharing of in-situ measurements following GEO and FAIR principles in the solar energy sector." (2022).
How to cite: Meddahi, A., Albergel, S., Chea, N., Tuomiranta, A., Guillon, S., Saint-Drenan, Y.-M., and Blanc, P.: Geographic validation of Satellite-Based Solar Irradiance Forecasting Models Across Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-886, https://doi.org/10.5194/ems2024-886, 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.
Norway has an ambition to assign areas for 30 GW offshore wind production by 2040. The first project areas for offshore wind at Utsira Nord and Sørlige Nordsjø II were announced last year 2023. Other areas that may be suitable for offshore wind have been analyzed and we expect high activity on the Norwegian continental shelf regarding offshore wind in the near future.
The Norwegian Meteorological institute (MET Norway) wants to support renewable energy. For the maritime sector, offshore wind will need meteorological and oceanographic data and services.
Today MET Norway delivers products and services to the offshore industry. The offshore wind project at MET Norway has an overall goal to further develop the services to fit the needs for offshore wind. This service includes statistical weather data, offshore forecasts delivered and visualized through a web portal called Luna, data delivered though Luna-API and also services such as providing a weather brief in video meetings and dedicated meteorologists.
For the weather forecasting services through the web portal, the project focuses on delivering wind forecasts at hub height, a good way to present forecasts of ocean surface current with ensemble data and make maritime observations available. Both monitoring and forecasting of lightning in a wind park are planned topics to work with as well as icing on wind turbines.
As well as predicting wind conditions in turbine heights, another purpose of these services is to give operators working with offshore wind the necessary information to perform operations and maintenance related to the wind park with the lowest possible risk for personnel and equipment.
The offshore forecasting services at MET Norway plays an important role to develop waves and ocean models that further benefits the society. In addition, providing this service gives MET Norway a valuable competence in maritime forecasting. This project will prepare MET Norway for the green industry and the needs of MET data, products and services in the future.
How to cite: Palm, A. H. and Albertsen, C. G.: Offshore wind project at MET Norway, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-689, https://doi.org/10.5194/ems2024-689, 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.
Under high variable renewable energy (VRE) penetration, the occurrence of low VRE production, such as dark doldrums (Dunkelflaute) and wind-solar drought, can threaten a secure and continuous energy supply owing to an imbalance between electricity demand and supply. In this study, historically reconstructed long-term VRE generation and electricity demand were used to investigate the relationship between extremely high residual load (demand minus VRE output) with dark doldrums, and weather/climate in Japan. The impact of changes in the VRE's installed capacity on this relationship was also investigated using three simple future target scenarios. The results showed that the increase in installed VRE capacity causes greater daily and weekly residual load variabilities and affects the seasonality of its peaks. To study the weather patterns associated with high residual load events, self-organizing maps were applied to atmospheric circulation fields derived from atmospheric reanalysis data. The high residual load was associated with enhanced cold surge-type weather patterns during winter at the current low VRE installation level. However, under future increased VRE penetration, the weather patterns leading to high residual load will change to cloudy-windless types typically caused by a southern coastal extratropical cyclone. There is also considerable interannual variability in the frequency of dark doldrums and high residual load events, that is strongly connected to climate variations in the tropical Indo-Pacific. However, this linkages are changed significantly with increasing VRE capacity. It is crucial to incorporate the dependence of climatic conditions into designing power systems to maintain the stability of a power system under future conditions.
How to cite: Ohba, M.: Climatology of dark doldrums and extremely high residual loads events and their future changes in Japan, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-4, https://doi.org/10.5194/ems2024-4, 2024.
Solar energy generation using photovoltaic technologies has emerged as one of the core renewable energy sources. Therefore, its prediction, which highly relies on meteorological information, is of paramount importance for various applications including power grid planning, integrating solar power effectively into the power grid, resource allocation, and sustainable energy management. This study presents the results of our investigation on the impact of the temporal resolution of meteorological data on solar energy generation predictions using machine learning (ML) models and a methodology that we developed to improve these predictions. Two independent solar energy generation datasets (one for urban rooftop solar panels in San Antonio, Texas, U.S., and the other one for a solar farm in India) along with the corresponding meteorological datasets that include solar irradiation, wind, temperature, and humidity information were utilized. Our results demonstrate that the temporal resolution of the meteorological data has a profound influence in predicting solar energy during rapid meteorological changes, particularly during sunrise and sunset times when the time rate of change of the meteorological data may be relatively large. We developed a simple, yet very effective, method to enhance machine learning model predictions through modifying the temporal resolution of the available meteorological data. We will present, using quantitative metrics, the predictive capabilities of our ML-based model, the impact of the temporal resolution of meteorological data on prediction accuracy, and the method that we developed to enhance prediction accuracy. This research was supported by the funds provided by CPS Energy (San Antonio, Texas, USA) through UTSA-TSERI.
How to cite: Testik, F. Y., Marino, D., Ortega, L., Le, T., and Testik, M. C.: Improvements to Solar Energy Generation Predictions via Temporal Resolution of Meteorological Data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-145, https://doi.org/10.5194/ems2024-145, 2024.
Offshore wind energy development in the United States is accelerating, with projects currently representing 40 gigawatts of proposed installed capacity. However, there are still substantial, unsolved challenges with forecasting winds and turbulence over the ocean. To help overcome the challenges, the US Department of Energy (DOE) and the National Oceanic and Atmospheric Administration (NOAA) are currently conducting a multi-seasonal offshore field campaign off the coast of New England in the Eastern United States. In collaboration with public and private partners, WFIP3 aims to boost offshore wind generation through better forecasting for existing, constructed, and planned wind farms in the area. WFIP3 builds on the success of the first and second wind forecast improvement projects (WFIP1 and WFIP2) which collected data to improve the accuracy of short-term wind forecasts over land. WFIP3 seeks to improve the understanding of the physical phenomena in the marine atmospheric boundary layer that impact wind and turbulence within turbine rotor planes that are critical for wind energy.
Since November 2023, a comprehensive set of remote sensing and in situ meteorological instruments have been installed at several sites at the coast and on islands. These continuous land-based observations are complemented by observations on a barge and ship during several multi-week-long periods. The observations will be used to evaluate and improve NOAA’s currently operational High-Resolution-Rapid-Refresh model as well as its successor the Rapid Refresh Forecast System. We present an overview of the campaign, research questions, and measurement strategy and will show some preliminary results from the ongoing campaign.
How to cite: Adler, B., Wilczak, J. M., Turner, D. D., Krishnamurthy, R. (., Kirincich, A., Bianco, L., and Myers, T.: Improving offshore wind forecasts off the coast of New England in the United States – The Third Wind Forecast Improvement Project (WFIP3), EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-199, https://doi.org/10.5194/ems2024-199, 2024.
Average wind speeds are projected to decrease annually, mainly in the Northern Hemisphere, owing to the progression of global warming. Although the momentum for the spread of offshore wind power generation is growing in Japan, the average surface wind speeds in the region are also decreasing, and wind resources are expected to decline in the future. However, the risk of low wind power generation due to wind conditions exists not only when the wind is weak, but also when the wind is strong. This study investigated future changes in the frequency and duration of extremely low and high winds around mainland Japan.
In this study, we used 2°C warming climate datasets (31 years × 12 members) and present climate datasets (31 years × 2 members) from the 5 km mesh downscaling data from the Database for Policy Decision-Making for Future Climate Change (d4PDF). The annual frequency of occurrence and maximum duration of weak (below cut-in wind speeds) and strong (above cut-out wind speeds) winds in the future (2°C warming) and present climates were compared in ensemble averages.
Under the present climate, weak wind events were more pronounced over land in summer, with a frequency of up to 70% and a maximum duration of approximately 96 h, depending on the location. Under future climate conditions, the weak wind trend will intensify further, increasing the frequency by up to 2.5% and the maximum duration by up to approximately 24 h. Under the present climate, strong-wind events were less conspicuous than weak-wind events and occurred mainly over the ocean in both summer and winter, with a frequency of up to 1–2% and a maximum duration of up to 12 h. In the future, the frequency will increase by approximately 1%, and the maximum duration will increase by approximately 6 h. The maximum duration of strong winds during the 31-year analysis period was approximately 48 and 60 h in summer and winter, respectively, under the present climate and increased by up to 24 h each under the future climate. Factors contributing to these changes include an increase in the number of strong typhoons, a decrease in movement speed in summer, and a shift in the Japan Sea cold air mass convergence zone (JPCZ) with strong northwesterly winds to the north in winter.
These findings suggest that under future climatic conditions, the probability of a long-term duration of extremely high winds will increase, potentially resulting in more severe low-generating events at offshore wind facilities than in the present climate.
How to cite: Iwanaga, H. and Yamaguchi, K.: Future Changes in the Frequency and Duration of Extreme Winds in Japan — Focusing on the Risk of Low Wind Power Generation due to Strong Winds, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-394, https://doi.org/10.5194/ems2024-394, 2024.
In solar power forecasting, the consideration of uncertainties in the forecast is becoming increasingly important and is being taken into account more and more. Ensemble predictions systems can provide information about the uncertainty of cloud cover and solar irradiance. The goal of this study is to evaluate the quality of different operational ensemble predictions systems (EPS) in Germany in terms of global horizontal irradiance (GHI). Therefore, the models ICON-EU-EPS (horizontal grid spacing approx. 20 km, 40 members), IFS-ENS (horizontal grid spacing approx. 20 km, 50 members), GEFS (horizontal grid spacing approx. 28 km, 30 members), and WRF-Solar-EPS (horizontal grid spacing 3 km, 30 members) are evaluated. The 00UTC run is considered for all models. The evaluation period is summer 2022 with observational GHI data for 25 sites from Deutscher Wetterdienst (DWD). A quality check of the observational data with the python library libinsitu reveals that more than 95% of the observational data is of high quality. For reasons of computational time, WRF-Solar-EPS forecasts are evaluated for selected days only. The forecast quality is evaluated using rank histograms and the continuous ranked probability score (CRPS) among other metrics. The results show that the quality is similar for all models, while WRF-Solar-EPS does not necessarily stand out despite its higher spatial resolution. The rank histograms reveal that all models are highly over-confident, meaning that most observations lie outside of the range of all members. Post-processing methods and model calibration is not part of this study but has the potential to increase the forecast quality.
How to cite: Kurzrock, F., Mähnert, M., Mayer, B., Gregor, P., Ben Slama, M., and Schmutz, N.: Evaluation of ensemble prediction systems in terms of global horizontal irradiance in Germany, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-416, https://doi.org/10.5194/ems2024-416, 2024.
Nowadays, the number of operational renewable energy plants is steadily increasing. It is necessary to analyse how climate change might affect their energy production. Motivated by this need, this study analyses the Mutriku Wave Energy Plant, which has been operating since July 2011 and has a total capacity of 296 kW.
ECMWF Reanalysis v5 (ERA5) data and Commonwealth Scientific and Industrial Research Organisation (CSIRO) wave climate projections are used between 2015 and 2100. The projections are derived from two CMIP6 models: ACCESS-CM2 and EC-EARTH3, along with their respective parameterizations CDFAC1.08 and CDFAC1. Specifically, we employ six different climate projections, two based on the low-emission SSP1-2.6 scenario and four based on the higher-emission SSP5-8.5 scenario.
Bias correction for vector-valued variables are performed using the multivariate bias technique based on the MBC N-pdf bivariate pdf, while for univariate variables; the classical Quantile Mapping (QM) technique is applied. Additionally, the self-organising map, SOM, technique is also used to classify the daily frequencies and powers of sea type. Finally, the Smirnov test is employed to assess if the shapes of the probability distributions from the different datasets differed statistically. A significance level of 0.05 is used.
Our results show that wave energy production in the Bay of Biscay remains stable in the 21st century. This stability is reflected in a significantly way in the analysis of sea type frequencies and in the energy production. This consistent wave scenario ensures reliable and predictable energy generation, making the Bay of Biscay a valuable source of renewable energy both now and in the future.
How to cite: Carreno-Madinabeitia, S., Serras, P., Ibarra-Berastegui, G., Sáenz, J., and Ulazia, A.: CMIP6 climate projections based wave energy production analysis for Mutriku Wave Energy Plant in the XXI century, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-441, https://doi.org/10.5194/ems2024-441, 2024.
The construction of offshore wind farms in the North Sea and Baltic Sea in Germany's Exclusive Economic Zone (EEZ) is crucial for the successful implementation of the energy transition. Since 2021, the Federal Network Agency has put potential sites out to tender every year. Therefore, detailed information on the wind conditions at the sites is essential for the bidders' energy yield calculations. The tender preparation is conducted in cooperation with the Federal Maritime and Hydrographic Agency (BSH) according to the Offshore Wind Energy Act (WindSeeG). The German Meteorological Service (DWD) supports the BSH in compiling the required wind information.
To assess the local wind conditions, an externally contracted one-year floating LiDAR measurement campaign is carried out for the potential site. This data is combined with the long-term in-situ measurements from the FINO1, FINO2 and FINO3 research platforms in the North Sea and Baltic Sea (https://www.fino-offshore.de/de/index.html). Additionally, the DWD provides corresponding model data and evaluation results of the COSMO-REA6 and ERA5 reanalyses. These data sets form the basis for the preparation of the comprehensive wind reports for the sites. All reports and the data are made publicly available to bidders by the BSH via the PINTA portal (https://pinta.bsh.de). The first two tendering processes have been successfully completed for three sites in the south-eastern North Sea (N-3.7, N-3.8, N-7.2) and for one site in the Baltic Sea (O-1.3) in September 2021 and September 2022, respectively. In 2023, a call for tender for sites N-3.5, N-3.6, N-6.6 and N-6.7, located in the North Sea, has been published and in 2024 for the North Sea sites N-9.1, N-9.2 and N-9.3.
A detailed investigation of the seasonal variability as well as an in-depth assessment of the current and historical wind conditions for the respective site is possible with the unique measurement and reanalysis data. The floating LiDAR wind measurements focus on the heights relevant for future wind turbine types and extend up to 250 m. The reanalyses are evaluated for the grid points closest to the sites and for the surrounding grid points. The data is validated using existing measurement data. Previous evaluations show a very good correlation, highlighting the added value of the reanalyses for determining the wind conditions at the sites. As future wind farms are planned for about 30 years, information on long-term variability is required. Long-term time series of geostrophic wind, derived from air pressure data from coastal stations from 1877 onwards enable an assessment of multi-decadal variations.
Future wind farms in the North Sea will be located even further off the coast. This will pose new challenges for the pre-investigations in all disciplines.
How to cite: Möller, T., Michaelis, J., Hansen, A., Spangehl, T., Hüttl-Kabus, S., Brast, M., Hahn, J., Andersson, A., Leiding, T., Grüter, M., and Kühn, B.: Opportunities and challenges in providing wind information for the German offshore wind auctions according to WindSeeG , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-781, https://doi.org/10.5194/ems2024-781, 2024.
The climate and energy crises are going hand in hand. The need to adapt to the excess heat and the energy demand - especially for cooling - will increase. The biggest relative change in Cooling Degrees Days will particularly affect the population of Central and Northern Europe. Thus, a good plan is needed to organise the energy system, preferably designing the use of RES resources as adopted by the European Commission in the 'fit for 55' package. Poland's gross domestic consumption still falls short of achieving a 20% share of green energy in the energy mix. The share of RES in domestic production is steadily increasing, especially in the prosumer installations segment. Due to local problems with energy connections and storage, one of the key issues remains maximising self-consumption from in-house installations. On an annual basis, it can be approximated that wind turbines will operate at rated capacity for an average of 25% of hours per year, and photovoltaic installations will average about 12% of hours per year. However, the expected energy yields can be realistically assessed only by taking into account the synoptic variability of cloud cover, irradiation, and airflow conditions. As well as their variation in neighbouring locations or on consecutive dates for the same location.
In October 2023, the - first in this part of Europe – public free-of-charge RES forecast service for micro-installations in Poland was launched. Constant and averaged solar irradiance values and wind speed generated from the ECMWF HRES 0.1° model fields were assumed in hourly intervals. The forecast values were expressed as a percentage [%] of the rated power yield of a wind or photovoltaic installation according to the assumed installation parameters: a wind turbine with a diameter of 1 m2, an installed rated capacity of 8.2 kW, and a threshold value of useful wind speed of 3 ms-1. As a model, the photovoltaic installation was one PV module of southern exposure, the slope of the unit 30% from the ground, and the power generated under standard conditions of 1 kW, where the maximum daily intensity of solar radiation under standard conditions was set at 1000 Wm-2. The module operating temperature under real conditions was set at 50°C, and the overall system efficiency, considering losses on the inverter, cabling, or module dirt, was set at 80%.
How to cite: Wieczorek, J., Bochenek, B., Jurasz, J., Figurski, M., Jaczewski, A., Gruszczynska, M., Mazur, A., and Strzyzewski, T.: Numerical weather forecasts supporting the Renewable Energy Sector (RES) in Poland, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-517, https://doi.org/10.5194/ems2024-517, 2024.
The Mediterranean Sea heavily relies on fossil fuels for its energy supply, but the shift toward green energy is imminent. Offshore wind farms represent a pivotal step in this transition. However, deploying these wind farms effectively requires an accurate assessment of the wind potential in offshore areas.
Unlike Northern Europe, research on Mediterranean wind energy is sparse due to its intricate topography and bathymetry. The Mediterranean's coastlines, hemmed in by mountain ranges, create unique wind climates, posing challenges for modeling offshore wind conditions. Additionally, existing in-situ observations are limited to specific points.
Sentinel-1 satellite Synthetic Aperture Radar (SAR) wind fields can resolve wind variability at sub-km scales. The strength of satellite wind fields lies in the observation of large spatial domains over broad temporal periods. Therefore, this technology holds promise for uncovering the wind resource potential.
This study focuses on the Gulf of Lion, located in the NW Mediterranean Sea and currently the Mediterranean's most promising area for floating wind turbine installation. Here, two dominant local winds—the Mistral and Tramontane—prevail. The Mistral, a northerly wind, forms between the Alps and the Massif Central, while the Tramontane, a northwesterly wind, sweeps through the Aude valley between the Massif Central and the Pyrénées.
SAR records images of the radar backscatter from the Earth’s surface, which is commonly known as the Normalized Radar Cross Section (NRCS). NRCS values can be used as inputs in a Geophysical Model Function (GMF), along with other radar parameters, to retrieve the SAR ocean wind fields. For the SAR wind speed retrieval, a necessary input is the wind direction, commonly provided by numerical models. This study utilizes SAR wind speed retrievals driven by three numerical model wind directions—GFS and ERA-5 with 27 km spatial resolution, and New European Wind Atlas - NEWA (WRF) with 3 km spatial resolution—over a year-long period. We first compare the three model wind directions against in situ measurements from two buoys located in the greater area of the Gulf of Lion, which are provided by the Copernicus Marin Service In Situ TAC data platform.
Then, we compare the in-situ wind speeds against the three SAR wind speeds and the three model wind speeds. Our objective is to determine which SAR-model data synergy best reflects true wind conditions in the area. This study also aims to highlight the benefits of involving satellite-derived wind fields for wind resource estimations in upcoming wind farm areas characterized by complex wind climates.
This work is supported by the ESAWAAI project, funded by the European Space Agency under the grant agreement 4000142170/23/DT.
How to cite: Dimitriadou, K., Olsen, B. T., Badger, M., and Hasager, C. B.: Assessment of SAR offshore wind fields in the Gulf of Lion , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-600, https://doi.org/10.5194/ems2024-600, 2024.
The residual load is defined by energy demand minus energy production from renewable sources (wind and solar power). High values of residual load correspond to a high energy demand that cannot be covered by renewable energies and must be compensated with fossil energies. On the opposite, low/negative values of residual load correspond to a surplus of renewable energies with a too low demand. In this case, energy must be exported, or wind turbines and solar panels curtailed.
We compute the residual load using a demand model and a solar and wind power model. For the solar power model, we use the CAMS solar energy dataset interpolated to the position of all single solar panels installed in Germany and model the solar power output with the PVlib Python package. The wind power model is using ERA5 windspeed and air density interpolated to turbine location and turbine hub height to be later fed in the power curve of all single turbines in Germany. The demand model is a linear regression of 2m-temperature from ERA5, industry production index, day-of-week, public holidays and installed solar capacity. The residual load is then given by: ResLoad = Demand - (Wind + Solar)
We look at both tails of the distribution of the residual load. High values of residual load are mostly observed in winter when temperatures and renewable energy production are low (mostly during blocked regimes). These events correspond to high energy prices. Negative values of residual load are mostly observed on Sundays, public holidays and in summer, where demand is low and temperatures and solar and wind power production are high. They usually correspond to negative energy prices. Both events described above are critical for the energy industry. Skillful forecasts of these events in the sub-seasonal range help to reduce stress to the energy market. The results presented herein lay the foundation for a more comprehensive, statistical analysis of the predictability characteristics of residual load extremes.
How to cite: Chabert, L., Späth, S., Baumgart, M., and Riemer, M.: Case studies of extreme values of residual load., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-678, https://doi.org/10.5194/ems2024-678, 2024.
Wind farms affect the atmosphere due to blockage, wakes and speed-ups. These modifications also affect other meteorological variables such as temperature, humidity and clouds locally under certain meteorological conditions. Therefore, there is a need to include wind farm effects in weather forecasts both for good-quality power predictions, but also for improved weather forecast in general.
To evaluate the influence of currently existing on- and offshore wind turbines in Europe, we perform forecasts with the operational NWP model HARMONIE-AROME. The HARMONIE-AROME model is equipped with two wind farm parameterizations (WFPs), namely the WFP by Fitch et al. (2012) implemented by van Stratum et al. (2022) and the Explicit Wake Parameterization (EWP) by Volker et al. (2015) implemented by Fischereit et al. (2024).
To represent the existing wind turbines in the simulations, we assembled an European wind turbine database for existing on- and offshore turbines that contains turbine locations and turbine characteristics such as hub height, rotor diameter and thrust curves. For the database we combined seven different data sets with a machine learning gap-filling approach to fill missing information.
Using the database, we simulate a winter and a summer month with both WFP and compare them to a control simulation without wind farms for central and northern Europe. The simulations indicate that wind speed, temperature and humidity are affected locally by the presence of wind turbines. The wind farm effects differ in magnitude and sometimes in sign for the two WFPs.
Fischereit, J., Vedel, H., Theeuwes, N. E., Larsén, X. G., Giebel, G., & Kaas, E. (2024). Modelling wind farm effects in HARMONIE-AROME - part 1: Implementation and evaluation. Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, 2024
Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K., Michalakes, J., & Barstad, I. (2012). Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model. Monthly Weather Review, 140(9), 3017–3038. https://doi.org/10.1175/MWR-D-11-00352.1
van Stratum, B., Theeuwes, N., Barkmeijer, J., van Ulft, B., & Wijnant, I. (2022). A one-year-long evaluation of a wind-farm parameterization in HARMONIE-AROME. Journal of Advances in Modeling Earth Systems, 14, e2021MS002947. https://doi.org/10.1029/2021MS002947
Volker, P. J. H., Badger, J., Hahmann, A. N., & Ott, S. (2015). The Explicit Wake Parametrisation V1.0: a wind farm parametrisation in the mesoscale model WRF. Geoscientific Model Development, 8(11), 3715–3731. https://doi.org/10.5194/gmd-8-3715-2015
How to cite: Fischereit, J., Vedel, H., Olsen, B. T., Imberger, M., Larsén, X. G., Hahmann, A. N., and Giebel, G.: Wind farm effects on weather forecast using HARMONIE-AROME, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1046, https://doi.org/10.5194/ems2024-1046, 2024.
The energy sector has a wide range of requirements for weather and climate data and services. As in many other countries, the share of renewable energies has increased in Germany during the last years and the German government aims at a further increase within the next decades. As a consequence, there is an increasing need for meteorological information on several time scales. In this presentation, we will discuss the experience of Germany’s national meteorological service (Deutscher Wetterdienst, DWD) with the provision of user-oriented services for various stakeholders in the energy sector.
DWD hosts several national and international climate data centres that provide observation-based climatological data suitable for assessments of the site-specific renewable energy potential, but also suitable for evaluating weather variability and the resulting risks of shortfall events or complementarity of energy resources (e.g. https://doi.org/10.1016/j.renene.2020.10.102). The datasets are derived from traditional weather observations, satellite data (e.g. https://www.cmsaf.eu) or model-based atmospheric reanalyses (e.g. https://doi.org/10.5194/asr-17-115-2020). Climate simulations can help to assess whether relevant changes in critical parameters are to be expected in the future. The debates in the recent winters about the risks of a shortage in gas availability have also led to an increased interest in seasonal forecasts.
Weather forecasts are of particular interest for the daily operation of the energy system, the predictive balancing of production and consumption, the management of congestion and the weather dependent thermal rating of the overhead lines in the electrical grid. In close contact with our key user groups, DWD optimizes and co-designs forecast products and services tailored to the user needs. One special focus lies on the co-development of products that make use of ensemble forecast information and to foster its integration in the user’s decision process.
DWD's open data policy (https://www.dwd.de/opendata) also supports the cooperation with this community. In addition to the national data, DWD’s international data centres also provide relevant datasets for Europe, or beyond (e.g. https://doi.org/10.3389/fclim.2021.815043).
In the presentation we will discuss the results and lessons learned of the cooperation with our users and we will also provide information on products that may be of interest to other users.
How to cite: Kaspar, F. and Fundel, V.: Weather and Climate Services in Support of the German Energy Transition: Use cases from Germany’s national meteorological service (Deutscher Wetterdienst, DWD), EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1092, https://doi.org/10.5194/ems2024-1092, 2024.
In the context of climate change, countries are increasing their proportion of renewable energy generation, such as wind and solar. The integration of renewable energy sources into the current energy network is a challenging task, as these sources are highly dependent on weather. The main challenge is to balance energy demand and supply, as both are now weather dependent.
During the winter season, cold temperatures lead to high demand. If these cold conditions coincide with periods of low winds, renewable generation becomes low at the same times as energy demand is high. These periods of high demand and low generation have gained a lot interest in the scientific literature and are defined as periods of high energy shortfall.
Recent studies have highlighted the influence of weather regimes or large-scale circulation patterns on both renewable energy generation and energy demand and shortfall.
In this research, the influence of weather regimes on energy shortfall days is investigated across 28 European countries during the winter. To this end, modelled energy data is analysed with respect to a weather regime classification.
The results show how blocking type regimes such as the Scandinavian Blocking, the Atlantic Ridge and the negative North Atlantic Oscillation, are most likely to favour periods of high shortfall. Additionally, large regions of Europe, and therefore multiple countries, are most likely to experience high shortfall during the same regime. This would suggest that multiple countries can simultaneously experience high shortfalls. In these circumstances, connection between the energy networks of multiple countries might not be sufficient to mitigate such high energy shortfall. The coldest winter (1962-1963) of the 20th century is used to highlight worst case scenarios, for which current and future energy networks need to be prepared.
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.
The integration of renewable energies into the electricity systems of European countries plays a key role in the European Union's ambitious goal of achieving a neutral climate by 2050. The geostrategic position of the Iberian Peninsula (IP) makes Spain and Portugal a benchmark in renewable energies, where their share exceeds the European average (around 50% in Spain and 60% in Portugal). Currently, solar photovoltaic (PV) and onshore wind power are the main sources of renewable energies in these countries. However, the offshore wind resource, which is set to play an important role in future energy systems, has great potential for exploitation in the IP. In this context, the Spanish and Portuguese governments have provided a legislative framework to encourage the development of offshore wind farms in the coming years. Furthermore, the annual cycle of energy demand in the IP peaks during the winter and summer. Yet, neither the annual cycle of onshore wind nor solar PV energy generation adjust alone with this demand curve. Previous studies agree that spatial and/or temporal complementarity between these resources can be used to increase renewable energy production and smooth the temporal variability of the combined energy supply. Although in the IP only a few studies analyzing these issues, they conclude that this region presents a great potential in terms of complementarity between these sources. Thus, the objective of this work is to determine whether offshore wind energy presents substantial temporal complementarity with onshore wind and solar PV energies in the IP in winter and summer. For this, hourly solar PV and onshore wind real time generation were obtained from Spanish and Portuguese electrical networks and normalized by the installed capacity. With respect to the offshore wind resource, hourly wind speed at 150 meters hub height was extracted for different locations marked as suitable zones by the Spanish and Portuguese governments from the COSMO-REA6 high-resolution reanalysis for 2015-2019. Once the wind speed was translated into capacity factor, we established a capacity factor threshold representing an appropriate efficient resource. We then analyzed the availability and length of generation/non-generation episodes for each resource based on that threshold. Finally, we analyzed the complementarity and synergy between resources, i.e., cases in which the generation of one resource complements the non-generation of another resource and cases in which any of the resources complements the rest. Some of the initial results show that offshore wind complements solar photovoltaic in winter and onshore wind in summer. This indicates that the exploitation of the offshore wind resource can contribute to diversify and improve the integration of renewable generation in the energy mix of the Iberian electricity system.
How to cite: López de la Franca Arema, N., Gaertner, M. Á., Gutiérrez, C., Sánchez, E., Gallardo, C., and Ortega, M.: Temporal complementarity analysis of solar photovoltaic, onshore, and offshore wind resources in the Iberian Peninsula , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-605, https://doi.org/10.5194/ems2024-605, 2024.
Abstract: Transitioning to renewable energy (RE) sources such as solar, wind, and hydropower is crucial for mitigating climate change by reducing greenhouse gas emissions. In 2019, 640 million people in Africa lacked access to clean energy. In Rwanda, the energy context revealed a 52% share of non-renewable and a 44% share of renewable sources. However, Rwanda faces a shortage of electricity to meet demand, even with imports from neighboring countries. Therefore, Rwanda set a goal to electrify all households by 2024, aiming for 100% electricity access through enhanced generation capacity and cross-border interconnections. The country has significant potential for RE, particularly in hydro, solar, methane gas, peat, and geothermal resources. However, Rwanda's wind resource has not yet been exploited for power generation. Although wind power in Rwanda has provided possible solutions in some areas, such as water pumping, windmills, and electricity generation, its full potential remains untapped. The main goal of this study is to investigate the wind resource assessment of Rwanda, a landlocked country in East Africa, as part of its efforts to achieve a 60% RE share in the generation mix by 2030. This study focuses on assessing Rwanda's wind potential through comprehensive data analysis, using hourly wind speed and direction data at 10 m above ground level for the year 2023 from various Meteo Rwanda sites across the country, along with gridded dataset of ERA5 reanalysis data at approximately 25 km spatial resolution at 10 m and 100 m. Additionally, the study used the WAsP model to validate by comparison the measured and modeled wind data from annual means to diurnal cycles. The methods used include the Weibull distribution and logarithmic law to describe wind speed distributions, and extrapolating values to different hub heights by using ERA5 reanalysis data. Furthermore, capacitor factors are computed, and wind power density is determined for each site given. This research aims to investigate the wind potential of Rwanda, contributing to its sustainable energy transition.
How to cite: Uwajeneza, L., Tang, C., Morel, B., and Sylla, B.: Investigating Wind Potential in Rwanda, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-806, https://doi.org/10.5194/ems2024-806, 2024.
Short-term forecasts of solar radiation are gaining importance for power grid operators, providing estimates of surface solar irradiance (SSI) and anticipated photovoltaic (PV) power generation for minutes to hours ahead. Geostationary satellites such as the Meteosat Second Generation equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) provide valuable spectral imagery for deriving SSI across wide geographical areas. While these spectral measurements are foundational for many SSI estimation and forecasting methods, current approaches still use Level-2 products of SSI, which leads to significant delays in the forecasting process. We demonstrate that a convolutional residual network can accurately emulate SSI produced by the SARAH-3 algorithm.
A generalized deep-learning model is trained to estimate the instantaneous SARAH-3 SSI from all channels of the SEVIRI imager over a large region in Europe. The SSI emulator shows a similar bias and root mean square error (RMSE) for a large validation pyranometer set across Europe and Northern Africa. The emulator directly infers SSI from the Level-1.5 spectral imagery of SEVIRI within just 15 seconds, strongly reducing the 10+ minutes runtime of non-machine-learning radiation retrievals such as SARAH-3. We present a characterisation of the SSI emulator's performance depending on location and season and quantify the channel importance for different regions and surface albedos. We also present the SSI emulator's performance over snowy surfaces where retrieval algorithms such as SARAH-3 have been struggling to distinguish between snow and clouds. To further improve the emulator, advanced fine-tuning methods are applied based on ground observations without degradation in regional bias. The fine-tuning improves the RMSE on average from 80 to 65 W/m^2 and outperforms the SARAH-3 algorithm.
How to cite: Schuurman, K. and Meyer, A.: Retrieving global radiation from Meteosat Level-1 with deep learning and pyranometer fine-tuning, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-531, https://doi.org/10.5194/ems2024-531, 2024.
