A novel lidar prototype for horizontal Doppler wind measurements with more than 30 km maximum range is presented. The request for such long-range measurements arose from the development of methods for improved prediction of potential and actual feed-in of wind power from offshore wind farms in the project WindRamp. The target is a short-term prediction horizon of up to 30 minutes.

The coherent lidar module is based on a robust fiber amplifier architecture developed within the project. This enables deployment in harsh environments in the future, e.g. at offshore wind farms. The emitted laser beam is eye save (class 1M).

In order to emulate operating conditions of an offshore platform, the system was deployed at the mouth of the Elbe river at 10 m above sea level with unobstructed view in a broad SW-sector. Scans between 204° and 304° azimuth at 0.35° Elevation were performed. The averaging time was 1 s and the angular speed 0.6° s^-1.

The lidar performance is demonstrated by observations of wind fronts propagating through the observed area. The weather in North Germany during winter 2023/24 was characterized by unusual persistent precipitation, low hanging clouds and fog, which are unfavourable conditions for lidar operation. Therefore, the observed availability of valid data versus range represents a conservative estimate of the system’s potential.

How to cite: Bade, J., Kirtzel, H.-J., Heinze, L., Markmann, P., Peters, G., Bollig, C., Ulonska, S., Jordan, F., and Huschenbeth, G.: Long range lidar for short term wind predictions for offshore wind parks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17552, https://doi.org/10.5194/egusphere-egu24-17552, 2024.

As wind turbines have grown in size, it has become ever more costly to make the necessary tower-based wind observations needed both for the pre-operation (siting) phase and for wind turbine operations. In response to this challenge, the wind energy scientific community has - over the last decades - focused on evaluating and improving ground-based remote-sensing technology. The development has often been done in close collaboration with the innovative companies dedicated to providing the new solutions for replacing the expensive meteorological towers to the market.

The project EARS4WindEnergy, which started in March 2023, represents one such effort. The project is focused on a re-exploration of the sodar technology, which preceded the later focus on wind lidars. Here, we present a benchmarking of the AQ510 sodar equipped with new signal processing technology with tall-tower data focusing on the three “must-perform” criteria of accurate wind speed, accurate turbulence intensity and a reliable identification of erroneous data. The complementary aspects of data availability and robustness in relation to current wind lidars is also discussed. Most of the presented data are taken at the Østerild test site in Northern Denmark, where a 244m tall tower allows for accuracy quantification over most of the sodar’s measurement range.

How to cite: Dellwik, E., Rodén, S.-O., Arnqvist, J., Sjöholm, M., Möhrlen, C., and Gräsman, A.: Replacement of meteorological towers with ground-based remote-remote sensing sodars: How close are we? , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7587, https://doi.org/10.5194/egusphere-egu24-7587, 2024.

Wind resource assessment studies over large regions provide the basis for the preliminary identification of locations with promising wind energy prospects. In past studies, several authors have mapped the mean wind speed across large regions using spatial interpolation methods or machine learning models. In recent studies, more emphasis has been placed on mapping the entire wind speed distribution to evaluate the wind resource variability at unsampled locations. Most of these studies have assumed that the wind speed distribution across the entire region belongs to a single family of probability distribution functions and then processed to map the distribution parameters. A flexible non-parametric approach for wind speed distribution mapping is proposed in this study. The new approach is based on mapping various wind speed quantiles at some fixed percentile points in the region using a machine learning model. Then, at any unsampled location, these quantiles are used as input of an asymmetric kernel estimator of cumulative distribution function to recover the whole wind speed distribution. Asymmetric kernel estimators solve the probability leakage problem that appears when fitting symmetric kernels to bounded variables such as wind speed. The non-parametric approach for wind speed distribution mapping was more effective than a traditional approach based on mapping the parameters of a distribution function. In the best scenario, an improvement was observed between 6% (test samples) and 9% (cross-validation) of the Kolmogorov-Smirnov statistic between the observed and estimated wind speed distribution. The non-parametric approach is recommended for regions with highly variable wind regimes that cannot be captured by a single family of distribution functions.

How to cite: Houndekindo, F. and Ouarda, T.: Mapping wind speed distribution across large regions using machine learning and asymmetric kernel estimators., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1432, https://doi.org/10.5194/egusphere-egu24-1432, 2024.

With the rapid development of wind energy, the imperative for precise wind power predictions has intensified, with the crux lying in forecasting wind speeds. The accurate short-term (1 to 3 days) forecast of wind speeds at the hub height in boundary layer poses a significant scientific challenge. Generating such forecasts for wind farms 1 to 3 days in lead time necessitates reliance on global weather forecast products and the WRF model. In pursuit of heightened accuracy, artificial intelligence (AI) algorithms are employed to refine WRF-predicted wind speeds based on observational data.

This study draws upon observational data from five operational wind farms over three years, employing diverse deep time-series models, to examine the effectiveness and limitations of these models in post-processing corrections for WRF-predicted wind speeds. Based on our examination, we conclude that: 1) Transformer-based models have significant untapped potential, with the Pyraformer model emerging as a well-suited temporal model for post-processing corrections in wind speed and power predictions. 2) Traditional full-attention mechanisms are less effective, highlighting the importance of sparse attention as a vital approach for capturing temporal correlations in such problems. 3) The optimal model demonstrates a reduction of approximately 20% in RMSE for single-point post-processing corrections. In addition, wind speed prediction accuracy reaches around 86%, and power prediction accuracy is approximately 82%. 4) AI-based post-processing corrections may encounter challenges, including the underestimation for high-value and difficulties in reproducing forecasts below the average value.

How to cite: Xia, X. and Luo, Y.: Application of WRF-Based Single-Point Data Artificial Intelligence Post-Processing Correction Method in Practical Short-Term Wind Speed and Power Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16521, https://doi.org/10.5194/egusphere-egu24-16521, 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 during July 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 will provide a foundation for future analysis which will more robustly verify WRF output using additional WiValdi instrumentation.

How to cite: Thayer, J., Kilroy, G., Wildmann, N., and Englberger, A.: How do convective cold pools influence the stability and turbulence conditions in the vicinity of wind turbines in Northern Germany?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17394, https://doi.org/10.5194/egusphere-egu24-17394, 2024.

The rainfall directly affects wind turbine operation by eroding the turbine blades and changing their aerodynamic performance, however, little research has been conducted on the effects of rainfall on wake evolution. The present study simulates the impact of rainfall on wind turbine wake using a coupled LES-ADMR model, in which a double Euler method is employed for the rainfall injection. The numerical simulation results indicate that the rainfall reduces the wake wind speed in the sweep area while increasing it in the outer region of the upper blade tip, reaching up to 2.1% for increment. Rainfall also weakens the turbulence in the near wake and the outer region of the top tip (as much as 2.0%), with the influence extending up to 10 diameters downstream the wind turbine. These modifications are positively correlated with the rainfall intensity and inversely correlated with wind speed. By analyzing the rainfall-induced changes in MKE (Mean Kinetic Energy) and TKE (Turbulent Kinetic Energy) budget terms, the study reveals that the alteration of turbulent radial transport of MKE is the main cause of changes in wind speed, while the variation of shear production of  TKE is responsible for the turbulent intensity changes. The rainfall-induced change of  reynold stress u'w' is the root cause of the above phenomenons.

How to cite: Yang, X. and Chen, S.: Effect of Rainfall on Evolution of the wind turbine wake：A LES Study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4945, https://doi.org/10.5194/egusphere-egu24-4945, 2024.

As China strides towards its carbon neutrality target by 2060, the strategic planning of renewable energy distribution and power plant installations becomes imperative to fulfill the renewable energy penetration goals. This study presents a comprehensive assessment of the future projections of solar and wind power resources in China, utilizing the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We examine various CMIP6 scenarios to project the geographical and temporal variations of solar and wind energy potential up to 2100. A verification assessment was carried out using terrestrial solar radiation and wind speed data sourced from 17 stations operated by the China Meteorological Administration (CMA). This evaluation revealed that the Meteorological Research Institute Earth System Model version 2-0 (MRI-ESM2-0) demonstrated overall superior performance in terms of correlation coefficients (R) and Root Mean Square Error (RMSE). Then MRI-ESM2-0 was selected to examine the spatial and temporal shifts in solar and wind potential in China. Notably, in the SSP585 scenario, a marked decrease in both PV power potential and wind power potential was observed. Additionally, the future spatial complementarity between solar and wind power in China was evaluated using the Pearson correlation coefficient and Kendall rank correlation coefficient and this was juxtaposed with the present complementarity. These maps provide a crucial reference for guiding the planning and management of renewable energy resources in China.

How to cite: Liao, Z., Xia, X., and Luo, Y.: Future Projections and Complementarity Assessment of Solar and Wind Power in China Using CMIP6 Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18234, https://doi.org/10.5194/egusphere-egu24-18234, 2024.

The growing importance of the offshore wind energy sector emphasizes the need for projections of the long-term energy yield for existing and planned wind farm installations. In the North Sea, where wind farms are already pivotal to the electricity mix of the surrounding countries, the production capacity is set to increase tenfold by 2050. Studies suggest that, by 2050, the wind climate over the North Sea basin may differ significantly from the historical climate (Carvalho et al., 2021; Hahmann et al., 2022). Here, we combine an analysis of CMIP6 projections with an ERA5-driven, mesoscale wind farm simulation to further explore the impact of near-future wind climate changes over the North Sea on the energy production. First, an ensemble of 17 GCMs is reduced to 12 GCMs based on an analysis of the ability to represent the historical wind rose at 100 m MSL (1985-2014). Next, we identify future decades for each season where the wind rose exceeds the range of the historical decadal variability. Based on these extreme wind roses, we then apply a sub-sampling to a 30-year, ERA5-driven COSMO-CLM simulation covering the North Sea and incorporating a projected, 250 GW wind farm layout. Based on the sub-sampled datasets, we then quantify the impact of these extreme 10-year wind roses on the energy production of different wind farm clusters and compare this against an historical baseline.

How to cite: Borgers, R., Pinto, J., Meyers, J., and van Lipzig, N.: Future wind energy production over the North Sea for extreme, 10-year wind roses based on CMIP6-informed subsampling of an ERA5-driven RCM simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20273, https://doi.org/10.5194/egusphere-egu24-20273, 2024.

Power production from a renewable energy (RE) source such as a wind farm or urban roof-top solar panel installation is highly sensitive to the obstacles around it, particularly those which are in the upstream direction. RE installations can avoid or minimize the effects of obstacles using proper planning. However, obstacles that come up after the plant is operational can lead to significant loss in power production and revenue. In this study we quantitatively explore two common examples – shading effect of neighbouring buildings on roof-top solar plants and wake effects of upstream wind turbines on offshore wind farms.

The first example considers a horizontal solar panel atop an urban building in a relatively congested neighbourhood. We built a model to quantify the shading effects of neighbouring tall buildings on the solar panel. The model calculates the position of the Sun on the celestial dome at every minute with astronomical accuracy. Then the solar irradiance is calculated for a clear-sky environment. After that the shadow profile is calculated and visualized for obstacle buildings with any height and at any distance. And finally, the loss in available insolation and the power production is calculated. The results show significant power loss due to the building shading effect. For example, a roof-top solar panel surrounded by a 20m taller building at 20m distance can reduce power generation by more than 50%.

The second example is where a new wind farm is constructed upstream of an existing wind farm. We used two different models to quantify the meteorological effects of the upstream wind turbines on downwind turbines. The first one involves Jensen Wake Model (JWM), a static wake recovery model to simulate the wake effects of upstream obstacle turbine on downwind turbine. The second approach makes use of the Wind Turbine Parameterization (WTP) in WRF. This method implements wake loss using a wind turbine power curve data and wake recovery through atmospheric vertical mixing. A case study has been conducted for a hypothetical offshore wind farm situated in Palk Strait between India and Sri Lanka by placing wind farms of different shapes and dimensions in the upwind direction. The results show a range of losses in annual power production between 3 – 12 MW, which roughly converts into €1.1M – €4.1M.

This study demonstrates that the effects of upstream obstacles on RE sources are non-trivial and can have serious impacts on the performance on RE installations. Currently, local zoning laws in India and many countries do not protect RE installations from future constructions that can act as obstacles. Hence, effective policies are required to safeguard the return on investments in the RE industry.

How to cite: Bhattacharya, A. and Baidya Roy, S.: Effects of Upstream Obstacles on Energy Production of Solar and Wind Farms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4688, https://doi.org/10.5194/egusphere-egu24-4688, 2024.

Particulate matter (PM) in the atmosphere and deposited on solar photovoltaic (PV) panels reduce PV energy generation. Reducing anthropogenic PM sources will therefore increase carbon-free energy generation. However, we lack a global understanding of the sectors that would be the most effective at achieving the necessary reductions in PM sources. We combine well-evaluated models of solar PV performance and atmospheric composition to show that deep cuts in air pollutant emissions from the residential sector substantially benefit Asian PV power output. Specifically, halving residential emissions of PM would lead to an additional 10.3 TWh yr^-1 and 2.5 TWh yr^-1 of PV energy generation in China and India in 2020, respectively. Compared to the 2020 electricity generation of 261.6 TWh yr^-1 and 54.4 TWh yr^-1 from solar PV technology in China and India, respectively, these unrealised sources of energy generation represent an improvement of approximately 4-5%. While anthropogenic PM sources originate mainly from producers, they are responding to changes in domestic and international consumer demand. This raises a critical question about the extent to which consumers, who benefit from the emission process, should be responsible for the resulting unrealised, cleaner PV energy generation. Focusing on Northeast Asia (NEA), we investigate the source-receptor relationship of PV energy losses attributable to PM pollution among China, South Korea, and Japan by incorporating a new input-output model into the combined models of solar PV performance and atmospheric composition. Our findings reveal that the solar energy generation losses attributable to PM pollution in NEA caused by emissions produced in China surpass those linked to China’s consumption that stimulates emissions in China and elsewhere, with the disparity amounting to 9.3 TWh yr^-1. Conversely, a reverse pattern is observed for solar energy generation losses linked to emissions produced versus induced by consumption in South Korea and Japan, where the disparities are found to be -0.023 TWh yr^-1 and -0.231 TWh yr^-1, respectively. In other words, when we consider international trade across NEA, we find there is diminished (augmented) responsibility for China (South Korea and Japan) in explaining PV-related energy losses attributable to PM pollution.

How to cite: Yao, F., Palmer, P., Liu, J., Chen, H., and Wang, Y.: Impacts of Air Pollutant Emissions on Solar Energy Generation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4715, https://doi.org/10.5194/egusphere-egu24-4715, 2024.

With its commitment to reduce greenhouse gas emissions and harnessing the potential of renewable energy, the West African region is at the forefront of global environmental challenges. This work focuses on the specific aspect of solar energy, which holds significant promise in the region. High quality solar energy forecasts are necessary for solar plants and power systems management, while they remain poorly developed in this region, in particular because of the specificities of the West African climate. We evaluate the errors in Global Horizontal Irradiance (GHI) operational forecast models for two Sahelian solar power plants, Zagtouli in Burkina Faso and Sococim in Senegal, and investigate their links with local meteorological conditions, with a specific focus on clouds and dust aerosols.

This work begins by assessing aerosol products and our results support the use of the CAMS reanalysis for the assessment of Aerosol Optical Depth (AOD), particularly with respect to dust aerosols. We then assess the performance of three operational GHI forecast products: the Global Forecast System (GFS, NCEP/NOAA), the Integrated Forecast System (IFS, ECMWF), and SteadyMet (SM), developed by French company Steadysun, which is computed from the previously mentioned Numerical Weather Prediction (NWP) model outputs. The analysis reveals that IFS and SM outperform GFS in terms of forecast accuracy, with SM showing a slight advantage due to its probabilistic nature, which provides valuable information on forecast uncertainty.

Closer examination reveals a significant relationship between GHI forecast errors and local meteorological characteristics. These errors are more pronounced during the wet season, primarily attributed to cloud occurrence. Dust events are found to play a secondary role, particularly during the dry season. Correlation analyses underline the main link between forecast errors and cloudiness, while co-occurrence analyses highlight the fact that dust aerosol loading is a secondary factor in forecast errors for the GHI directly or for cloud representation (aerosol-cloud interaction).

How to cite: Anquetin, S., Clauzel, L., Lavaysse, C., Tremoy, G., and Raynaud, D.: West African operational daily solar forecast errors and their links with meteorological conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5042, https://doi.org/10.5194/egusphere-egu24-5042, 2024.

The increase in renewable energy production demands forecasting of wind and solar radiation due to their greater variability compared to non-renewable energy sources. The variability in solar energy is primarily caused by clouds. Large Eddy Simulation (LES) proves effective for high-resolution solar radiation prediction, capturing clouds like stratocumulus where large-scale models cannot. LES uncertainty in clouds primarily stems from initial conditions taken from these large-scale models. In this study, an ensemble Kalman filter assimilates observations into LES initial conditions. A large ensemble is created from a limited amount of LES runs by taking advantage of their internal variability. From this ensemble and measurements from the Cabauw measurement site, improved initial conditions are calculated for a range of stratocumulus cases. These cases are simulated without further interference. The method shows a 60% reduction in Root Mean Square Error (RMSE) for shortwave down solar radiation at the initial condition over the unfiltered initial condition. This improvement persists at 45% after 3 hours of simulation, showing the lasting impact of assimilated observational data on predictive accuracy. The decrease can be accounted to a combination of microphysical processes, energy fluxes from the lower boundary condition and the advective tendencies in the model. In future work, possible improvements to these processes will be identified and the method will be evaluated for other sites and cloud conditions.

How to cite: van Soest, M., Jonker, H., and de Roode, S.: Solar Radiation Forecasts from Large Eddy Simulations and Observations using Ensemble Kalman Filtering, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18854, https://doi.org/10.5194/egusphere-egu24-18854, 2024.

Energy systems across the globe are evolving to meet climate mitigation targets set by the Paris Agreement. This process requires a rapid reduction on nations’ reliance on fossil fuels and significant uptake of renewable generation (such as wind power, solar power, and hydropower). In parallel to the decarbonisation of the electricity sector, both the heat and transport sectors are electrifying to reduce their carbon intensity. Renewable energy sources are weather-dependent, causing production to vary on timescales from minutes to decades. A consequence of this variability is that there may be periods of low renewable energy production, here termed ‘renewable energy droughts’. This energy security challenge needs to be addressed to provide a consistent power supply and to ensure grid stability. India is chosen here as a study area as a region that already has a large existing proportion of renewable generation (42 GW of wind power, 61 GW of solar power and 51 GW of hydropower were installed as of October 2022) and a region that experiences good sub-seasonal predictability in large-scale patterns.

In this study, we use broad variety of data sources to quantify potential and realised capacity over India from 1979 to 2022 using the ERA5 reanalysis and a range of open source renewable energy installation data. Using gridded estimates of existing installed renewable capacity combined with our historical capacity factor dataset, we create a simple but effective renewable production model for each Indian state and at national level. We use this model to identify the timing of historical renewable energy droughts and then discuss potential weaknesses in the existing grid – particularly a lack of complementarity between wind and solar production in north India – and vulnerability to high deficit generation in the winter. The data produced here have all been made open access and the methods could easily be reproduced over any region of interest.

We then consider the weather patterns that could cause the largest renewable energy droughts over India and investigate potential sources of predictability. Existing large-scale daily weather types (based on large-scale wind map clustering) as well as novel patterns created by k-means clustering of more relevant variables for wind and solar power are used to investigate the different weather patterns causing renewable energy droughts. Renewable energy droughts largely occur during the winter season (January and February) and are caused by low seasonal wind speeds in combination with weather patterns bringing high cloud cover. These are mainly winter anticyclones and western disturbances.

Sources of potential sub-seasonal predictability are considered for the largest renewable energy droughts, including the Madden Julian Oscillation and Boreal Summer Intra-Seasonal Oscillation. Although both have a stronger relationship with high energy production days, links between phases of these two modes of variability and renewable energy droughts have been identified. These could help to provide early warnings for conditions that challenge supply security in the future.

How to cite: Bloomfield, H., Hunt, K., and Dijkstra, I.: Identifying weather patterns responsible for renewable energy production droughts in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7725, https://doi.org/10.5194/egusphere-egu24-7725, 2024.

The production of renewable energy from wind and solar sources is intricately linked to meteorological conditions, where wind speed and solar radiation play critical roles. Due to the success of renewable energies, wind turbines are increasingly placed in sites with complex terrain, while solar panels are increasingly situated in alpine areas. However, current weather models often struggle to accurately forecast the weather, especially over complicated topography, due to limitations in spatial resolution. This leads to inaccurate predictions of power production, impacting the efficiency and reliability of renewable energy systems. To address this challenge, Meteomatics developed the EURO1k model, the first pan-European weather model with a 1 km² spatial resolution, providing optimal forecasting for wind and solar power.

The EURO1k model offers a 48-hour forecast horizon, generating a new forecast every hour. In addition to standard data sources such as weather stations, radar, satellite data, and radiosondes, the EURO1k model also incorporates data from a network of Meteodrones - small, unmanned aircraft systems developed by Meteomatics - which collect vertical atmospheric profiles up to 6000m in altitude. The high resolution of the EURO1k model enables accurate representation of small-scale weather patterns, resulting in highly accurate and precise forecasts.

Meteomatics uses a forecast system that combines various global and regional weather models to predict wind and solar power, aiming to reduce average errors. Recently, EURO1k has been integrated into this system, improving intraday and day-ahead power production forecasts. The normalized root mean square error (nRMSE) was reduced by up to 8.1% for intraday and by up to 8.5% for the day-ahead wind power forecast. Furthermore, a comparison of day-ahead forecasts with actual production data, combined with balancing energy costs, demonstrates improved earnings with the addition of the EURO1k model. Indeed, the EURO1k shows especially better performance in weather situations with large uncertainties. This underscores the added value of EURO1k in power forecasting, enhancing the cost efficiency of renewable energies and fostering greater integration into the energy mix, thereby reducing CO₂ emissions.

How to cite: Pasquier, J. T., Rausch, J., Piot, M., Schmoeckel, J., Thaler, M., Schluchter, C., and Fengler, M.: Improving Renewable Energy Forecasting with Meteomatics EURO1k Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17204, https://doi.org/10.5194/egusphere-egu24-17204, 2024.

The applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States is evaluated using wind speed and irradiance variables from multiple observational data sets.  After converting ERA5 and observed meteorological variables into wind power and solar power, comparisons demonstrate that significant errors in the ERA5 reanalysis exist limiting its direct applicability for a wind and solar energy analysis.  Overall, ERA5-derived solar power is biased high, while ERA5-derived wind power is biased low.  Errors for the shortest duration, most extreme solar negative anomaly events are found to be statistically reasonably well represented in the ERA5, when completely overcast conditions occur in both ERA5 and observations.  Longer duration events on weekly to monthly timescales, which include partially cloudy days or a mix of cloud conditions, have ERA5-derived solar power errors as large as 40%.  ERA5-derived solar power errors are found to have consistent characteristics across the CONUS region.  The negative bias errors in the ERA5 windspeeds and wind power are largely consistent across the central and northwestern US, and offshore, while the eastern US has an overall small net bias.  For weekly to monthly timescales, the uncorrected ERA5-derived wind power errors approach 50%.  Corrections to the ERA5 are derived using a quantile-quantile method for solar power, and linear regression of wind speed for wind power.  These corrections greatly reduce the ERA5 errors, including for extreme events associated with wind and solar energy droughts, that will be most challenging for electric grid operation, while also avoiding potential over-inflation of the reanalysis variability resulting from differences between point-measurements and the temporally and spatially smoother reanalysis values.

How to cite: Wilczak, J. M., Akish, E., Capotondi, A., and Compo, G.: Evaluation and Bias Correction of the ERA5 Reanalysis for Wind and Solar Energy Applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12053, https://doi.org/10.5194/egusphere-egu24-12053, 2024.

Renewable energies (RES) will play a central role in national energy systems worldwide in the near future, boosted by the climate change issue and the ever-growing competitiveness of these energies. An example is the Spanish roadmap to produce 80% of its electricity from renewables by 2030.
However, the transition from the current generation mix to decarbonized energy systems is a formidable challenge, as they must be technically reliable and economically viable. To design such systems, the spatial and temporal variability of RES, combined with proper simulation tools, are determinant. In recent decades, energy system models have emerged as valuable tools for conducting these analyses. These models allow, for a specific region, the analysis of the optimal allocation and sizing of new renewable plants, taking into account the variability of generation and demand, energy costs, integration and the issue of transmission. The key input to these models is a database of RES resources in the study region. However, in many cases, the extent to which these databases represent the actual RES for a given country is far from optimal, reducing confidence in the results. In general, current RES databases face two main problems: 1) low reliability of energy estimates and 2) lack of adequate spatial and/or temporal resolution. In most cases, these problems arise from the lack of actual measurements for model training and validation.
In this work, we present SHIRENDA (Spanish High-resolution Renewable ENergies and Demand database), an enhanced open access database of Spanish renewable energies resources and demand. The database consists of hourly values of wind, solar photovoltaic and hydroelectric capacity factors (CF), together with electricity demand, covering the period 1990-2020, for each of the Spanish NUTS3 regions, which is an unprecedented spatial resolution so far. CFs and demand values were derived using state-of-the-art machine learning models based on: 1) actual values of installed RES capacities (Jiménez-Garrote et al, 2023); 2) real energy and demand data derived from the Spanish TSO and 3) meteorological data derived from the ERA5 reanalysis. The database covers the period 1990-2020, with the period 2014-2020 used for model training and validation purposes.
The SHIRENDA database has been developed within the framework of the MET4LOWCAR project, funded by the Government of Spain, and aims to gather the desirable characteristics to carry out reliable studies on modeling and analysis of energy systems, thus contributing to an adequate energy transition. Notably, the high spatial resolution allows the very high spatial variability of RES resources in the study region to be properly taken into account. At the same time, the high temporal resolution, along with the temporal coverage, allows for properly assessing the impact of climate variability, extreme meteorological conditions and compound events in a future decarbonized energy systems in Spain. 

Reference: Jimenez-Garrote et al, 2023. https://doi.org/10.1016/j.solener.2023.03.009

How to cite: Pozo-Vázquez, D., Sánchez-Hernandez, G., Jiménez-Garrote, A., López-Cuesta, M., Galván-León, I., Aler-Mur, R., Tovar-Pescador, J., Ruiz-Arias, J. A., and Santos-Alamilllos, F.: SHIRENDA: A long-term high-resolution database of electricity demand and wind, hydro and PV renewable resources for Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1609, https://doi.org/10.5194/egusphere-egu24-1609, 2024.