Displays

Solar photovoltaic (PV) capacity has risen exponentially, with the majority deployed as ground-mounted solar parks, across the world. Deployments are projected to continue, leading to further land use change with implications for the hosting environment, including perturbations in ecological processes that underpin the supply of natural capital and ecosystem services. Whilst alterations to within solar park climate of magnitudes known to effect ecosystems processes have been quantified, the spatial extent remains unclear. In this study, we use remote sensing and field data to provide evidence of a solar park land surface temperature (LST) cool island. Specifically, we quantify a LST cooling of up to 2.3 ℃ outside the solar park boundary, with the effect declining rapidly with distance from the solar park but extending up to 730 m away. The magnitude of cooling observed is sufficient to alter ecosystem processes, including greenhouse gas emissions with implications for the carbon intensity of the electricity produced. Consequently, we need to better understand the local climatic impacts of solar parks and associated cascading impacts on ecosystem function to establish the broader environmental co-benefits and costs of this rapidly growing means of low carbon electricity production.

How to cite: Armstrong, A., Hernandez, R. R., Blackburn, G. A., Davies, G., Hunt, M., Whyatt, J. D., and Guoqing, L.: Local microclimatic impacts of utility scale photovoltaic solar parks , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8452, https://doi.org/10.5194/egusphere-egu2020-8452, 2020.

We have examined the applicability of a new set of 8 tailored weather regimes (WRs) to reproduce wind power variability in Western Europe. These WRs have been defined using a substantially smaller domain than those traditionally used to derive WRs for the North Atlantic-European sector, in order to maximize the large-scale circulation signal on wind power in the region of study. Wind power is characterized here by wind capacity factors (CFs) from a meteorological reanalysis dataset and from high-resolution data simulated by the Weather Research and Forecasting (WRF) model. We first show that WRs capture effectively year-round onshore wind power production variability across Europe, especially over northwestern / central Europe and Iberia. Since the influence of the large-scale circulation on wind energy production is regionally dependent, we have then examined the high-resolution CF data interpolated to the location of more than 100 wind farms in two regions with different orography and climatological features, the UK and the Iberian Peninsula.

The use of WRs allows discriminating situations with varied wind speed distributions and power production in both regions. In addition, the use of their monthly frequencies of occurrence as predictors in a multi-linear regression model allows explaining up to two thirds of the month-to-month CF variability for most seasons and sub-regions. These results outperform those previously reported based on Euro-Atlantic modes of atmospheric circulation. The improvement achieved by the spatial adaptation of WRs to a relatively small domain seems to compensate for the reduction in explained variance that may occur when using yearly as compared to monthly or seasonal WR classifications. In addition, our annual WR classification has the advantage that it allows applying a consistent group of WRs to reproduce day-to-day wind speed variability during extreme events regardless of the time of the year. As an illustration, we have applied these WRs to two recent periods such as the wind energy deficit of summer 2018 in the UK and the surplus of March 2018 in Iberia, which can be explained consistently by the different combinations of WRs.

How to cite: García-Herrera, R., Garrido-Perez, J. M., Ordóñez, C., Barriopedro, D., and Paredes, D.: Impact of weather regimes on wind power variability in western Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18797, https://doi.org/10.5194/egusphere-egu2020-18797, 2020.

How to cite: Gonzalez, P., Brayshaw, D., and Schiemann, R.: Impact of blocking on low wind events and its representation by high-resolution GCMs: An energy perspective , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20614, https://doi.org/10.5194/egusphere-egu2020-20614, 2020.

The New European Wind Atlas (https://map.neweuropeanwindatlas.eu) is developed based on the simulated wind field over Europe from a mesoscale model coupled to a microscale component through a statistical downscaling approach. The simulation that provides mesoscale inputs within the model chain has been decided upon a careful sensitivity analysis of potential model configurations. In order to accomplish model resolutions of 3 km over Europe, the broader European domain is partitioned into a set of 10 partially overlapping tiles. The wind field is simulated with the WRF model over these tiles and finally blended into a single domain. The wind outputs from a reference simulation is evaluated on the basis of its comparison with an observational database specifically compiled and quality controlled for the purpose of validating the wind atlas over the complete European domain. The observational database includes surface wind observations at ca. 4000 sites as well as 16 masts datasets. The observational dataset of surface wind (WISED) is informative about the spatial and temporal variability of the wind climatology, punctuated with singular masts that provide information of wind velocities at height. The validation of the mesoscale simulation aims at investigating the ability of the high-resolution simulation to reproduce the observed intra-annual variability of daily wind within the entire domain.

Observed and simulated winds are higher at the British, North Sea and Baltic shores and lowlands. Correlations are typically over 0.8. Surface wind variability tends to be overestimated in the northern coasts and underestimated elsewhere and inland. Mast wind variability tends to be overestimated except for some southern sites. Seasonal differences seem minor in these respects. Biases and RMSE can help identifying if systematic errors in specific tiles take place.

Therefore, performing model simulations of a high horizontal resolution over the broader European domain is possible. We can learn about the variability of surface and height wind both from observations and model simulations. Model observations are not perfect, but observations also present uncertainties. Good quality wind data, both at the surface and in masts are a requisite for robust evaluation of models. European wide features of wind variability can be recognized both in observations and simulations.

How to cite: García-Bustamante, E., Navarro, J., González-Rouco, J. F., Lucio- Eceiza, E. E., Rojas-Labanda, C., and Palomares, A.: The European wind from observational and simulated databases, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19846, https://doi.org/10.5194/egusphere-egu2020-19846, 2020.

Wind farm far wakes are of particular interest for offshore installations, as turbulence intensity, which is the main driver for wake dissipation, is much lower over the ocean than over land. Therefore, wakes behind offshore wind turbines and wind parks are expected to be much longer than behind onshore parks. 

In situ measurements of the far wakes were missing before the initiation of the research project WIPAFF (WInd PArk Far Fields) in 2015. The main results of which are reported here. WIPAFF has been funded by the German Federal Ministry for Economic Affairs and Energy and ran from November 2015 to April 2019.  The main goal of WIPAFF was to perform a large number of in situ measurements from aircraft operations at hub height behind wind parks in the German Bight (North Sea), to evaluate further SAR images and to update and validate existing meso-scale and industrial models on the basis of the observations to enable a holistic coverage of the downstream wakes.
 
A  unique  dataset  from  airborne in situ data,  remote sensing  by  laser  scanner  and  SAR  gained  during  the WIPAFF  project  proves  that  wakes  up to  several  tens of kilometers exist downstream of offshore wind farms during stable conditions, while under neutral/unstable conditions, the wake length amounts to 15 km or less. Turbulence occurs at the lateral boundaries of the wakes, due to shear between the reduced wind speed inside the wake and the undisturbed flow. Data also indicates that a denser wind park layout increases the wake length additionally due to a higher initial wind speed deficit. The recovery of the decelerated flow in the wake can be modeled as a first order approximation by an exponential function. The project could also reveal that wind-farm parameterizations in the numerical meso-scale WRF model show a feasible agreement with the observations. 

How to cite: Platis, A., Bange, J., Bärfuss, K., Canadillas, B., Hundhausen, M., Djath, B., Lampert, A., Schulz-Stellenfleth, J., Siedersleben, S., Neumann, T., and Emeis, S.: Offshore wind farm far field - Results of the project WIPAFF, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10087, https://doi.org/10.5194/egusphere-egu2020-10087, 2020.

Offshore wind power is seen as a large renewable energy resource due to the high and continuous wind speeds over the ocean.However, as wind farms expand in scale, wind turbines increasingly remove kinetic energy from the atmospheric flow, reducing wind speeds and expected electricity yields.Here we show that this removal effect of large wind farms and the drop in yields can be estimated in a relatively simple way by considering the kinetic energy budget of the lower atmosphere, which we refer to as the KEBA approach.We first show that KEBA can reproduce the estimated, climatological yields of wind farms of different sizes and locations using previously published numerical model simulations with an explicit wind farm representation.  We then show the relevance of these reductions by evaluating the contribution of offshore wind energy in specific scenarios of Germany’s energy transition in the year 2050.Our estimates suggest that due to reduced wind speeds, mean capacity factors of wind farms are reduced to 33 - 39%, which is notably less than capacity factors above 50% that are commonly assumed in energy scenarios.This reduction is explained by KEBA by the depletion of the horizontal flow of kinetic energy by the wind farms and the low vertical renewal rate, which limits large-scale wind energy potentials to less than 1 W m^-2 of surface area.We conclude that wind speed reductions are likely to play a substantial role in the further expansion of offshore wind energy and need to be considered in the planning process.These reduced yields can be estimated by a comparatively simple approach based on budgeting the kinetic energy of the atmosphere surrounding the wind farms.

How to cite: Kleidon, A. and Miller, L.: Estimating offshore wind power potentials that account for the kinetic energy removal by wind turbines: the Kinetic Energy Budget of the Atmosphere (KEBA) approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4988, https://doi.org/10.5194/egusphere-egu2020-4988, 2020.

Variability of solar irradiance is an important factor concerning large-scale integration of solar photovoltaics (PV) systems onto the electricity grid. Calculations of irradiance are computationally expensive, leaving operational meso-scale forecasting models struggling to achieve accurate results. Moreover, such models deliver outputs at a temporal resolution in the order of hours, whereas from a grid-integration point of view, minute-to-minute variability is a major concern. In previous work, we found that absolute power peaks in the order of seconds are up to 18% higher compared to 15-minute resolution for irradiance and even upwards of 22% higher for household PV systems. Moreover, these maximum peaks in output power are solely observed under mixed-cloud conditions, for which alse the greatest variability is found. In this work we present a machine-learning model which can forecast sub-resolution variability of irradiance, based on standard meso-scale outputs of the HARMONIE model of the The Royal Netherlands Meteorological Institute (KNMI). For training and validation, irradiance measurements obtained at a 1-second interval are used of the Baseline Surface Radiation Network (BSRN) site of Cabauw. A tree-based model was employed, for which the optimum members were constructed using extreme gradient boosting. In this work, we explore the dominant features of the model and link the machine-learned-relations to meteorological processes and dynamics. This research was executed in collaboration with the Distribution Grid Operator Alliander.

How to cite: Kreuwel, F. and van Heerwaarden, C.: Forecasting sub-resolution temporal variability of irradiance, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10152, https://doi.org/10.5194/egusphere-egu2020-10152, 2020.

The precise forecasting of ramps in production of windparks is a problem that is not satifsfactorily solved. This is of particular interest because ramps contribute to a major part to the forecasting error in power production, in particular for offshore wind parks.

Since ramps are often due to fronts passing a location, we developed a method for the correction of front speed and -direction using a combination of wind park meteorological measurements and numerical weather prediction (NWP). On one hand we use conventional methods like the Canny algorithm for NWP data, on the other hand, we use data from a collection of wind parks to determine a passing front. By the front speed, and the relative location of wind parks, the front speed is computed and a correction can  be applied to downstream wind parks.

The results can be  validated and a corresponding error measure can be computed on the basis of measured and numerical data. Our method shall be implemented into a proprietary forecast system with the goal of an automatized detection and correction mechanism.

How to cite: Feldtkeller, F., Kirsch, A., Denisenko, G., and Abel, M.: Front detection using wind park data and NWP, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7187, https://doi.org/10.5194/egusphere-egu2020-7187, 2020.

Abstract

Over the past years, environmental concerns have played a key role in the development of renewable energy sources (RES). In Europe, the installed capacity of photovoltaic (PV) has increased from around 10 GW in 2008 to nearly 119 GW in 2018 [1]. Due to this high penetration rate and the intermittent nature of RES, several challenges appear related to the economic and secure operation of a power system. To overcome these challenges, it is necessary to develop reliable forecasts of RES, and namely of PV production, for the next hours to days to adjust production planning, while intra-hourly forecasts may contribute to optimize operation of storage units coupled to RES plants.

The aim of this paper is to present a novel spatio-temporal (ST) spot forecasting approach able to use multiple heterogeneous sources of data as inputs to forecast short-term PV production (i.e. from 15 minutes up to a day ahead).

First, we consider measured production data from nearby power plants as input to forecast the output of a specific PV plant. These data permit to exploit the correlation between the production data of spatially distributed PV sites. The classical ST approach in the literature, based only on this source of data [2], permits to improve predictability for the next few minutes up to 6 hours ahead.

Then, we extend the model by the use of satellite images (i.e. global horizontal irradiance (GHI)) which provide meaningful spatial information at a larger extent.

Finally, we consider Numerical Weather Predictions (NWPs) as input, which permit to extend the applicability of the model to day-ahead lead times, so that, overall, the resulting model covers efficiently horizons ranging from a few minutes to day ahead.

The spatio-temporal relationships being dependent on the particular meteorological situation of the day at hand, we apply an analog ensemble approach, to condition the learning process with historical observations corresponding to similar meteorological situation. We used the analogue approach to select a subset of similar historical situations over which a dynamical calibration of the forecasted model is done, as it was for example suggested by [3,4]. In our paper we extend the analogs ensemble approach by considering geographically distributed observations of the physical variables of interest (as suggested by [4] for hydrological issues) rather than only those at the level of the PV plant.

The performance of the proposed ST model with heterogeneous inputs is compared with reference models and advanced ones such as the Random Forest model. Historical production data collected from 9 PV plants of CNR are considered. The power units, located in the South-East France, exhibit relevant spatial correlations which make them suitable for the proposed ST model.

References

• [1] IRENA - https://www.irena.org/Statistics/Download-Data
• [2] Agoua, Girard, Kariniotakis. Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production. IEEE Transactions on Sustainable Energy , IEEE, 2018, 9 (2), pp. 538 - 546. https://doi.org/10.1109/TSTE.2017.2747765
• [3] Alessandrini, Delle Monache, Sperati, Cervone. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 2015. https://doi.org/10.1016/j.apenergy.2015.08.011
• [4] Bellier, Bontron, Zin. Using meteorological analogues for reordering postprocessed precipitation ensembles in hydrological forecasting. Water Resources Research, 2017. https://doi.org/10.1002/2017wr021245

How to cite: Bellinguer, K., Girard, R., Bontron, G., and Kariniotakis, G.: Short-term photovoltaic generation forecasting using multiple heterogenous sources of data based on an analog approach., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13790, https://doi.org/10.5194/egusphere-egu2020-13790, 2020.

Trading of photovoltaic (PV) energy generation involves several decision making processes at different times with different objectives. For example, a PV power plant coupled with a Battery Energy Storage System (BESS) has to provide bids in the day-ahead electricity market, but can also provide ancillary services. On the delivery day, it can also participate in intra-day trading sessions, and must decide which quantity to charge or discharge from the BESS in real-time. These successive decision-making processes all require forecasts of the energy production level for different forecast horizons. Besides, such decisions are generally not taken for a single plant at a single location but for a collection of several geographically distributed plants.

However, the models and the inputs used for the different forecast horizons are often different. In situ measurements are more accurate for very-short term forecasts (real-time to one hour ahead forecasts), satellite data is used for short-term forecasts (up to 6 hours ahead), and Numerical Weather Predictions (NWP) are used for long-term forecasts (day-ahead and longer). Models also vary, with auto-regressive approaches being commonly used for very-short term forecasts, while longer forecast horizons use a wide range of machine learning models. PV producers have thus to develop and maintain numerous forecasting models for the different decision-making processes they are involved in, usually fitted for each power plant. This increases further the complexity of the decision-making processes.

In this work we propose a forecasting model that can use all the inputs mentioned before, and weights them according to the forecasting horizon. It can thus operate from very short-term to day-ahead forecast horizons with state-of-the-art performance. It can also directly provide probabilistic forecasts for an aggregation of power plants, thus allowing having a single forecasting model for managing a virtual power plant. The model follows the “lazy learning” paradigm, where generalization from the training set is only computed when a forecast is requested. Thus, the model is resilient to changes in the neighborhood of the plant (surrounding environment, partial outage, soiling, etc.). The model is based on the Analog Ensemble (AnEn) method. However it is structurally expanded to allow the method to use an arbitrary large number of inputs. Each input is then weighted depending on the forecast horizon, which allows dynamically selecting the most relevant inputs depending on the horizon.

The model is evaluated for short-term and day-ahead forecasts, and compared with a Quantile Regression Forest (QRF) and Bayesian Automatic Relevance Determination (ARD) for day-ahead forecasts, and a linear Auto-Regressive Integrated Moving Average (ARIMA) model for short term forecasts. Results show that the AnEn model is competitive with the QRF and ARD models in day-ahead forecasting, while requiring less computational resources and without a need for regular retraining. It is also better than the ARIMA model for short-term forecasting. An evaluation conditional to the the weather variability allow to assess the model performance in the best and worst condition.

How to cite: Carriere, T. and Kariniotakis, G.: Towards a seamless approach for photovoltaïc forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21753, https://doi.org/10.5194/egusphere-egu2020-21753, 2020.

The aggregation of multiple renewable plants located in distinct climate zones, using different energy sources, enables to reduce the production uncertainty when compared to the production of a single plant. Such aggregations, controlled by a Virtual Power Plant (VPP) system, are good candidates for the provision of ancillary services. Stochastic optimization models are available to optimize biddings on ancillary services and energy markets (see for instance [1]). These models require trajectories of the renewable VPP production that anticipate production uncertainty and reproduce correctly the temporal correlations observed in the production signal. This is particularly important in ancillary services markets, where a reserve bid must be guaranteed over a production duration or validity period during which power fluctuations are significant (e.g. lasting currently 24 hours on the European common market for Frequency Containment Reserve, with a foreseen evolution to 4 hours by July 2020 [2]). 
Production trajectories may be obtained by coupling probabilistic forecasts and a model of temporal dependencies between forecast horizons [3] and possibly spatial dependencies in the case of a multivariate forecast at the scale of a region or a portfolio [4]. In the case of a renewable VPP, the aggregated production is primarily of interest. In this work, we propose a methodology to generate trajectories of aggregated production from probabilistic forecasts obtained with decision-tree based models or neural networks. A copula models the dependency between forecast horizons and the space defined by the plants contained in the aggregation. The model is tested in a day-ahead forecasting configuration on a 54 MW VPP comprising 15 plants with 3 different energy sources (Photovoltaics, Wind, Hydro). The comparison of trajectories generated from a direct forecast of the aggregated production and from forecasts at lower levels of the aggregation shows that the latter solution reproduces with more accuracy the temporal variability of the aggregated production over the whole horizon range, especially when Photovoltaics dominates the production capacities in the aggregation (15 % improvement of the Variogram Score).
 [1]: Soares, T., & Pinson, P. (2017). Renewable energy sources offering flexibility through electricity markets. Technical University of Denmark.
[2]: ENTSO-E. (2018). TSO’s proposal for the establishment of common and harmonised rules and processes for the exchange and procurement of Balancing Capacity for Frequency Containment Reserves (FCR) TSOs’ proposal for the establishment of common and harmonised rules and pro-c, (October), 1–9.
[3]: Pinson, P., Madsen, H., Nielsen, H. A., Papaefthymiou, G., & Klöckl, B. (2009). From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy, 12(1), 51–62. 
[4]: Golestaneh, F., Gooi, H. B., & Pinson, P. (2016). Generation and evaluation of space–time trajectories of photovoltaic power. Applied Energy, 176, 80–91. 

How to cite: Camal, S., Michiorri, A., and Kariniotakis, G.: Forecast trajectories for the production of a renewable virtual power plant able to provide ancillary services, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19081, https://doi.org/10.5194/egusphere-egu2020-19081, 2020.

Multiple uncertainties, including from the uncertainty of a single power (wind power or photovoltaic power) output forecasting to the uncertainty of the combined power output of wind and photovoltaic forecasting to the power shortage after hydropower compensation for wind and photovoltaic power output, exist in the wind-photovoltaic-hydropower system. Furthermore, as the forecast is updated, the above uncertainty will evolve accordingly. Revealing the evolution of multiple uncertainties is of great significance for the hydropower compensation for the combined power output of wind and photovoltaic. We use a generalized martingale model of forecast evolution to describe the uncertainty of a single power output. We then superimpose the single power output to obtain the combined power output of wind and photovoltaic. we establish a stochastic programming with recourse model for optimal scheduling of the hydropower compensation for wind and photovoltaic power output. The results indicate that the uncertainty of the combined power output of wind and photovoltaic forecasting is less than that of wind power output forecasting, and greater than that of photovoltaic power output forecasting. After hydropower compensates for combined power output of wind and photovoltaic, compared with the uncertainty of combined wind and photovoltaic power output forecasting, the uncertainty of power shortage is greatly reduced by 90%, which has significant benefits. And with the dynamic update of the forecast, the uncertainty of the single power output forecast, the uncertainty of the combined power output forecast, and the uncertainty of the power shortage will decrease accordingly.

How to cite: Liu, W., Wang, C., Lei, X., Zhong, P., and Lu, Q.: Stochastic optimal scheduling of hydropower compensation for wind and photovoltaic power output considering multiple uncertainties, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3641, https://doi.org/10.5194/egusphere-egu2020-3641, 2020.

Net carbon dioxide emissions have to be brought down to zero in the coming decades to hold the rise in global temperature in this century below the 2°C from pre-industrial levels. This target implies a fundamental transformation of the global energy system that will have to rely heavily on renewable energy sources. Among these, the harvesting of electricity from the wind plays an important role. Yet, climate change itself can impact the supply of renewable energy. Therefore, national climate mitigation plans need to make informed decisions regarding any changes to future extractable wind resources to consider the possible risks.

In this work, we explore the changes in wind climatology over the North Sea in the different shared socioeconomic pathways (SSP) emission scenarios as identified by the output of a selection of CMIP6 simulations. Many northern European countries rely on the wind resources of the North Sea for climate mitigation. As a first step, however, we validate various aspects of the wind speed and direction and their variability in the historical CMIP6 simulations as compared to multiple long-term reanalyses. The work also includes calculations of annual energy production for existing and planned wind farms in the North Sea and how these could change in the coming decades.

How to cite: Hahmann, A. N., Peña, A., Pryor, S. C., and Luzia, G.: Future wind energy resources in the North Sea as predicted by CMIP6 models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9093, https://doi.org/10.5194/egusphere-egu2020-9093, 2020.

Several international and national initiatives allow access to data from wind measurements, e. g. NREL wind prospector or the international wind atlases. But there is no adequate platform for combined commercial and scientific needs.

Kraken realizes open data concepts for scientific access combined with corresponding licenses for commercial use. To this end, we offer all users to provide links and a possibility for direct upload of wind-related data. Information on data provenance and usability concepts should be provided.

This way, we can collect and provide data of different quality (possibly not to scientific standards) and for different use cases. Scientifically, this may allow access to, e. g., wind park data which would otherwise not be accessible. Commercially, an access to the enormous data bases is possible at one place, a problem that often is underestimated.

On the long run, automatized intelligent analysis of the data will be implemented and the corresponding reports may be published, depending on the licenses related to the underlying data. The whole project is intended to be community-based and extensible to all kind of renewable energy data.

In a first attempt, a website has been launched with limited functionality, now we are trying to involve as many as possible data sources. In addition, we welcome the open implementation of analyses as already offered by other sites (site characterization, feature engineering, improved weather parameterization).

How to cite: Kirsch, A., Feldtkeller, F., and Abel, M.: Kraken – a scientific and commercial data meta-platform for wind energy resources, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7184, https://doi.org/10.5194/egusphere-egu2020-7184, 2020.

Wind energy represents the leading source of renewable energy in many developed countries. South Korea has recently introduced large-scale programs to promote the transition from fossil fuels and nuclear power to renewable energy as a source of power. The Korean government has set an energy policy goal to increase the ratio of renewable energy to 20% by 2030. To this end, it is necessary to supply renewable energy facilities with a total capacity of 48.7GW including 30.8GW of photovoltaic power generation and 16.5GW of wind power generation by the target year. Accordingly, we should plan now for the regulation of the location to meet this developing need. However, in South Korea, forests cover 63% of the country's land area so that there is a limit to find a location for the installation of large-scale power generation facilities without occupying forest lands. For example, it is mainly located in forests or farmlands where land costs are relatively low, resulting in a decrease in forest resources and negative impacts on ecosystems and landscapes. Renewable energyexpansion planning should ensure that environmental criteria, of the type outlined in this study, are given appropriate considerations in onshore wind power project site selection. Many of the more problematic wind power sites are best left mountainous forest under the natural conditions, because the environmental or related social impacts are likely to be unacceptably high. Obviously, no plans are likely to be more environmentally desirable in those cases. The alternatives for onshore wind power siting considered the environmental criteria to achieve the goal of wind energy will be suggested.

How to cite: Park, J.-Y. and Lee, Y.-J.: Environmental criteria for site selection of wind power projects in South Korea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21386, https://doi.org/10.5194/egusphere-egu2020-21386, 2020.

Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The International Energy Agency (IEA) Wind Task on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, ...), forecast vendors and forecast users.
Collaboration is open to IEA Wind member states, 12 countries are already therein.

The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks for NWP models. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions.

The main result is the IEA Recommended Practice for Selecting Renewable Power Forecasting Solutions. This document in three parts (Forecast solution selection process, and Designing and executing forecasting benchmarks and trials, and their Evaluation) takes its outset from the recurrent problem at forecast user companies of how to choose a forecast vendor. The first report describes how to tackle the general situation, while the second report specifically describes how to set up a forecasting trial so that the result is what the client intended. Many of the pitfalls which we have seen over the years, are avoided.

Other results include a paper on possible uses of uncertainty forecasts, an assessment of the uncertainty chain within the forecasts, and meteorological data on an information portal for wind power forecasting. This meteorological data is used for a benchmark exercise, to be announced at the conference. The poster will present the latest developments from the Task, and announce the next activities.

How to cite: Giebel, G., Shaw, W., Frank, H., Pinson, P., Draxl, C., Zack, J., Möhrlen, C., Kariniotakis, G., and Bessa, R.: IEA Wind Task 36 Forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14253, https://doi.org/10.5194/egusphere-egu2020-14253, 2020.

The aim of this paper is to present the objectives, research directions and first highlight results of the Smart4RES project, which was launched in November 2019, under the Horizon 2020 Framework Programme. Smart4RES is a research project that aims to bring substantial performance improvements to the whole model and value chain in renewable energy (RES) forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of renewable energy forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological forecasting. Smart4RES concentrates on novel developments towards very high-resolution and dedicated weather forecasting solutions. It makes optimal use of varied and distributed sources of data e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to renewable energy forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including distributed and privacy-preserving learning and forecasting methods, as well as the advent of platform-enabled data-markets, with associated pricing strategies. Smart4RES places a strong emphasis on maximizing the value from the use of forecasts in applications through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.

How to cite: Kariniotakis, G., Camal, S., Bessa, R., Pinson, P., Giebel, G., Libois, Q., Legrand, R., Lange, M., Wilbert, S., Nouri, B., Neto, A., Verzijlbergh, R., Sauba, G., Sideratos, G., Korka, E., and Petit, S.: Smart4RES: Towards next generation forecasting tools of renewable energy production, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20205, https://doi.org/10.5194/egusphere-egu2020-20205, 2020.

Renewable energy plays a key role to play in the transition towards a low-carbon society and many countries have been investing in R&D and deployment of renewables over the last few decades. Despite its importance, relatively little attention has been focused on the crucial impact of weather and climate on energy demand and supply, or on the seasonal forecast generation or operational planning of renewable technologies. In particular, to improve the operation and longer-term planning of renewables it is essential to consider seasonal and subseasonal weather forecasting. Unfortunately, reports that focus on these issues are not common in the scientific literature.
Here we present a systematic review of the seasonal forecasting of wind and wind power for the Iberian peninsula and the Canary Islands, a region leading the world in the development of renewable energies (particularly wind), and thus an important illustration in global terms. To this end, we consider the scientific literature published over the last eleven years (2008-2018). An initial search of this literature produced 8355 documents, but our review suggests that only around 0.3% are actually relevant to our purposes. The results show that the teleconnection patterns (NAO, EA, and SCAND) and the stratosphere are important sources of predictability in the Iberian Peninsula and that GloSea5 is an effective model for seasonal wind forecasting for the region. We conclude that the existing literature in this crucial area is very limited, which points to the need for increased research efforts. Moreover, the approach and methods developed here could be applied to other areas for which systematic reviews might be either useful or necessary.

How to cite: Añel, J. A., Bayo-Besteiro, S., García-Rodríguez, M., and Labandeira, X.: State of the art of Seasonal and Subseasonal Wind and Wind Power Forecasting for the Iberian Peninsula and the Canary islands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12262, https://doi.org/10.5194/egusphere-egu2020-12262, 2020.

Climate change is evident at present with threatening effects as intense hurricanes, rising sea level, increase number of droughts, and shifting weather patterns. Burning of fossil fuels and anthropogenic activities increase the greenhouse gases concentration in atmosphere, which is a major cause behind the climate change. Renewable energy as solar is a good source for combating the causes of climate change by producing clean energy.  

The efficient integration of solar energy into electrical grids requires an accurate prediction of solar irradiance. The solar irradiance is the flux of radiant energy received per unit area of the earth from the sun. Existing techniques use basic stochastic (such Gaussian model, hidden Markov model, etc.) and ensemble neural network models for solar forecasting. However, recent literature reflects the potential of deep-learning models over the statistical model.

In this paper, we propose a deep-learning-based one-dimensional, multi-quantile convolution neural network for predicting the solar irradiance. The network employs dilation in its convolution kernel, which helps capturing the long-term dependencies between instances of the input climatic variables. Additionally, we also incorporate the attention mechanism between the input and learned representation from the convolution, which allows attending to the temporal instance of features for improved prediction. We perform both short-term (three hours ahead) and long-term (twenty-four hours ahead) solar irradiance prediction. We exhaustively present the forecast for all four seasons (spring, summer, fall, and winter) as well as for the whole year. We provide a point solar forecast along with forecast at different quantiles. Quantile forecast provides a range of estimates with varying confidence intervals, which allows better interpretation as compared to point forecast. This notion of confidence associated with each quantile makes the forecasting probabilistic.

In order to validate our approach, we consider two cities (Boulder and Fort Peck) from the SURFAD network and examine twenty climatic features as input to our model.  Additionally, we learned embedded reduced input dimension using an autoencoder. The proposed architecture is trained with all the input features and reduced features, independently. We observe the prediction error for Boulder is higher than Fort Peck, which can be due to the volatile weather of Boulder. The proposed model forecasts the solar irradiance for winter with a higher accuracy as compared to spring, summer, or fall. We observe the correlation coefficients as 0.90 (Boulder) and 0.92 (Fort Peck) between the actual and predicted solar irradiance.  The long-term forecast shows average improvements of 37.1% and 33.1% in root mean square error (RMSE) over existing numerical weather prediction model for Boulder and Fort Peck, respectively. Similarly, the short-term forecast shows improvements of 33.7% and 34.2% for the respective cities.

How to cite: Saha, M., Naik, B., and Monteleoni, C.: Probabilistic and Point Solar Forecasting Using Attention Based Dilated Convolutional Neural Network, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12818, https://doi.org/10.5194/egusphere-egu2020-12818, 2020.

Numerical weather predictions are often too coarse to represent single turbines in a wind park and post-processing of the individual turbines is necessary. However, individual post-processing can lead to inconsistencies in forecasts for a wind farm. Using standardized anomalies allows to forecast all turbines simultaneously. Therefore, a climatological mean is subtracted from observations/predictions and then divided by a climatological spread which eliminates any site-specific characteristics.

Additionally, different sources of input can be used, such as variables from a global model, a mesoscale model or observations to improve forecasts. However, to prevent overfitting a variable selection method is needed to determine the most important predictors. The combination of standardized anomalies and a variable selection method provides a convenient method for good forecasts of wind farms.

How to cite: Dabernig, M., Kann, A., and Schicker, I.: Statistical Post-Processing with Standardized Anomalies and Variable Selection for Wind Farm Forecasts , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16810, https://doi.org/10.5194/egusphere-egu2020-16810, 2020.

Wind gusts and high wind speeds need to be considered in wind power industry and power grid management as they affect construction, material, siting and maintenance of turbines and power lines. Furthermore, gusts are an important information source on turbulence conditions in the atmosphere at the respective sites.
Often, the wind farm operators only provide basic data of the turbines such as average wind speed, direction, power and temperature. However, they require forecasts of gusts, too. Thus, a simple gust estimation algorithm based on the average wind speed was developed. The algorithm is tested at different mast measurement sites and WFIP2 data and applied to selected wind turbines. Results show that the algorithm is skillful enough to be used as a first guess gust estimation for single turbines and is, thus, used for nowcasting.
For nowcasting for the first two hours with a temporal fequency of ten minutes solely observations are used. A high-frequency wind speed and gust nowcasting ensemble based on different machine learning methodologies, including an ensemble for every method, was developd. Used are boosting, random forest, linear regression, a simple monte carlo method and a feed forward neural network. Results show that perturbing the observations provides a good forecasting spread for at least some of the methods. However, for other methods the spread is reduced significantly. Most of the used methods are able to provide good forecastst. However, hyperparameter tuning for the lightGBM boosting algorithm and the neural network is still needed.

How to cite: Schicker, I. and Papazek, P.: A simple gust estimation algorithm and machine learning based nowcasting for wind turbines, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3034, https://doi.org/10.5194/egusphere-egu2020-3034, 2020.

The rapid development of offshore wind farms has raised concerns about the local environment and ecosystem. Wind farms influence the local meteorology by extracting kinetic energy from the wind field and by generating a large wake. The North Sea is one of the main regions of the world where the growth of offshore wind farms is rapidly increasing. In this study, we analyze the impact of large-scale offshore wind farms in the North Sea on local meteorology using regional climate model COSMO-CLM. For this purpose, the parametrization for wind turbine driven by Fitch et al. (2012) and Blahak et al. (2010), previously implemented in COSMO-CLM v 4.8 at KU-Leuven (Chatterjee et al. 2016), has been implemented in the latest version 5 of COSMO-CLM. Here we present the first results of COSMO-CLM long-term simulations with and without wind farms using mesoscale resolving high-resolution horizontal atmospheric grid spacing (~ 2 km).

How to cite: Akhtar, N. and Rockel, B.: Mesoscale resolving high-resolution simulation of wind farms in COSMO-CLM 5, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7178, https://doi.org/10.5194/egusphere-egu2020-7178, 2020.

The purpose of this study is to develop an ensemble-based data assimilation method to accurately predict wind speed in wind farm and provide it for the use of wind energy intelligent forecasting platform. As Taiwan government aimed to increase the share of renewable energy generation to 20% by 2025, among them, the uncertain wind energy output will cause electricity company has to reserve a considerable reserve capacity when dispatching power, and it is usually high cost natural gas power generation. In view of this, we will develop wind energy intelligent forecasting platform with an error of 10% within 72 hours and expect to save hundred millions of dollars of unnecessary natural gas generators investment. Once the wind energy can be predicted more accurately, the electricity company can fully utilize the robustness and economy of smart grid supply. Therefore, the mastery of the change of wind speed is one of the key factors that can reduce the minimum error of wind energy intelligent forecasting.

There are many uncertainties in the numerical meteorological models, including errors in the initial conditions or defects in the model, which may affect the accuracy of the prediction. Since the deterministic prediction cannot fully grasp the uncertainty in the prediction process, so it is difficult to obtain all possible wind field changes. The development of ensemble-based data assimilation prediction is to make up for the weakness of deterministic prediction. With the prediction of 20 wind fields as ensemble members, it is expected to include the uncertainty of prediction, quantify the uncertainty, and integrate the wind speed observations of wind farms as well to provide the optimal prediction of wind speed for the next 72 hours. The results show that the prediction error of wind speed within 72 hours is 6% under different weather conditions (excluding typhoons), which proves that the accuracy of wind speed prediction by combining data assimilation technology and ensemble approach is better.

How to cite: Fan, Y.-M.: Ensemble-Based Data Assimilation for Wind Forecasting – Application to Wind Farm, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18144, https://doi.org/10.5194/egusphere-egu2020-18144, 2020.

The operation cost for wind parks make up a major fraction of the park’s overall lifetime cost. To facilitate an optimal wind park operation and maintenance, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behaviour in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. We demonstrate the successful detection and diagnosis of anomalous power production for a case study of a German onshore wind park for turbines of 3 MW rated power.

How to cite: Meyer, A.: Machine-learning based wind turbine operating state detection and diagnosis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6532, https://doi.org/10.5194/egusphere-egu2020-6532, 2020.

With the rapid development of social economy, China's energy demand has been growing at an alarming rate. The annual cumulative power generation is about  6.8 trillion kilowatts hour in 2017, and 70% of them is provided by fossil fuel resources, so it is important to promote the use of renewable and clean energy, such as solar power generation technology. The advantages of using solar panel roof in urban areas include reduction of the need of land use in the crowed city and less dependence on fossil fuels. However, there is need to understand impacts of solar roof on local climate, on energy supply during heatwaves, and associated economic benefits in China. This study selected a heatwave event in Jiangsu province, China to simulate the impact of solar panel roof on local thermal environment and energy supply. During that time, the cooling energy consumption reached more than half of the total electricity consumption. A new heat transfer scheme of solar panel roof was introduced into WRF/BEP/BEM model, which include layers (glass protective panel, solar panel, bottom plate) and was divided into two types for heat transfer calculation: bracket and non-bracket. The results showed that the urban average 2-m daytime temperature decreased by 0.3℃ in non-bracket case which is better than that of bracket case, while its cooling effect on nighttime temperature was small. For the bracket case, its cooling effect on daytime and nighttime air temperature were equal (0.2^oC). Both solar panel roofs can reduce indoor daytime air temperature with the maximum cooling effect around 11:00 local time for non-bracket roof and 14:00 for bracket roof. However, bracket roof increased nighttime indoor air temperature and air-conditioning energy consumption. Solar panel roofs also reduce daytime turbulent kinetic energy and constrain the development of boundary layer. Results also show that with solar photoelectric conversion efficiency being 0.14, the photovoltaic power generation can meet 84.1%, 61.3% and 35.9% of the cooling energy consumption for high-density, low-density residential areas and commercial areas, respectively, during this heatwave event.

How to cite: Wang, Y., Chen, F., Hao, X., and Wang, F.: The influence of solar panel roof on urban thermal environment and cooling energy demand during a heat wave event in 2017, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1437, https://doi.org/10.5194/egusphere-egu2020-1437, 2020.

This study investigates the potential impact of rooftop solar photovoltaic (PV) installation on local convection in urban area. Rooftop solar PV system is a space-efficient option to deploy renewable energies in the crowded urban area. However, as the installation scale increases, unintentional impact on local climate may emerge. In particular, PV array deployment can change the surface radiative balance and thus enhance or reduce the urban heat island effect. The urban heat island effect has been hypothesized to influence afternoon thunderstorm activity in the tropical island, Taiwan. Therefore, temperature change due to PV installation may also alter the local circulation and convection. This research takes the Taipei City, which is a metropolitan area in northern Taiwan, for a case study. Citywide rooftop solar PV installation experiments are conducted by using the Weather Research and Forecasting (WRF) Model coupled with urban canopy model. Different PV conversion efficiency scenarios, including currently and future technology levels, are simulated to evaluate the potential impact on local circulation and convection.

How to cite: Chang, P.-S., Chen, J.-P., and Hsieh, C.-I.: The Potential Impact of Solar Photovoltaic Installation on Local Circulation and Convection in Taipei, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13349, https://doi.org/10.5194/egusphere-egu2020-13349, 2020.

The Weather Research and Forecasting (WRF) model fitted with the Fitch et al. (2012) scheme for parameterization of the effect of wind energy extraction is used to study the effects of very large wind farms on regional weather. Two real data cases have been run in a high spatial resolution (grid size 500 m). Both cases are characterized by a convective westerly flow. The inner model domain covers the North Sea and Denmark. The largest windfarm consists of 200.000 wind turbines each with a capacity of 8MW. The model is run for up to 12 hours with and without the wind farm. The impact on the regional weather of these very large wind farms are studied and presented. Furthermore, the effect of horizontal spacing between wind turbines is investigated. Significant impact on the regional weather from the very large wind farms was found. Horizontal wind speed changes occur up to 3500m above the surface. The precipitation pattern is greatly affected by the very large wind farms due to the enhanced mixing in the boundary layer. Increased precipitation occurs at the front? within the wind farm, thus leaving the airmass relatively dry downstream when it reaches the Danish coast, resulting in a decrease in precipitation here compared to the control run. The formation of a small low level jet is found above the very large wind farm. Furthermore, wake effects from individual wind turbines decrease the total power production. The wind speed in the real data cases are well above the speed of maximum power production of the wind turbines. Yet most of the 200.000 wind turbines are producing only 1MW due the wake effects. A simulation run with a wind farm of 50.000 8MW wind turbines was also run. This windfarm covers the same area as the previous one, but horizontal distance between wind turbines are 1000m instead of 500m. This configuration was found to produce a similar amount of power as the 200.000 configuration. However, the atmospheric impact on regional weather is smaller but still large with 50.000 wind turbines.

How to cite: Jacobsen, S. and Walløe Hansen, A.: A study of the impact of very large wind farms on regional weather using the WRF model in high resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16884, https://doi.org/10.5194/egusphere-egu2020-16884, 2020.

Storm Xaver impacted the northern Europe on 5-6 December 2013.  It developed southeast of Greenland and passed north of Scotland and across southern Norway on a trajectory that led to a cold air outbreak across the North Sea and intense convection activity in northern Europe.  Strong sustained north winds led to a high storm surge that impacted all countries bordering the North Sea.  Storm Xaver was a century scale event with certain locations around the North Sea reporting their highest ever water levels since the start of modern records.  Media reports from the time of the storm chronicle the scale of the disruptions, including many cancelled flights, interrupted rail networks, closed bridges and roads, coastal building collapses, and power blackouts across northern Europe.  Much of this was due to the strong winds, but coastal storm surge flooding was important in the UK, and it led to interrupted port operations around the North Sea.

The storm was important for energy infrastructure and particularly for wind energy infrastructure.  In the northern North Sea, petroleum platforms were evacuated and operations closed ahead of the storm as a precautionary measure.  A number of onshore wind turbines were badly damaged by high winds and lightning strikes in the UK and Germany.  Over the North Sea, wind speeds exceeded the turbine shutdown threshold of 25 m/s for an extended period of time, with economic impacts from the loss of power generation.   In the German Bight, the FINO1 offshore wind energy research platform was damaged at the 15 m level by large waves.  This was the third report of storm damage to this platform after Storm Britta in 2006 and Storm Tilo in 2007.  Researchers have highlighted the need to reassess  the design criteria for offshore wind turbines based on these kinds of extreme meteorological events.  For the offshore wind industry, an important element of energy meteorology is to characterize both the evolving wind and wave fields during severe storms as both elements contribute to turbine loads and potential damage.

The present conference contribution presents a literature review of the major events during Storm Xaver and impacts on energy infrastructure.  Tide gauge records are reanalyzed to trace the progress of the storm surge wave around the North Sea.  A spectral analysis is used to separate the long period storm surge component, diurnal/semidiurnal tide, and short period components in the original water level record.  The short period component of the tide gauge record is important as it may be linked with infragravity waves that have been implicated in certain cases of offshore infrastructure damage in addition to coastal erosion.  Discussion is made of offshore wave records during the storm.  Storm Xaver is compared with two damaging offshore storms in 2006 and 2007.

How to cite: Kettle, A.: Storm Xaver over Europe in December 2013 and its energy meteorological impacts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6029, https://doi.org/10.5194/egusphere-egu2020-6029, 2020.

Many wind energy applications rely on engineering models that simulate the interaction between the wind and the turbine(s). These models often represent the wind in an idealised fashion, which introduces uncertainties that translate into financial risk for investors.

Over the past four years, we investigated these uncertainties by re-evaluating common assumptions about the (offshore) wind field, studying the physics that govern winds in coastal areas, evaluating the representation of offshore winds in weather models, and proposing alternative methods to represent the offshore wind climate in engineering models.

Uncertainties in the wind climate were studied through a number of ‘anomalous wind events’. An important and illustrative example is the low-level jet, which can substantially impact power production and wind loads on the turbine. We found that low-level jets occur often over the North Sea. Moreover, numerical weather prediction models struggle to adequately represent this phenomenon. A climatology based only on observations is also biased, because the observations are limited in time and space. Thus, we combined field observations with output of reanalysis products to obtain a reliable climatology.

At the 2020 general assembly, we will present a new evaluation of three recent wind atlases over the North Sea: ERA5, The New European Wind Atlas (NEWA), and the Dutch Offshore Wind Atals (DOWA). With virtually no bias, DOWA outperforms the other datasets in terms of the mean wind profile and also in the representation of wind shear. The high resolution offered by DOWA (2.5 km) and NEWA (3 km) leads to substantial improvements in the frequency and the level of detail with which low-level jets are captured. However, the timing of the events is a bit off in NEWA. By contrast, DOWA was produced using continuous three-hourly data-assimilation updates, which imposes a much stronger constraint on the simulations. Consequently, the timing of low-level jets in DOWA is much better represented. This makes for a low-level jet climatology with unprecedented accuracy and detail, facilitating resource assessment and future studies on the characteristics of the offshore wind climate.

How to cite: Kalverla, P. C., Holtslag, A. A. M., Ronda, R. J., and Steeneveld, G.-J.: Quality of wind characteristics in recent wind atlases over the North Sea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13916, https://doi.org/10.5194/egusphere-egu2020-13916, 2020.

A large-eddy simulation (LES) model, coupled with a dynamic actuator-disk model, is used to investigate the turbine power production and the turbine wake distribution in large wind farms where the streamwise turbine spacing of 7, 9, 12, 15, and 18 rotor diameters are considered. Two incoming flow conditions, three wind turbine arrangements, as well as the five turbine spacings are involved in this study, which leads to a total of 30 LES wind farm scenarios. The two incoming flow conditions have the same mean velocity of 9 m s^-1 but different turbulence intensity levels (i.e., 7% and 11%) at the hub height level. The considered turbine arrangements are the perfectly-aligned, laterally-staggered, and vertically-staggered layouts. The simulated results show that the turbine power production has a significant improvement by increasing the streamwise turbine spacing. With increasing the streamwise turbine spacing from 7 to 18 rotor diameters, the overall averaged power outputs are raised by about 27% in the staggered wind farms and about 38% in the aligned wind farms. The wind farm scenarios with the turbine spacing of 12d or greater in a large wind farm can lead to an increasing trend in the power production from the downstream turbines in the high-turbulence inflow condition, or also avoids the degradation of the power output on the turbines with the low-turbulence inflow condition. The flow adjustment above the wind farm results in the generation of the internal boundary layer (IBL), which grows up vertically along with the wake-wise direction. The growth of the IBL is found to be affected by the changes in the inflow condition and the turbine spacing. The IBL depth above the wind farms is found to be influenced by the turbine spacing, whereas the IBL depth in the downstream wake region of the wind farms shows a rapid increase under the high-turbulence inflow condition.

How to cite: Wu, Y.-T. and Tsao, Y.-H.: Power Output Efficiency in Large Wind Farms with Different Streamwise Turbine Spacing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4484, https://doi.org/10.5194/egusphere-egu2020-4484, 2020.

The large number of already existing and planned offshore wind parks in the German Bight leads to challenging requirements with regard to reliable information on various processes in the atmosphere and the ocean. In particular wind shadowing effects play a major role for the optimal planning and operation of wind park installations. Synthetic Aperture Radar (SAR) satellites have proved their capability of giving a 2D view of the wakes generated behind wind farms at a  high spatial resolution. However, the estimation of wind speed deficits from SAR data is still a challenge, because undisturbed reference wind fields are usually not available at the exact location of the wake. A common approach is therefore to identify some reference areas on SAR scenes outside the wake region, which naturally leads to errors in the deficit computations. 
In this study a new filter approach for the deficit estimation is proposed, which allows to derive error bars for the deficits. The filter is based on a 2D convolution operation with a filter kernel, which has a shape depending on the wind park geometry and the wind direction. The errors depend on spectral properties of the background wind fields, which are estimated from SAR data as well. In this context the stability of the atmospheric boundary layer is shown to play a major role. Examples are shown using data acquired by the SENTINEL-1A/B satellites. The approach is seen as a contribution to make SAR based deficit computations more objective and automised, which is essential for the application of the method to larger data sets and to make wake analysis done in different regions more comparable.

How to cite: Schulz-Stellenfleth, J., Djath, B., and Haid, V.: Satellite based estimation of atmospheric wakes downstream offhore windparks using a new objective filter technique , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18585, https://doi.org/10.5194/egusphere-egu2020-18585, 2020.

Wind Farm Layout Optimization Problem (WFLOP) is an important issue to be addressed when installing a large wind farm. Many studies have focused on the WFLOP but only for a limited number of turbines (10 – 100 turbines) and idealized wind speed distributions. In this study, we apply the Genetic Algorithm (GA) to solve the WFLOP for large wind farms using real wind data.

The study site is the Palk Strait located between India and Sri Lanka. This site is considered to be one of the two potential hotspots of offshore wind in India. An interesting feature of the site is that the winds here are dominated by two major monsoons: southwesterly summer monsoon (June-September) and northeasterly winter monsoon (November to January). As a consequence, the wind directions do not drastically change, unlike other sites which can have winds distributed over 360^o. This allowed us to design a wind farm with a 5D X 3D spacing, where 5D is in the dominant wind direction and 3D is in the transverse direction (D- rotor diameter of the turbine - 150 m in this study).

Jensen wake model is used to calculate the wake losses. The optimization of the layout using GA involves building a population of layouts at each generation. This population consists of, the best layouts of the previous generation, crossovers or offspring from the best layouts of the previous generation and few mutated layouts. The best layout at each generation is assessed using the fitness or objective functions that consist of annual power production by the layout, cost incurred by layout per unit power produced, and the efficiency of the layout. GA mimics the natural selection process observed in nature, which can be summarised as survival of the fittest. At each generation, the layouts performing the best would enter the next generation where a new population is created from the best performing layouts.

GA is used to produce 3 different optimal layouts as described below. Results show that:

A ~5GW layout – has 738 turbines, producing 2.37 GW of power at an efficiency of 0.79

Layout along the coast – has 1091 turbines, producing 3.665 GW of power at an efficiency of 0.82.

Layout for the total area – has 2612 turbines, producing 7.82 GW of power at an efficiency of 0.74.

Thus, placing the turbines along the coast is more efficient as it makes the maximum use of the available wind energy and it would be cost-effective as well by placing the turbines closer to the shores.

Wind energy is growing at an unprecedented rate in India. Easily accessible terrestrial resources are almost saturated and offshore is the new frontier. This study can play an important role in the offshore expansion of renewables in India.

How to cite: Reddy, K. N. and Roy, S. B.: Layout optimization for a large offshore wind farm using Genetic Algorithm, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12654, https://doi.org/10.5194/egusphere-egu2020-12654, 2020.

Renewable energies, like solar and wind energy, play an important role in current and future energy supply in Germany and Europe. The renewable energy production highly depends on weather, which leads to an increasing impact of the meteorological fluctuations on energy production.

Here, climatological datasets with high spatial and temporal resolution are used to simulate the electrical energy production from photovoltaic (PV) installations and wind turbines. For the solar radiation the CM SAF SARAH 2.1 dataset is used, which includes global and direct radiation with a temporal resolution of 30 minutes and a grid spacing of 0.05°. The data is available from 1983 to 2017. The regional reanalysis COSMO-REA6 provides hourly wind speed data from 1995 to 2015 with a spatial resolution of 6km. Based on these datasets capacity factors are calculated for PV and wind energy for Germany. Using the spatial distribution of solar panels and wind turbines as well as electrical power generation data from 2015 the simulated capacity factors were converted into (potential) hourly power generation in Germany from 1995 to 2015. 

The main aim of this study is to identify weather regimes where renewable energy production from solar and wind was comparable low. Due to high power production from solar radiation, which exhibits a comparable low variability and high predictability, in summer, all low production events occur in winter. During winter, wind power is the main contributor to renewable energy production. On the basis of the hourly time series of simulated power production the weather regimes that are associated with multiple days of low renewable energy production are identified and analysed. European regions are identified that exhibit comparably high potential renewable power production for those weather regimes with low energy production in Germany.  

How to cite: Drücke, J., Borsche, M., James, P., Kaspar, F., Pfeifroth, U., Ahrens, B., and Trentmann, J.: Climatological Analysis of the Potential of Solar and Wind Energy in Germany, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14412, https://doi.org/10.5194/egusphere-egu2020-14412, 2020.

Wind energy investments have met a quick growth during the last decades due to the stricter climate policies, the need for energy independence and the higher profits coming from the smaller costs of such applications. Moreover the evolution of technology leads to the characterization of more areas as suitable for energy applications. Offshore wind farms are a nice example of how to build bigger, more efficient and resistant in extreme conditions wind power plants.

The present work is focused on the determination of the suitability of an offshore marine area for the development of wind farm structures. More specifically the region of interest is the marine area on the south of France including the Gulf of Leon. For the needs of the study a 10-year database, produced employing state of the art atmospheric and wave models, is utilized. The wind and wave parameters used, have a spatial resolution of 6 km and a frequency of one hour.

Wind speed and power probability distribution characteristics are discussed in different heights throughout the domain. Particular locations are selected for a more comprehensive analysis. At the same time extreme wind and wave conditions and their 50-years return period are analyzed and used to define the safety level of the wind farms structural characteristics. The outcome could lead to a review of the area suitability for wind farm development, providing a new tool for technical/research teams and decision makers.

How to cite: Patlakas, P., Stathopoulos, C., Gavriil, A., Galanis, G., and Kallos, G.: Wind energy potential assessment in western Mediterranean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18130, https://doi.org/10.5194/egusphere-egu2020-18130, 2020.

In this talk, we investigate the causes of the strongest and weakest winds observed across Mexico and explore the consequences of these to current and future wind energy production in the country. Using 40 years of the ERA-5 atmospheric reanalysis data, we find that the strongest winds in this region are caused by cold surges, where an anticyclone moves South from the Central United States of America resulting in strong Northerly winds across the Gulf of Mexico which channel through the gap in the mountains to the South of Mexico. Other regions have different drivers for high and low wind speed events. The strongest winds across the East coast of Mexico originate from Easterly trade winds propagating across the Gulf of Mexico, whereas those in Baja California Sur are influenced by the proximity of the North Pacific High. These regions in Mexico have peak (and sustained low) wind speeds at different times of year which suggests that wind farms in different regions could compliment one another to optimise wind power generation. However, all stations but Baja California Sur see the same weather patterns associated with weak wind events, meaning that low wind power production may be unavoidable at these times. The conditions that proceed these sustained periods of strong and weak winds are explored to gain some predictability for wind power applications. The El Nino Southern Oscillation is found to influence wind speeds at some locations across Mexico at sub-seasonal time-scales.

How to cite: Thomas, S., Martinez-Alvarado, O., Drew, D., and Bloomfield, H.: Drivers of Extreme Wind Events in Mexico for Wind Power Applications, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19833, https://doi.org/10.5194/egusphere-egu2020-19833, 2020.

The potential of increasing the amount of offshore wind energy production in the Baltic Sea has been of great interest for many countries and wind power companies for a long time. From a meteorological point of view, there are several special wind characteristics that are observed in this area that needs to be taken into consideration when planning for a wind farm. For example, as the Baltic Sea is a semi-enclosed basin surrounded by coastlines in all directions, phenomenon such as low-level jets occur frequently.

In order to create a climatology of the wind conditions over the Baltic Sea, with wind power applications in mind, four different state-of-the-art reanalysis data sets (MERRA2, ERA5, UERRA and NEWA) have been compared with measurements from LIDAR systems and high meteorological towers (Anholt, Finnish Utö, FINO2 and Östergarnsholm). The performance of the data sets has been analyzed in terms of stability and governing synoptic weather conditions as well as seasonal and diurnal variations. By selecting the most suitable reanalysis data set and using the observations to make corrections, a climatology for wind conditions over the Baltic Sea, focusing on the low-level jets, has then been constructed.

How to cite: Hallgren, C., Sahlée, E., Ivanell, S., Körnich, H., and Vakkari, V.: Wind conditions over the Baltic Sea – comparing reanalysis data sets with observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21897, https://doi.org/10.5194/egusphere-egu2020-21897, 2020.

In this study, we present methodology to create synthetic multi-year wind generation dataset at minute-scale granularity at the existing and future Australian wind farms. The purpose of the dataset  is to assist studies of penetration of large scale and distributed renewable generation into the electricity systems and its impact on power system security in the National Energy Market (NEM).

Synthetic historical records are based on a spatial and temporal blend of reanalysis datasets with the minute-scale wind speeds observations at Bureau of Meteorology weather station network. Strengths and weaknesses of reanalysis data are illustrated and a correction methodology discussed. A method to introduce minute-scale and sub-hourly fluctuations absent in the reanalyses records is presented. Expected statistical properties of sub-hourly fluctuations in the wind generation records are derived from the characteristics of the background atmospheric state in the vicinity of the wind farms.

The accuracy of the dataset is validated in terms of  power spectra and ramping frequencies in the simulated timeseries against existing minute-scale observations of wind generation at Australian Wind farms. The statistical properties of the observed and simulated timeseries match reasonably well, overall making the dataset suitable for the investigations of  the implications of wind ramping on energy demand and generation at the existing and foreseeable infrastructure build in the NEM.

How to cite: Titova, E. and Mittal, R.: Synthetic Wind Generation Records for Australian Wind Farms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6426, https://doi.org/10.5194/egusphere-egu2020-6426, 2020.

A crosswind spectrum model S_v(f) is proposed that covers both the meso and the microscale, ranging in frequency 1/5 day^-1 to the turbulence inertial subrange. The purpose is to improve the calculation of flow meandering effect over areas of the sizes of offshore wind farms and clusters.

The development is based on measurement (from Høvsøre) analysis over this broad frequency range for cases where wind direction does not change much during a day. The model reads:

fS_v(f) = Boundary-layer model   for f>f₁,

         = Constant                           for f₂<f<f₁,

         = a₁f ^-2/3 + a₂f ^-2                     for f<f₂

Here, the frequency range f₂ to f₁ defines the gap region, and the constant in this subrange is determined by the spectra on both sides of this range; the boundary-layer model used here is either the Kaimal model or the Mikkelsen-Tchen model; a₁ and a₂ are climatological coefficients. The credibility of the model is evaluated against with measurements from another wind test site Østerild, with measurements ranging in height from surface layer to a height of 241 m.   

The model is used together with a similar model for the longitudinal wind component u to obtain time series u(t) and v(t), from which direction statistics are obtained and compared with those from measurements. It was found that the boundary-layer models could only describe stationary time series for wind vectors. The new v-spectral model improves significantly the statistics of wind direction variation over scales that correspond to offshore wind farms and clusters.

How to cite: Larsén, X., Larsen, S., Petersen, E., and Mikkelsen, T.: A new spectrum model for the atmospheric crosswind component applicable from mesoscales to microscales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5240, https://doi.org/10.5194/egusphere-egu2020-5240, 2020.

Wind power generation is a promising technology to reduce greenhouse gas emissions in line with the Paris Agreement.  In the recent years, the global offshore wind market grew around 30% per year but the full potential of this technology is still not fully exploited. In fact, offshore wind power has the potential to generate more than the worldwide energy demand of today. The high variability of wind on many different timescales does, however, pose serious technical challenges for system integration and system security.  With a few exceptions, little focus has been given to multi-decadal variability. Our research therefore focuses on timescales exceeding ten years.

Based on detrended wind data from the coupled centennial reanalysis CERA-20C, we calculate long-term offshore wind power generation time series across Europe and analyze their variability with a focus on the North Sea, the Mediterranean Sea and the Atlantic Ocean. Our approach is based on two independent spectral analysis methods, namely power spectral density and singular spectrum analysis. The latter is particularly well suited for relatively short and noisy time series. In both methods an AR(1)-process is considered as a realistic model for the noisy background. The analysis is complemented by computing the 20yr running mean to also gain insight into long term developments and quantify benefits of large-scale balancing.

We find strong indications for two significant multidecadal modes, which appear consistently independent of the statistical method and at all locations subject to our investigation. Moreover, we reveal potential to mitigate multidecadal offshore wind power generation variability via spatial balancing in Europe. In particular, optimized allocations off the Portuguese coast and in the North Sea allow for considerably more stable wind power generation on multi-decadal time scales.

How to cite: Neubacher, C., Wohland, J., and Witthaut, D.: Multi-decadal offshore wind power variability can be mitigated through optimized European allocation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9538, https://doi.org/10.5194/egusphere-egu2020-9538, 2020.

Analysis of correlation among precipitation, wind, and solar resources could explore their complementary features, enhance the utilization efficiency of renewable energy and further alleviate the carbon emission issues caused by fossil energy. In this study, we discuss the correlation between precipitation and wind, wind and solar, precipitation and solar from various Spatio-temporal perspectives (from east to west in China, in terms of plain, plateau, hill, and mountain, from daily to ten days and monthly) with observed data. With investigation of daily time series of precipitation, wind speed and solar radiation ranging from 1961-1-1 to 2016-12-31 of 726 meteorological stations located in various landform and distributed dispersedly in China, the results show that 1) the fluctuation value, quantified by Mei-Wang Fluctuation index, denotes the descending tendency when the time resolution increases, and this tendency is stronger in the southern and eastern China; 2) the correlation coefficient, characterized by Kendall’s rank correlation coefficient, changes from east to west in China, and the strength of this correlation displays certain connection to the local topography (e.g., in Qinghai province which is located in the plateau region the complementarity between precipitation and wind speed is stronger than that between precipitation and solar, the mid-stream basin of Yangtze River where the topography is scattered and complex has the weaker complementarity compared to other areas in China). According to the results of this research, it is helpful from the temporal perspective to understand the requirement of complementarity in the utilization of wind, and solar resources which are intermittent, and from the spatial perspective to know the solution of mitigating fluctuation via integration of multi-renewable energy situated in different locations.

How to cite: Wang, X.: Analysis of Fluctuation and Correlation Tendency among Precipitation, Wind, and Solar from Various Spatio-temporal Perspectives: a Case Study in China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4931, https://doi.org/10.5194/egusphere-egu2020-4931, 2020.

The Faroe Islands is a small mountainous island group in the north east Atlantic Ocean, located far from any other mainland. The closes adjacent land being Shetland ~300 km away. One electrical power company exists on the islands, distributing power to the ~50.000 citizens. Approximately half of the electrical power comes from renewable energy sources (wind and hydro) and the other half from oil [1]. The political goal is to have the electrical system running 100% on renewable energy sources by 2030. This will presumable be achieved by implementing a significant amount of wind power [2]. The climate in the Faroe Islands is very windy, making it a good area for harvesting wind energy.

As wind is a fluctuating power source, analyzing the wind field and its characteristics is of great importance, when planning implementation of a significant amount of wind power into the power grid. Smoothening of the wind power can be achieved different ways, one being with spatial dispersion of wind farms seen in other studies [3,4]. The spectral characteristics and the smoothening effect of spatial dispersed sites based on wind farm data and meteorological wind speed measurements in the Faroe Islands was shown in a poster presentation at EMS2019 [5]. However, implementing more wind farms requires knowledge of new sites. There have been made NWP calculations of the wind in the Faroe Islands for the period July 2016 to June 2017. NWP are beneficial in the way that they give valuable information at unknown sites, which may be used for wind farm planning. However, NWP calculations are based on a given setup of a simplified reality. Hence, validating any NWP model is needed.

There exists wind measurements at various heights from two meteorological masts at the time period of the mentioned NWP model calculations in the Faroe Islands. The aim of this study is to compare auto- and cross-spectral characteristics of the sets of modelled and measured data. The results will give an insight on the value of NWP derived data for grid integration studies in a region with complex topography.

How to cite: Poulsen, T. and Beyer, H.-G.: Cross spectral characteristics of modelled and measured sets of spatially distributed wind power in the Faroe Islands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3063, https://doi.org/10.5194/egusphere-egu2020-3063, 2020.

West Africa is one of the least developed regions in the world regarding the energy availability and energy security. Located close to the equator West Africa receives high amounts of global horizontal irradiance (GHI). Thus, solar power and especially photovoltaic (PV) systems seem to be a promising solution to provide electricity with low environmental impact. To plan and to dimension a PV power system climatological data for global horizontal irradiance (GHI) and its variability need to be taken into account. However, ground based measurements of irradiances are not available continuously and cover only a few discrete locations.

Data records of surface irradiance based on satellite measurements have the advantage of covering wide spatial regions and being available over long time periods. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) provides the Surface Solar Radiation Data Set-Heliosat, Edition 2.1 (SARAH-2.1), a 35 year long climate data record in an half hourly resolution, covering the whole of Africa and Europe.

In this study, the SARAH-2.1 data record (1983-2017) is used to analyze the impact of 35 years atmospheric variability and trend on GHI and PV yields over West Africa (defined as the region from 3°N to 20°N and 20°W to 16°E). The trend and the variability of solar irradiance is analyzed separately for the wet and dry season as well as for annual data. Furthermore, a simplified model provides high-resolution potential PV yields.

According to the SARAH-2.1 data record, solar irradiance is largest (with up to 300 W/m 2 daily average) in the Sahara and the Sahel zone with a positive trend (up to 5 W/m²/decade). Whereas, the solar irradiance is lower in southern West Africa with a negative trend (up to -5 W/m²/decade). The positive trend is mostly connected to the dry season, while the negative trend occurs during the wet season. PV yields show a strong meridional gradient with lowest values around 4 kWh/kWp in southern West Africa and reach more than 5 kWh/kWp in the Sahara and Sahel zone.

This poster will discuss the long-term trend and variability analysis of solar irradiance and highlight the implications for photovoltaic-based power systems in West Africa.

How to cite: Neher, I., Crewell, S., Meilinger, S., Pfeifroth, U., and Trentmann, J.: Long-term variability of solar irradiance and its implications for photovoltaic power in West Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19283, https://doi.org/10.5194/egusphere-egu2020-19283, 2020.

Energy production from the utilization of wind energy potential depends on the variability of the wind field as determined by the interaction of natural processes on different scales. Global climate change can cause alterations in the surface wind and thus it may affect the geographical distribution and the wind energy potential variability. Wind energy production is sensitive to wind speed changes, especially in the upper percentile of the wind speed distributions, where energy production is more effective. The importance of wind energy production changes is enhanced by the fact that wind energy investments are long-term and are characterized by high initial costs and low operating costs. In the present study, these changes are examined for the southeastern Mediterranean region, based on simulations of the Regional Climate Model ALADIN 5.2 extracted from the Med-CORDEX database for the climatic scenarios RCP4.5 and RCP8.5. The results indicate a wind power density increase over the Aegean Sea, the Ionian Sea, the Dardanelles and the Black Sea, with similar levels of increase for both climatic scenarios. In contrast, during the winter period there is a decline across the southeastern Mediterranean, which is more significant in the case of the RCP8.5 scenario. Finally, for most areas of eastern Greece, there is a reduction in the number of wind speed cases for both below and above cut-in and cut-out wind speeds, while there is an increase in the number of wind speed cases that wind turbines operate at their maximum power. The results are expected to reduce the uncertainty associated with the impact of climate change on wind energy production. 

How to cite: Tzanis, C. G., Philippopoulos, K., Cartalis, C., Granakis, K., Alimissis, A., and Koutsogiannis, I.: Climate change impacts on wind power density over southeastern Mediterranean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1546, https://doi.org/10.5194/egusphere-egu2020-1546, 2020.

The development of renewable electricity in Africa could be massive in coming decades, as a response to the rapid rising electricity demand while complying with the Paris Agreements. This study shows that in the high-resolution climate experiments of CORDEX-AFRICA, the annual mean solar potential is expected to decrease on average by 4% over most of the continent by the end of the century, reaching up to 6% over the Horn of Africa, as a direct result of decrease in solar radiation and increase in air surface temperature. These projections are associated with large uncertainties, in particular over the Sahel and the elevated terrains of eastern Africa. While the expected decrease may affect the sizing of the numerous solar projects planned in Africa for the next decades, this study suggests that it does not endanger their viability. At last, this study indicates that the design of such projects also needs to account for the non-negligible uncertainties associated with the resource.

How to cite: Bichet, A., Hingray, B., Evin, G., Diedhiou, A., Kebe, F., and Anquetin, S.: Potential impact of climate change on solar resource in Africa for photovoltaic energy: analyses from CORDEX-AFRICA climate experiments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22015, https://doi.org/10.5194/egusphere-egu2020-22015, 2020.