ObjectivesThe project adresses the following scientific & technical objectives :
- Definition and identification of extreme events
Forecasters, energy producers and grid operators have different views on what extremes related to wind generation are. One of the objectives of the project is to produce a catalogue which identifies and classifies extreme situations as a function of their origin, nature or impact.
- Large-scale vision of wind power forecasting by the development of an adequate information management infrastructure
The focus is to consistently collect, store and distribute the measurement data that are needed to assess the prevailing meteorological conditions over Europe with regard to wind energy. The related vision consists in having for the first time a coherent dataset that is based on standard meteorological data as well as wind farm data, which in combination represents the European “wind energy weather”. The knowledge of the prevailing weather situation at a certain time is a pre-requisite to detect relevant large-scale extreme events.
The work in this part will be directed towards the design and implementation of a slim but robust data management system that will host the data.
- Alerting & Data Assimilation Techniques for Improved Short-term Wind Power Predictability
The aim here is to develop methods to adequately monitor and assess the weather situation over Europe in order to detect severe deviations in the wind power forecast due to extreme events. Then react on such a deviation by issuing suitable alerts to users that a forecast error is occurring, and by producing improved updates of the prediction in the short-term (0-6 h).
In order to compare the latest forecast field with the prevailing meteorological situation over Europe different data assimilation techniques are developed ranging from the use of rather basic pattern recognition methods towards more advanced meteorological analysis techniques based on surface measurements and wind farm data.
The aim is to obtain a fairly precise picture of the current “wind energy weather” including not only synoptic weather data but also wind farm measurements. This snapshot of the current weather situation is used to detection extreme errors in wind field and wind power prediction and is the starting point to calculate short-term updates.
A major innovation of this work, lies in the direct inclusion of wind farm data into advanced data assimilation techniques focussing on the variables that mainly determine the wind power output and.
- Optimized Ensemble Forecast Systems Applied to Wind Power Prediction
The project aims to deliver the meteorological component for skilful, probabilistic wind power predictions based on ECMWF’s Ensemble Prediction System (EPS). The work on the EPS aims at improving ensemble forecasts (wind and wind power) at all forecast ranges (0−15 days). Special attention is paid to forecast extreme events in the medium-range more accurate to facilitate the integration of wind power in the power system in any weather situation. A set of EPS configurations will be evaluated using newly developed probabilistic skill score measures that are based on observations of different sources (e.g. certified WMO measurement sites, installed LIDAR measurements, etc.).
Emphasis will be given to the combination of high resolved deterministic forecast products with ensemble predictions by minimizing probabilistic skill scores. The optimally combined meteorological forecast system will be linked to wind power forecasting tools and improvements in wind power forecasts will be evaluated on all relevant time scales that are interesting to end-users (wind park operators, TSOs, energy trader, etc.).
- Novel Methods for Wind Power Forecasting and Extremes
Specific attention is given to predicting extremes situations in the short to medium term (up to 2-3 days ahead) with purely statistical and probabilistic methods accounting for the evolution of meteorological variables, possibly indicating different weather regimes.
Due to this time scale, this work relies more on Numerical Weather Predictions (NWPs) than on the use of measurements. A large component of this work relates to the question of estimating and communicating prediction uncertainty, as it is a crucial aspect of wind power forecasting and for warning forecast users about potential forthcoming extremes.
In parallel, other objectives are related to the aim of decreasing the average prediction error, the risk exposure due to the large errors in wind power forecasts for the case of point forecasts, and to a more robust prediction of extreme events. For that purpose, novel methods will be developed for regime-switching modelling (and forecasting), for conditional forecast combination, as well as for better accounting for cut-off events when modelling the curve for the conversion of wind to power.
- Wind Resource Assessment vs Predictability
One of the objectives of the project is to explore synergies between the forecasting and resources assessment areas. The use of information produced by meteorological models in the determination of Vref parameter will be analysed and a new dynamic risk analysis technique will be developed. An innovative concept will be examined which consists in taking into account the predictability of a potential site for a wind farm as a design parameter in the resources assessment study. This is expected to be of beneficial for the case of projected wind farms which are going to participate in an electricity market. In that case, the pay back of the investement depends among others on the revenue from the participation in the electricity market. However, such revenue is reduced because of penalties induced from forecast errors.
- Assessment of benefits from new measuring technologies for better estimation of external conditions, resource assessment and forecasting.
Wind speed measurements are the basis for power production calculations and forecasting. As wind turbines have grown larger and higher, with their rotors covering even larger portions of the boundary layer, larger discrepancies are being reported. There are strong indications that these discrepancies are mainly due to the measurement of the wind speed at hub height, which however cannot any longer be considered as adequate. New remote sensing techniques such as the Lidar device, being investigated within the UpWind project, offer the possibility to measure the wind profile (wind speed, wind direction and turbulence) at more heights both below and above hub height in a competitive way without the need of huge met towers. The knowledge of the wind profile over the rotor will remove much of the uncertainty associated to the power measurement and will make power predictions more accurate. A more detailed input at more levels will also enhance the performance of the forecasting software.
Since the aim of SafeWind is not to design or develop new measurement devices, links will be developed with UpWind or other projects to exchange measurements from previous measuring campaigns. In complement to these data, and due to the fact that for the purposes of this project campaigns of at least one year are required, two campaigns are planned in flat and complex terrain environments. In addition to Lidars, other measuring devices such as Sodars or ultrasonic anemometers will be used for benchmarking results and evaluating the benefits in terms of accuracy obtained by the new technologies especially in extreme conditions (i.e. determination of wind gusts).
- Demonstration of operational benefits
A validation phase, in synergy with the ANEMOS.plus project, is foreseen to evaulate operationally the most promising models developed here.
