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Using Ensemble Forecasting for Wind Power Preprint from the EWEC in Madrid, June 16-20, 2003 Using Ensemble Forecasting for Wind Power 1 1 1 Gregor Giebel , Lars Landberg , Jake Badger , Kai Sattler 2, Henrik Feddersen 2, Torben Skov Nielsen 3, Henrik Aalborg Nielsen 3 and Henrik Madsen 3 1 Risø National Laboratory, P.O. Box 49, DK-4000 Roskilde, risoe.dk Tel/Fax: +45 4677 5095 / 5970. E-mail: [email protected]. 2 Danish Meteorological Institute, DK-2100 København Ø, dmi.dk 3 Informatics and Mathematical Modelling, Technical University of Denmark, DK-2800 Lyngby, imm.dtu.dk Abstract: Short-term prediction of wind power has a long tradition in Denmark. It is an essential tool for the operators to keep the grid from becoming unstable in a region like Jutland, where more than 27% of the electricity consumption comes from wind power. This means that the minimum load is already lower than the maximum production from wind energy alone. Danish utilities have therefore used short-term prediction of wind energy since the mid-90ies. However, the accuracy is still far from being sufficient in the eyes of the utilities (used to have load forecasts accurate to within 5% on a one-week horizon). The Ensemble project tries to alleviate the dependency of the forecast quality on one model by using multiple models, and also will investigate the possibilities of using the model spread of multiple models or of dedicated ensemble runs for a prediction of the uncertainty of the forecast. Usually, short-term forecasting works (especially for the horizon beyond 6 hours) by gathering input from a Numerical Weather Prediction (NWP) model. This input data is used together with online data in statistical models (this is the case eg in Zephyr/WPPT) to yield the output of the wind farms or of a whole region for the next 48 hours (only limited by the NWP model horizon). For the accuracy of the final production forecast, the accuracy of the NWP prediction is paramount. While many efforts are underway to increase the accuracy of the NWP forecasts themselves (which ultimately are limited by the amount of computing power available, the lack of a tight observational network on the Atlantic and limited physics modelling), another approach is to use ensembles of different models or different model runs. This can be either an ensemble of different models output for the same area, using different data assimilation schemes and different model physics, or a dedicated ensemble run by a large institution, where the same model is run with slight variations in initial conditions and/or parameterizations. Two of the large ensembles run this way are available from the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, and from the National Center for Environmental Protection (NCEP) in the US. These are used to calculate the uncertainty of the prediction from the model spread. However, since the model domains are global, it is not certain that this approach will work, due to insufficient spread in Denmark. Additionally, we will try to establish an ensemble of members of DMIs forecasts together with forecasts from the Deutscher Wetterdienst. The project is funded by the Danish PSO funds under the reference no. ORDRE-101295 (FU 2101). 1 INTRODUCTION use a proper Numerical Weather Prediction (NWP) model, of the type used in the large meteorological centres. Actually, The high regional penetrations of wind energy can only be short-term prediction models based on NWP outperform time- integrated successfully using a short-term prediction model, to series models already for 4-6 hours lead-time. However, for a predict the wind power production for some hours ahead. How week-ahead prediction, even the best NWP models are not many hours look-ahead time one needs, depends on the quite good enough, and a month-ahead prediction is also application. For the scheduling of power plants, the most theoretically hardly conceivable due to the inherent chaos in important time scale is determined by the start-up times of the the atmosphere. For an introduction into short-term prediction other power plants in the grid, from 1 hour for gas turbines up models, refer to [1]. A much larger text on the topic is to 8 hours or more for the largest coal fired blocks. The underway, too [2]. markets determine the second time scale of interest. In The typical short-term prediction model uses NWP data from Denmark, the main market for electricity is the NordPool, one operational model, run by a meteorological service. Eg , followed by the German market. NordPool regulations the Danish Zephyr model uses the four daily runs of the mandate trading for the next 24-h day at noon the day before. HIRLAM model of the Danish Meteorological Institute (DMI) This means that the wind power forecasts have to be accurate as input. This is usually the local met. institute, since they for about 37 hours lead-time. A third time scale is involved in know the best parameterisations for the local conditions and maintenance planning, of power plants or the electrical grid. run the model with the highest resolution for the particular The ideal lead-time for this would be weeks or even months country. ahead. However, using data from more than one met. institute can The different lead-times can be served with different short- increase the resilience against errors, and possibly also the term prediction models. The scheduling of a quick-response accuracy of the forecasts. grid can be done with time-series analysis models alone, based on past production. For the trading horizon, it is necessary to Besides the forecasted value for the power, the utilities also would like to have an estimate on the accuracy of the current prediction. It has been established [3, 4] that the errors of the NWP models are not highly dependent on the level of the wind speed. That means that the shape of the power curve has a large influence on the uncertainty of the forecasts: where the power curve is steep, the error is amplified, while in the flat areas of the power curve, the error becomes less relevant. Combining this with the historical performance of the model and a term depending on the horizon is the state-of-the-art in uncertainty prediction these days. A completely different class of uncertainty could be introduced if we could assess the predictability of the current weather situation. This is where ensemble forecasting comes in. The idea in ensemble forecasting is to cover a larger part of the possible futures through introduction of variation in the initial conditions. This can be used as a sensitivity analysis on the Figure 1: A spaghetti plot of storm tracks over the influence of variations in different factors. There are four main continental US. Image Peter Houtekamer, Environment Canada possibilities for the calculation of ensembles of forecasts: • Every model run is started with a little variation in the initial conditions. In this way it can be investigated how 2 PREVIOUS WORKS sensitive the result is against small changes in the initial A number of groups in the field are currently investigating the conditions (a.k.a. the butterfly effect). These small benefits of ensemble forecasts. variations are still compatible with the likely error in the Giebel et al [5] and Waldl and Giebel [6,7] investigated the analysis at time zero. Keep in mind that the meteorological relative merits of the Danish HIRLAM model, the observational network has a density over land in the order Deutschlandmodell of the DWD and a combination of both for of 20-50 km. The ECMWF (European Centre for Medium a wind farm in Germany. There, the RMSE of the Range Weather Forecasts in Reading) runs this kind of Deutschlandmodell was slightly better than the one of the ensemble twice a day with 50 members. NCEP in the US Danish model, while a simple arithmetic mean of both models run another one with 11 members. yields an even lower RMSE. • A multi-scheme ensemble starts with identical data input, Moehrlen et al [8] use a multi-model ensemble of different but uses different variants of the same model. This can be parameterisation schemes within HIRLAM. They make the different data assimilation techniques (optimal interpolation, point that, with the spacing of the observational network being 3D-Var or 4D-Var), different numerical integration schemes 30-40 km, it might be a better use of resources to run the NWP (Eulerian or Lagrangian) or different physical model not in the highest possible resolution (in the study 1.4 parameterisations. Dependent on the choices made, the km), but use the computer cycles instead for calculating model behaviour changes. Note that this does not ensembles. A doubling of resolution means a factor 8 in necessarily mean that the different combinations do better running time (since one has to double the number of points in or worse in the traditional verifications scores. all four dimensions). The same effort could therefore be used • A variation on the scheme above is a proper multi-model to generate 8 ensemble members. The effects of lower ensemble, using results from the operation models of resolution might not be so bad, since effects well below the different institutes, eg the Deutscher Wetterdienst or the US spacing of the observational grid are mainly invented by the National Weather Service. model anyway, and could be taken care of by using direction • The easiest possibility is to use a poor man’s ensemble. dependent roughness instead. This is only valid if the Since for instance DMI delivers a new model run every 6 resolution is already good enough to properly represent fronts hours for 48 hours in advance, this means that at every point and meso-scale developments.
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