17:00 - 18:30 Annual energy production - let's get it right!
To improve investor confidence in our energy yield assessments it’s vital that we can demonstrate improvements in the accuracy of our predictions. Only through doing this will be able to access cheaper sources of capital which will in turn reduce the cost of energy.
- Identification of the errors in the model chain.
- How using multiple models can improve prediction accuracy.
- How to use vertical extrapolation models in combination with long term correlation techniques.
- Using the due diligence process to add project value.
Lead Session Chair:
Mike Anderson, RES Ltd.
Bjarke Tobias Olsen (1) F Andrea Hahmann (1) Anna Maria Sempreviva (1) Jake Badger (1) Hans Ejsin Joergensen (1)
(1) Technical University of Denmark, DTU, Roskilde, Denmark
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Presenter's biographyBiographies are supplied directly by presenters at EWEA 2015 and are published here unedited
Bjarke is a PhD student at The Technical University of Denmark (DTU). He is working on coupling meso- and microscale models for improving wind climate estimation at complicated sites. Before coming to DTU Bjarke worked on forecasting heavy precipitation events using mesocale models at the Danish Meteorological Institute (DMI). Bjarke is B.Sc. in Meteorology and M.Sc. in Physics.
Simulating wind energy resources with mesoscale models: Intercomparison of state-of-the-art models
Mesoscale models are increasingly being used to estimate wind conditions to identify perspective areas and sites where to develop wind farm projects. Mesoscale models are useful because they give information over extensive areas with various terrain complexities where measurements are scarce and measurement campaigns costly. Various mesoscale models and families of mesoscale models are being used, with thousands of setup options. Since long-term integrations are expensive and tedious to carry out, only limited comparisons exist. To contribute evaluating the capabilities of mesoscale models used in wind energy to estimate site wind conditions, a tailored benchmarking study has been co-organized by the European Wind Energy Association (EWEA) and the European Energy Research Alliance Joint Programme Wind Energy (EERA JP WIND). EWEA has been the host of this comparison exercise insuring that the identity of each participant is kept anonymous. The blind evaluation was performed at the Wind Energy Department of the Technical University of Denmark (DTU) by the authors.
There were two principal objectives: (1) To highlight common issues on mesoscale modelling of wind conditions on sites with different characteristics, and (2) To identify gaps and strengths of models and understand the root conditions for further evaluating uncertainties.
Preliminary results of this mesoscale benchmarking exercise were presented at the EWEA Technology Workshop in Wind Resource Assessment in Helsinki, Finland in June 2-3 2015. Here, we present an updated and more comprehensive summary of the results.
We selected three experimental sites: FINO3 (offshore, GE), Høvsore (coastal, DK), and Cabauw (land-based, NL), and three other sites without observations. Year 2011 was chosen because the availability of concurrent suitable time series of vertical profiles of winds speed and other surface parameters at the three sites. The participants were asked to provide hourly time series of wind speed, wind direction, temperature, etc., at various vertical heights for the complete year. The methodology used to derive the time series was left to the choice of the participants, but they were asked for a brief description of their model and many other parameters (e.g., horizontal and vertical resolution, model parameterizations, surface roughness length) that could be used to group the various models and interpret the results of the intercomparison.
Main body of abstract
Twenty separate entries were received by the deadline of 31 March 2015. They included simulations done with various versions of the Weather Research and Forecast (WRF) model, but also of six other well-known mesoscale models. The various entries represent an excellent sample of the various models used in by the wind energy industry today.
The analysis of the submitted time series included comparison to observations, summarized with well-known measures such as biases, RMSE, correlations, and of sector-wise statistics, e.g. frequency and Weibull A and k. The comparison also includes the observed and modeled temporal spectra. The various statistics were grouped as a function of the various models, their spatial resolution, forcing data, and the various integration methods. Many statistics have been computed and will be presented in addition to those shown in the Helsinki presentation.
The analysis of the time series from twenty entries has shown to be an invaluable source of information about state of the art in wind modeling with mesoscale models.
Biases between the simulated and observed wind speeds at hub heights (80-100 m AGL) from the various models are around ±1.0 m/s and fairly independent of the site and do not seem to be directly related to the model horizontal resolution used in the modeling. As probably expected, the wind speeds from the simulations using the various version of the WRF model cluster close to each other, especially in their description of the wind profile.
Cloud computing allows now the use of mesoscale models by non-expert users for site assessment studies. This tool is very useful and powerful, but users must be aware of the different issues that might be encountered in working with different setups. The results presented in the model intercomparison provide such guidance.