rchavez-arroyo/owabench

OWA Wake Modeling Challenge

Javier Sanz Rodrigo, Pawel Gancarski, Pedro Miguel Fernandes Correia

Background

The Offshore Wind Accelerator (OWA) Wake Modeling Challenge aims to improve confidence in wake models in the prediction of array efficiency. A benchmarking process comprising 5 wind farms allows wake model developers and end-users test their array efficiency prediction methodologies over a wide range of wind climate and wind farm layout conditions.

The project is integrated in the Phase 3 of the IEA Task 31 Wakebench international framework for wind farm modeling and evaluation.

Scope and Objectives

The primary objective of the project is to understand the limitations of wake models used by industry for the prediction of array efficiency over a relevant range of operational conditions in the offshore environment. To this end, the following three objectives were defined to guide the Wake Modelling Challenge:

• Evaluate wake modelling and power prediction methods and validate the results with measured data.
• Examine the accuracy of specific models, quantify uncertainty bands and highlight modelling trends.
• Define an open-access model evaluation methodology that can systematically improve consistency and traceability in the assessment of state-of-the-art wake models for power prediction as more datasets are added.

Benchmark Guides

The following blog posts were used to guide benchmark participants: * Anholt benchmark * 5 additional offshore wind farms

Data

Benchmark input data and simulation data is published open-access in the following data repository: https://doi.org/10.5281/zenodo.3715198

Citation

You can cite the github repo in the following way:

Sanz Rodrigo J., Gancarski P. and Fernandes Correia P. M. (2020). OWA Wake Modelling Challenge (Version v2.0.1). Zenodo. http://doi.org/10.5281/zenodo.3773129

Installation

We use Jupyter notebooks based on Python 3. We recomend the Anaconda distribution to install python.

Dependencies

The libraries used by the notebooks can be installed with

`bash pip install -r requirements.txt `

License

Copyright 2020 CENER Licensed under the GNU General Public License v3.0

Acknowledgements

The authors would like to thank Carbon Trust and the OWA Technical Working Group for their support providing funding, operational data and guidance throughout the project. We would like to thank all the benchmark participants for their simulations and in-kind support in fine-tuning the benchmark set-up and evaluation methodology as well as submitting simulation outputs.