Abstract
Researchers within the International Energy Agency (IEA) Task 31: Wakebench have created a framework for the evaluation of wind farm flow models operating at the microscale level. The framework consists of a model evaluation protocol integrated with a web-based portal for model benchmarking (www.windbench.net). This paper provides an overview of the building-block validation approach applied to wind farm wake models, including best practices for the benchmarking and data processing procedures for validation datasets from wind farm SCADA and meteorological databases. A hierarchy of test cases has been proposed for wake model evaluation, from similarity theory of the axisymmetric wake and idealized infinite wind farm, to single-wake wind tunnel (UMN-EPFL) and field experiments (Sexbierum), to wind farm arrays in offshore (Horns Rev, Lillgrund) and complex terrain conditions (San Gregorio). A summary of results from the axisymmetric wake, Sexbierum, Horns Rev and Lillgrund benchmarks are used to discuss the state-of-the-art of wake model validation and highlight the most relevant issues for future development.
Highlights
The International Energy Agency (IEA) Task 31 Wakebench was initiated in 2011 to establish an international research collaboration in the field of wind farm flow modeling
This paper provides an overview of the building-block validation approach applied to wind farm wake models, including best practices for the benchmarking and data processing procedures for validation datasets from wind farm SCADA and meteorological databases
This paper provides an overview of the building-block validation approach applied to wind farm wake models, including best practices for the benchmarking from meteorological and wind farm SCADA databases
Summary
The IEA Task 31 Wakebench was initiated in 2011 to establish an international research collaboration in the field of wind farm flow modeling. Published under licence by IOP Publishing Ltd environmental conditions, by subdividing it in subsystems and unit problems to form a hierarchy of benchmarks with systematically increasing levels of complexity [2][3][4] This approach allows isolation of individual or combined elements of the model-chain and evaluation of the potential impact of each element on the full system performance. During this initial stage the focus is on identifying consistency among groups of models instead of evaluating details of individual models This task is left to the user, who benefits from the benchmarking activities by having access to detailed information about the simulations of the group. Generating validation data from operational wind farms deserves some attention since this operation requires a rather exhaustive filtering for quality control [27]
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