Mesoscale to microscale wind farm flow modeling and evaluation

Abstract

The increasing size of wind turbines, with rotors already spanning more than 150 m diameter and hub heights above 100 m, requires proper modeling of the atmospheric boundary layer (ABL) from the surface to the free atmosphere. Furthermore, large wind farm arrays create their own boundary layer structure with unique physics. This poses significant challenges to traditional wind engineering models that rely on surface-layer theories and engineering wind farm models to simulate the flow in and around wind farms. However, adopting an ABL approach offers the opportunity to better integrate wind farm design tools and meteorological models. The challenge is how to build the bridge between atmospheric and wind engineering model communities and how to establish a comprehensive evaluation process that identifies relevant physical phenomena for wind energy applications with modeling and experimental requirements. A framework for model verification, validation, and uncertainty quantification is established to guide this process by a systematic evaluation of the modeling system at increasing levels of complexity. In terms of atmospheric physics, 'building the bridge' means developing models for the so-called 'terra incognita,' a term used to designate the turbulent scales that transition from mesoscale to microscale. This range of scales within atmospheric research deals with the transition from parameterized to resolved turbulence and the improvement of surface boundary-layer parameterizations. The coupling of meteorological and wind engineering flow models and the definition of a formal model evaluation methodology, is a strong area of research for the next generation of wind conditions assessment and wind farm and wind turbine design tools. Some fundamental challenges are identified in order to guide future research in this area. mesoscale model at~4 km horizontal resolution (a), nested down with WRF to 1.67 and 0.55 km resolution, and introducing microscale speedups with WAsP at 100 m resolution (b). (Reprinted with permission from Ref 35.

A methodology for the design and testing of atmospheric boundary layer models for wind energy applications

“…Mesoscale forcing is derived from simulations with the Advanced Research Weather Forecasting model (WRF), version 3.8 (Skamarock et al, 2008). Kleczek et al (2014) made a sensitivity study of WRF for different grid setups, boundary layer schemes, boundary conditions and spin-up time.…”

“…The dynamics of these forcings determine the interplay between the wind climatology, relevant for the assessment of the wind resource, and the wind conditions relevant for wind turbine siting. Sanz Rodrigo et al (2016) reviews the state-of-the-art wind farm flow modeling, methodologies and challenges for mesoscale-microscale coupling.…”

“…The design of ABL models for wind energy requires a systematic approach to verification and validation in order to demonstrate consistency of the computational code with the conceptual physical model and to quantifying deviations with respect to the real world (Sanz Rodrigo et al, 2016). The verification process is carried out using idealized test cases where the solution is known from theory or from a higherfidelity model (code-to-code comparison).…”

Evaluation of three mainstream numerical weather prediction models with observations from meteorological mast IJmuiden at the North Sea

“…Applications range from resource assessment to load calculations and from wake simulations to power forecasting . A comprehensive review of flow modeling in the wind energy sector was recently published by Sanz Rogdrigo et al In that study, the authors distinguish between mesoscale and microscale models.…”

Statistical post‐processing of turbulence‐resolving weather forecasts for offshore wind power forecasting

“…Moving from the standard ECMWF forecast to the ultrahigh‐resolution turbulence‐resolving model represents a significant jump in resolution. For future work looking to benchmark against all possible methods, it would be prudent to include a comparison of a middle‐ground between the two and evaluate performance against a mesoscale model, typically used in commercial power forecasting systems. Additional benchmark comparisons should certainly include direct comparisons with ensemble members or an ensemble of weather prediction sources; however, it should be noted that a robust numerical comparison in this context would necessarily require inclusion of ultrahigh‐resolution ensembles generated by the LES simulation.…”