Knowledge Center

Our commitment to making renewable energy smarter can be found in the research we conduct, the products we develop, and the results we produce. Recognized as a global leader and innovator in renewable energy consulting, AWS Truepower frequently presents at and attends industry conferences and symposiums.

Papers

  • Evaluation of Four Numerical Wind Flow Models for Wind Resource Mapping

  • Philippe Beaucage*, Michael C. Brower, Jeremy Tensen,  AWS Truepower, LLC

    (Presented at AWEA Windpower 2011)

    ABSTRACT

    A wide range of numerical wind flow models are available to simulate atmospheric flows. For wind resource mapping, the traditional approach has been to rely on linear Jackson–Hunt type wind flow models. Mesoscale numerical weather prediction (NWP) models coupled to linear wind flow models have been in use since the end of the 1990s. In the last few years, computational fluid dynamics (CFD) methods, in particular Reynolds-averaged Navier–Stokes (RANS) models, have entered the mainstream, whereas more advanced CFD models such as large-eddy simulations (LES) have been explored in research but remain computationally intensive. The present study aims to evaluate the ability of four numerical models to predict the variation in mean wind speed across sites with a wide range of terrain complexities, surface characteristics and wind climates. The four are (1) Jackson–Hunt type model, (2) CFD/RANS model, (3) coupled NWP and mass-consistent model and (4) coupled NWP and LES model. The wind flow model predictions are compared against high-quality observations from a total of 26 meteorological masts in four project areas. The coupled NWP model and NWP-LES model produced the lowest root mean square error (RMSE) as measured between the predicted and observed mean wind speeds. The RMSE for the linear Jackson-Hunt type model was 29% greater than the coupled NWP models and for the RANS model 58% greater than the coupled NWP models. The key advantage of the coupled NWP models appears to be their ability to simulate the unsteadiness of the flow as well as phenomena due to atmospheric stability and other thermal effects.

    Copyright © 2012 John Wiley & Sons, Ltd.

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  • White Paper: Reducing Uncertainty in Solar Energy Estimates

  • With solar photovoltaic (PV) projects, a major area of risk is quantifying the expected annual energy production and uncertainty. One of the most significant drivers is the uncertainty in solar irradiance data.

    With more financial stakeholders becoming aware of the risks of using modeled data alone to estimate energy and project cash flows, the collection of on‐site measurements is coming to the forefront as a critical area for project planning and evaluated in project due diligence.

    To demonstrate how solar irradiance data affects uncertainty in energy production estimates, a case study was conducted for 11 sites in the U.S. to show how energy estimates using only modeled data compared to those derived from on‐site measured data correlated to a long‐term reference.

    Click here to download the white paper.

  • Wind Flow Model Performance

  • This study characterizes mean flow at four sites with different terrain complexity and wind climates (e.g. mesoscale circulations). In this paper five numerical wind flow models are compared: WAsP, Meteodyn WT, WindMap / openWind Enterprise, SiteWind and ARPS.

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