From lidar scans to roughness maps for wind resource modelling in forested areas

Abstract

Abstract. Applying erroneous roughness lengths can have a large impact on the estimated performance of wind turbines, particularly in forested areas. In this study, a new method called the objective roughness approach (ORA), which converts tree height maps created using airborne lidar scans to roughness maps suitable for wind modelling, is evaluated via cross predictions among different anemometers at a complex forested site with seven tall meteorological masts using the Wind Atlas Analysis and Application Program (WAsP). The cross predictions were made using ORA maps created at four spatial resolutions and from four freely available roughness maps based on land use classifications. The validation showed that the use of ORA maps resulted in a closer agreement with observational data for all investigated resolutions compared to the land use maps. Further, when using the ORA maps, the risk of making large errors (> 25 %) in predicted power density was reduced by 40–50 % compared to satellite-based products with the same resolution. The results could be further improved for high-resolution ORA maps by adding the displacement height. The improvements when using the ORA maps were both due to a higher roughness length and due to the higher resolution.

Characterizing wind gusts in complex terrain

“…Since two or more distributional forms may exhibit relatively good fits to the empirical distributions, we also note results wherein a second distribution type exhibits equivalent NLL values (i.e., those within 0.1 % of the best fit). The tails of probability distributions are typically of the greatest importance to wind loading (e.g., turbine design and control systems; IEC, 2005) and are not always well described by distributional forms that best represent the body of the distributions (Friederichs and Thorarinsdottir, 2012). Thus, the effectiveness of each distribution type in representing the 99th percentile gust magnitude and gust amplitude and the first percentile rise time are evaluated by comparing the parametric estimate derived from the fitted distribution to the empirically derived percentile value.…”

“…The resulting parametric descriptions of gust properties are potentially of utility to the engineering community because they permit estimation of extreme values (IEC, 2005; ASCE, 1998) (e.g., using Rice theory; Gomes and Vickery, 1977), facilitate development of joint distributions of gust parameters, allow characterization of gusts that contribute to structural fatigue, and are used with design standards (for example, extreme gusts are modeled in wind turbine design standards based on mean wind speeds and turbulence intensity; IEC, 2005). They are potentially also of use within the meteorological community since they could afford a methodology for downscaling of wind gusts in either weather forecasting (Friederichs and Thorarinsdottir, 2012;Suomi and Vihma, 2018) or climate downscaling contexts (Cheng et al, 2014). Further, fluctuating wind loads on engineering structures requires estimates of multiple components of the flow, including characteristics that have previously received relatively little attention (e.g., the shape of wind gusts) (Mücke et al, 2011;Suomi et al, 2013).…”

“…Various parametric distributions are evaluated in terms of their goodness of fit to the empirical data and their accuracy at the distribution tails and are used to develop joint probability distributions of different gust properties at a single location and of the same gust property across space (where the latter can be used to develop bivariate extreme value copulas; Bonazzi et al, 2012). Where possible the distributional forms for each gust parameter are compared with previous work in flat or moderately complex terrain (Morgan et al, 2011;Cheng and Bierbooms, 2001;Friederichs and Thorarinsdottir, 2012;Hu et al, 2018).…”

Estimating Air Density Using Observations and Re-Analysis Outputs for Wind Energy Purposes

“…The wind power density at a turbine site depends on the wind speed cubed, so there is usually a strong focus on measuring and modelling the wind speed frequency distribution with an accuracy that is as high as possible [2][3][4]. However, the wind power density and thus the AEP also depends on the air density at hub height, but the estimation of air density has received very little attention in literature.…”

Estimating wind conditions in forests using roughness lengths: A matter of data input

“…Floors et al (2018) described how roughness lengths can be applied for wind resource assessments, and the study furthermore stressed that online dataset of roughness lengths cannot replace converted in situ data. Therefore, this research aims at testing and comparing optimized roughness approach (ORA) presented by Enevoldsen (2016b) and ranking it against the different data sources presented by Floors et al (2018), in order to provide information to academia and the wind industry about which models to trust when examining wind conditions in forested areas dominated by coniferous tree types. Thus, this research focuses only on finding the best approach using roughness as an indicator of the trees’ impact on the wind conditions.…”