WAs PForest GALES a merged tool for improved

WAs. P-Forest. GALES: a merged tool for improved forest wind damage prediction Ebba Dellwik, Duncan Heathfield, Barry Gardiner WORLD INABOX

Starting point • Currently, the Forest. GALES model does not include a spatially varying wind field. We insert WAs. P wind field predictions into Forest. GALES and propose an automated way of doing this. • A simple flow model (such as WAs. P) is better than no flow model. 2 DTU Wind Energy, Technical University of Denmark 29 June 2017

Risø national laboratory and the orgin of WAs. P 3 DTU Wind Energy, Technical University of Denmark 29 June 2017

Risø national laboratory and the origin of WAs. P In 2017, WAs. P is still the most widely used siting tool for wind turbines. 4 DTU Wind Energy, Technical University of Denmark 29 June 2017

Key model properties WAs. P: Surface parametrization Elevation map Roughness map for large area. Displacement height map (optional) Wind input Observations fit to a Weibull distribution Forest. GALES: Stand parametrization Compartments where the forest properties are constant, added by user. Some of the forest parameters are recalculated to {z 0, d} using Raupach (1994, 1995). Wind input DAMS score based on Wind Zone and local elevation or Weibull distribution from data. Output Prediction based on a linearized model (IBZ) for terrain elevation and Output a parametrized non-linear roughness Wind risk assessment change model. Gridded wind field “resource grid” 5 DTU Wind Energy, Technical University of Denmark 29 June 2017

Example case: Aberfoyle in Southern Scotland Intention: Predict a map of time until first damage for a small area of Aberfoyle forest and discuss how the map would have looked without the coupling. 6 DTU Wind Energy, Technical University of Denmark 29 June 2017

Recipe for “TOMBERONT”: Raw ingredients • Map of DAMS windiness zones for UK (Source: Forest Research) • Ordnance Survey explorer elevation contour data for tile NS, NT (Source: OS) • Aerodynamic roughness data for a 50 km square area around Aberfoyle (Source: GWA Map Server for Online WAs. P) • Forest stand data (polygons and properties) for Aberfoyle area (Source: Juan Suarez) 7 DTU Wind Energy, Technical University of Denmark Method • Make a Was. P *. tab file representing DAMS windiness score, and make a Was. P *. lib wind atlas file from the DAMS tab file • Make a WAs. P-optimised elevation vector map • Use Forest. Gales to get roughness lengths from forest map • Blend forest-derived roughness map with background roughness map • Construct WAs. P workspace with small resource grid, and run • Get Weibull A and K for sector with maximum speed for each node • Use Forest. Gales to get predicted return period for each node 29 June 2017

Work flow: trial coupling at Aberfoyle WAs. P: Surface parametrization Elevation map Roughness map Displacement height map Wind data input Weibull distribution of observed wind fields. Output Gridded wind field “resource grid” with Weibull parameters for each grid point 8 DTU Wind Energy, Technical University of Denmark Forest. GALES: Stand parametrization Compartments where the forest properties are constant added by user. Some of the forest parameters are recalculated to {z 0, d} using Raupach (1994, 1995). Wind DAMS score based on Wind Zone and local elevation. Output Wind risk assessment 29 June 2017

Making the WAs. P roughness map 9 DTU Wind Energy, Technical University of Denmark 29 June 2017

Addition of Corinne roughness 10 DTU Wind Energy, Technical University of Denmark 29 June 2017

Comparison of roughness values from stand data and Corinne map Z 0 forest = 1. 2 m Corinne Stand data Juan Suarez, Raupach (1994) 11 DTU Wind Energy, Technical University of Denmark 29 June 2017

Final roughness map LARGE area 12 DTU Wind Energy, Technical University of Denmark Example zoom in 29 June 2017

Preparing for fast simulations Low res High res 20 km 13 DTU Wind Energy, Technical University of Denmark 29 June 2017

WAs. P output 14 DTU Wind Energy, Technical University of Denmark 29 June 2017

Increasing risk Forest risk map, Forest. GALES 15 DTU Wind Energy, Technical University of Denmark 29 June 2017

Other possible recipes • Using WAs. P CFD, WAs. P Engineering instead of WASP IBZ (the traditional linear orography model + nonlinear roughness change model) • Using a global extreme wind climate from WAs. P Engineering • Using Gumbel distribution instead of Weibull • Add the displacement height to the elevation map in WAs. P • Use airborne lidar data to estimate roughness + displacement height values 16 DTU Wind Energy, Technical University of Denmark 29 June 2017

Lidar scan of Aberfoyle DTU Wind Energy, Technical University of Denmark 29 June 2017

Orography DTU Wind Energy, Technical University of Denmark 29 June 2017

Max. Tree height was set to 35 m DTU Wind Energy, Technical University of Denmark 29 June 2017

Max PAI = 6 DTU Wind Energy, Technical University of Denmark 29 June 2017

Roughness (h/10), could be adjusted to different forest height fraction. 60 m resolution DTU Wind Energy, Technical University of Denmark 300 m resolution 29 June 2017

Conclusions • It seems possible to integrate Forest. GALES with WAs. P. • The integrated models provide more detailed spatial information on wind risk. • There are several possible ways of improvement. • Next step: Automated beta-version testing by expert users after September. • Anybody interested ? 22 DTU Wind Energy, Technical University of Denmark 29 June 2017

Thank you for listening! Contact: ebde@dtu. dk Read more about wasp: www. wasp. dk 200 years 100 years 50 years 23 DTU Wind Energy, Technical University of Denmark 29 June 2017

Scan density (first reflections) Empty square corresponds to half-empty. las file. DTU Wind Energy, Technical University of Denmark 29 June 2017