Net AEP maps for windfarm optimization Morten Nielsen Vindkraftnet meeting November 13, 2018 at DTU Lyngby Campus

Outline • What is a Net AEP map • Greedy ‘optimization’ algorithms • Example 1 - Gwynt y Môr offshore wind farm • Example 2 – Jing Bian complex terrain wind farm

WAsP resource grids

Combined wake map by FFT calculus Calculation of depleted wind resource maps

• Find production for all wind speeds, wind

directions and turbine types • Make probability-weighted integral over all

wind conditions • Find power density or AEP or reference

turbine at grid nodes

Apply speedup-factors for complex terrain (but no directional deflection) Poster and paper from EWEA 2015 conference

Depleted wind resource maps

Gross AEP Net AEP

Greedy ‘optimization’ algorithms • Greedy v1

Recalculate the Net AEP map for each iteration and place the turbine at the positions with the highest net wind resource

• Greedy v2 As version I, but using a Net AEP map corrected for losses at existing turbines due to the added turbine

Geometric constraints • Grid mask (coarse) • Shapes (better accuracy)

– WF boundary (polygons) – Turbine distances (circles)

Example 1: Gwynt y Môr offshore wind farm Base case dist≈6.6D Geometric dist≥5.5D

Greedy v1 Greedy v2

Constrain on turbine separation Greedy v2 dist≥3D Greedy v2 dist≥4D

Greedy v2 dist≥5D Turbine separation statistics

WF Layout Mean StDev Min Max

Existing 6.67 0.10 6.29 6.72

Geometrical 5.54 0.14 5.50 6.86

Greedy v1 3.95 1.56 1.07 7.91

Greedy v2 4.76 1.29 1.73 8.11

Distance ≥ 3D 4.65 1.12 3.00 8.02

Distance ≥ 4D 5.03 0.74 4.00 6.67

Distance ≥ 5D 5.41 0.46 5.00 7.21

Comparison of production estimates

20252030203520402045205020552060206520702075

GyM Net AEP [GWh]

WF layout Net AEP [GWh]

Diff

Existing 2067.734 -

Geometry (5.5D) 2042.483 -1.22%

Greedy v1 2062.643 -0.25%

Greedy v2 2071.124 0.16%

Distance ≥ 3D 2070.909 0.15%

Distance ≥ 4D 2069.383 0.08%

Distance ≥ 5D 2064.159 -0.17%

Minimum spanning tree Existing Geometric Distance≥3D

Greedy v1 Greedy v2 Distance≥5D

580

600

620

640

660

680

700

720

[GWh] Production

Gross AEP

Net AEP

Complex terrain – Jing Bian wind farm

Existing plan dist≥7D

dist≥5D dist≥3D

Restricted areas IEC standard Edition 3 (2010) Edition 4 (planned) Shear 0 ≤ α ≤ 0.2

sector frequency weighted 0.05 ≤ α ≤ 0.25

sector energy weighted

Flow inclination I<8° worst sector

I<8° sector energy weighted

Edition 3 Edition 4

Conclusions • Efficient method for adding wake effects to wind resource maps

– Works for linear wake models like Jensen and Fuga – Speed depends on grid domain size, no. of turbine types and total no. of turbines

• Greedy layout algorithm for offshore site – Tends to place turbines at wind farm boundary, particularly at the windward side – Better results with version 2 including losses for already positioned turbines – Modest gain in AEP and turbine separations unacceptable – Need constraints on turbine separation – Optimized layout may have shorter cable distances

• Greedy layout algorithm for complex terrain site – This problem is not just about wake losses but also about differences in wind resource – Shows some shortcomings of the greedy ‘optimization’ approach – Need to consider wind conditions in the IEC standard – Shear and flow inclination criteria seems to be relaxed in Ed. 4 of IEC standard

Thanks to project Wind Farm Layout Optimization in Complex Terrain, EUDP 2014-2017