wind farm siting in complex terrain using cfd

Wind Farm Siting in Complex Terrain

using CFD

Ben Martinez,

Vattenfall Wind R&D

Vindkraftnet meeting Kolding, Denmark

01-06-2017

Confidentiality - Low (C2)

Outline

Intro:

Vattenfall’s wind assets

Our needs for wind farm siting CFD

Vattenfall’s approach to micro-siting modelling

An insight into Vattenfall’s siting CFD tool capabilities

CFD experiences in Sweden

Modelling atmospheric stability

Vattenfall’s wind assets (2016)

Total operating capacity in 2016 ~ 2,2 GW

~60% offshore versus ~40% onshore

Vattenfall’s need for CFD…

Growth in onshore wind power portfolio mainly in Sweden & UK- Areas affected by forest in complex terrain

- Areas affected by severe icing

Sweden has 60-65% forest cover - About 18% of all forest in Europe

- Forest coverage in comparison: • Denmark: 11%

• United Kingdom: 12% (Scotland 15%)

• Germany: 31%

• European average: 35-45%

CFD can better model site conditions in forested & complex terrains- Non-linear effects are larger

- High turbulence and wind shear

- A matter of techno-economical risk mitigation

It’s hilly!!

It’s full of forest!!

It’s icy!!

Quick review: What is micro-siting?

Micro-siting considers the detailed

layout of a wind farm

Aim is to map the ‘expected’ average

atmospheric conditions (wind speed,

shear, vear, turbulence intensity…)

to be expected at every wind turbine

position

Quick review: micro-siting models

TEM 75 Topical Expert meeting | Ben Martinez | 12/11/2013

CFD Linearized

Non-linear

RANS / LES approach

Linear

(Perturbation theory)

3D

No vertical extrapolation

necessary

2D

Vertical profiles assumed

logarithmic

Flow separation predicted

Suitable for complex terrainSeparation not predicted

Computationally intensive Runs fast

Models Turbulence No Turbulence modeled

Forest modelling:

Turbulence source and

momentum sink terms;

different tree heights and

types

Forest modelling:

Simplified parametric model

; constant tree height

WAKES: Actuator Disk

Actuator Line

WAKES: Semi-empirical or

linealized CFD



CFD Linearized

Non-linear

RANS / LES approach

Linear


3D


necessary

2D


logarithmic





Forest modelling:




types

Forest modelling:




Actuator Line


linealized CFD

Flat versus complex terrain

15 deg. critical angle



CFD Linearized

Non-linear

RANS / LES approach

Linear


3D


necessary

2D


logarithmic





Forest modelling:




types

Forest modelling:




Actuator Line


linealized CFD



Forest treatment in CFD:



CFD Linearized

Non-linear

RANS / LES approach

Linear


3D


necessary

2D


logarithmic





Forest modelling:




types

Forest modelling:




Actuator Line


linealized CFD



Forest treatment (linear):

Uses

Displacement

height

Onshore site classification

1. CFD

Terrain

type

1. Flat 2. Forest 3. Hilly 4. Complex 5. Icy

Model(s)

Linearized

Mesoscale modelling

Wind Farm Terrain type CFD requirement

Klim (DK) 1 No

Juktan (SE) 2 + 3 + 5 Yes

Clashindarroch (UK) 2 + 4 Yes

Hoge Vag (SE) 2 + 3 Yes

Pen y Cymoedd (UK) 2 + 4 Yes

Many onshore sites require CFD Different level of need

Combine linear and

CFD model runs

Modelling strategy

Terrain

type

1. Flat 2. Forest 3. Hilly 4. Complex 5. Icy

Model(s)

If site is 2, 3, 4 or any combination including at least 2,3 or 4:

Similar P50 and wind statistics results:

Lowers modelling uncertainty

Much higher confidence

Distant P50 and wind statistics results:

Detects divergence in results! Raises the flag

A deeper investigation should clarify which

model to trust depending on type of terrain.

Our CFD tool for micro-siting

Customized CFD tool based on

the CFX RANS solver (Ansys)

Graphical user interphase is developed to

ease its use

Fully integrated into our high performance

computing cluster (HPC)

Additional Post-processing add-on’s are

implemented at R&D

Site prospecting and constraint mapping tool

Cross-prediction module

Energy assessment module

Report generation tool

Workflow

Straight forward Workflow: Pre-processing (serial)

Solver (parallelized)

Post-processing (serial)

Scripts can be easily modified (Perl based)

Terrain data Mesh generation CFD pre-processing24 / 36 sectors

CFD solutionCFD post-processing

Roughness

& Forest description

Energy

Assessment

Cross

predictions

Site Prospecting /

Constraint map

Report

Mast &

(WT locations)

Generating Mesh

Standalone automatic mesher

Creates block structured hexahedral meshes

The mesh is projected on to the STL terrain geometry

Peripheral blending or extention is applied by default

Vertical and horizontal cell expansion factors are applied ( r < 1.2)

Solving strategy

K-epsilon or K-omega SST models

Assumption: Reynolds number independent flow

Neutral atmospheric stability good assumption in moderate to high speed winds

Speed-up’s are wind speed independent 1 simulation per wind sector

Generally 24 to 36 simulations per site

Reconstruction of target (wind turbine) time series

i = 1,…., num of turbines

j = 1,…., num of sectors

Post-processing: Site prospecting / constraint map tool

Tool aimed to map unwanted wind turbine positions

and to choose wind turbine type following IEC 61400-1 Ed3 Standard

Site assessment checklist:

1. 0 < Average shear coefficient < 0.2

2. Abs(Flow inclination) < 8 deg (for any direction)

3. Extreme Wind speed < Vref

4. Wind distribution <= IEC design distribution

5. Effective TI <= Representative TI

(Tool provides Average ambient TI

or TI at specific wind speed)

Example: Mapping average TI and applying contraints

Average Ambient TI < 14 %

Example: Mapping contraints

Variable constraint inputs allows flexibility

Constraint setup at 80 @ agl:

1. min: 0 max: 14 (Average Ambient TI)

2. min: 0 max: 0.2 (Shear coefficient)

3. min: 0 max: 50 (Extreme 50 year ws class I)

4. min: 0 max: 6 (Terrain Inclination)

5. min: -8 max: 8 (Flow inclination)

6. wind distribution: No design constraint

Example: Mapping contraints

COMBINED CONSTRAINTS

Post-Processing: Cross-Prediction

Purpose: to check your model predictions on known data

(mast to mast prediction)


Sectorwise cross-predictions for wind speed and ambient TI

Wind standard deviation extrapolated using the Reynolds number independent

assumption


Wind climatology at every WT position

Sectorwise shear coefficient and Turbulence intensity at 10 m/s tables

highlighting problematic sectors

Report generation tool

Results from site prospecting / Cross-prediction / Energy assessment

are sumarized in .pdf report.


Common practice is to run: CFD + WAsP

Most of the projects are hilly and forested

Site classification (2 + 3, hilly and forest)

• Main difference forest treatment

a) Forest heights seen by CFD at site X b) Wasp only sees roughness


Common pattern observed: WAsP tends to overpredict wind speeds at some

turbine locations

tendency to trust CFD on such locations

High shear and Turbulence values (can be reduced quite a lot with higher hub

heights)

Modelling atmospheric stability

In-house R&D project was set to investigate the potential added value

of modelling atmospheric stability using the available Ansys-CFX windmodeller

stability model.

transient model solving an extra model equation on Enthalpy

buoyancy term is present in the momentum equations

Due to the lack of 2-height temperature measurement met-masts in our assets

it has been difficult to arrive to statistically meaningful conclusions

Modelling challenges:

Not a steady-state flow

spurious gravity waves difficult to handle

longer simulation times

convergence issues (unstable flows especially)

Risk of garbage-in - garbage-out

Non-Reynolds dependant flow >>> several simulations per wind sector

(time consuming)

THANK YOU FOR YOUR ATTENTION !

wind farm siting in complex terrain using cfd

Documents