ACP R&T_2022 - Poster - Long-Distance Wakes_Stoelinga

1. Blockage
Internal wakes
External wakes
Photo: Krogh (2019) 𝑈!
Wake rotation
Motivation
• Interactions between wind flow and turbines are complex, and becoming more
so with an ever-increasing number and size of turbines being installed in the
stable marine atmospheric environment.
• Developers must be concerned not just with project wakes, but with long-
distance external wakes, project-scale blockage, lateral acceleration, wake
recovery, etc.
• Standard engineering wake models are overly simplistic and do not account for
long-range external wakes, while a growing body of literature is identifying such
wakes at greater and greater distances from their source in the offshore
environment.
Solution
• Weather prediction models are ideally suited to simulate all of the complex phenomena that play a role in
the interaction between turbines and wind flow, because they’ve already been designed to simulate these
phenomena for weather forecasting.
• Wind turbines can be represented as sources of deceleration in the Weather Research and Forecast (WRF)
weather prediction model, using the Wind Farm Parameterization (WFP) (Fitch et al 2012).
• In the WFP, turbine wakes fully interact with the 3D wind flow within the weather prediction model.
Important effects such as flow curvature and atmospheric stability are accounted for.
• Waked wind speed deficits simulated by WRF/WFP have been validated in peer-reviewed studies (Hasagar et
al. 2015; Platis et al. 2018; Siedersleben et al. 2018).
• ArcVera also validated WRF/WFP for an onshore project in the central U.S., using 54 days of SCADA data from
an existing wind farm, half of which occurred before and half after operation began at an upwind wind farm
5 km away—see schematic at right. Also shown is the simulated hub-height waked wind speed deficit at one
instant in time during north-northeasterly winds.
• Results of the validation study are summarized in the table below. Note, only times when the waking project
was directly upwind were included, leading to the large percent energy deficits. Stable and unstable times
were examined separately. Two engineering wake models were also tested: OpenWind’s Eddy Viscosity-Deep
Array Wake Model (EV-DAWN); and ArcVera’s Wind Farm Atmosphere Interaction (WFAI) Model. They greatly
underestimated the energy wake loss, whereas WRF-WFP overpredicted the energy wake loss by just 16%.
Long-Range Offshore Wakes Experiment
• What does the future hold in terms of wind farm wakes when very l large “turbines of the
future” are deployed within the New York Bight lease areas?
• Used IEA Wind Reference Turbine (15 MW capacity, 160 m rotor diameter)
• Determined, for example, the wake loss for turbines in the area 0538 from wakes generated
by turbines in areas 0539, 0541, and 0542.
Key Results
• Wind farm-size wake swaths extend 100 km downwind of turbine arrays
• Wind speed reduced 1-3 m/s within wake, when unwaked wind speed was ~10 m/s
• During a 16-day period selected for predominantly south-southwesterly flow, wakes
from area 0539 reduced wind energy at 0538 by 13%; and wakes from 0539, 0541 and
0542 reduced wind energy at 0538 by 29%. Engineering wake models EV-DAWM and
WFAI predicted ≤ 5% energy wake loss in either scenario for the same 16-day period.
Download the white paper
and view the animation video
of simulated offshore wakes
in the New York Bight!
Lead author email:
mark.stoelinga@arcvera.com
References
Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K., Michalakes, J., and Barstad, I., 2012: Local and Mesoscale Impacts of Wind Farms as Parameterized in a
Mesoscale NWP Model, Mon. Weather Rev., 140, 3017–3038, https://doi.org/10.1175/MWRD-11-00352.1.
Hasager, C., P. Vincent, H. Vincent, R. Husson, A. Mouche, M. Badger, A. Peña, P. Volker, J. Badger, A Di Bella, A. Palomares, E. Cantero, P. Correia, 2015: Comparing satellite
SAR and wind farm wake models. Journal of Physics: Conference Series. 625. 10.1088/1742-6596/625/1/012035.
Krogh, H., 2019: Amazing view of the Horns Rev 2 offshore wind farm. Post on twitter.com, 12 July 2019.
https://twitter.com/Orsted/status/1149593626520903681?s=20&t=w5ZJUoLhDEjPyLSgrVddog
Platis, A., Siedersleben, S. K., Bange, J., Lampert, A., Baerfuss, K., Hankers, R., Canadillas, B., Foreman, R., Schulz-Stellenfleth, J., Djath, B., Neuman, T., and Emeis, S., 2018:
First in situ evidence of wakes in the far field behind offshore wind farms, Sci. Rep., 8, 2163, https://doi.org/10.1038/s41598-018-20389-y.
Siedersleben, S. K., Platis, A., Lundquist, J. K., Lampert, A., Bärfuss, K., Cañadillas, B., Djath, B., Schulz-Stellenfleth, J., Bange, J., Neumann, T., and Emeis, S., 2018: Evaluation
of a wind farm parametrization for mesoscale atmospheric flow models with aircraft measurements, Meteorol. Z., 27, 401–415,
https://doi.org/10.1127/metz/2018/0900.
Source of Estimate
Long-Range External Wake Loss at Central US Wind Farm
All Times Stable Conditions Unstable Conditions
SCADA 23.8% 29.1% 12.8%
EV-DAWM 5.7% not tested not tested
ArcVera WFAI Model 0.2% not tested not tested
WRF-WFP 27.7% 32.6% 17.5%
5 km
5 km
Project-scale
wake in
northerly wind
New Project
Target Project
(built first)
10 km
New Project
Target Project
(built first)
Atlantic Ocean
0499
N
e
w
J
e
r
s
e
y
E06
E05
0541
0542
0539
0538
0537
New York Bight
0544
0512
0498
Lateral deflection
American Clean Power Resource & Technology Conference
Las Vegas, Nevada, 7-9 September 2022
Onshore Validation Study