Dr. Rubén DelgadoUMBC/Joint Center for Earth Systems Technology

Dr. Belay Demoz, Alexandra St. Pé, Brian Carroll, Farrah Daham, Qin Liu, Julio Roman, Christian Sias, Shelbi Tippett, Daniel Wesloh

UMBC Atmospheric Lidar Group

1

Business Network for Offshore WindNational Renewable Energy Laboratory

July 1, 2014

Energy-Meteorology Research Questions

2

Why high wind farm energy loss?

•Average Loss 20% (up to 35%)

•6% = Wake Effect

•4%= Turbine Performance

Best method to estimate variability in

wind profile?

Best method to account for impact of

meteorological controls in expected

turbine power output?

UMBC Wind Energy Research

3

lidar.umbc.edu/wind-energy

Mid-Atlantic meteorological controls impact on seasonal & diurnal variations in wind regimes

Optimal turbine design and wind farm layout strategies

Doppler wind lidar retrievals of turbine’s wake effect

Validation/improvements of Numerical Weather Prediction and weather industry’s model output

Land/marine boundary layer dynamics

UMBC Offshore Wind Measurements Maryland Energy Administration (2013)

Data Collection 2013-2016 to support wind resource assessment

4

Phase 1 (July-August 2013): Offshore Measurements during MEA Geophysical Survey

(Windcube V2 Offshore)

Buoy 44009 Underestimates Wind Speed Resource

( bias -3m/s )

5

Maryland WEA Campaign Results

Weibull parameters Buoy LidarShape parameter (k) 1.39 2.19Scale parameter (c) - m/s 5.04 7.89

NWP & Reanalysis Data Underestimates the Wind Speed Resource & Error in Wind Direction

6

Maryland WEA Campaign Results

Maryland WEA Campaign Results Why differences?

Frequent Development of nocturnal offshore LLJs

Coastal upwelling of cooler water in WEA? Land-Sea-breeze circulation?

7

α = 0.14

High rotor layer winds shear (+20% High (-) wind shear exponents)

Maryland WEA Campaign Results Sensitivity of Potential Power Output when accounting for met

controls (shear & TI)

NREL 5MW Reference Turbine

8

Next Steps:• 2 sec variability

[1-2]

DOE Wind Energy R&D Focus 2015-2021

Wind Plant Optimization R&D: ‘Smart Wind Plants’

Design for wind farm operation at optimal profitability

Strategy: Develop new technologies that ‘exploit interactionsamong turbines, wind resource & operating environment’

9

Lidar Uncertainty Measurement Experiment (LUMEX)

CIRES: A. Choukulkar, R. M. Hardesty, Y. Pichugina, C. Senff, A. Weickmann,NOAA ESRL: W.A. Brewer, R.M. Banta, S. Sandberg, D. Wolfe; UMBC: G. Antoszewski, B. Carroll, R. Delgado; UC-Boulder: J. Lundquist, M. Rhodes, NW Res. Assoc.: A. Muschinski

10

Measurements at NOAA Boulder Atmospheric Observatory in June-July 2014.

The major goals of this experiment were the following:

Intercomparison of Doppler wind lidars and error analysis of spatial representativeness to establish scanning strategies and retrieval algorithm for PBL studies and wind energy.

Validation of turbulence analysis techniques and retrievals from Doppler lidars.

Lidar 1

Lidar 2

70o

45o

tower

profiler

Lidar Measurements To Support DOE/NREL XPIA Spring 2015: EXperimental Measurement Campaign for Planetary

Boundary Layer Instrument Assessment

Scanning Doppler Wind Lidar (Leosphere 200s) wind validation at Boulder Atmospheric Observatory (300 m tower)

Radiosondes, Microwave Radiometer Temperature Profiles (Atm. Stability)

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XPIA: Assessment of Motion Correction in Doppler Wind Lidar Retrievals

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• NOAA Boulder Atmospheric Observatory April-May 2014

• Comparison of static Doppler Wind Lidar and sonic anemometers at BAO 300m tower to determine uncertainties of hardware and software motion compensation in commercial Wind lidars for offshore wind assessments.

sparling@umbc.edu

Offshore Lidar Measurements To Support DOE/NREL

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Phase 2 (MEA) (2014-2016): Scanning Doppler Wind Lidar Measurements Offshore from Ocean City, MD

=

Offshore Lidar Measurements To Support DOE/NREL

14

Future Work (?): Deploy Scanning Doppler Lidar Offshore in WEAs to address wind plant performance issues (pre-construction)

[3]

Aligns well with DOE strategy: … ‘exploitinteractions among turbines, windresource & operating environment’

Maritime Applied Physics Corp. (MAPC)

15

mrice@mapcorp.com

• Remote Offshore Sensor System(ROSS)

• Three Surveys-one buoy• Avian• Marine• Wind

• Stabilized Mast

Delmarva Wind Energy Research Facility (DWERF)-University of Maryland Eastern Shore

100 meter met. tower

Solid rod lattice design rated for Cat 2 hurricane

NRG Instruments at 10m, 50m, 80m, 100m

DNV-GL, IEC guidelines for resource assessment

Symphonie Plus3 Data Logger, WindLinx data com system

POC- Bruce M. Williams –bwilliams@PERIHQ.com

DWERF-University of Maryland Eastern Shore

17

A6

A5

A4

A3

A2

A1

Location LAT LONG

BASE 38.21860° -75.67785°

A1 38.21887° -75.67793°

A2 38.21852° -75.67753°

A3 38.21841° -75.67811°

A4 38.21931° -75.67803°

A5 38.21838° -75.67700°

A6 38.21808° -75.67849°

Model Uncertainty/Sensitivity Testing in MD WEA

Domain (volume) configurationtesting Horizontal resolution Spatial coverage Update cycle Vertical resolution

Physics sensitivity testing PBL schemes. Land Surface schemes

Initial Conditiontesting Improved geophysical

input Optimal data source

PBL schemes

sparling@umbc.edu

Future Work VERTEX: Univ. of Delaware (Archer)/UMBC (Delgado)

Vertical mixing enhancement/reduction within turbine wake: field campaign and numerical simulations (Sept-Oct 2016). Proposed to NSF.

Saint Louis University (Pasken)/UMBC(Halverson, Delgado)/Howard University (Demoz) Improved characterization of thermal and wind regime variability

for enhancing prediction of extreme icing events that impact utilities: Winter 2015 and 2016.

Remote sensing (lidar/microwave radiometer), radiosondes, dropsonde instrumented aircraft.

19

Thank you.Questions?

20

Acknowledgements

• Maryland Energy Administration, DOD/Army Research Laboratory: Partnership in

Research Transition (PIRT) Program, NOAA-CREST, Leosphere/Renewable NRG

Systems, CB&I/Coastal Planning and Engineering Environmental and Infrastructure,

NREL(Andrew Clifton)

References [1] Sumner J and Masson C 2006 Influence of atmospheric stability on wind turbine power performance

curves J. Sol. Energy Eng. 128 531–7.

[2] Wagner R, Antoniou I, Pedersen S M, Courtney M S and Jørgensen H E 2009 The influence of the wind

speed profile on wind turbine performance measurements Wind Energy 12 348–62.

[3] Koch, Grady J., et al. "Three-dimensional wind profiling of offshore wind energy areas with airborne

Doppler lidar." Journal of Applied Remote Sensing 8.1 (2014): 083662-083662.

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Extra

22

Wind Speed Statistics MD WEA Summer 2013

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Method Mean Median Mode

uEQ 7.014 6.998 6.485

uEQ,TI 7.505 7.453 6.937

uEQ,TID 7.505 7.453 6.937

lidar@100m 6.940 6.920 6.443

Buoy (ext.) 3.252 3.337 1.734

*Wind speeds (m/s)

Model/Reanalysis Resolution

NAM-218 12.19km

RAP-130 13.54 km

NARR-221 32.36 km

CFSv2 0.5 x 0.5 degree (55 x43 km)

ERA-I 0.703 x 0.702 degree (78 x 60km)

NNRP/R1 2.5 x 2.5 degrees (277 x 216km)

24

25

Example: Offshore Scanning Lidar Measurements