Wind Plant Modeling and Simulation: 

The amazing interrelationships of 

physics and engineering in wind 

plant design

Paul Veers, NWTC Chief Engineer, NREL

AIAA Science and Technology Forum and Exposition

 January 7, 2019

San Francisco, California NREL/PR-5000-73088

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract 

No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views 

expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for 

publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or 

allow others to do so, for U.S. Government purposes.
New Book: Modeling and Simulation in Wind Plant Design and 

Analysis (to be published by IET in 2019)

• Volume 1: Atmosphere and Plant

1. The Exascale Computational Challenge (Sprague and

Robinson, NREL)

2. Bladed-resolved modeling with fluid-structure

interaction (Vijayakumar and Brasseur, NREL and CU)

3. Meso-scale modeling of the atmosphere (Haupt et al.,

NCAR)

4. Mesoscale to Microscale Coupling for High-Fidelity

Wind Plant Simulation (Mirocha, LLNL)

5. Atmospheric turbulence modeling and simulation

(Berg and Kelley, DTU)

6. Modeling the flow through full wind plants: Wakes and

Wake Interactions (Churchfield and Moriarty, NREL)

7. Control of Wind Plants and Power Output (van

Wingerden et al., TU Delft and NREL)

8. Forecasting for Wind Power Production and Grid

Operations(Zack, UL)

9. Cost of Energy and Financial Structures Modeling

(Hand, et al., CCEC and NREL)

• Volume 2: Turbine and System

1. Aerodynamics of wind turbines (MOL Hansen, DTU)

2. Structural Dynamics: the turbine as an aeroelastic

system (Morten Hansen, LM/GE)

3. Blade/Rotor design and analysis and optimization

(Bottasso and Bortolotti, TU Munich and NREL)

4. Drive Train Analysis for Reliable Design (Zhang, et

al., Romax and NREL)

5. Offshore turbines with bottom-mounted or floating

support systems (Matha, et al., Ramboll and NTNU)

6. Turbine Controller design (Wright, et al., NREL, CU, CSM, TU Delft)

7. System Engineering and optimization of wind

turbines and plants (Ning and Dykes, BYU and NREL)

8. Wind Plant Electrical System: Electrical Generation, Machines, Power Electronics and Collector Systems

(Muljadi and Gevorgian, Auburn and NREL)

9. Grid Modeling with Wind Plants (Miller and

Stenclik, GE)
NREL | 3

Outline

Technology deployment status

Drivers of success – past turbine focus, future system perspective

The revolution in computational capabilities

Upstream of the turbine

Downstream of the turbine

Controlling the power plant

Grid connection and integration
4

Wind Energy: Past and 

Present (end of 2017)

Decreased Costs in Wind Energy 

have led to Exponentially 

Increasing Deployment

• Lowest cost electricity

available in 2016 (Lazard)

• Current costs: 3-7 cents/kWh

• Wind provided over 6% of

U.S. electricity in 2017

• US Wind capacity (89GW)

now exceeds hydropower

(80GW)
Globally, the U.S. Placed 2nd in Annual Wind Power Capacity Additions in 2017, and in 

Cumulative Wind Power Capacity

• U.S. also remains a distant second to China in cumulative capacity

• Global wind additions in 2017 were below the 54,600 MW added in 2016 and the

record level of 63,000 MW added in 2015

Annual Capacity

(2017, MW)

Cumulative Capacity

(end of 2017, MW)

China 19,660 China 188,392

United States 7,017 United States 88,973 

Germany 6,581 Germany 56,132

United Kingdom 4,270 India 32,848

India 4,148 Spain 23,170

Brazil 2,022 United Kingdom 18,872

France 1,694 France 13,759

Turkey 766 Brazil 12,763

South Africa 618 Canada 12,239

Finland 535 Italy 9,479

Rest of World 5,182 Rest of World 82,391

TOTAL 52,492 TOTAL 539,019

Courtesy Ryan Wiser and Mark Bolinger of LBNL for data from the 2017 Market Report.

https://emp.lbl.gov/publications/2017-wind-technologies-market-report
0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

Denmark

Ireland

Portugal

Spain

U.K.

Germany

Romania

Sweden

Belgium

Netherlands

Austria

Poland

Turkey

Brazil

United States

France

Canada

Australia

Italy

China

Mexico

India

Japan

GLOBAL

Approximate Cumulative Wind Penetration, end of 2017

Approximate Cumulative Wind Penetration, end of 2016

Estimated Wind Generation as a

Proportion of Electricity Consumption

The United States is Lagging Other Countries in Wind as a Percentage of Electricity Consumption

Note: Figure only includes the 

countries with the most installed 

wind power capacity at the end 

Courtesy Ryan Wiser and Mark Bolinger of LBNL for data from the 2017 Market Report. of 2017

https://emp.lbl.gov/publications/2017-wind-technologies-market-report

5%
The Geographic Spread of Wind Power Projects Across the United States Is Broad, with the 

Exception of the Southeast

Note: Numbers within 

states represent 

cumulative installed 

wind capacity and, in 

brackets, annual 

additions in 2017 Courtesy Ryan Wiser and Mark Bolinger of LBNL for data from the 2017 Market Report.

https://emp.lbl.gov/publications/2017-wind-technologies-market-report
NATIONAL RENEWABLE ENERGY LABORATORY 8

Manufacturing Facilities in the US (>500)

The global wind 

energy business is 

well over $100 billion 

per year

“Investments in wind in 2018, 

were $25.7 billion offshore and 

$100.8 billion land-based.”

Financial Times, January, 2019

Courtesy Ryan Wiser and Mark Bolinger of LBNL for data from the 2017 Market Report.

https://emp.lbl.gov/publications/2017-wind-technologies-market-report
NREL | 9

Historical Look
NATIONAL RENEWABLE ENERGY LABORATORY 10

Historically, the secret to wind energy has been SURVIVAL

Nansen and Johansen arctic expedition, 14 March 1895. (Public Domain)
NATIONAL RENEWABLE ENERGY LABORATORY 11

US: wind energy for everyone

Charles Brush windmill, Cleveland, Ohio, USA, 1888

Over on million multi-vane turbines 

installed on US farms before 1940.

(Public Domain) https://www.pinterest.com/pin/11470174030722354/
NATIONAL RENEWABLE ENERGY LABORATORY 12

Basics

• Earth’s atmosphere has wind

• Wind is moving air

• Air is a fluid

• A fluid is a continuum

Early View of 

Wake and 

Energy 

Extraction

Planform and 

solidity

Passive twist bend Airfoils

Trailing edge add-ons for 

noise reduction Add-ons

Tip shape

Courtesy Kenneth Thomsen, Siemens 1980’s blade

2010’s blade
Extrapolation of turbine markets in 2030

13

0.0

1.0

2.0

3.0

4.0

5.0

6.0

1995 2000 2005 2010 2015 2020 2025 2030

Average Nameplate Capacity (MW)

IEA Wind Expert Survey: 

max, 75th, median, 25th, min

Historical data with linear extrapolation

MAKE Americas 

projection

0

20

40

60

80

100

120

140

160

1995 2000 2005 2010 2015 2020 2025 2030

Average Hub Height (meters)

IEA Wind Expert Survey: 

max, 75th, median, 25th, min

Historical data with linear extrapolation

MAKE Americas 

projection

Note: Projects located in lower wind speed sites have utilized 

towers that are, on average, ~ 5 meters taller than shown

0

20

40

60

80

100

120

140

160

180

200

1995 2000 2005 2010 2015 2020 2025 2030

Average Rotor Diameter (meters)

IEA Wind Expert Survey: 

max, 75th, median, 25th, min

Historical data with linear extrapolation MAKE Americas 

projection

Capacity – 3.3MW

Hub Height – 111m

Rotor Diameter – 156m Courtesy Ryan Wiser and Mark 

Bolinger of LBNL for data from the 

2017 Market Report.

https://emp.lbl.gov/publications/201

7-wind-technologies-market-report

• Everything is getting

bigger

• Rotor diameter has

more than doubled

over the last 20 years
NATIONAL RENEWABLE ENERGY LABORATORY 14

R&D Focus Transition: Turbine → Plant

Yesterday

(Individual Turbines)

2020 (and beyond)

(SMART Wind Plants)

Individual wind turbine R&D

 >90 GW Deployed (~6% of U.S. Electricity)

 Land-based wind: 3-6 ₵/kWh (beating coal)

 Multi-Billion dollar industry with involvement dominated by multinational corporations

 Advances in fundamental science driving major innovation:

validation of internal tools and engineering processes

Wind plant optimization R&D

 Develop new technologies that exploit interactions among turbines,

resource, & operating environment

 Design for operation at optimal project profitability and Internal Rate

of Return (IRR)

 Wind plant physics & science challenges require new core

competencies

Largest offering:

Vestas 9.5 MW

164m DIA

140m hub height
NREL | 15

Wind Plant design spans several orders of magnitude

The design of the wind plant is a 

multi-scale, multi-physics 

problem

Wind plants are connected to 

the largest machine on the 

planet – the grid 

Courtesy Katherine Dykes, NREL
NREL | 16

Computational Revolution

(Especially in Fluid Dynamics)
NREL | 17

Can we solve Navier-Stokes equations?

• Analytical solutions only exist for the most simple

laminar problems without turbulence

• Turbulence brings in orders more complexity

“Big whirls have little whirls that feed on their 

velocity, and little whirls have lesser whirls and so on 

to viscosity.” -- Lewis Richardson

Atmospheric Turbulence Scales:

• Largest ~1 km, Smallest ~1 mm

• Resolving the smallest scale in a domain

that contains the largest scale requites a

billion-billion computational cells

(10^18)

• The answer is Not DNS!

https://www.azquotes.com/quote/590633

Courtesy Matt Churchfield, NREL
NATIONAL RENEWABLE ENERGY LABORATORY 18

5-MW turbine example: Discretization of the fluid domain

*Jonkman et al., 2009, “Definition of a 5-MW Reference Wind Turbine for Offshore System Development 

https://www.nrel.gov/docs/fy09osti/38060.pdf

NREL 5-MW reference turbine* 

• Rotor diameter: 126 m

• Rated tip speed: 80 m/s

• Hub height: 90 m

Fluid domain is broken 

up into a mesh 

composed of “cells” or 

“elements” defined by 

“nodes”

Mesh created by M. Lawson

Example 8-node hex element
NATIONAL RENEWABLE ENERGY LABORATORY 19

Mesh created by Mike Lawson via Pointwise

5-MW turbine example: Extreme scale separation in the mesh

504 m

1080 m

342 m

603 m

In order to capture 

boundary layers near 

blades, near-blade 

elements have length 

of only 10^(-6) m

• The overall domain size is large

but not large enough to capture

all the scales of turbulence

• There are 761,112,204 nodes in

this mesh
NATIONAL RENEWABLE ENERGY LABORATORY 20

5-MW turbine example: A HUGE system of equations

If your wrote out these equations with 10-pt font, your paper 

could stretch between Washington, D.C. and San Francisco

761,112,204 equations and 761,112,204 unknowns; need to be solved every time step 
NATIONAL RENEWABLE ENERGY LABORATORY 21

We are now moving from PetaFLOPS to ExaFLOPS

A BIGcomputer!

NREL Eagle: 8 PetaFLOPS

NERSC Cori: 14 PetaFLOPS system

OLCF Summit: 200 PetaFLOPS

• Speed characterized in floatingpoint operations per second

(FLOPS)

• PetaFLOPS: one thousand

million million (10^15) FLOPS

Courtesy: 

Lawrence 

Berkeley 

Labs

Courtesy: 

NREL Courtesy: 

Oak Ridge 

National 

Laboratory
NATIONAL RENEWABLE ENERGY LABORATORY 22

Strong-scaling limit: An example

Strong scaling: For a fixed problem size, use more of the machine

• i.e., reduce the number of equations for each processor

Ideal strong scaling: Using twice as many processors cuts simulation time in half

Example shows how CFD codes typically 

exhibit good strong scaling down to about 

40,000 equations per processor

Data transfer between processors 

becomes the bottleneck

For a given problems size, we can only 

use so much of a supercomputer
NATIONAL RENEWABLE ENERGY LABORATORY 23

Blade-resolved simulation of the NREL 5-MW turbine using Nalu-Wind

NREL 5-MW turbine simulation (“coarse”, 25-million-node mesh) under 

uniform inflow of 8 m/s. The wake is visualized by contours of velocity 

magnitude of 5.5 m/s. Simulation performed on the NERSC Cori machine.

https://www.exascaleproject.org/exawind-project-demonstrates-bladeresolved-simulation-of-the-nrel-5-mw-reference-wind-turbine/

Team: Lawson, 

Melvin, Ananthan, 

Gruchalla, Rood, 

Sprague

ExaWind Project 

(Office of Science)

Participants

• NREL

• Sandia Labs

• ORNL

• Univ. of Texas
NREL | 24

Critical models are required both upstream and downstream

• Weather

• Meso-scale flows

• Plant

Turbine

• Subcomponents

• Foundation

• Rotor

• Drive Train

• Electrical

• Plant

• Grid

Upstream

Downstream

Energy 

Cascade

System 

Optimization

Courtesy Jason Jonkman, NREL
NREL | 25

Atmospheric Modeling
Forcing and Transfer of Energy Across Scales

Visualization by James Neher

Courtesy Sue 

Haupt of NCAR 

and colleagues Courtesy Jeff 

Mirocha, LLNL
NATIONAL RENEWABLE ENERGY LABORATORY 27

Challenges at the Mesoscale, Microscale Interface

Source: “Can mesoscale models reach the 

microscale?”

http://www.ewea.org/events/workshop

s/wp-content/uploads/2015/06/LIZCA

Alex Montornes(Vortex)

Pau Casso (Vortex)

Branko Kosovic (NCAR)
NREL | 28

Global Weather Phenomena Drive the System

Each of the Scales is discretized – sub-grid models aggregate from physics at finer scales

Figures Courtesy Sue Haupt, NCAR

Reproduced with permission from Modeling and Simulation in Wind 

Plant Design and Analysis edited by Paul Veers. Copyright 2019 IET.
NREL | 29

Example of Nesting in Atmospheric Modelling

Examples of mesoscale atmospheric model 

mesh refinement strategies, including grid 

nesting (e.g. WRF; a,b), Voronoi tesselations

(e.g. MPAS; c) and adaptive (e.g. NUMA; d).

(Courtesy Jeff Mirocha, LLNL) 

• Models are nested to 

attempt to resolve the 

atmospheric flow at finer 

and finer scales

Reproduced with permission from Modeling and Simulation in Wind 

Plant Design and Analysis edited by Paul Veers. Copyright 2019 IET. Courtesy Jeff Mirocha, LLNL
NATIONAL RENEWABLE ENERGY LABORATORY 30

Mesoscale to Microscale Coupling in Complex Terrain 

Courtesy Branko Kosovic & 

Pedro Jimenez, NCAR
NATIONAL RENEWABLE ENERGY LABORATORY 31

Diurnal Case – SOWFA - Nov 8, 2014

Matt Churchfield, NREL
NREL | 32

Wind Plant Flow Modeling
Courtesy Matt Churchfield, NREL
NREL | 34

Wakes in Wind Plants

Average Wake

Large Eddy 

Simulation (LES) 

Wake

Dynamic Wake 

Meandering

Reproduced with permission from Modeling and Simulation in Wind 

Plant Design and Analysis edited by Paul Veers. Copyright 2019 IET.

Courtesy Churchfield and Moriarty, NREL
Unstable 

Atmospheric 

Boundary Layer 

(Daytime)

Stable 

Atmospheric 

Boundary Layer 

(Nighttime) Courtesy 

Churchfield and 

Moriarty, NREL

Wakes depend on 

atmospheric 

stability
NREL | 36

Reduced Order Modeling Tools

FLORIS FAST.FARM SOWFA

• Control-oriented 

model

• Runs in fractions of 

seconds

• Can be used to find 

optimal control 

settings and analyze 

across wind rose to 

estimate AEP

• New code which 

overlays DWM wakes on 

pre-computed CFD 

inflows

• Includes embedded 

FAST models of turbines

• Runs on few cores, near 

real time, allowing load 

suite analysis

• Wind farm simulator 

based on large-eddy 

simulation

• Allows detailed 

investigation of wake 

physics, but requires 

many cores and time 

to run simulations 
NREL | 37

Turbine Subsystems and Design Optimization
Modeling focus has typically begun with the machine

Torque

Bending

Detailed Aeroelastic Model

Reproduced with permission from Modeling and Simulation in Wind 

Plant Design and Analysis edited by Paul Veers. Copyright 2019 IET.

Courtesy Morten Hansen, LM/GE Courtesy Jason Jonkman, NREL
Reproduced with 

permission from 

Modeling and 

Simulation in Wind Plant 

Design and Analysis

edited by Paul Veers. 

Copyright 2019 IET.

Turbine System Design Optimization

Courtesy Carlo Bottasso, 

University of Munich
Modeling of the Aeroelastic System Depends on Interfaces with 

all the critical subcomponents

Similar drill-down in scale and 

complexity for each subcomponent in 

the system – all of which contribute 

to system cost of energy and 

reliability.

Electrical and Power Electronics

Control System

Hub & 

Pitch 

control

Yaw drive

Tower

Foundation and Floating Systems

Courtesy Jason Jonkman, NREL
NREL | 41

Rotor Optimization
Design approach – Modeling Cascade

Scales represent different 

criteria in the design space of 

wind turbine blades

All

Loads

Boundary Conditions: 

Interface Loads

Order 1,000,000 dof 

shell model - Buckling

Order 100 dof beam model – Internal loads and strains 3 dof modal 

representation 

of the blade

Detailed Materials 

Model

Courtesy Montana State University

Courtesy Jason 

Jonkman, NREL

Courtesy Joshua Paquette, 

Sandia Labs
2D FEM sectional model

Blade and tower 

beam models

Structural design parameters

Aeroservoelastic multibody 

model

Aerodynamic design 

parameters

Constraints:

• Max tip deflection

• Ultimate & fatigue loads

• Natural frequencies

• Buckling

• Manufacturing constraints

• Geometric

• Noise

• ...

Optimizer

min CoE

subject to constraints 

Control synthesis

Load & performance analysis: 

• DLCs

• AEP

• Campbell

• Noise

• ...

CoE model

Configurational design

parameters

Sub-system models

Rotor 

Optimization 

Framework 

Courtesy Carlo Bottasso, 

University of Munich

Reproduced with 

permission from 

Modeling and 

Simulation in Wind Plant 

Design and Analysis

edited by Paul Veers. 

Copyright 2019 IET.
Automatic 3D CAD 

generation

“Fine” level: 3D FEM

“Coarse” level: 2D FEM & beam models

Analyses:

- Max tip deflection

- Max stress/strain

- Fatigue

- Buckling

Verification of design constraints

Constraint/model update

Joint & laminate analysis Automatic 3D FEM meshing Root 3D CAD model - Bolt preload calculation

- Max stress/strain

- Fatigue

Automatic 3D FEM meshing

Blade Design Modeling 

Cascade 

Courtesy Carlo Bottasso, 

University of Munich

Reproduced with 

permission from 

Modeling and 

Simulation in Wind Plant 

Design and Analysis

edited by Paul Veers. 

Copyright 2019 IET.

Checks on Blade Nominal 

Design Adequacy
NREL | 45

Drive Train Modeling
Drive Train Design Modeling Cascade

All

Loads

Internal Gearbox 

Loads

• Aeroelastic models often describe the drive

train as a few springs and dampers

• Actual behavior may be much more complex,

including nonlinear flexibility and damping

Reproduced with permission 

from Modeling and Simulation 

in Wind Plant Design and 

Analysis edited by Paul Veers. 

Copyright 2019 IET.

Courtesy Zhiwei Zhang, Romax
NREL | 47

Drive Train Models – Including Flexibility

Drive Train 

Configuration

• Gear shape

• # stages

• # planets

• location of

bearings

• Bearing type

• CRB

• TRB

• Journal

An example of an integrated drivetrain configuration (deflection 

exaggerated in a RomaxDESIGNER simulation model) [4]

Courtesy Zhiwei Zhang, Romax

Elastic deformation in the drive system leads 

to uneven loading on gear teeth and bearings 

Reproduced with permission from Modeling and Simulation in Wind 

Plant Design and Analysis edited by Paul Veers. Copyright 2019 IET.
NREL | 48

Bearing Failures Influenced by Tribology

Reproduced with permission from Modeling and Simulation in Wind 

Plant Design and Analysis edited by Paul Veers. Copyright 2019 IET.

Roller loading zone

Bearing damage is 

dependent on not 

only torque and 

lateral loading, but 

skidding and 

friction – requiring 

detailed bearing 

models

Load Sharing is never equal

Movie courtesy Yi Guo, NREL
NREL | 49

Offshore Foundations
NREL | 50

First US Offshore Wind Plant

Deepwater Wind’s Block Island Wind Farm, the first 

commercial wind farm in the United States, features five GE 

Haliade 150-6MW wind turbines to come online by end of 

2016. 

Map credit: AWS 

Truepower

Block 

Island

Rhode Island

Video courtesy: Paul Veers, NREL

NREL | 52

Foundations involve air, sea, and soil interactions

Reproduced with permission from Modeling and Simulation in Wind 

Plant Design and Analysis edited by Paul Veers. Copyright 2019 IET.

Courtesy Denis Matha, Ramboll

Bottom Mounted Structures

Floating Foundations show great promise
NREL | 53

Turbine and Plant Controls
NREL | 54

History of Wind Energy Controls

NREL – Atmosphere to Electrons (A2e)

Industry Industry

Nat. Lab Research

What if turbines could talk to neighboring turbines?

Courtesy Paul Fleming, NREL
NREL | 55

Control Exploration

Courtesy Paul Fleming, NREL
NREL | 56

Control Design

• With TU Delft, developed the Reduced 

Order Model (ROM) FLORIS, an opensource wind farm control design and 

analysis code and control-oriented 

wake model

• FLORIS is used to design the controller, 

SOWFA is used to evaluate 

effectiveness

• Critical and central to all wind farm 

controls research

• Has an active and growing user base

Courtesy Paul Fleming, NREL
NREL | 57

Continuing Exploration – Refining ROMs

New engineering 

model (Martinez)

Courtesy Paul Fleming, NREL
NREL | 58

Wind Plant Optimization
NREL | 59

Control Analysis and Plant Optimization

Wake models can be used to:

• Design wind farm controllers

• Predict performance of wind

farm controllers

• Estimate impact on annual

energy production from wind

farm control

• However, the LCOE value can be

significantly amplified if coupled

into the design phase, for

example, joined to layout

optimization

Reproduced with permission from Modeling and Simulation in Wind 

Plant Design and Analysis edited by Paul Veers. Copyright 2019 IET.
NREL | 60

Analysis

FLORIS typically predicts a 1-2% AEP gain for a typical wind farm, with 

possible gain primarily determined by baseline wake losses

These gains have proven very interesting to industry

However, the LCOE value can be significantly amplified if coupled into the 

design phase, for example, joined to layout optimization
NREL | 61

Consensus Control
NREL | 62

Grid Modeling
Grid penetration levels are already very high in the U.S.

• 2017 Wind Penetration by ISO: SPP: 23.2%; ERCOT: 17.4%; MISO: 7.7%;

CAISO: 6.0%; NYISO: 2.7%; PJM: 2.7%; ISO-NE: 2.6%

Installed Capacity (MW) 2017 Wind Generation as a Percentage of:

Annual (2017) Cumulative (end of 2017) In-State Generation In-State Load

Texas 2,305 Texas 22,599 Iowa 36.9% North Dakota 58.3%

Oklahoma 851 Oklahoma 7,495 Kansas 36.0% Kansas 47.1%

Kansas 659 Iowa 7,308 Oklahoma 31.9% Iowa 43.0%

New Mexico 570 California 5,555 South Dakota 30.1% Oklahoma 40.9%

Iowa 397 Kansas 5,110 North Dakota 26.8% Wyoming 26.3%

Illinois 306 Illinois 4,332 Maine 19.9% South Dakota 25.7%

Missouri 300 Minnesota 3,699 Minnesota 18.2% New Mexico 19.7%

North Dakota 249 Oregon 3,213 Colorado 17.6% Maine 19.5%

Michigan 249 Colorado 3,106 Idaho 15.4% Colorado 17.5%

Indiana 220 Washington 3,075 Texas 14.8% Nebraska 17.4%

North Carolina 208 North Dakota 2,996 Nebraska 14.6% Texas 17.3%

Minnesota 200 Indiana 2,117 New Mexico 13.5% Minnesota 16.7%

Nebraska 99 Michigan 1,860 Vermont 13.4% Montana 14.8%

Wisconsin 98 New York 1,829 Oregon 11.1% Oregon 13.5%

Colorado 75 New Mexico 1,682 Wyoming 9.4% Idaho 10.4%

Ohio 72 Wyoming 1,489 Montana 7.6% Illinois 8.3%

Oregon 50 Nebraska 1,415 California 6.8% Washington 8.3%

California 50 Pennsylvania 1,369 Hawaii 6.5% Hawaii 6.9%

Vermont 30 South Dakota 977 Washington 6.5% California 5.5%

Maine 23 Idaho 973 Illinois 6.2% Vermont 5.2%

Rest of U.S. 7 Rest of U.S. 6,774 Rest of U.S. 1.1% Rest of U.S. 1.2%

TOTAL 7,017 TOTAL 88,973 TOTAL 6.3% TOTAL 6.9% Courtesy Ryan Wiser and Mark 

Bolinger of LBNL for data from the 

2017 Market Report.

https://emp.lbl.gov/publications/20

17-wind-technologies-market-report

• 15 States exceed

10% of demand with

wind energy on

average

• 4 States exceed 30%

• One state generates

over half of its load
NREL | 64

Wind Plants Need be Actively controlled

• This is the leading edge 

of a move away from 

inertia-dominated 

generation

• New wind plants will 

need to provide the 

ancillary services 

traditionally supplied by 

thermal plants

Reproduced with permission from Modeling and 

Simulation in Wind Plant Design and Analysis

edited by Paul Veers. Copyright 2019 IET.

Courtesy Nick Miller, GE
NATIONAL RENEWABLE ENERGY LABORATORY 65

Wind Plant Hardware in the Loop

Optimal electrical control depends on atmospheric conditions and grid

Courtesy 

Patrick 

Moriarty, 

NREL
NATIONAL RENEWABLE ENERGY LABORATORY 66

Modeling at Scale

Coluirtesy Beth Clark, NREL

https://www.youtube.com/playlist?list=PLmIn8Hncs7bEl4P8z6-KCliwbYrwANv4p
DNV GL Energy Transition Outlook 2017 - Electricity

“... a base or ’central’ case, ... is the aim of this present exercise, which is a 

forecast, not a scenario.” Remi Eriksen, Group President & CEO DNV GL

In 2050: 1/3 Wind, 1/3 Solar PV, 1/3 everything else. The Wind is 1/3 offshore.

Source: DNV GL Energy Transition Outlook 2017

https://eto.dnvgl.com/2017/main-report
Sample Section Divider

Thank You!

paul.veers@nrel.gov