A iea wind

April 2019

IEA Wind TCP

Results of IEA Wind TCP 

Workshop on a Grand Vision 

for Wind Energy Technology
IEA Wind TCP Task 11 Technical Report

Technical Report

Results of IEA Wind TCP Workshop on a 

Grand Vision for Wind Energy Technology

Prepared for the

International Energy Agency Wind Implementing Agreement

Authors

Katherine Dykes, National Renewable Energy Laboratory (NREL) 

Paul Veers, NREL

Eric Lantz, NREL

Hannele Holttinen, VTT Technical Research Centre of Finland

Ola Carlson, Chalmers University of Technology 

Aidan Tuohy, Electric Power Research Institute

Anna Maria Sempreviva, Danish Technical University (DTU) Wind Energy 

Andrew Clifton, WindForS - Wind Energy Research Cluster 

Javier Sanz Rodrigo, National Renewable Energy Center CENER 

Derek Berry, NREL

Daniel Laird, NREL

Scott Carron, NREL

Patrick Moriarty, NREL 

Melinda Marquis, National Oceanic and Atmospheric Administration (NOAA)

Charles Meneveau, John Hopkins University 

Joachim Peinke, University of Oldenburg 

Joshua Paquette, Sandia National Laboratories

Nick Johnson, NREL 

Lucy Pao, University of Colorado at Boulder 

Paul Fleming, NREL 

Carlo Bottasso, Technical University of Munich 

Ville Lehtomaki, VTT Technical Research Centre of Finland

Amy Robertson, NREL 

Michael Muskulus, National Technical University of Norway (NTNU) 

Jim Manwell, University of Massachusetts at Amherst

John Olav Tande, SINTEF Energy Research

Latha Sethuraman, NREL

Owen Roberts, NREL

Jason Fields, NREL

April 2019
IEA Wind TCP Task 11 Technical Report

IEA Wind TCP functions within a framework created by the International Energy 

Agency (IEA). Views, findings, and publications of IEA Wind do not necessarily 

represent the views or policies of the IEA Secretariat or of all its individual member 

countries. IEA Wind is part of IEA’s Technology Collaboration Programme (TCP).

This work was authored in part 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 National Renewable Energy Laboratory Directed Research and Development 

(LDRD) Program. The views expressed herein do not necessarily represent the views of 

the DOE or the U.S. Government.
IEA Wind TCP Task 11 Technical Report

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Preface

The wind industry has realized substantial growth reaching over 500 gigawatts (0.5 

terawatts) of installed capacity in 2017 (Global Wind Energy Council 2018) and producing 

about 5% of global electricity demand in 2016 (Wiser and Bolinger 2018). The levelized cost 

of energy (LCOE) for wind energy projects both on land and offshore has fallen as a result of

substantial innovation over the last several decades. More specifically, equipment, 

installation, and operation costs have decreased while energy production per turbine has 

increased (Wiser et al. 2016). 

At the same time as LCOE has been decreasing, integration challenges in the broader electric 

system have been successfully addressed in many markets, thereby enabling the level of wind 

energy generation to grow to more than 10% of electricity consumption in at least eight 

countries around the world and more than 30% in Portugal and Denmark (Wiser and 

Bolinger 2018). 

What does the future of the wind industry hold?

Looking ahead, a variety of scenarios for future global wind deployment illustrate 

possibilities ranging from a plateau in deployment near current levels to a rapid growth in 

global installations. Based on analysis by DNV GL (2018), wind-generated electricity 

projected growth will provide over a third of global electricity demand and accordingly, wind 

technology could become a primary electricity generation technology.

However, there are challenges facing continued growth of wind energy deployments 

globally. Competitive technologies for electricity generation including solar photovoltaics 

and shale gas have seen rapidly declining costs in recent years (Haegel et al. 2017). In many 

countries, conditions also favor continued interest and investment in coal and nuclear power

(International Energy Agency [IEA] 2018). Finally, many stakeholders still question the 

ability to maintain reliable and stable electric grid system operation as wind-generated 

electricity increases to levels of 10%, 20%, and 30% or more. In these future scenarios, wind 

power plants will not only provide energy resources, but also significant capacity value and 

reliability services to the grid (Ahlstrom et al. 2015).

Therefore, realizing the full potential of wind technology will require a paradigm shift in how 

wind turbines and power plants are designed, controlled, and operated. Notwithstanding the 

accomplishments of the wind industry to date in driving down costs and increasing 

performance, there is still an immense opportunity for innovation to enable continued 

expansion of wind power around the world. 

The IEA Wind Topical Experts Meeting (TEM) #89 “Grand Vision for Wind Energy” 

workshop sought to bring together a group of experts to consider the question of how to 

enable a future in which wind energy supplies more than 50% of global electricity 

consumption. Over 70 experts representing 15 different countries attended the workshop and 

provided a diverse set of perspectives for the Grand Vision for Wind Energy. The experts 

participated in one or more meetings to develop the vision including: 1) the main IEA Wind 

TEM #89 held at the National Renewable Energy Laboratory in Golden, Colorado, on 

October 22‒23, 2017, 2) a meeting at the Utility Variable Integration Group 2017 Fall 

Technical Workshop in Nashville, Tennessee, on October 12, 2017, and 3) the IEA Wind 
IEA Wind TCP Task 11 Technical Report

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Task 25 Fall 2017 Meeting held at the Idaho National Engineering and Environmental 

Laboratory in Cuernacava, Mexico, on November 20, 2017. The list of participants from the 

workshop and follow-on meetings are provided in Appendix A.

Attendees were asked to create a vision for wind where the:

● Cost of energy for wind is less than that of natural gas fuel and solar electricity 

generation (i.e., serving as the cheapest global electricity resource)

● Wind power plants can operate in a manner commensurate with traditional thermal 

plants to provide reliability, flexibility, and resilience for the power system (i.e., wind 

energy can become a new backbone for the electricity system of the future).

The workshop was organized into four working sessions (see Appendix A for the core 

meeting agenda). The first was meant to solidify the context (e.g., grid architecture, markets) 

for the vision of wind energy supplying over 50% of global electricity demand. In the next 

two sessions, participants explored technology innovation opportunities for wind energy 

within this context. Specifically, the second session looked at driving the LCOE to make 

wind the least-expensive energy generation technology and thus spur the needed continuous 

expansion of wind energy deployment. The third session looked beyond LCOE to increasing 

the overall system value that wind energy provides to the grid in terms of reliability and 

resilience—resulting in significant capacity value, dependable ancillary services, and rapid 

response to system perturbations. The fourth and final session considered the research and 

development (R&D) challenges critical to realizing the wind energy LCOE and system value 

improvements identified in the prior two sessions. Workshop findings are broken out by the 

working group in the areas of 1) atmospheric science and forecasting, 2) turbine technology 

and design, 3) manufacturing and industrialization, 4) plant controls and operations, 5) grid 

integration, and 6) offshore-specific technologies. Outcomes of the workshop for each of the 

working group areas included:

● Identification and ranking of high-priority wind energy technology opportunities for 

significantly reducing LCOE and improving grid system value

● Identification and ranking of R&D challenges associated with the technology 

opportunities along with recommended actions to address those challenges.

The workshop focused on the economics of wind energy in the system and did not consider 

deployment concerns related to environmental and societal impacts. In addition, the 

workshop did not address policy concerns in terms of how they might shape the opportunity 

and requirements for wind energy looking forward.

Sections 1, 2, and 3 of this report provide the findings from the IEA Wind TEM #89 “Grand 

Vision for Wind Energy” workshop including the future landscape of the electricity system 

with over 50% of the electricity supply from wind energy, a review of key innovation 

pathways in different topic areas that will be embodied in the wind power plant of the future 

that supports the Grand Vision (including innovations that both reduce LCOE and increase 

system value of wind energy), and identification of the key R&D challenges that must be 

addressed to realize the wind power plant of the future and the Grand Vision.

Through the process of identifying key R&D challenges in each of the working group areas, 

cross-cutting themes emerged: data science and multiscale/multidisciplinary modeling 
IEA Wind TCP Task 11 Technical Report

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capabilities. Section 4 of this report looks at synergies between wind energy science and 

enabling research advances in sensing technologies and measurement techniques, computer 

and computational sciences, multiscale and multidisciplinary modeling, digitalization, big 

data, and information science. The result of the effort led to an alignment between the key 

R&D challenges and scientific activities in data and modeling.

Overall, the integrative and coupled nature of wind energy physics across the full system led 

the authors to conclude that there is a need for overarching coordination efforts and 

frameworks to fully address the R&D challenges identified by the experts. Follow-on work 

will need to articulate a wind energy science discipline to address the grand challenges in 

wind energy R&D and realize the Grand Vision for wind energy as a foundation of the future 

electricity system.
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Table of Contents

1 Historical Innovation, Status, and Challenges Ahead To Motivate a Grand Vision for Wind 

Energy..............................................................................................................................................1

1.1 Historical Wind Energy Development ....................................................................................1

1.2 Description of Expert Meetings and the Grand Vision for Wind Energy Technology ...........7

2 The Future of Global Energy and Wind Energy Abundance......................................................9

2.1 Context of the Grand Vision Including Global Megatrends ...................................................9

2.2 The Future Electric Grid Sets New Requirements for Power Generation Sources, Including 

Wind......................................................................................................................................10

3 Research Needs for Wind Energy at 50% ..................................................................................17

3.1 Breakout Group 1: Atmospheric Science and Forecasting....................................................18

3.2 Breakout Group 2: Turbine Technology and Design ............................................................29

3.3 Breakout Group 3: Manufacturing and Industrialization ......................................................37

3.4 Breakout Group 4: Plant Controls and Operations................................................................48

3.5 Breakout Group 5: Grid Integration ......................................................................................55

3.6 Breakout Group 6: Offshore-Specific Technologies.............................................................62

4 Cross-Cutting Needs for Data Science and Multiscale, Multidisciplinary Modeling.............75

4.1 Novel Sensing Technologies and Measurement Techniques................................................76

4.2 Computer and Computational Sciences ................................................................................79

4.3 Multiscale and Multidisciplinary Computational Models.....................................................80

4.4 Digitalization, Big Data, and Information/Data Science.......................................................84

4.5 Intersection of the Grand Vision with Emerging Science and Technologies........................90

5 Summary .......................................................................................................................................94

References...........................................................................................................................................95

Section 4 References....................................................................................................................118

List of Figures

Figure 1. Global cumulative installed wind capacity from 2001 through 2017. Note that prior to 

2001, a similar growth trend is present from the mid-1980s to today (Source: GWEC 2018)

..........................................................................................................................................................2

Figure 2. Wind energy LCOE from 1980 through 2016 (Source: IEA Wind Task 26 [Lantz et al. 

2012; Wiser et al. 2016]).................................................................................................................2

Figure 3. A small selection of turbines from a few global wind turbine manufacturers illustrate 

the growth in wind turbine sizes over time for land-based wind turbines in terms of hub 

height, rotor diameter, and power rating (in megawatts [MW]) (Source: National 

Renewable Energy Laboratory [NREL]) .......................................................................................3

Figure 4. Innovation in blade design from the 1980s to present day—incorporating significant 

technological innovation in aerodynamics, structures, materials, controls, and 

manufacturing. (Source: NREL—based on graphic from Kenneth Thomsen, formerly 

Siemens Gamesa Renewable Energy) .........................................................................................4

Figure 5. Analysis of cost of energy across different technologies as of 2018 (Source: Lazard 

2018).................................................................................................................................................5

Figure 6. Potential shift in electricity market structure with increased renewables deployment. 

Renewables that take advantage of “free fuel” provide energy at near-zero marginal cost. 

When energy is free some of the time, the current paradigm in which energy markets make 

up the bulk of revenue for generation assets is unsustainable (Ahlstrom et al. 2015), 

unless scarcity pricing is allowed. New innovative uses of low-cost electricity could take 

much of the surplus energy (Helisto et al. 2017). New market designs that focus on realtime pricing and improving revenues from capacity and grid support (ancillary) service 

markets will pose challenges as well as opportunities to renewable and variable energy 

technologies, such as wind energy. (Source: NREL based on Ahlstrom 2015) ....................6

Figure 7. In 2050: 29% wind, 40% solar PV, and one-third everything else; “... a base or central 

case, ... is the aim of this present exercise, which is a forecast, not a scenario,” stated 
IEA Wind TCP Task 11 Technical Report

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Remi Eriksen, Group President and chief executive officer of DNV GL (Source: DNV GL 

2018)...............................................................................................................................................11

Figure 8. The challenge of stability in the converter-dominated electric grid system. The red 

circles represent traditional power plants. If there are many such masses or inertial 

elements in the system, losing one mass (by cutting the string) will shift to a new 

equilibrium quickly, with the other system masses dampening out the effects of the loss. 

However, as more and more wind turbines with low inertia, but considerable embedded 

intelligence, are added to the system, significant coordination and advanced control 

become necessary to ensure that the grid is resilient to perturbations. (Source: NREL 

based on a figure created by Nick Miller, formerly GE Energy Services) ..............................13

Figure 9. Future wind power plants will have to consider trade-offs in design and operation 

between LCOE and system value. Depending on the architecture and makeup of the grid, 

the relative importance of each objective will change. Thus, this workshop considered 

each objective separately and then sought to harmonize across them for an integrated 

Grand Vision for Wind Energy (Source: NREL based on graphic from Ryan Wiser, 

Lawrence Berkeley National Laboratory)...................................................................................18

Figure 10. Wind power plants at every scale—from large-scale atmospheric effects to local 

climatology and topography to inter and intraplant flows and turbines responses to 

dynamic interaction with the electric grid system (Source: NREL) ........................................75

Figure 11. Enabling technologies/research for advancing wind energy science ........................76

Figure 12. Illustration of Industry 4.0, showing the four "industrial revolutions" (Source: Rose 

2016)...............................................................................................................................................84

List of Tables

Table 1. Summary of Global Energy System of Systems Attributes for the Grand Vision of 

Wind Energy (in 2050)..................................................................................................................12

Table 2. Scenarios for Wind Energy Grand Vision Breakout Sessions ........................................17

Table 3. Innovations in Atmospheric Science and Forecasting To Improve Wind Power Plant 

LCOE..............................................................................................................................................21

Table 4. Innovations in Atmospheric Science and Forecasting To Improve Wind Power Plant 

System Value ................................................................................................................................24

Table 5. Atmospheric Science and Forecasting R&D Challenges.................................................27

Table 6. Innovations in Turbine Technology To Reduce LCOE.....................................................30

Table 7. Innovations in Turbine Technology To Increase System Value......................................34

Table 8. Turbine Technology R&D Grand Challenges ....................................................................35

Table 9. Innovations in Manufacturing and Industrialization To Reduce LCOE...........................38

Table 10. Manufacturing and Industrialization R&D Grand Challenges........................................45

Table 11. Innovations in Plant Control and Operations To Improve Wind Power Plant LCOE...49

Table 12. Innovations in Plant Control and Operations To Improve Wind Power Plant System 

Value ..............................................................................................................................................51

Table 13. Plant Control and Operations R&D Grand Challenges...................................................53

Table 14. Innovations in Grid Integration To Improve Wind Power Plant System Value ............55

Table 15. Grid Integration R&D Grand Challenges..........................................................................61

Table 16. Innovations in Offshore Wind Energy Technology To Improve Wind Power Plant 

LCOE..............................................................................................................................................64

Table 17. Innovations in Offshore Wind Energy Technology To Improve Wind Power Plant 

System Value ................................................................................................................................69

Table 18. Offshore Wind Energy Technology R&D Grand Challenges To Realize LCOE and 

System Value Potential ................................................................................................................71

Table 19. Objectives of Digitalization According to ETIPWind ......................................................85

Table 20. The Challenges and Opportunities of the Wind Energy Transition to Digitalization 

and “Openness” Toward the Grand Vision ...............................................................................91
IEA Wind TCP Task 11 Technical Report

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1 Historical Innovation, Status, and Challenges 

Ahead To Motivate a Grand Vision for Wind 

Energy

Wind energy has evolved over the last several decades from a niche technology to one that 

provides a significant share of electricity generation in grid systems across the world (Wiser 

and Bolinger 2018; Wingfield 2017; Kleckner 2017). A key factor in the success of wind 

energy to date is innovation that has increased energy production for a given project while 

simultaneously reducing overall wind turbine and power plant costs (Lantz et al. 2012). In 

other words, innovation has lowered the cost of energy for wind power plants. Looking 

ahead, the prospects for increased global wind energy deployment are compelling but 

significant competition is also anticipated from solar photovoltaics (PV) and other energy 

resources. Moreover, as the share of wind and other variable renewable energy sources—

such as solar PV—grow within electric grid systems, there is an increased need for power 

system services that extend beyond cost-effective energy. With continued innovation, wind 

power is expected to be capable of providing both highly competitive energy supply (Dykes 

et al. 2017) and power system support services that are essential to the reliable and resilient 

operation of the grid (Ackermann et al. 2017). 

Recognizing the need for continued innovation in wind energy to enable the full potential for 

wind power in the future electricity system, a group of more than 70 international experts 

came together to examine: 1) the current challenges to continued large-scale deployment of 

wind energy, 2) the opportunities for innovation to address those challenges, and 3) the 

necessary research and development (R&D) to realize those innovations. This report 

documents the overall findings of these experts. The report also highlights trends in science 

and technology that are expected to enable wind energy R&D as identified by the experts.

1.1 Historical Wind Energy Development

Global electricity generation from wind was estimated at approximately 5% of total 

electricity supply in 2017 (Wiser and Bolinger 2018). To arrive at this level, global 

installations have grown rapidly since the turn of the century (Figure 1). By the end of 2017, 

global installations of wind power capacity exceeded half a terawatt (Global Wind Energy 

Council [GWEC] 2018). 
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Figure 1. Global cumulative installed wind capacity from 2001 through 2017. Note that prior to 

2001, a similar growth trend is present from the mid-1980s to today 

(Source: GWEC 2018)

Growth in wind energy has been spurred by policy supports in different locations around the 

world. However, as global installations have grown, innovation driven by technology scaling, 

technology learning, and R&D investment have led to a corresponding drop in the levelized 

cost of energy (LCOE). Figure 2 illustrates how costs have generally fallen through time. As 

of 2016, a composite characterization of wind power costs among Northern Europe (e.g., 

Denmark, Germany, Ireland, Sweden, Norway, and the European Union) and the United 

States indicated an LCOE of approximately $50/megawatt-hour (MWh) for new installations.

Figure 2. Wind energy LCOE from 1980 through 2016 (Source: IEA Wind Task 26 [Lantz et al. 

2012; Wiser et al. 2016])

The main drivers for LCOE reduction have been technology scaling to larger wind turbines 

coupled with innovation in several areas of wind turbine and plant design, operations, and 

reliability (Wood Mackenzie 2018; Lantz et al. 2012, Wiser et al. 2016). In terms of scaling, 
IEA Wind TCP Task 11 Technical Report

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turbines have grown in rotor diameter, power rating, and hub height consistently since 

commercial deployments first began in the 1980s. Rated power has increased by a factor of 

approximately 30-50 for land-based machines such that wind power plants today can produce 

far more energy with a much smaller number of machines. These size increases have led to 

significant turbine- and plant-level economies of scale. At the same time, rotor diameter and 

hub heights have also increased. These changes allow turbines to capture more energy at 

greater heights above ground level where the wind resource quality is better. As a result, the 

energy per turbine and per-unit cost has fallen—also contributing to lower LCOE. 

Figure 3 illustrates how turbines have grown since the early 1980s. Although the basic 

platform configuration of the technology, three-bladed horizontal-axis upwind wind turbine 

on a monopole tower, has not changed, the size has increased 6‒7 times in terms of hub 

height, 6‒8 times in terms of rotor diameter, and 30‒50 times in terms of power rating. The 

size of wind turbines today rivals large-scale monuments and buildings while withstanding 

dynamic and complex loading throughout the turbine’s lifetime. 

Figure 3. A small selection of turbines from a few global wind turbine manufacturers illustrate 

the growth in wind turbine sizes over time for land-based wind turbines in terms of hub height, 

rotor diameter, and power rating (in megawatts [MW])

(Source: National Renewable Energy Laboratory [NREL])

In addition to scaling and the associated performance improvements, the relative cost of wind 

power plant development and operations has also decreased. These reductions are, in part, a 

function of technology learning, R&D, and innovation but are facilitated by increases in 

turbine size, and in some locations, plant size, allowing for fewer moving parts overall and 

economies of scale with larger facilities. Combined, these trends have helped enable overall 

capital expenditures (CapEx) and operational expenditures (OpEx) per unit of energy to 

decrease over the decades (Wiser and Bolinger 2018; Lantz et al. 2012). 
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An important example of the innovation that has evolved in wind energy is evident in wind 

turbine blade designs—which are far more sophisticated in aerodynamic design, use of 

materials, manufacturing process, special features, and structure than ever before. Figure 4 

shows a comparison of the design features for a state-of-the-art blade design in 2015 versus 

one of the early mass-produced blades of the 1980s. A selection of design features is shown,

including custom airfoils that were developed specifically for wind energy applications;

advances in aerodynamic design, such as optimization of the planform, solidity, and tip 

shape; and advances in reducing weight through load mitigation by coupled aerostructural 

design (e.g., passive twist bend coupling). Current blades also incorporate various add-ons 

that may improve aerodynamic performance, reduce structural loads, or mitigate 

aerodynamic noise produced by the turbines (e.g., trailing-edge serrations).

Figure 4. Innovation in blade design from the 1980s to present day—incorporating significant 

technological innovation in aerodynamics, structures, materials, controls, and manufacturing. 

(Source: NREL—based on graphic from Kenneth Thomsen, formerly Siemens Gamesa Renewable 

Energy)

Typical blades of the first generation of large-scale deployment turbines in the 1980s had a 

length of around 7.5 meters (m) (rotor diameter of 15 m) and weighed roughly 1 ton for a 

machine rating of 55‒65 kilowatts (kW). Without innovation, scaling of these blades for 

current large-scale offshore machines of roughly 6 megawatts (MW) in power rating and 154 

m rotor diameter (75-m blade) would result in blade weights of nearly 1,000 tons. However, 

with innovation, a blade of this size in 2015 weighed only 80 tons. Thus, the rotor weight 

scaled with rotor diameter by an exponent of closer to 2 rather than an exponent of 3, which 

would have been the case without technology improvement. The “square-cube law” is known 

in the wind community as the rule that as you scale the size of the rotor diameter, the power 

increases by a power of 2 (power is directly proportional to rotor area) but the mass of the 

blade would increase by a factor of 3 (proportional to the volume increase). Through 

innovation, the blades have become slenderer and less material intensive such that the 

industry has been able to “beat the square-cube law” as rotor sizes have increased. 

Innovation in blades as well as the rest of the system have resulted in substantial cost savings 
IEA Wind TCP Task 11 Technical Report

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on a per-unit energy production basis. 

Lower LCOE driven by these technological innovations has given rise to wind energy 

projects that are now competitive in a growing number of regions of the world (Wiser and 

Bolinger 2018; Lazard 2018). Depending on the resource quality, wind energy may be the 

most cost-effective new electricity generation resource available. According to Lazard’s 

Levelized Cost of Energy Analysis (2018), which estimates unsubsidized energy costs for an 

array of electricity generation sources ranging from $29‒$56 USD/MWh for wind energy can 

be compared to $41‒$74 USD/MWh for combined-cycle natural gas generation, and $36‒

$44 USD/MWh for utility-scale solar PV energy (Lazard 2018). It is important to note that 

these LCOE comparisons do not consider system value and so cannot be used in isolation to 

assess competitiveness.

Figure 5. Analysis of cost of energy across different technologies as of 2018

(Source: Lazard 2018)

1.2 Challenges to Future Wind Energy Deployment

Continued growth of wind energy deployments may require continued technology 

innovation. Even as wind energy has become among the most competitive new sources of 

electricity generation, persistent challenges could limit continued growth of wind energy.

One of the most significant challenges to continued large-scale deployment of wind energy 

has and continues to be LCOE competition from other electricity generation technologies. In 

recent years, the cost of energy for natural gas and solar PV have dropped substantially 

(Haegel et al. 2017; International Energy Agency [IEA] 2018; Fu et al. 2018; Lazard 2018)

and cost of energy for coal remains low as well. Although utility-scale wind power prices 

remained lower than for solar PV in 2018 for some projects (Lazard 2018), depending on the 

region of world and availability of respective wind or solar resources, LCOE for utility-scale 

solar PV can be lower than utility-scale wind energy. The relative forecasts of solar, wind,

and natural gas prices have a significant impact on the expected future global electricity 

generation portfolio (Mai et al. 2017). Thus, for wind energy to remain competitive, further 

efforts to drive down LCOE through research and innovation will be needed.
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At the same time as LCOE has been decreasing, integration challenges in the broader electric 

system have been successfully addressed in many markets, thereby enabling wind energy 

generation to grow to more than 10% of electricity consumption in at least eight countries 

and more than 30% in two: Portugal and Denmark (Wiser and Bolinger 2018). Moreover, 

subnational system-level instantaneous wind power share has frequently exceeded the 50% 

threshold in several U.S. systems (see, for example, Wingfield 2017; Kleckner 2017). 

Generation in Denmark has exceeded 100% of the entire national electricity demand on some 

days during and since 2015 (Nelson 2015). 

However, wind energy deployment is still challenged on several fronts by concerns about the 

ability to integrate a variable resource into the electric grid system in a reliable, resilient, and 

sustainable way. Current market structures based on marginal operating costs respond to 

renewables with a “free” fuel source that supplies electricity at low marginal costs by 

lowering energy prices when large amounts of wind are available. This therefore reduces the 

available revenue for all electricity generation sources (including wind energy), and 

especially reduces the system value of wind energy as the share of wind energy generation 

increases (Hirth 2013; Helisto et al. 2017; Ahlstrom et al. 2015; Wiser et al. 2017; ZamaniDehkordi et al. 2016). As more wind energy, near-zero marginal cost, and nondispatchable 

operation is deployed in a given energy system, energy prices and revenues to generation 

assets fall, leading to a negative feedback loop in which wind energy cannibalizes its own 

profit opportunities. Ensuring enough revenue for capital recovery and availability of critical 

reliability services may require a change in electricity market structures to explicitly consider 

additional elements of overall electricity system operation reliability—namely capacity and 

system service markets (Ahlstrom et al. 2015).

Figure 6. Potential shift in electricity market structure with increased renewables deployment. 

Renewables that take advantage of “free fuel” provide energy at near-zero marginal cost. 

When energy is free some of the time, the current paradigm in which energy markets make up 

the bulk of revenue for generation assets is unsustainable (Ahlstrom et al. 2015), unless 

scarcity pricing is allowed. New innovative uses of low-cost electricity could take much of the 

surplus energy (Helisto et al. 2017). New market designs that focus on real-time pricing and 

improving revenues from capacity and grid support (ancillary) service markets will pose 

challenges as well as opportunities to renewable and variable energy technologies, such as 

wind energy. 

(Source: NREL based on Ahlstrom 2015) 
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For wind power plants to remain competitive as market structures and revenue streams 

evolve, there may be an increasing emphasis on the value of wind power plants in providing 

capacity value and other system services. To ensure profitability and become a primary 

source of electricity generation in the future grid system, wind power plants may need to seek

value in forward capacity markets, provide “dispatchable” operation in peak energy pricing 

periods, and participate in ancillary service markets. Thus, innovations for wind energy may 

need to target not just reduced LCOE but also increased value to the electricity system. Wind 

plants that provide ancillary services will likely need to forgo some energy generation 

opportunities to deliver the service. For such an outcome to be financially feasible, either the 

market needs to compensate for that missed opportunity, or the capital cost of wind systems 

will need to be low enough that the missed energy revenue does not adversely impact energy 

cost.

Large-scale deployment of wind energy in the future will also need to address concerns 

related to nontechnical/nonmarket impacts. Social acceptance, transmission availability, and 

a variety of related system, social, and environmental factors are expected to influence 

physical design needs and constraints and ultimately the deployment of wind turbines and 

plants. Although these elements are also critical to the current deployment of wind energy,

they extend beyond the scope of the current work. In addition, discussions around policy 

strategies that attempt to affect the evolution of the electricity system are beyond the scope of 

this effort. Instead, the focus of the current work is on research-based innovation that can 

affect the economics of wind energy from an LCOE and a system-value perspective to 

support wind energy deployment reaching shares of 50% or more in the global electricity 

system. 

1.2 Description of Expert Meetings and the Grand Vision for Wind 

Energy Technology

For wind energy to achieve its full potential as an electricity generation resource for the 

future global energy system, further reducing LCOE and increasing system value is essential. 

To support and realize critical innovations, new R&D efforts and findings are expected to 

play a significant role. The IEA Wind Topical Experts Meeting (TEM) #89 “Grand Vision 

for Wind Energy” workshop sought to bring together a group of experts to consider the 

question of how to enable wind power to reach its potential. In this context, a future in which

wind supplies more than 50% of the global electricity consumption was determined to be 

within the scope of wind energy’s overall potential. Over 70 experts representing 15 different 

countries attended and provided a diverse set of perspectives for the Grand Vision for Wind 

Energy. The experts participated in one or more meetings including: 1) the main IEA Wind 

TEM #89 held at the National Renewable Energy Laboratory (NREL) in Golden, Colorado, 

on Oct. 22‒23, 2017 (see Appendix A for the meeting agenda), 2) a meeting at the Utility 

Variable Integration Group 2017 Fall Technical Workshop in Nashville, Tennessee, on 

October 12, 2017, and 3) the IEA Wind Task 25 Fall 2017 Meeting held at INEEL in

Cuernacava, Mexico, on November 20, 2017. The list of participants from the workshop and 

follow-on meetings are provided in Appendix B.
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Attendees were asked to identify innovations and research needs that would enable wind 

power to:

● Achieve a cost of energy that is less than that of all other electricity generation

technologies (i.e., serving as the cheapest global electricity resource)

● Operate in a manner that provides reliability, flexibility, and resilience for the power 

system (i.e., to enable wind energy to supply as much as 50% of total electricity 

consumption and serve as a new backbone for the electricity system of the 21st

century).

The workshop was organized into four working sessions (Appendix A). The first was meant 

to solidify the context (e.g., grid architecture, markets) for the potential of wind energy 

supplying over 50% of global electricity demand. In the next two sessions, participants 

explored technology innovation opportunities for wind energy within this context. The 

second session looked at driving down LCOE to allow for wind power to continue to be 

available as an abundant, low-cost, consumer-friendly energy resource. The third session 

looked beyond LCOE to increase the overall system value that wind energy provides to the 

grid in terms of reliability and resilience—specifically to provide capacity value, ancillary 

services, and rapid response to system perturbations. The fourth and final session considered 

the R&D challenges critical to realizing the wind energy LCOE and system-value 

improvements identified in the prior two sessions. Outcomes for the workshop included:

● Identification and ranking of high-priority wind energy technology opportunities for 

significantly reducing LCOE and improving grid system value

● Identification and ranking of R&D challenges associated with the technology 

opportunities.

This report provides the workshop findings including the future landscape of the electricity 

system with over 50% of the electricity supply from wind energy, a review of key innovation 

opportunities in different topic areas that will be embodied in the wind power plant of the 

future (including innovations that both reduce LCOE and increase system value of wind 

energy), and identification of the key R&D challenges that must be addressed to realize the 

wind power plant of the future and the potential for wind energy as a major supplier of 

electricity generation in the 21st century.

The report is structured as follows. Section 2 discusses the future context considered by the 

IEA Wind TEM. Section 3 describes the key findings related to innovation needs to reduce 

LCOE and increase system value and identifies challenges in wind energy to enable those 

innovations. Section 4 describes advances in related fields and enabling technologies that 

support the vision, and Section 5 summarizes the overall report findings.
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2 The Future of Global Energy and Wind Energy 

Abundance

The IEA Wind TEM #89 “Grand Vision for Wind Energy” workshop identified trends of the 

energy system in the year 2050 to understand and subsequently define the future 

requirements for wind power plants operating in this hypothetical future. This approach 

helped determine the innovations and R&D efforts needed to realize the future potential for 

wind energy.

2.1 Context of the Grand Vision Including Global Megatrends

To characterize the effort, a wider context regarding global megatrends in terms of society 

and technology development was needed. Several analyses about global megatrends have 

been made in recent years to better understand how the world may look in 2050. Among 

these megatrends is continued population growth coupled with an increased standard of 

living for broad swaths of the global populace as well as increased mobility and 

electrification; continued sizable demands on the global agricultural and modern 

infrastructure systems are also anticipated (United Nations). Adding to these are trends in

decarbonization of the electricity, heating, and transport sectors, as well as industrial use of 

energy and carbon. Moreover, climate change adaptation and mitigation are expected to 

support deployment of clean energy solutions, including wind power, in the decades to come 

(IEA 2018; DNV GL 2018). 

Deployment and Social Acceptance of Wind Energy

Social acceptance and other aspects of large-scale deployment of wind energy were outside 

the scope of the workshop but are critical to the realization of the full potential of wind 

energy. As with all energy technologies, deployment of wind energy, especially at large 

scales, will have increasing impacts on society and the environment. In this vein, it is 

expected that social acceptance, transmission availability, and a variety of related system, 

social, and environmental factors will influence physical design needs and constraints for the 

technology, thereby ultimately affecting the deployment of wind turbines and power plants. 

We anticipate that with time and increasing wind power share around the globe, the ability to 

design and optimize wind turbines and plants to best integrate with the landscape, existing 

infrastructure, and local social and environmental considerations, as well as multiple landuse, ocean, and airspace needs, will become increasingly necessary. 

Planning, policy, environmental, and broader social science research will help realize a 21st

century society in which wind power contributes substantially to the global energy system. 

Although these additional factors and implications are an essential pillar in enabling the 

energy future envisioned by the participants in the IEA Wind TEM #89, a full elaboration of 

these social and environmental challenges and associated research needs is beyond the scope 

of this report. The authors suggest considering a similar comprehensive effort focused on 

these issues that complements the current effort, to address all the opportunities and 

challenges related to the future of wind energy.
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2.2 The Future Electric Grid Sets New Requirements for Power 

Generation Sources, Including Wind

Continuing recent trends, increasing shares of solar and wind energy production are expected 

to be integrated into the world’s energy and electrical systems. Many energy scenarios show 

wind power as becoming one of the main sources of electricity by midcentury. The IEA 

World Energy Outlook 2018 forecasts that renewables, led by wind and solar PV, will make

up two-thirds of new power plant investments through 2040, leading to a scenario in which

they provide 40% of global electricity generation in that year (IEA 2018). Bloomberg New 

Energy Finance forecasts similar trends with renewables making up 72% of global electricity 

generation investments through 2040; wind and solar energy provide 34% of global 

electricity generation in that year (Bloomberg New Energy Finance [BNEF] 2017). Even if 

solar energy tends to dominate global investment and installed capacity of these scenarios, 

electricity generation is estimated to be dominated by wind energy in the northern 

hemisphere (Pursiheimo et al. 2018). Broadly, analyses predict a share of renewable 

electricity in the generation system of at least one-third globally by 2040 (Energy 

Information Administration [EIA] 2017; BP Energy Economics 2018; BNEF 2018; IEA 

2018). An even higher wind energy generation scenario comes from the International 

Renewable Energy Agency (IRENA), which estimates over 60% of global electricity 

generation in 2050 from solar and wind energy alone (26% and 36%, respectively) (IRENA 

2018).

A recent study from DNV GL, a global services corporation for the maritime, oil and gas, 

renewable energy, food, and healthcare sectors, found that their “central” case for the 

electricity generation portfolio in 2050 was comprised of more than two-thirds renewables,

with approximately 29% coming from wind energy and 40% from solar PV (DNV GL 2018). 

Figure 7 shows the composition of the electricity generation mix forecast by DNV GL from 

1980 through 2050. In 2050, the projected electricity generation from onshore, or land-based, 

and offshore wind combined is 29%. A key feature seen in future energy scenarios is that in

addition to a renewable-energy-dominated electricity system, there is significant 

electrification of other energy sectors (e.g., transport, heating), such that electricity demand 

doubles and the role of renewable energy in larger energy systems is even more critical. 
IEA Wind TCP Task 11 Technical Report

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Figure 7. In 2050: 29% wind, 40% solar PV, and one-third everything else; “... a base or central 

case, ... is the aim of this present exercise, which is a forecast, not a scenario,” stated Remi 

Eriksen, Group President and chief executive officer of DNV GL (Source: DNV GL 2018)

Although the previously mentioned forecasts project substantial growth for wind energy over 

the next several decades, there is potential for wind power to provide an even larger share of 

future energy demand. The “Grand Vision of Wind Energy” workshop characterized a 

potential future in which wind energy is the “backbone” element of the future electricity and 

broader energy system. More specifically, the international experts from the workshop 

pondered a particularly aggressive scenario, with 80% of electricity generation coming from 

renewables in 2050. For wind energy specifically, the group identified a level of interest 

wherein wind provides more than 50% of global electricity generation by 2050 (versus the 

29% observed from the DNV GL study). Although this target is aggressive, it helps provide a 

critical context for pushing beyond the boundaries of current thinking with respect to the

innovation and R&D needed to realize the full potential for the future of wind energy. Table 

1 summarizes the attributes that makeup the overall energy system that was the focal scenario 

articulated and considered by the participants, with full integration of bulk and distributed 

electricity systems into a single dynamic and holistic system. 
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Table 1. Summary of Global Energy System of Systems Attributes for the Grand Vision of 

Wind Energy (in 2050)

Global Energy 

System of 

Systems

2050 Future Scenario Attributes

Electricity 

Generation

• Renewables at 80% (wind energy provides > 50% of electricity supply) – instantaneous 

generation > 100% of demand in some regions

• Retirement of thermal assets (few thermal assets in the electrical system)

• Large amounts of generation in the distribution system 

System 

Architecture

• Converter-based system (little to no physical inertia in the system)

o Fast/transient stability challenges overcome

o Wind and solar have converters that include “grid-forming capabilities”

• Integrated generation, transmission, distribution, and customer systems into a single 

dynamic and holistic system

o Distribution assets actively participate in all electricity markets

o Many distributed systems/microgrids/customer assets/cellular networks building up the 

bulk system

Transmission 

Infrastructure

• Optimized use of existing assets/corridors—dynamic line/transformer ratings, upgrades, 

and reuse of transmission wherein thermal assets are retired 

• New technologies for AC and DC transmission and modular power flow control

• Cost-competitive long-distance underground transmission is available

Storage 

Technologies

• Cost-effective storage may be widely available to balance diurnal and short-term 

fluctuations in variable resources

• Power to X is cost effective/seasonal storage is widely available

• Storage reacts to real-time price signals and gives grid support through fast response 

services

Electricity 

Consumption

• Massive electrification of the global energy system (e.g., heat, transport, buildings, 

industrial demand)

o Significant growth in energy use in non-OECD countries

o Increased energy efficiency for reductions in energy use in OECD countries

• Assets responsive to real-time price signals or similar methods of demand response (highly 

elastic load profile)

o Demand response provides grid support through fast response services

Market 

Design and 

Coordination

• Transformation of electricity markets (e.g., capacity, energy, ancillary (grid support) 

services)

o Real-time retail pricing 

o Predominantly zero-marginal cost of energy with significant renewable share of 

electricity generation

o All assets participate in capacity and grid-support-service markets

o Increased coordination between neighboring markets

Institutional 

and 

Regulatory 

Context

• Larger balancing areas

• Breakthrough institutional settings (including harmonization of regulation)

• Local systems/markets capable of autonomy when needed

As mentioned, the future scenario resulting from the discussions includes a generation mix 

with 80% of annual demand being met by renewables, meaning regular periods where 

instantaneous generation from variable renewables exceeds 100% of load made up from

variable renewables in some regions. 
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The shift toward an electricity system in which the main contributors are weather-driven and 

variable would likely require a paradigm shift in grid architecture. This paradigm shift is 

caused by a few key features that stem from proliferation of wind and solar energy in the 

system. First, the retirement of thermal/synchronous assets means the loss of mechanical 

inertia that has been key to supporting grid stability for more than a century (Bömer et al.

2010; EirGrid and SONI 2014). Figure 8 depicts the role of inertia in ensuring electric system 

stability where the lack of physical inertia from traditional synchronous machines creates a 

paradigm shift in ensuring electric system stability.

Figure 8. The challenge of stability in the converter-dominated electric grid system. The red

circles represent traditional power plants. If there are many such masses or inertial elements

in the system, losing one mass (by cutting the string) will shift to a new equilibrium quickly, 

with the other system masses dampening out the effects of the loss. However, as more and 

more wind turbines with low inertia, but considerable embedded intelligence, are added to the 

system, significant coordination and advanced control become necessary to ensure that the 

grid is resilient to perturbations. 

(Source: NREL based on a figure created by Nick Miller, formerly GE Energy Services)

This new system must remain stable, which requires advances in the services wind power 

plants and other generators need to provide to the system of the future. In fact, wind power 

plants would need to provide grid-forming converters, which can set system frequency to 

support normal operation and are able to provide “black-start capability” to the grid in the 

event of a system outage (Halley et al. 2018).

The other significant shift for this electric system architecture has less to do with the 

incorporation of wind energy explicitly but is related to other trends in the broader electricity 

and energy system. Increased deployment of generation and storage assets on the distribution 

side of the system would lead to a new overall architecture for the grid system wherein the 

lines between generation, transmission, and distribution are increasingly blurred. Currently, 

there are trends toward stronger, more interconnected, and more closely coordinated bulk 

power systems and local, smart, bidirectional distribution systems and microgrids that are 

able to communicate with each other and the larger power system (McCalley et al. 2017;

Bobinaite et al. 2018). In the scenario considered by the experts of the IEA Wind TEM #89,
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these trends are likely to continue and come to dominate the operation of the grid. Perhaps 

most notably, these smaller and more independent distribution systems/microgrids/cellular 

networks could operate autonomously when needed (Bobinaite et al. 2018; Hu et al. 2018). 

Subsystems in this larger, more integrated network would be like a web of cells capable of 

interacting with neighboring cells in partly autonomous operation alone or with any number 

of neighboring cells as needed, but otherwise providing smart resources for global system use

(Kroposki et al. 2017). The operation of such a grid system could place additional 

requirements on wind power plants and other generators, such that they must provide gridforming capabilities, control, and services both at the utility scale in the bulk system and as 

part of microgrids when operating autonomously (Persson 2017). 

A more dynamic and optimized transmission system would also be required to support the 

future electric grid made up of many microgrids that provide their own generation and 

storage alongside bulk generation systems comprising large-scale generation and 

transmission assets. In this world, transmission improvements would likely need to optimize 

and maximize the use of existing assets and corridors. In many areas of the world, 

contractual mechanisms and lack of technology upgrades (i.e., dynamic line/transformer 

ratings) could limit further deployment and integration of wind energy in regions with 

transmission congestion (Bhattarai et al. 2018; Greenwood et al. 2014; Gentle et al. 2014, 

Estanquiero et al. 2018). At the same time, development of and investment in new 

transmission technologies (i.e., high-voltage alternating current [HVAC] and high-voltage 

direct current [HVDC] transmission, modular power flow control, and cost-competitive 

underground transmission) would enable a more robust, reliable, and accommodating 

transmission system for the 80% renewables electric system (European Technology and 

Innovation Platform on Wind Energy [ETIPWind] 2016). Some of these technologies (e.g.,

HVAC and HVDC transmission, modular flow control) could allow energy to be transmitted 

across greater distances more efficiently, thereby opening new resource areas and allowing 

for improved integration as a result of geographic diversity, whereas other technologies (e.g.,

dynamic ratings) will allow for improved utilization of existing assets and more reliable 

system operations. In order to realize many of the benefits of these new technologies, 

cooperation of multiple legislative, regulatory, and operational entities across broad 

geographical regions is required. 

The other major changes in the future electric system as defined by the workshop are in the

end use of electricity including storage, demand response, and electrification of other energy 

sectors. Using electric power for heat and transport will give new, potentially more flexible 

demand than the current electrical load. Heating loads have cost-efficient thermal storage 

options available today and vehicles are typically used much less than half of the hours in a 

year. Because of falling costs in utility-scale storage (predominantly batteries), there is likely 

to be an increasing amount of economic storage available in the grid system at both the bulkand distributed-system levels. Storage would be an important technology for mitigating the 

challenges associated with the inherent variability of both wind and solar technologies at 

high penetrations. For many applications, there is a trend toward either colocated or virtual 

“hybrid power plants” with combined wind, solar, storage, and other generation assets. 

Storage technologies serve as electricity sinks during periods of overproduction from wind 

and solar assets relative to demand and to provide that energy as a source back to the grid 

during periods of low solar and wind generation. For many decades, systems with large 
IEA Wind TCP Task 11 Technical Report

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hydropower resources through their reservoirs have served as energy storage systems and 

enabled large-scale deployment of wind energy in some markets, such as in Denmark and 

Portugal (Holttinen et al. 2016; Farahmand et al. 2013, 2015). 

However, storage may also come in new forms. One significant new source of storage being 

investigated is “Power to X,” in which electricity is used to produce various products, such as 

storable synthetic gases or liquids, that can be used in diverse industrial processes, heating, 

transportation, or other uses (Neo Carbon Energy 2017). Wind energy systems of the future 

may be able to provide significant “Power to X” resources either through the electric grid 

system or in off-grid applications (i.e., in remote regions or far offshore). Large and wellinterconnected grids and “Power to X” will be available to shift power production from the 

grid to other systems across large geographic and temporal scales (even seasonally)—making 

the variability of wind energy generation less critical for large levels of wind energy 

deployment. An example application is steel fabrication, as demonstrated in the Finnish 

Hydrogen Breakthrough Ironmaking Technology project (HYBRIT 2018).

The electrification of other energy sectors (e.g., heat, transportation, buildings, industrial 

demand) would result in a substantial increase in overall electricity consumption in the years 

to come. However, overall energy usage in OECD countries is expected to stabilize or 

decline, whereas in non-OECD countries it is expected to substantially increase (like

predictions from DNV GL and other forecasts suggest). With electrification and growth of 

overall electricity consumption globally, there would be a significant change in how that 

demand interacts with the overall electricity system. Some technologies, such as in the case 

of electric vehicles or “Power-to-X,” would provide storage on various timescales. More 

generally, many assets will likely be responsive to real-time price signals or to similar 

methods that provide demand-side flexibility, such that the electricity demand profile of the 

future will be much more elastic than at present (Institute for Sustainable Development and 

International Relations 2015).

A final potential component of the future energy system pondered by the experts in the IEA 

Wind TEM is the need for (and value of) breakthroughs across the globe in institutional and 

regulatory contexts. If geographic and regulatory barriers can be broken down, utilities and 

system operators could take advantage of the potential of generation, transmission, storage,

and demand assets that are geographically dispersed and can complement each other when 

integrated into larger balancing areas (McCalley et al. 2017). Several studies have shown that 

the geographically dispersed deployment of renewable energy resources within one 

technology area (i.e., wind) or across technology areas (i.e., solar and wind energy) can be 

beneficial to smoothing out the variability inherent with those resources. By spreading wind 

energy technologies across large geographic areas, there is significantly less variation in the 

expected generation from those assets over time—for example, from the north to south of 

Europe, or the north to south of the Atlantic Coast in the United States (Kempton et al. 2010;

Grams et al. 2017; Olauson et al. 2015). In many areas of the world, solar and wind energy 

profiles are such that when combined, the variability of the combined resource is less than 

either technology by itself (Seel et al. 2018). Larger balancing areas and harmonized 

regulations would allow the electric system to take advantage of the complementarity of 

dispersed and diverse renewable generation assets; this could likely require increased 

transmission buildout in some locations as described earlier.
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Benefits and Challenges of Wind Energy As the Electric System Backbone

The future electric system explored and pondered by the IEA Wind TEM #89 is one that 

departs from the electric system architecture and design that has been dominant since first 

introduced at the end of the 19th and beginning of the 20th century. The global electric 

system of the 21st century considered by the experts to inform future R&D needs would be a 

converter-dominated system largely made up of renewable energies, such as solar and wind. 

The system would be designed for increasing levels of interconnectivity across huge 

geographic areas coupled with a simultaneous increase in the level of autonomy for a given 

locale. The combination of autonomy and large-scale interconnectivity along with the 

proliferation of storage and demand response assets was determined to be capable of leading

to a robust system with increased redundancy, distributed security, and resiliency, if the 

generation technologies can offer the required controllability.

As the electric system adapts, so do the requirements placed on the generation assets that 

provide its primary supply of electricity. For wind to serve as a potential backbone of this

future electricity system, it will need to provide low-cost energy around the world, while at 

the same time supporting grid stability and reliability at all times, necessitating the 

innovation and research described in the remainder of this report.
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3 Research Needs for Wind Energy at 50%

To assist in focusing and calibrating participant input, two opposing and extreme scenarios of 

the future energy system in year 2050 were created that would then guide objectives for wind 

power plant innovation discussions. In the first scenario, the grid would support large 

amounts of wind energy without significant burden being placed on wind technology itself. 

Large amounts of transmission, storage, changes in the distribution system, advanced power 

electronics, and other means would allow for high levels of wind in the system without 

requiring significant capacity and grid services from wind plants. Thus, the focus for wind 

energy innovation would be, as it has been historically, on decreasing LCOE. In the second 

scenario, however, constraints on all other aspects of the system described earlier would 

force wind to be “designed for curtailment” and provide significant capacity value and 

system services to the grid. In this case, wind power plants would have to be designed to 

provide increased system value. The two scenarios for LCOE and system value are shown 

in the Table 2.

Table 2. Scenarios for Wind Energy Grand Vision Breakout Sessions

Scenario 1: Objective of Lowest Possible Wind 

Power Plant LCOE

Scenario 2: Objective of Highest Possible Wind 

Power Plant Contributed System Value

Generation:

• Wind energy > 50% global electricity supply

• Renewables > 90% global electricity supply

• Global energy is predominantly electric (massive electrification of transportation, heating)

• Transmission and storage are ubiquitous 

(seasonal storage available)

• Transmission is constrained and storage is 

limited (no seasonal storage available

Distribution

• Large amounts of electric vehicles, 

demand response, solar; bulk and 

distributed systems are both significant

• Limited amount of distribution-side 

capacity (e.g., electric vehicles, demand 

response, solar); mostly a bulk system

• Load is highly elastic, large deployment of 

Power-to-X technologies

• Load is highly inelastic, little deployment 

of Power-to-X technologies

Market/System Design

• Advanced energy, capacity, and service 

markets where wind participates (but 

energy is the dominant revenue stream)

• Advanced energy, capacity, and service 

markets where wind participates (capacity 

and service payments are substantial)

Socio-economics

• Few constraints on siting (environmental, 

social) with increased deployment

• More constraints on siting (environmental, 

social) with increased deployment

• Lowest cost of electricity prioritized • Local economic development prioritized

The scenarios provided in Table 2 served as a guide for the Grand Vision for Wind Energy 

workshop to consider, in sequence, innovations to 1) reduce wind energy LCOE, and 2) 

increase the value of wind energy to electric grid system operation and reliability. These 

were considered separately because as Figure 9 demonstrates, innovations that reduce the 

cost of energy may actual decrease system value and vice versa. A combination of these 
IEA Wind TCP Task 11 Technical Report

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innovations will lead to the realization of the wind power plant of the future capable of 

realizing an electric system future in which wind energy provides more than 50% of overall 

global electricity generation. Significant investment in wind energy research will be required 

to realize these innovations and create the Grand Vision for Wind Energy. The workshop 

also sought to identify R&D needs in wind energy to support the identified LCOE and 

system value innovations. 

Figure 9. Future wind power plants will have to consider trade-offs in design and operation 

between LCOE and system value. Depending on the architecture and makeup of the grid, the 

relative importance of each objective will change. Thus, this workshop considered each 

objective separately and then sought to harmonize across them for an integrated Grand Vision 

for Wind Energy (Source: NREL based on graphic from Ryan Wiser, Lawrence Berkeley 

National Laboratory)

This section provides a high-level summary of the workshop findings broken out by the 

working group in the areas of 1) atmospheric science and forecasting, 2) turbine technology 

and design, 3) manufacturing and industrialization, and 4) plant controls and operations, 5) 

grid integration, and 6) offshore-specific technologies. What follows are the LCOE, system 

value, and R&D priorities identified by each breakout group. There is overlap across the 

categories as it is impossible to fully decouple a wind power plant system into distinct areas 

where innovations are isolated to single subsystems. In fact, many identified innovations and 

R&D challenges are common to several working group areas and this commonality is used in 

Section 4 to identify cross-cutting needs in wind energy research.

3.1 Breakout Group 1: Atmospheric Science and Forecasting

As the amount of wind energy increases to a considerable percentage of the world’s energy 

supply, improving the knowledge base focused on the wind resource itself and its interaction 

with wind power plants is imperative. 

Better understanding of the atmospheric environment is needed for three general timescales: 

short term for minutes, hours, and days ahead and medium-term energy and maintenance 

forecasting and long term for predictions of lifetime energy production and system loads and 

reliability. Short-term forecasting is becoming more important as the percentage of wind 

power on the grid increases (Marquis et al. 2011), the stability of the grid is more dependent 

on renewables, and the amount of ancillary services provided by wind energy increases 

(Shapiro et al. 2017). Modeling and prediction of the wind resource by numerical weather 

prediction (NWP), including various components of NWP, such as data assimilation, model 

physics, and parameterizations, and initial conditions from observations, are key to 
IEA Wind TCP Task 11 Technical Report

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advancing knowledge in this area (Haupt et al. 2017). Forecasts will also require improved 

models for transformation of NWP predictions to quantities of power and energy including 

turbine performance in complex flows. Long-term energy estimates depend on many of the 

same physical processes that are important for short-term forecasting, but they are also 

influenced by longer-term sources of variability, such as the El Niño-Southern Oscillation, 

and those induced by future climate change (Pryor and Barthelmie 2010; Clifton and 

Lundquist 2012; Karnauskas et al. 2018). Longer-term forecasting also affects industry’s 

ability to predict how and when there will be reliability issues with turbines and, thus, will 

support improved operation and maintenance strategies as well as better turbine and plant 

design in the future.

Uncertainty estimates for both timescales are important, and a deeper understanding and 

separation of compounding and canceling errors for different physical processes will be 

important for improving uncertainty quantification and reducing epistemic uncertainty in 

models across scales. Improved understanding of uncertainties in models as well as 

uncertainties in observations will help reduce the cost of energy with time (Pinson 2013). 

Better understanding of the resource and the uncertainties involved can help improve many 

aspects of wind energy systems, from turbine and plant layout design to optimal grid 

interaction and energy market structure. 

The innovation pathways to address these issues include new approaches for multifidelity, 

multiscale simulations along with rigorous methods for uncertainty quantification. This 

involves coupling existing models in new ways as well as leveraging capabilities of new 

models as they are developed. Such models will be employed over a variety of timescales 

with forecasting time windows from less than a day to decades for wind resource and energy 

prediction. The models must be well-validated by researchers using large-scale data sets 

organized by innovative data science tools as well as high-resolution observations from 

advanced remote-sensing, unmanned aerial vehicle sensing systems, and other in situ 

technologies. Forecasting tools will not only predict the resources of wind energy and other 

renewable technologies, but the performance of energy storage technologies, the grid load, 

and grid behavior including transmission lines.

The benefits of the innovation pathways on LCOE include lower fixed charge rates from 

reduced uncertainty, higher annual energy production from better optimized wind power 

plants in both design and operation, and lower operating expenses from more optimally 

designed and operated wind plants. The system-value impacts include reduced uncertainty 

and variability in wind power plant energy production as well as an increased ability of wind 

power plants to provide services to the grid for reliability. Better forecasting tools in the wind 

energy space will also support optimization of the overall grid system with high levels of 

renewables to balance the use of wind energy with other generation assets as well as storage 

and distribution-side technologies such as electric vehicles and demand-response programs. 

The benefits of each of the innovation pathways is described in more detail in the following 

subsections.

The innovation pathways and their benefits will be realized through the undertaking of three 

research challenges in atmospheric science and forecasting. First, the scientific community 

must endeavor to fundamentally understand the wind resource as well as how wind plants 
IEA Wind TCP Task 11 Technical Report

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change the resource (Fitch et al. 2012; Lundquist et al. 2019), and once the understanding is 

gained, the community must identify how to optimize the electricity system to work 

seamlessly with the resource. Second, the industry needs an improved model chain to 

accurately predict the varying energy resources provided by the atmosphere. Third, to ensure 

broad acceptance and long-term viability, atmospheric science can help inform industry to 

enable better decision-making regarding environmental and societal impacts from wind 

energy. For example, atmospheric scientists should determine the potential impact of wind 

farms on crop yield to mitigate potential negative consequences of large-scale deployments 

in the U.S. Midwest.

The innovation pathways, their impacts to LCOE, system-level benefits, and grand 

challenges to improve the fundamental understanding of the wind resource and its influence 

on wind power plants are described in more detail in the following sections.

Innovations To Reduce LCOE

Numerous pathways can reduce the cost of energy through the application of atmospheric 

science both for short-term forecasting and long-term energy estimation. As highlighted in 

Table 3, many of these benefits arise from enhanced models of the atmosphere that will 

improve the accuracy of forecasts at a range of timescales and, equally important, help 

quantify the uncertainty of energy estimates. These model improvements will require 

validation using new high-fidelity observational studies. Such observational studies will 

provide quantification of model skill in estimating and predicting physical processes that 

affect the wind resource. For instance, a recent 5-year public-private partnership project in 

the Pacific Northwest collected atmospheric measurements through all four seasons to 

measure the skill of weather forecasts in this area of very complex terrain. Changes were 

made to the underlying NWP models, particularly by revising the mixing length in the 

planetary boundary layer scheme, improved numerical methods for horizontal diffusion, and 

the introduction of small-scale gravity wave drag. (Shaw et al. forthcoming; Wilczak et al. 

forthcoming; Olson et al. forthcoming). Revisions to the model components were tested in 

retrospective model runs that were compared against the collected measurements. Revisions 

were made iteratively until optimized against the observations.
IEA Wind TCP Task 11 Technical Report

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Table 3. Innovations in Atmospheric Science and Forecasting To Improve Wind Power Plant 

LCOE

Innovation Category Innovation LCOE Impact

Increase Accuracy of Long-Term 

Resource and Energy Estimates 

and Reduce Uncertainties 

Development of multiscale,

multifidelity modeling 

approaches—a spectrum ranging

from numerical weather 

prediction to large eddy 

simulations to reduced-order 

models, with improved 

integration with observations;

tools that will predict interactions 

between and among multiple 

turbines and wind plants

• Reductions in fixed charge rates 

through improved energy estimates

• Increased annual energy production 

(AEP) through optimized 

micrositing, array design, and 

interplant effects

• Lower OpEx through more 

optimized and efficient wind plant 

layouts for greater reliability and 

reduced interplant effects

Improved tools with scientifically 

rigorous quantification of 

uncertainty for observational 

sensors and models; tools that 

more accurately predict P99/P50 

uncertainty estimates

• Reductions in fixed charge rates 

through improved energy estimates 

with accurate uncertainty 

quantification

Organized, connected, and 

distributed platforms for sharing 

open wind resource data or data 

“marketplace” 

• Increased AEP through better plant 

siting 

Improved remote sensing across 

scales to observe wind plant 

inflow, wake effects, and regional 

impacts 

• Reductions in fixed charge rates 

through improved energy estimates 

and uncertainty quantification

• Improved AEP as a result of more 

optimized wind plant layouts and 

operation tuned to the observed 

resource

• Lower OpEx as a result of more 

optimal operation of plant

More accurate model tools to 

predict longer-term wind and 

other renewable resource trends 

including interannual variability 

and climate change at improved 

spatial and temporal resolutions 

• Increased AEP through improved 

energy system planning and siting

• Lower fixed charge rates through 

improved prediction of long-range 

variability

Multiscale, multifidelity models are key for developing more optimized wind plants in the 

future (Schreck et al. 2008). Such models are necessary to better understand the fundamental 

physical phenomena driving wind plant performance and wind plant interactions. Further 

research on the most relevant flow phenomena (Stevens and Meneveau 2017), such as wind 

turbine wakes (low energy bodies of flow in a plant created by upstream turbines extracting 

energy and imparting vortices into the flow), is expected to yield better tools for optimal 

siting of individual turbines within a given wind plant. Optimal siting strategies will decrease 

the negative impacts of turbulence on turbines that are downwind of others in the wind plant 

and maximize total energy production of that plant. Additionally, reduced wind power 
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production at established wind plants has been identified after new wind plants have been 

developed many kilometers upwind (in the prevailing wind direction) of the established wind 

plants (Nygaard 2014), which can lead to power losses at the downwind wind farm of 

millions of dollars (Lundquist et al. 2019). Increased understanding of wind plant wakes on 

downstream wind resources is expected to lead to improved plant siting and optimized power 

production.

Wind energy project financing is dominated by prediction of risks (Schwabe et al. 2017). 

One of the largest and dominant risks is the preconstruction estimates of energy production 

over the lifetime of the wind plant. Developers currently use nonscientific approaches (Lee et 

al. 2018) primarily based on experience to estimate the risk from various sources of 

uncertainty (Clifton et al. 2016), often underestimating interannual variability (Bodini et al. 

2016). A more rigorous, accurate method for predicting uncertainty in long-term energy 

estimates will lower the finance costs across all wind project development.

Long-term and short-term energy estimates of wind plants are heavily influenced by the 

observational data used to make predictions. For short-term forecasting, the initial conditions 

and data assimilation schemes using real-time observations can produce high sensitivities. 

Assimilated observational data includes observations from distributed meteorological towers, 

but also aircraft observations, and even wind turbine supervisory control and data acquisition 

(SCADA) data output. For long-term predictions, longer-term atmospheric observational 

records make significant impacts through the measure-correlate-predict process. New 

observational data sets could be similar to reanalysis data sets (e.g., Modern EraRetrospective Analysis for Research and Applications [MERRA-2]; Gelaro et. al 2017),

which are a combination of simulations and observation over a long period of time, but with 

higher fidelity and validated using more observational data. For example, NREL’s Wind 

Integration National Dataset Toolkit (Draxl et al. 2015) has a 2-km spatial resolution and 

could be extended for several decades and validated with many thousands of observational 

sites. These data should carefully catalogue any potential long-term variability from artifacts 

including sensor replacement or calibration and even local environmental changes, such as 

changing surface roughness (Vautard et al. 2014). As such, more advanced publicly available 

resource data sets will improve overall predictions across timescales. Data should include 

detailed metadata and taxonomies for making catalogues and finding atmospheric details. An 

example of taxonomy would be a catalogue of vegetation immediately within and 

surrounding a wind farm site, which may change with seasons or longer time periods. 

As turbine heights continue to increase (Wiser and Bolinger 2018), tower-based 

measurement systems need to be augmented with advanced remote-sensing devices to 

measure atmospheric conditions over a wide range of heights and spatial distributions across 

potential wind power plants over the long time periods (multiple years) required for wind 

resource assessment. These advanced observations will serve to help validate new wind 

resource models and can also be assimilated into shorter-term predictions of the resource in 

the operational environment. Data assimilation of remote-sensing devices has been shown to 

increase accuracy of short-term forecasts (Wilczak et al. 2015). High-resolution Doppler 

lidar instruments can observe the wakes of individual wind turbines (Aitken et al. 2014), 

providing valuable data for wind plant model validation and real-time operational control.
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The impacts of naturally occurring, large-scale climate drivers, such as the El Niño-Southern 

Oscillation and the Pacific Decadal Oscillation, on wind resources will vary by location. This 

important phenomenon is often not acknowledged in terms of how critical it is to the impact 

of the overall wind resource for both intra and interannual variation. As a result, these 

climate oscillation impacts are not well-understood and thus difficult to predict in both the 

short and long term (DNV GL 2016). Similarly, the likely impacts of various climate-change 

scenarios on wind resources (Karnauskas et al. [2018] and references cited therein) also 

exhibit high uncertainty and will vary by location. Further research to understand how both 

natural climate variability and human-caused climate change will affect wind resources is 

needed to reduce risk to the financial investments made to deploy wind plants based on 

resource assessments. Given the increasing deployments of wind plants in locations 

vulnerable to extreme events like hurricanes (Hallowell et al. 2018) that will be affected by 

climate variability, assessments of the impacts of these extreme events on turbine-relevant 

atmospheric parameters (Worsnop et al. 2017) will become more important. 

Innovations To Increase System Value

Beyond reductions in the cost of wind energy, improved understanding of atmospheric 

science has other benefits to society including 1) improved short-term forecasting of wind 

power plant output for participation in merchant markets, and 2) improved understanding and 

design of interactions of wind energy with the grid and its subcomponents. Each of these 

values is described further in Table 4.
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Table 4. Innovations in Atmospheric Science and Forecasting To Improve Wind Power Plant 

System Value

Innovation Category Innovation System Value Impact

Short-Term Operational 

Forecasting -

Postconstruction 

Short-term wind inflow 

forecast adoption to 

improve bidding to 

merchant markets

• Reduced variability/uncertainty of 

supply

• Ancillary service support

• Increased energy value

• Reduced curtailment impacts

Forecasting wind turbine 

and plant performance in a 

wide range of operating 

environments

• Reduced variability/uncertainty of 

supply

• Ancillary service support

Grid Interaction 

Improvements

Multigeneration type 

forecasts (e.g., wind, solar, 

and storage) to optimize 

production, storage, 

markets, and so on

• Reduced variability/uncertainty of 

supply

• Ancillary service support

• Increased capacity value 

• Increased energy value

• Electrification support

• Reduced curtailment impacts

Forecast power line 

temperatures for 

estimating transmission 

“dynamic line rating”

• Reduced variability/uncertainty of 

supply

• More efficient transmission use

Coupled forecasting of 

wind and electricity load 

and grid state together

• Reduced variability/uncertainty of 

supply

• Ancillary service support

Smoothing power (diurnal) • Reduced variability/uncertainty of 

supply

Seasonal storage • Reduced variability/uncertainty of 

supply over medium-term operations 

(seasonally)

Shared platforms providing 

forecasting that supports 

demand response, time-ofuse pricing, decision 

support; weather reports 

of day-before and dayahead wind energy 

production

• More efficient demand management 

if energy forecasts are accessible by 

the public

As the amount of wind energy penetration increases, so does the value of energy 

forecasting—particularly in fast-acting merchant markets, where resources are more quickly 

dispatchable (Soman et al. 2010).

Improved physical understanding of processes that influence wind plants can lead to better 

optimized wind plant layouts and operational strategies. For example, understanding how 

turbine wakes interact with each other throughout a wind plant, in various kinds of terrain, on
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land or offshore, opens up strategies for optimal siting of individual turbines and for steering 

wakes to optimize power production (Fleming et al. 2016a). 

For such improvements to take place, there is a need to develop a full understanding of sitespecific atmospheric phenomena including but not limited to frontal systems, flows in 

complex terrain, sea breezes, cold pool erosion, the turbulent daytime and nocturnal stable 

atmospheric boundary layer, low-level jets, canopy effects, coherent structures, and turbulent 

wind gusts. Episodic field campaigns such as the Perdigão experiment (Fernando et al. 2018) 

can provide useful insights into the effects of atmospheric stability on wind turbine wakes 

(Wildmann et al. 2018) and terrain-driven recirculation on turbine-height winds (Menke et al. 

2019). Through multiple, large, public-private partnerships, some improvements in 

understanding of atmospheric processes that affect winds at the height of wind turbines have 

been made and incorporated into operational weather forecast models. 

In a recent project, the models used to predict temperature and timing of mix-outs of cold 

pools, among other processes in the Pacific Northwest (mentioned earlier), have been 

improved through revised planetary boundary layer mixing and horizontal diffusion schemes 

and introduction of small-scale gravity wave drag (Shaw et al. forthcoming; Wilczak et al. 

forthcoming; Olson et al. forthcoming). The private sector has made decision support tools to 

utilize such advances (Grimit et al. forthcoming). Such forecast accuracy advances support 

more efficient bidding into energy markets and wind plant controls. The latter facilitates sitespecific designs for lower operation and maintenance (O&M) costs. 

Improved and/or integrated forecasts of wind, solar, and water (for hydropower and pumped 

storage) support decision-making and optimization of power generation and storage 

(charging and discharging), as well as energy market operations (Hodge et al. 2018).

Improved forecasting of temperature, irradiance, and wind speed and direction can support 

dynamic transmission line rating. Dynamic line rating can allow greater use of existing (and 

new) transmission lines because static line ratings, which do not change throughout the year 

or change only once or twice annually, limit the maximum current that a conductor can 

continuously carry without exceeding its temperature rating. Increased use of transmission 

lines is particularly helpful to renewable energy sources because they are spatially sparse 

(diffuse) compared to conventional energy sources (Bhattarai et al. 2018). Ultimately, this 

dynamic use of transmission lines would lead to a more dispatchable energy supply

(Estanqueiro et al. 2018).

Coupled forecasting of the resource, load, and grid state, including transmission line 

capacity, can help regulators more accurately balance grid operation, leading to more grid 

stability and lower supply uncertainty (Bhattarai et al. 2018; Grams et al. 2017; Holttinen 

2018).

Forecasts on a seasonal and longer-term basis could be used to optimally design, locate, and 

operate seasonal storage opportunities to ensure greater supply reliability. They can also be 

used to manage the storage devices themselves (e.g., pumped hydropower that will be 

influenced by rainfall and evaporation rate into the atmosphere (Wang et al. 2016).
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Finally, through greater publicity of energy resource forecasts on a regular basis along with 

public education efforts, further demand management may be possible. If the forecasts can 

also be connected to publicized costs that vary daily and along with smart appliances, more 

optimal and reliable grid operation is also possible (e.g., Dietrich et al. 2012, Nistor et al. 

2015).

R&D Challenges To Realize Innovations

The grand science and engineering challenges to realize innovations include 1) a fundamental 

understanding of the wind resource and how it will be modified, locally and regionally, by 

large-scale deployment of wind, and 2) an improved model chain to accurately predict the 

varying energy resources provided by the atmosphere.

A fundamental understanding of the wind resource and how it will be modified, locally and 

regionally, by large-scale deployment of wind energy is key to many of the innovations 

identified across the various breakout sessions. Atmospheric science has developed over the 

years, focusing on length and timescales most relevant for weather forecasting and climate 

prediction. Wind energy requires information about the wind resource and its availability at 

different levels of spatial and temporal resolutions (e.g., wind speed statistics at hub height 

and corresponding scales in between surface layer and regional-scale phenomena). This inbetween region between micro and mesoscale meteorology has not been modeled well in the 

past because weather models focus on near-surface precipitation and temperature. In the 

vertical direction, model accuracy can be improved with increased model resolution and also 

improved surface layer parameterizations. In the horizontal direction, this region has already 

been termed “terra incognita” in the context of atmospheric turbulence modeling by 

Wyngaard (2004), a still-relevant description of the continuing insufficient knowledge of the 

relevant phenomena and underlying principles, although model improvements are currently 

being tested. 

An improved model chain to predict accurately the varying energy resources provided by the 

atmosphere will support the ability to estimate the long-term resource, design wind turbines 

and power plants, as well as forecast energy production in the short term. At present, most of 

the modeling studies of wind energy systems are focused on a small range of scales. As a 

result, they may claim accuracy in a limited range of scales, but it remains a challenge to 

provide accurate and efficient predictions while capturing the relevant phenomena that range 

over many orders of magnitude of length and timescales, from micrometers to hundreds of 

kilometers, and from milliseconds to years. Although active work in this area is underway 

(Haupt et al. forthcoming), new advances in modeling coupling, such as cell perturbation 

methods (Muñoz-Esparza et al. 2014a, 2014b), will need development and refinement.

Each of these research challenges requires addressing more specialized subchallenges, which 

are described in Table 5.
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Table 5. Atmospheric Science and Forecasting R&D Challenges

Grand Challenge Science and Engineering Challenge

Grand Challenge #1 –

Fully Understand the 

Wind Resource and How 

It Interacts with Wind 

Power Plants 

Understand, model, predict, and forecast the atmosphere and its impact 

on wind energy systems to develop a renewable energy system that 

“dances with the wind”

Develop more accurate and higher-fidelity representations of the 

atmosphere using remote sensing, turbines, unmanned aerial vehicle 

sensing systems, and wind plant systems as sensors.

Develop novel means for uncertainty quantification of the resource.

Build mathematical theory for improved descriptions of a multipoint,

time-evolving structure of highly non-Gaussian fields.

Identify canonical cases that cover a wide range of operating behavior. 

Provide individual turbine inflow prediction and interface with control.

Optimization of siting, layout design, and operation of wind plants, 

including possible effects of interfarm wakes and climate change 

influence

Atmospheric science studies require access to industry data for 

comparison to meteorological measurements. Aligning meteorological 

phenomena with turbine performance and power production is 

necessary for ensuring relevance of atmospheric science results. 

Grand Challenge #2 -

Improved Model Chain 

To Better Predict 

Renewable Energy 

Resources 

Complete model chain of atmosphere/wind plant interactions that 

seamlessly bridges scales

Machine-learning-based forecasts 

Information and data science for information retrieval, data analytics,

and machine learning; large, well-organized open-access data archives to 

enable accurate machine-learning techniques

Probabilistic forecasting interfaced with deterministic forecasting

Advanced, high-performance computing hardware and software 

architecture to exploit next-generation computing hardware; common 

model software architecture framework for software development

High-fidelity model validation experiments

Knowledge must bridge scales that are important for turbine operation and reliability, with 

scales ranging from the large mesoscale to individual turbine wakes (Schreck et al. 2008). 

Improved multiscale models that capture the important physical processes across a variety of 

atmospheric and topographic environments will improve understanding of wind power plant 

performance, not only at the large plant level, but at each individual turbine location over the 

entire lifetime of a given wind power plant (Petersen and Troen 2012). As wind plants 

continue to be built worldwide, the interactions among plants at the largest scales (Nygaard 

2014) will become more important: offshore wakes are already observed to extend 50 km in 

some circumstances (Platis et al. 2018). The occurrences, behavior, and predictability of 
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these wakes require further examination (Siedersleben et al. 2018a, b). New models to 

simulate wind plant interactions with the atmosphere (Fitch et al. 2012) as well as new 

methods to access and share data on existing operational wind plants will be necessary. To 

advance R&D, researchers must have expanded and open access to meteorological and plant 

operational data.

The first research challenge is achieving a full fundamental understanding of the wind 

resource and how it will be modified, locally and regionally, by large-scale deployment of 

wind power, such that the industry can predict wind power output within 1% of reality 99% 

of the time. Addressing this grand challenge will require field campaigns with deployment of 

instruments to collect observations for model verification as well as permanent deployment 

of instrument networks to collect observations for assimilation into weather models to yield 

improved data for defining initial conditions for atmospheric simulations. In addition to 

anemometers that are already routinely mounted on towers and turbine nacelles, other 

instruments, such as lidars, can be mounted on turbines to provide situational awareness of 

the wind resource within and around wind plants. 

Understanding the relationship of measured and modeled atmospheric phenomena to wind 

power production, at the level of individual turbines and wind plants, requires access to 

temporally and spatially matched data of wind power generation. At present, industry 

companies sign nondisclosure agreements with individual research teams for defined periods 

and locations; however, a national or international framework whereby companies could 

share power production data without compromising proprietary information would advance 

atmospheric science research for the industry. Additionally, the physical effects of turbines 

on the local environment can be modeled at various levels of fidelity, such as actuator disk 

and actuator line models in large-eddy simulations (Calaf et al. 2010; Martínez‐Tossas et al. 

2015; Vanderwende et al. 2016), and local momentum and kinetic energy sources included 

into weather models (Fitch et al. 2012). These methods have experienced limited testing than

turbine power production data (Lee and Lundquist 2017). Moreover, ensemble forecasts can 

be generated by multiple methods (Lee et al. 2012). One or more models with different 

physics can be combined into an ensemble, different initial conditions can be used with the 

same model to yield an ensemble, and various components of a model can be stochastically 

perturbed to yield an ensemble (Ollinaho et al. 2017). The goal with any approach to 

ensembles is to yield a spread of forecasts that accurately represents the uncertainty in the 

forecast. Quantifying uncertainty in forecasts can support improved decision-making by 

power systems operations and markets. 

The second grand challenge is the development of an improved model chain that seamlessly 

bridges scales to better predict renewable energy resources that are currently modeled 

separately. New multiscale atmospheric models developed must allow investigation into 

advanced understanding of the physics and also inform development of computational 

efficient models used by industry. A new set of tools across scales and with multifidelity will 

allow for prediction of fully coupled physics that drive performance and reliability of wind 

energy systems (Sanz Rodrigo et al. 2017). Such models may begin by coupling existing 

models across the mesoscale and microscale for which new numerical and physical methods 

for coupling must be developed and implemented (Muñoz-Esparza et al. 2014). These 
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models must be sufficiently validated with new high-fidelity observations of wind plants over 

long time periods (Petersen 2017). These models will include the dominant physical 

processes required for prediction of the energy resources on different timescales and reducedorder models will take advantage of machine-learning techniques tuned to observed and 

simulated data for more time-efficient calculations.

To support this next generation of models, new computing architectures at the exascale or 

larger (Hammond et al. 2015) are needed in addition to software frameworks that support the 

next generation of hardware (Sprague et al. 2015). New “big data” tools, such as open 

numerical laboratories (Kanov et al. 2015; Meneveau and Marusic 2016) should be deployed 

to more openly share results from exascale numerical wind power plant simulations. 

Inclusion of data assimilation and machine-learning techniques will require stronger 

interactions with the growing data science community. Models of all fidelity must be 

validated to acceptable levels of accuracy to ensure acceptance by the wind energy 

community. New observational data sets will be needed that will be obtained through largescale field campaigns to better understand the coupled physical processes and provide this 

validation data (Petersen 2017). Large, well-organized open-access data archives of 

meteorological and wind power plant operational data will also be useful for helping improve 

simulation accuracy. Because of sensitivities around intellectual property and other reasons, 

industry has historically been hesitant to share data. Discussion around more open access to 

data will be discussed further in Section 4 as a cross-cutting research need.

3.2 Breakout Group 2: Turbine Technology and Design

Turbine technology and design will continue to play a critical role in the deployment of wind 

energy, both in terms of reducing LCOE and offering greater value to the electrical grid. It is 

expected that turbine size will continue to grow, resulting in a trade-off between higher 

power ratings and higher capacity factors. This growth will require greater sophistication in 

rotor design to remove weight while controlling loads and deflections, and drivetrains that 

can reliably convert higher torques. Advanced sensing and control methodologies are critical 

to achieving these larger machines, as are new manufacturing and assembly methods to 

overcome logistics challenges. A deeper and more detailed understanding of the atmosphere 

discussed in other groups is also expected to lead to an enhanced ability to design for targeted 

conditions, extending design lifetime, reducing excessive safety factors, increasing energy 

capture, and enhancing grid support where needed.

One important note is that the breakout group focused on conventional technology for wind 

turbines (horizontal-axis turbines) and did not consider in any detail other architectures, such 

as airborne wind turbines or vertical-axis turbines. While there is room for innovation with 

novel architectures, the group focused more on opportunities that still exist for significant 

innovation on horizontal-axis turbines and identified many opportunities as discussed in the 

next section. 

Innovations To Reduce LCOE

The greatest opportunities to reduce LCOE are found in the design and manufacturing of 

larger rotors, reduction of the impact of wakes, and improvement in major component 
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reliability. Additionally, improved design standards and methods will allow for further 

optimization of turbine design. 

Table 6. Innovations in Turbine Technology To Reduce LCOE

Innovation Category Innovation Innovation LCOE Impact

Larger Rotors Blades optimized for structure and 

manufacturing over aerodynamic 

performance, where aerodynamic add-ons are

installed after the fact to regain lost 

performance

• Decreased CapEx through 

lower manufacturing costs

• Decreased OpEx through 

higher manufacturing quality 

and reliability

Low specific power, high capacity factor rotors • Increased AEP through 

higher energy capture in low 

and moderate winds

Ultraflexible rotors with active aerocontrol • Decreased CapEx through 

loads reduction on the rest 

of the machine

Larger wind turbines • Decreased CapEx through 

economies of scale

Airfoil design for high Reynolds numbers • Greater AEP through 

increased efficiency

Turbine- and plant-level flow sensing and 

design impacts (wind awareness)

• Decreased OpEx through 

increased reliability

Innovative materials (low-cost carbon) with 

highly tailored properties

• Decreased CapEx through 

loads reduction on the rest 

of the machine

High-torque drivetrains • Decreased OpEx through 

higher reliability

Modular hybrid and taller towers • Increased AEP through 

higher wind resource

Extreme load mitigation • Decreased CapEx through 

loads reduction

Distributed and redundant sensing and 

controls

• Decreased OpEx by 

anticipating need for 

maintenance

Wake Management Turbine rotor designed for wake 

dissipation/reenergization

• Decreased OpEx through 

increased reliability

• Increased AEP

Turbine rotor designed to extract energy from 

upper atmosphere

• Increased AEP through 

increased energy flow into 

plant

Turbine designed to steer wakes away from 

downwind turbines

• Increased AEP through lower 

performance losses

Turbines with Higher 

Reliability

Implement turbine (redundant) load sensing 

and health monitoring for rotors, towers, and 

other components

• Decreased OpEx through 

increased reliability

Adopting aerospace approaches that include 

inspection and damage assessment

• Decreased OpEx through 

more predictable failures

Improved materials for metallic bearing 

surfaces

• Decreased OpEx through 

increased reliability
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Innovation Category Innovation Innovation LCOE Impact

Improved Design 

Methods and Standards

Improved test methods for turbine 

components that more accurately simulate 

operational loading

• Decreased CapEx through 

optimized design

• Decreased OpEx through 

increased reliability

Probabilistic design (integrated into standards 

also) and increased understanding of 

uncertainties (for reduced safety factors)

• Decreased OpEx through 

increased reliability

• Decreased CapEx through 

optimized design

Improved aeroelastic and component models 

with lower uncertainty in loads and 

materials/manufacturing variability 

• Decreased CapEx and OpEx

through optimized, reliable 

designs

Turbines designed and operated for 50-year 

lifetimes

• Decreased finance costs 

through longer turbine 

lifetimes

Innovative turbine design approaches with 

unique site-specific criteria

• Increased AEP through sitespecific optimized design

• Decreased CapEx through 

site-optimized designs

• Decreased OpEx through 

more accurate site-suitability 

assessment

The industry trend over the past few decades has been toward larger rotors (Wiser and 

Bolinger 2018). As described in Section 1, there are many reasons for the upscaling. 

Generally, an increase in blade length has a squared-factor effect in the power that can be 

captured from a given wind turbine (available power is a function of the rotor swept area), 

thereby increasing the annual energy production (AEP) produced by a turbine (Manwell et al.

2010). In addition, larger rotors and towers reach higher heights with better wind resources,

and because power of the wind is related to the cube of the wind speed, this results in 

substantial increases in energy production at a given location. 

In addition, as deployment of wind energy across the world becomes more substantial, the 

availability of the most desirable sites is becoming more limited. It is therefore necessary to 

deploy wind turbines in areas with lower wind speeds. Further, increasing the size of the 

rotor relative to the power rating increases the power capture at lower wind speeds. Turbines

with larger rotors can capture more energy and make lower wind speed sites more 

economically attractive. Large rotor turbines with a lower specific power (rated power 

divided by swept area) also offer a higher capacity factor that not only increases AEP, but 

also increases the capacity value that the plant has to the electric grid. 

One of the challenges associated with larger blades is the acoustic emissions. However, for 

offshore applications, acoustic noise from blades may not be an issue. For large-scale, landbased machines, it is often an active design constraint that limits the maximum allowable 

blade tip speed. Acoustic emissions scale with tip speed to the fifth power, so even a small 

increase in tip speed has a large impact on acoustic emissions from the blade (Moriarty and 

Migliore 2003). To keep acoustic emissions within acceptable levels, it is typically necessary 

to operate the rotor at a suboptimal rotational speed. Doing so increases the torque in the 

drivetrain, which then drives up drivetrain mass. Therefore, innovations in medium- and low-
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speed drivetrains will offer concurrent design options to limit system weight and capital cost. 

If tip speed could increase, this would imply an increase in the Reynolds number for the 

airfoils. There are limited data on the aerodynamic performance of wind turbine airfoils at 

higher Reynolds numbers, yet the data that are available suggest an improvement in overall 

aerodynamic performance (Pires et al. 2016). Still, more research in this area is needed.

So far wind turbine blades have avoided the square/cubed law that governs blade volume and 

consequently mass as blade length increases. Current control technology, materials 

engineering, and manufacturing have enabled blades to grow in length with much less than a 

cubic increase in mass, thereby overcoming the square/cube law to the point where it is still 

economical to increase the length of the blade to reap the benefits of increased power 

produced (Fonseca 2017). Advances in turbine control, active aerodynamic control (Barlas et 

al. 2016), and remote sensing of the wind power plant flow environment may enable future 

blades to be even longer, lighter, and reliable while still adhering to the safety standards

required by industry. In response to larger rotors, taller towers will also become necessary 

(Dykes et al. 2018). Research is needed in this area to enable tall towers that conform to 

transportation and other logistical constraints as will be discussed in Section 3.3. Increasing 

the overall power rating of these machines will also take advantage of economies of scale, 

using fewer individual machines for a desired total power plant output. There are significant 

balance-of-system savings and potential O&M reductions in a plant with fewer turbines, such 

as the reduction in the number of foundations, material usage for cables and roads, and more.

A key consideration for turbine design is how individual turbine properties can be tailored 

such that the interactions with each other within a plant will result in optimum net energy 

production for the site (after the losses from interturbine interaction are considered). By 

coordinating and designing turbines based on their operation within a plant, future wind 

power plants will generate the maximum possible power for a given inflow condition at the 

plant and not at the individual turbine level. This approach will likely affect how each turbine 

within a plant is designed. For example, it may be beneficial to the overall wind power plant 

system value to not extract the maximum available wind power for every individual turbine, 

but instead for the upwind turbines to allow some wind energy to pass through to the

downwind turbines (Bossanyi 2018). This strategy may result in design of a low induction 

rotor that maximizes power generation for the wind plant. Another strategy may be to design 

turbines with a rotor tilt in such a way that allows for entrainment of flow from above the 

turbines (Annoni et al. 2017). This would reduce the effective swept area of the tilted turbine,

but it would encourage mixing of more energetic flow from above the wind power plant that 

would expedite reenergization of the wake deficit and thereby have a beneficial effect at the 

wind plant level. Future designs of wind turbine technology wherein the actual machine 

design is optimized for the full plant performance and cost will continue to drive down 

LCOE.

Turbine reliability remains a high-priority research area as more turbines are installed, and 

operational expenses will continue to be a driver for LCOE (U.S. Department of Energy

2015). For example, drivetrain bearings have been plagued by early failures because of axial 

cracking. This phenomenon is caused by a material failure, known as white etching area 

cracking, driven by a damage process that is not currently included in the design 

specification of wind turbines. This area requires further fundamental research to both 
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uncover the fundamental process, design to mitigate against it, and change the design criteria 

to make the failure mode rare. This is just one example of the continuous need to define, 

research, and solve issues that cause endemic reliability problems as they emerge so that 

super-reliable systems can be designed, manufactured, and deployed.

Another opportunity to improve reliability is a novel approach to the monitoring of critical 

components based on using a larger number of sensors and transducers—a classical approach 

in the monitoring of conventional power plants, and not typical in the wind sector. As 

methods for big data analysis and condition monitoring (Márquez et al. 2012) mature, they 

will have a dramatic impact on turbine monitoring and may lead to lower uncertainty in the 

operational environment, thus allowing for more efficient and robust designs. The fusion of 

the existing massive data sets, which are not accessible on a real-time basis, offers some 

near-term opportunities for enhancing both performance and reliability. Condition 

monitoring within the turbines and inflow sensors around the plant, used in conjunction with 

advanced data assimilation and machine-learning innovations, can open the door to 

significant improvements in plant operations that increase revenue and reduce costs. 

Design methodologies and standards must evolve to keep pace with changing regulatory and 

operational environments. Deterministic design approaches have been very successful in 

developing current multimegawatt wind turbines, but as power purchase markets evolve and 

industry subsidies expire, more emphasis will be placed on developing the most reliable and 

cost-effective technology available. As such, understanding uncertainties in the design 

process and moving toward a probabilistic design philosophy (Sørensen and Toft 2010) will 

be critical to developing the most efficient and reliable wind turbines of the future. By 

understanding these uncertainties, designers can safely move away from padding designs 

with safety factors that may add unnecessary mass, and therefore, cost to the system. This 

shift in design approach represents a fundamental alternative to the classic approaches to 

structural design embedded in the current design standard. New standards based on this 

approach will need to be developed.

A key caveat to much of the discussion is that there is often a trade-off between many of the 

design objectives that make up LCOE including the energy production, balance-of-system 

costs, and operational expenditures. For instance, an upstream design innovation that reduces 

capital expenditures may lower energy production and increase operational expenditures or 

vice versa. For instance, blade segmentation that allows the transportation of larger blades to 

a given site may allow for increased energy production, but the segment itself will introduce 

additional costs in terms of materials and manufacturing, on-site assembly of the jointed 

blade, and potential long-term issues for reliability and maintenance costs. A full system 

design perspective is necessary to evaluate technologies to appropriately assess their impacts 

on the LCOE (Dykes et al. 2011). 

Innovations To Increase System Value

As the share of wind and solar into the electrical grid increases, there will be greater need for 

wind turbines to replace the grid services currently offered by synchronous, dispatchable 

conventional power plants. Wind power plants have the potential to contribute greatly to grid 

reliability through the design of machines with a higher capacity factor, turbines designed to 
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allow for advanced grid service controllers, and improved operational strategies. 

Additionally, advances in turbine design that allow for deployment in a more diverse set of 

environmental and geophysical conditions will allow for more flexibility in plant location, 

reducing grid congestion and overall output variability. Finally, an opportunity exists for 

power plants to produce fuel and other outputs (Power to X), which would enable storage or 

use of energy for other applications such as transportation. 

Table 7. Innovations in Turbine Technology To Increase System Value

Innovation Category Innovation System Value Impact

Higher Capacity Factor Longer rotor blades for given rated power • Increased capacity value

• Increased energy value

• Ancillary service support

• Reduced variability/ 

uncertainty of supply

Low-Wind-Speed 

Turbines

Turbines that are deployable across larger 

geographic regions for reduced 

variability/uncertainty and ability to 

produce power at lower wind speeds

• Reduced variability/ 

uncertainty of supply

Optimized Operational 

Strategy

Develop detailed understanding of real-time 

turbine operational cost and revenue 

production

• Reduced variability/ 

uncertainty of supply

Design for Grid Services Integrate wind turbine with energy storage • Ancillary service support

• Increased capacity value

• Increased energy value

• Reduced variability/ 

uncertainty of supply

Controls for active and reactive power • Ancillary service support

Synthetic inertia • Ancillary service support

Alternative Plant-Side 

Energy Usage

Fuel production • Electrification support

Water pumping/desalinization • Reduced curtailment impacts

Higher-capacity-factor wind turbines and wind power plants offer several advantages, and 

there has been a clear industry trend toward such machines over the past decade (Wiser and 

Bolinger 2018). Larger rotors relative to rated power are the main avenue to achieving highcapacity-factor wind turbines. These machines are ideal for lower-wind-class sites to allow 

for operation at rated more of the time, but there are also advantages to the overall grid 

system when there is less variability in power production for large wind plants. A reduction 

in power production uncertainty will also reduce the cost of project financing. Further, 

turbines designed for low-wind-speed sites (often with larger rotors) will allow for wind 

plants to be better distributed geographically, which generates lower variability in overall 

power production and reduces congestion on power lines. 

For every unit of energy produced by wind turbines, there is an implicit trade-off of the 

revenue from that unit of energy and the cost of operating the turbine (largely in the loading 

and damage accumulation to the machine over time). Optimizing long-term operational 

strategies of wind turbines will allow for balancing turbine revenue and cost in the most 
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profitable way possible. In turn, this optimization will require a detailed understanding of the 

turbine operation over its lifetime and accurate predictions of the health of various 

components and ability to accurately predict when failures may occur. 

As more and more renewable energy services are integrated into the grid, wind turbines and 

power plants can provide services for reliability and stability to the grid. Wind turbines have 

physical inertia in the spinning rotor. The energy stored in this rotor inertia is accessible 

almost instantaneously to support a sudden grid fault, such as a major substation trip or loss 

of generation. If the wind power plant is coordinated to control all its rotational inertia assets, 

it can act in rapid response to feed the front end of a critical fault until other, slower assets 

can be brought on line to stabilize the grid. Generally, wind turbines with type 3 or type 4 

drivetrains (those with power converters) can provide several services to the grid including 

inertia, active and reactive power control, and fault ride-through capability (Kroposki et al.

2017).

Finally, wind plants can use energy on-site or at strategically located points on the 

transmission system for other purposes, such as fuel production and water processing, thus 

avoiding complete shutdown during curtailment periods. Additionally, wind plants in some 

areas can be used for water pumping and desalination.

R&D Challenges To Realize Innovations

For wind energy to continue to be a viable source of electricity at the scales envisioned, two 

grand R&D challenges have been identified as critical to unlocking the potential innovations 

highlighted in this document.

Table 8. Turbine Technology R&D Grand Challenges

R&D Grand Challenges Specific R&D Activities

Grand Challenge 1: Complete Understanding of 

Physics for Design and Operation of Very LargeScale Machines

Validation and verification of the simulation and 

design tools including new instrumentation, open 

data access to the research community, and “big 

science” grand challenges

Assess the inflow characteristics across very large 

rotor diameters, such as, veer, shear, threedimensional distribution turbulence, and wake 

inflow coupling

Grand Challenge 2: Physical Understanding of the 

Boundary Layer Physics - Multiscale 

Characterization Driving Turbine Performance and 

Loads

Assess impacts and potential for two-way coupling 

of physics from the turbine and the flow and effects 

on the boundary layer inflow

Assess wake dynamics (generation, meandering, 

dissipation) and subsequent array interaction 

effects

The first grand challenge is to improve the accuracy and reduce the uncertainty of the current 

design tools, as well as inflow characterization. This has a lot of complementarity with the 

challenges as discovered in the atmospheric science and forecasting breakout group as will 

be discussed in Section 3.2. There are still significant gaps in our knowledge around wind 

turbine and plant physics—especially as we go to larger machines and applications in 
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complex terrain or offshore (Hills et al. 2015; Womble et al. 2015; Damiani 2018).

Aeroelastic tools based on blade element momentum (BEM) theory have been used to design 

almost every turbine in the commercial market to date, including very large wind turbines;

however, there are limitations to the accuracy of BEM theory. For example, highly flexible 

blades have large deflections out of the plane of rotation, which breaks some of the 

fundamental assumptions of the method. Floating offshore systems with moving 

substructures will produce motions of the rotor that are not easily modeled in BEM theory. 

Momentum balance methods are also unable to directly resolve highly three-dimensional 

(3D) flows near the hub, which are increasingly important in understanding the wakes that 

impinge on downstream turbines. These issues will become increasingly more pronounced as 

the size of rotors continues to grow larger. Enhanced modeling techniques that resolve the 

actual geometry of the blades in a flow-field computation will be needed to compute and 

understand the behavior of these flexible, moving systems and their overall dynamics. Design 

tools that accurately capture the resulting loads at all times (and under all relevant 

atmospheric conditions) will also need to be derived from high-fidelity models to assess the 

adequacy of advanced, enlarged rotors for extreme loads and operating fatigue. 

A common practice to handle these limitations is to design blades and other components with 

greater-than-optimal factors of safety and to avoid design alternatives that are too far from 

the current configurations wherein there is institutional knowledge of what works and what 

does not. This approach leads to components that are costlier and heavier, leading to overall 

adverse system impacts, as well as stifled innovation. To improve the design process, it is 

necessary to fully validate and verify the simulation and design tools and understand the 

uncertainties associated with each model. A robust validation and verification process would 

require investment in new instruments capable of producing high-resolution experimental 

data (Hills et al. 2015). This challenge is so big that no institution can solve it on their own 

and will require a high degree of transparency and knowledge sharing among stakeholders. 

The use of open-source tools should be encouraged as it will facilitate the process.

A second and somewhat related grand challenge focuses more specifically on the interaction 

of wind turbines and wind plants with environmental conditions. Researchers have developed 

mesoscale models capable of describing atmospheric phenomena on a large spatial and 

temporal domain, and microscale models capable of describing atmospheric phenomena on a 

smaller spatial and temporal scale. Currently, there is a lack of understanding and modeling 

capability to accurately represent the interactions between these two modeling techniques. 

Developing an understanding of how these models interact with each other is important to 

reducing uncertainty in the physical processes driving turbine performance in the local 

environment. Computational models that bring the characteristics of the mesoscale flows 

down to the scale of the wind plant and compute the impact of local flow features on the 

wind plant operating production and turbine loads will have to be created (Womble et al.

2015). The needed detail resolution of the flow field and its impact on design criteria can 

only be observed from computational models. Measurement systems are still inadequate for 

observing the fine scale of turbulence, wakes, and other local effects important in generating 

the loads on these highly dynamic flexible machines. An especially important process to 

understand is that of wake generation and evolution through the wind plant. This cannot be 

modeled with a high degree of accuracy and low degree of uncertainty without this approach. 
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Solving this grand challenge will give engineers the appropriate tools to design optimal wind 

turbines and wind plants in the future and will allow for more efficient and reliable operation 

of wind plants. The topic of understanding the physics and creating validated modeling 

capability across all relevant spatial and temporal scales is a key focus of breakout group 3 

and will be discussed in even more detail in the following subsection on atmospheric science 

and forecasting.

3.3 Breakout Group 3: Manufacturing and Industrialization

As discussed, increasing turbine sizes over the last several decades has resulted in significant

economies of scale and lower cost of energy for wind power plants. The sizes of land-based 

machines that are the state of the art today have blades exceeding 70 m, with hub heights 

reaching toward 200 m, for total tip heights of 250 m or more, and have generator ratings of 

4 MW or greater (and offshore wind turbines are becoming even larger). The sheer size of 

major components for these machines presents new challenges for their manufacture, 

transport, and installation on-site. For the last few decades, the technologies and processes 

for manufacturing the largest components, such as wind turbine blades and towers, have

remained relatively unchanged—utilizing low-cost manufacturing processes and materials, as 

well as manual labor practices. In addition, advances in component design and manufacturing 

have been constrained significantly by transportation limits. For example, 4.3 m is a common 

maximum allowable chord dimension for traditional transport of a land-based turbine to 

avoid overhead obstructions on roads. The maximum allowable blade root diameter is only 

slightly above that value. The maximum diameter of most commercial wind turbine towers 

for land-based wind turbines, as well as the maximum chord of their blades, are still 

constrained by transportation limits on roads and rail (Cotrell et al. 2014). In addition, as 

land-based turbines reach higher heights and components are increasingly heavier, the 

industry will no longer be able to use traditional installation practices that have relied on 

cranes commonly available for other industrial applications. For offshore wind energy, 

specialized installation vessels and equipment have been introduced and their availability is 

one factor that has contributed to significant reductions in LCOE for offshore applications in 

Europe (Musial et al. 2017). 

Conventional manufacturing, transportation, and installation approaches have worked well 

enough over the past 20 years to support the overall system reductions in LCOE. However,

with the continued need for even lower LCOE as well as the desire to expand into new 

markets where turbines must reach even higher heights to access good wind resources, the 

demand for ever-larger wind turbine components is causing a rethinking of these approaches. 

This dynamic creates an opportunity to revisit the product development cycle with a focus on 

how investment in manufacturing, industrialization, and transportation and logistics 

innovations can enable future reductions in LCOE. 

The recent emergence of the fourth industrial revolution, or “Industry 4.0” (Cotteleer and 

Sniderman 2017), may promise technologies that can realize new levels of efficiency and 

cost reduction in manufacturing. These technologies may include advances in areas such as

additive manufacturing, advanced materials, advanced sensors, advanced automation, 

digitalization and digital twins, artificial intelligence, and high-performance computing. 

Combining extreme-scale sizes of wind turbine components with advances in the areas 
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mentioned earlier positions the global wind industry to innovate on the product development 

cycle and exploit R&D pathways in manufacturing and industrialization on its quest to 

continuing to lower LCOE.

Innovations To Reduce LCOE

For land-based wind turbine components, one pathway that might relax the design constraints 

imposed by transportation size limits is manufacturing of large components at the sites where 

wind power plants will be installed. In addition, potential innovation in the areas of advanced 

materials, additive manufacturing, standardization, and quality control have been identified 

that may enable the production of very large components including wind turbine blades in 

both traditional and on-site production facilities. These innovations are presented in Table 9

and discussed in more depth in the text that follows.

Table 9. Innovations in Manufacturing and Industrialization To Reduce LCOE

Innovation Category Innovation LCOE Impact

Additive Manufacturing Three-dimensional (3D) printed 

molds and tooling for blades, 

nacelles, and other components

• Reduces CapEx through lower-cost 

molds and tooling

• Reduces time to market through 

rapid printing of molds and tooling

3D-printed components (i.e.,

blades, towers, hubs, nacelle 

canopy, generators, and other 

drivetrain components) 

including power electronics

• Reduces CapEx by eliminating 

molds and tools

• Increases AEP through advanced 

wind blade geometries and better 

drivetrain efficiencies

• Reduces CapEx through lighter 

weight and subsequent reduction 

in structure

Manufacturing

Automation

Wind turbine blade molding 

automation

• Improves blade quality, which

increases field reliability, thus 

decreasing downtime and 

maintenance cost —leading to 

overall reduction in AEP

Wind turbine blade finishing 

automation

• Reduces labor costs for blade 

manufacturing

• Decreases overall required floor 

space, thereby decreasing CapEx

Wind turbine tower automation • Decreases labor costs for tower 

manufacturing

Advanced Materials Low-cost carbon fiber and other 

advanced materials for wind 

turbine blades

• Reduces CapEx and OpEx with 

system-level improvements 

• Increases AEP via longer, lighter 

weight blades

Advanced, 3D-printed and 

manufacturable materials for 

other wind turbine components

• Reduces CapEx and OpEx with 

system-level improvements 

• Increases recyclability

Machine Learning/Advanced 

Modeling

Advanced design optimization 

using high-performance 

computing, advanced 

• Reduces CapEx and OpEx with 

system-level improvements

• Increases recyclability
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Innovation Category Innovation LCOE Impact

algorithms, design for advanced 

manufacturing, and artificial 

intelligence

• Increases overall AEP

Industrialization and 

Standardization

Standardized or commercial offthe-shelf support structure

solutions for offshore wind

• Reduces CapEx

• Increases reliability of components

Standardization of supply chain • Reduces CapEx

Quality Control and 

Improvement

Reduction in scaling-related 

defects and nonconformance

• Reduces OpEx through less 

nonconformance and greater 

reliability

On-Site Manufacturing and/or 

Assembly

Segmented blades • Increases AEP by enabling larger 

rotors

• Decreases transportation costs

Thermoplastic composite blade 

structures with thermally 

welded joints

(also applies to advanced 

materials)

• Increases AEP by enabling larger 

rotors

• Decreases OpEx through reduced 

repair and replacement costs

On-site manufacture of large 

components (potentially 

including 3D printing, 

automation, and robotics)

• Increases AEP by enabling larger 

rotors 

• Decreases transportation costs

Technologies to enable landbased turbines of 10 MW or 

greater

• Increases AEP by enabling larger 

rotors and power ratings

Wind Turbine Blades

One of the emerging areas of innovation that could be deployed in the manufacturing of wind 

turbine blades is additive manufacturing. This technology could be utilized in two disparate 

areas: the production of tooling and the manufacturing of actual wind turbine blades and their 

subcomponents. As discussed in Section 1, innovation in blade design has been a key driver 

of performance improvement and cost reduction for the overall system. Still, manufacturing 

for blades has not realized revolutionary changes in process and equipment in the last few 

decades. On the equipment side, blade tooling, including molds and fixtures for wind turbine 

blade components such as high-pressure and low-pressure skins, root inserts, spar caps, 

trailing-edge stiffeners, and shear webs, is currently developed and produced using 

traditional manufacturing methods once a blade geometry is defined and before production 

begins. Future wind turbine mold production may be accomplished using additive 

manufacturing, or 3D printing (Post et al. 2017b). This advancement could reduce the time 

and overall cost of tool production and increase the efficiency of tool operation. 

Traditionally, tooling development and production consumes a large amount of time, often 

from 6 months to a year, and limits the speed to which new products can be introduced to 

market. To commercialize new turbine models in a timelier fashion, advances in the speed of 

3D printing could be used to shorten the time to print tooling. 
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Additive manufacturing could also enable new tooling features specially adapted to the needs 

of very large blade production (Post et al. 2017a). For example, current fiberglass/carbonfiber epoxy blades require heated molds to cure the resin and adhesive systems once the 

blade components have been infused and assembled. The common mold heating approach of 

built-in resistive heating elements, which are typically high in cost and take a long time to 

construct, could be replaced with discrete and distributed built-in heating and cooling 

cavities. An early development of additive manufacturing for wind turbine blade tooling has 

been achieved through 3D printing of a blade mold (Post el al. 2017b). Future innovation in 

3D printing could also lead to the direct use of additive manufacturing to produce actual 

wind blade components—or even full wind turbine blades—thus eliminating the need for 

blade tooling entirely. 

Targeted automation is another area of manufacturing innovation for both on-site and 

traditional factories. Particularly for wind turbine blades, their production has long been a 

labor-intensive process. Although there have been a few successes, such as in-root cutting, 

facing, and drilling, most attempts to automate labor steps have been unsuccessful. 

Opportunities to reduce costs must be identified in terms of understanding cost drivers in 

blade production as well as the potential for advanced manufacturing solutions, including 

automation, to reduce these costs. Examples of research that target use of automation in 

manufacturing include new methods of placing core material and other composite materials 

in an automated setting as well as automation of thermal welding processes to bond the wind 

turbine blade components together without adhesives. 

Another area of innovation for manufacturing and industrialization is the development and 

deployment of advanced materials in wind turbine components. While the discussion in the 

following paragraph emphasizes materials used in blade manufacturing, innovative materials 

in several areas would also benefit the design and manufacture of other major components,

such as towers and generators.

For transforming wind turbine blade production, two advanced material systems have been 

identified. The first of these materials is low-cost carbon fiber, including the potential use of 

low-cost textile-based carbon fiber (Oak Ridge National Laboratory 2010). Carbon fiber has 

long been utilized selectively in the design and production of wind turbine blades as they 

have increased in length over the past 20 years. However, by lowering the raw material cost 

of this high-strength/stiffness-to-weight ratio material, low-cost carbon fiber could be more 

broadly deployed in the design and manufacturing of wind turbine blades up to and beyond 

100 m in length. Whether used in pultruded (fabrication of a composite by drawing resincoated glass fibers through a heated die) spar caps, infused trailing-edge stiffeners, or other 

highly structural components of wind turbine blades, low-cost carbon fiber could further 

reduce LCOE. This larger tow, lower-cost carbon fiber has the potential to be optimized for 

performance in carrying the loads specifically related to the durability and survival of wind 

turbine blades. If successful, the development of this advanced material will transform how 

wind turbine blades are designed and produced.

A second material that could revolutionize the way that wind turbine blades are 

manufactured is a specialized in-situ polymerized thermoplastic resin system. Currently, 
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almost all megawatt-scale wind turbine blades are manufactured using traditional thermoset 

resin systems, such as epoxy, polyester, or vinyl ester resins. Although traditional 

thermoplastic resin systems have been evaluated in the past for use in wind turbine blade 

production, such drawbacks as very high temperature exotherm and elevated moisture 

sensitivity have prevented the adoption of thermoplastics in blade manufacturing. With the 

recent development of a two-part acrylic-based reactive thermoplastic resin system, including 

the successful demonstration of manufacturing a 9-m thermoplastic blade (Murray et al.

2017), thermoplastic resins have been gaining interest as a replacement for thermosets in 

wind turbine blades because of their room temperature cure, recyclability (Cousins et al.

2018), and decreased cycle times, which could lead to lower manufacturing costs (Bersee and 

Noi 2016; Murray et al. 2018; Wiser and Bolinger 2016).

Thermoplastics typically do not require a heated mold for cure and do not require a separate 

step in an oven for postcure, which would eliminate the need for some plant tooling and save 

substantial factory floor space. In addition, they could eliminate adhesives used to bond blade 

subcomponents. These adhesive joints, which are often the area of wind turbine blades that 

fail prematurely, could be replaced by a form of thermal welding (Stavrov and Bersee 2005). 

This fusing of thermoplastic components together, either in a traditional factory or in the 

field, may result in more robust and reliable wind turbine blade joints. 

Thermoplastic resin systems create the possibility of blades being widely recycled at the end 

of their lifetime (Cousins et al. 2018). The inability of current thermoset resin systems to be 

efficiently broken down and reused leads to most blades being disposed of in landfills after 

their useful life on a wind turbine (Larsen 2009; Ramirez-Tejeda et al. 2017).

Thermoplastics, however, can be reheated, recycled, and reused in future composite 

structures, whether for wind turbine blades or other industrial parts. The ability to recycle 

wind turbine blades will become more important as the rate of decommissioning and 

replacement of wind turbines increases. With increasing deployment of wind energy, the 

resulting composite waste from wind turbine blades alone could reach millions of metric tons 

per year by 2050 (Liu and Barlow 2017) unless recycled as in-fill, inert material, or other 

secondary uses of decommissioned blades. For those blades that are not recycled into 

secondary products, the adoption of thermoplastic resin systems in the design and production 

of wind turbine blades could transform the way that blades are recycled and avoid the 

deposition of millions of tons of composite waste in landfills.

Workshop attendees also identified on-site manufacturing as a leading enabler of the larger 

rotors needed to reduce LCOE as it avoids the transportation barriers altogether. Currently, 

transportation barriers are limiting the design dimensions of critical elements, such as blade 

chords and tower bases, resulting in suboptimal component designs and weights that are 

much greater than they would be with on-site manufactured dimensioning. The 

manufacturing capabilities that would allow production of wind turbine blades, towers, and 

other components directly at the location of planned wind power plants is still a research 

topic. However, to be economically viable, on-site manufacturing would require innovation 

in many of the technologies already mentioned earlier, such as 3D-printed tooling and 

advanced thermoplastic resin systems. For example, the ability to quickly and economically 

print blade tooling and molds on-site will eliminate the requirement to produce tooling at a 
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separate factory and then ship these tools in segments to the on-site location. This ability will

provide significant savings in the cost of the tooling and will also decrease the time required 

to build tooling. The use of thermoplastic resin systems in an on-site manufacturing 

environment can also have a positive impact on production by eliminating the need of heated 

tooling and postcure ovens. An in-situ thermoplastic resin system could also decrease the 

cycle time through the fast infusion of thick composite laminate resulting from the relatively 

low viscosity of that resin. The ultimate success of wind turbine blades and towers 

manufactured on-site will stem from an entire rethinking of both the design and 

manufacturing of these large composite (blades) and metallic (tower) structures. On-site 

manufacturing will be successful if a newly designed blade or tower, free from the 

constraints of transportation limitations, can save enough weight through the reduction in the 

overall bill of material, reduce turbine system loads with this decreased weight, and decrease 

overall labor and cycle times as mentioned earlier.

Finally, the reduction of LCOE through innovation in wind turbine blade, tower, and other 

component production requires the use of advanced nondestructive evaluation methods 

implemented upstream in the fabrication process to produce defect-free components with 

increased lifetime reliability in the field (Sutherland et al. 1994; Adams et al. 2011; Zwink 

2012). Most often in current wind turbine blade production, nondestructive evaluation is used 

to inspect, validate, and, if required, define repairs to already manufactured blades before 

they leave the factory. An example of this is the use of ultrasonic nondestructive evaluation

systems to inspect internal blade adhesive joints. However, innovation in nondestructive 

evaluation technology and deployment earlier in the manufacturing process will enable the 

production of better-quality component structures. 

Hub, Drivetrain, and Other Nacelle Components

As wind turbines get larger, innovations in rotor designs, drivetrain components, towers, and

substructures are equally critical as they have cascading effects. As weight and size are key 

cost drivers, turbine subsystem costs including materials and manufacturing for nacelle and 

drivetrain power systems can significantly add to the CapEx. Given the level of upscaling 

anticipated in the coming years and recent advances in metal additive manufacturing, the 

design and manufacturing processes for critical components such as the hub, nacelle, and 

drivetrain components may undergo a radical shift as there exists a great opportunity through 

better, low-cost materials and high-volume production, while ensuring the reliability is 

maintained. As much as being a great enabler for large-scale components, considering the 

costs, additive manufacturing is expected to bring in greater value for creating highly 

complex drivetrain components with added functionality. 

Additive manufacturing technologies have already entered the powertrain chain in the 

automobile industry for driveline components (Du 2019) and sealing and thermal 

management (Giffi et al. 2014). However, decisions about its transferability to wind power 

conversion is highly dependent on scalability in manufacturing, materials, costs, and the 

ability to address key performance drivers, such as weight reduction, added functionality, and 

increased strength. A variety of metals and alloys including titanium, aluminum, and steel are 

printable both directly and indirectly. In terms of direct metal printing, both electron beam 
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melting and direct metal laser sintering/melting techniques are popular, use a powder bed,

and are trending toward large-scale metal printing with a customizable and scalable build 

volume designed for use with multiple materials, including nonreactive and reactive. Several 

iron-based alloys are currently available on the market with well-developed process 

parameters and postprocessing solutions, including steel. 

Along with optimum processing parameters and posttreatment, the resulting additive 

manufacturing material mechanical properties are comparable or even better than the 

conventional production methods. However, additive manufacturing processes with various 

type of machines are presently far from being completely developed to manufacture the 

controlled-microstructure materials for some metals. It is safe to assume that direct metal 

printing has limitations in terms of usable materials and manufacturing precision in strict 

tolerance design situations. At the same time, the mechanical properties, stability, and fatigue 

performance of printed parts remain to be established as the components become bigger. 

Because of these challenges, few companies are considering indirect additive manufacturing 

as a feasible option to produce large, complicated geometries. Methods have advanced to 

make structural steel joints that will significantly reduce the time and cost needed to make 

complex nodes in tensile structures (Niche 2017). More recently, printing sand molds for use 

in a traditional metal casting process is gaining popularity. Build volumes of up to 4.0 m by 

2.0 m by1.0 m are now possible commercially (Voxeljet website). Using a printed sand mold, 

metal nodes can be produced as any other metal cast as a certified material. To be able to 

fully explore these opportunities, considering the complexity of metal additive 

manufacturing, all optimization domains should be integrated during the early design phase. 

This involves accurate materials selection, technology parameters optimization, 3D 

modeling, and finally part postprocessing. Yet, it is believed that layered technology has not 

proven to produce parts that can take a lot of torque or tension, but as advanced metal 

additive processes are being developed very quickly, the potential to disrupt the normal flow 

of gear manufacturing is a possibility (American Gear Manufacturers Association 2019),

although key challenges remain in demonstrating the requirements of material hardness, 

fatigue strength, surface finishes, and tolerances for components that are subjected to inservice wear and complex failure mechanisms. 

Three opportunities with metal additive manufacturing are already being considered in the 

wind industry with regards to drivetrain subsystems reliability and efficiency: integrating 

printed internal channels in gears for condition monitoring the structural health of gears,

making lightweight structures (Dodd 2017), and producing power electronics. NREL is 

leading the first research investigations on structural light weighting opportunities for large 

direct-drive generators and the potential for indirect metal additive manufacturing in 

realizing large-scale geometries (Hayes et al. 2018). The research identifies new 

opportunities to realize significant power densities and achieve savings in top-head mass. 

Three-dimensional-printing nacelle assembly (Post et al. 2017a) can provide new and 

improved design geometries with higher complexity and thermal management. Wide 

bandgap electrical components and additive manufacturing are already being used to 

redesign and develop higher efficiency power electronic converter systems. Additivemanufacturing-part-enabled converters have the potential to achieve up to 98% system 

efficiency, higher power densities, and specific power ratings. Considering the present costs 
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of metal additive manufacturing and printed materials, the feasibility for LCOE reduction 

would be more pronounced by implementing design for additive manufacturing methods and 

tools that can better exploit and efficiently tap into the advantages including geometric 

complexity, component integration, and light weighting.

Beyond additive manufacturing, advanced materials in electromagnetic and power electronic 

components also have the potential to improve performance and reduce system costs

(Bahmani 2016). Power electronics and generator research has been driven by the need to 

maximize efficiency at high power levels, incurring the greatest losses and need for heat 

transfer. Further, at low power levels, when the winds are low, it is vital that the system have 

the highest efficiency possible. In addition, weight of the electrical components is more 

critical for wind applications because weight reductions also have a multiplied impact on the 

cost of the supporting structural components of the turbine. Generators using DC-DC

converters with high-frequency transformers will gain weight reduction in the range of 90% 

compared to the standard transformer (Max 2009). For offshore applications, it is even more 

important to reduce volume and weight, as it can reduce the size of the offshore substation 

platforms or even eliminate them (Chatterjee et al. 2016).

 

Towers

For any type of on-site manufacturing, factory mobilization and total cost, time to 

deployment and operation, risk of project delays, required cycle times to meet other scopes of 

work on the wind farm, and intrawind farm logistics and transportation must be considered. 

Locating and acquiring usable land for a factory, civil works and foundations required for 

factory equipment, availability of utility energy for process requirements or self-generation, 

and reclamation are all considerations that may constrain the application of various on-site 

manufacturing approaches. Components must also be sized for intrawind farm transport and 

handling by cranes or trailers. Transporting 100-m-long blades (or longer) and complete 

towers is possible but terrain complexity and the cost implications of constructing site roads 

able to handle specialized loads must be considered. 

Towers for modern and near-future wind turbines are designed with transportability as one of 

the key drivers (Dykes et al. 2018). Increased energy production has been realized with 

higher hub heights that place turbines into higher-quality resource regimes as well as larger 

rotors that enable more of the wind passing by the turbine to be converted into electricity. 

Basic science R&D coupled with industry innovations have allowed hub heights and turbine 

rotors to grow and increase energy capture while simultaneously eliminating excess material, 

improving production processes, and maintaining reliability, thereby enabling this increased 

energy to be achieved at a negligible cost penalty. Specifically, advancements in turbine 

controls have significantly reduced these cost and mass penalties as turbines and hub heights 

increase in size.

However, as turbines continue to grow, the forces generated by the turbine have also

increased. This requires more material to be used for a constrained geometry, or alternatively,

changing the geometry of the tower. The constraints introduced by transportation are

allowing many alternative tower technologies to be explored and deployed to exploit the 
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ability to change the geometry outside of the transportability envelope. On-site 

manufacturing of steel and concrete towers is becoming more common to allow for the use of 

new tower geometries.

The most common approach for on-site manufacturing of tall towers is a hybrid tower 

composed of tubular steel sections and cast on site concrete lower tower sections (Lantz et al.

2017). Max Bogl has successfully deployed and produced concrete tower sections on-site 

with their mobile factory approach. Steel towers can be manufactured on-site as well. It is 

common practice in pipeline construction to construct tubular cylindrical steel sections using 

flay steel plate and rolling and welding the material on-site. Keystone Tower Systems has 

several patents and has demonstrated a similar technology but with the benefit of adding the 

capability to roll tapered conical towers, which dramatically improves the mass intensity of 

the tower compared to cylindrical towers. Lattice towers have a very low material cost, but 

much higher labor costs compared to tubular steel or concrete towers. GE recently 

demonstrated a lattice tower but has not made any commercial offerings to date. Other tower 

technologies, such as the Vestas Large Diameter Steel Tower and Siemens bolted shell are 

somewhere between a lattice structure and a conventional tubular steel tower. See Lantz et al.

(2017) for a review of different tall tower technology options.

The opportunity space for towers is constantly changing as advances in turbine controls are 

reducing cost penalties to conventional tower technologies. Automation of on-site 

manufacturing of steel or concrete may hold promise but the primary barriers that still exist

are time to production, mobilization cost, and land availability. A system optimization model 

considering the temporal aspect of farm construction is critical to understanding the 

feasibility of these on-site manufacturing technologies. 

R&D Challenges To Realize Innovations

Given the manufacturing-focused pathways to LCOE reductions described in the previous 

section, the three most important grand R&D challenges to realizing these innovations are 

advanced materials for wind turbine design and manufacturing, automation of wind turbine 

component manufacturing, and enabling on-site manufacturing of major turbine components. 

Table 10 summarizes these challenges and more detailed discussion follows.

Table 10. Manufacturing and Industrialization R&D Grand Challenges

R&D Grand Challenges Specific R&D Activities

Grand Challenge 1: Advanced 

Materials

Research new materials for the “circular economy” for energy materials,

allowing for the design, substitution, reuse, recycle, and 

remanufacturing of critical materials in the production of wind turbine 

components, including blades and towers

Continue to develop and implement low-cost carbon fiber for use in 

wind turbine blades, including use in pultruded carbon-fiber spar caps 

and preformed carbon-fiber trailing-edge and leading-edge stiffeners

Research the design and production of advanced core materials, 

including the use of additively manufactured core materials

Develop ideal material characteristics to enable advanced design and 

manufacture of wind turbine components 
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R&D Grand Challenges Specific R&D Activities

Research advanced erosion-resistant composite materials for use on the 

leading edge of wind turbine blades

Research, development, and scale-up of thermoplastic resin systems to 

reduce wind turbine blade manufacturing cycle times, reduce blade 

costs, enable thermal welding of bond lines, increase recyclability, and 

enable advanced in-field blade repair techniques

Research advanced thermoset materials that can be recovered and 

reused

Research metals and other noncomposite materials for development of 

lightweight, high-strength, and high-stiffness turbine components

Grand Challenge 2: Automation Develop comprehensive techno-economic models for automation and a 

virtual factory (or digital twin of a factory) to understand advantages 

and disadvantages of targeted automation for both wind turbine blade 

molding and finishing operations

Identify and quantify all cost inputs for wind turbine component 

manufacturing (e.g., blades, towers) to identify production steps that 

would benefit from automation

Develop automated production methods for the continuous 

manufacturing of one-piece wind turbine towers for on-site 

manufacturing

Develop advanced design tools to optimize blade and tower design with 

respect to manufacturing with automation

Research and develop automation technology able to locate complex 

wind turbine blade geometry in space to allow for effective automated 

finishing operations, such as sanding, drilling, and cutting

Develop methods to automate the delivery, placement, and inspection 

of core material into blade skin molds during the composite laminate 

skin lay-up

Integrate real-time inspection and quality control into automated 

production steps for wind turbine blades, towers, and other 

components

Develop large-scale, low-cost, high-throughput, high-performance 

automated additive manufacturing technologies (e.g., 3D printing, 

automated fiber placement and tape layup, filament winding) for the 

production of towers, blade skins, blade spars, and other turbine 

components

Develop embedded metrology and out-of-mold indexing technologies

Grand Challenge 3: On-Site 

Manufacturing

Develop robust composite materials for use in broad on-site 

environmental conditions

Research the chemistry and processing of in-situ thermoplastic resin 

systems to enable thermally welded joints in an on-site manufacturing 

environment

Develop targeted wind turbine blade finishing automation techniques 

designed to reduce the overall footprint of on-site finishing operations 

and minimize production facility floor space

Research additive manufacturing to print wind turbine blade molds and 

tooling at on-site manufacturing locations
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R&D Grand Challenges Specific R&D Activities

Develop optimized factory designs and manufacturing techno-economic 

models to identify key areas for cost savings in an on-site manufacturing 

facility

Research and develop “digital twins” for the structures produced in an 

on-site manufacturing facility to enhance structural health monitoring in 

the field and to increase reliability of structures

Research optimized blade and tower designs for on-site manufacturing 

without the constraints imposed by typical transportation logistics

Research advanced nondestructive evaluation methods to be 

implemented upstream in the production environment in on-site 

manufacturing facilities to ensure robust manufacturing methods

Develop manufacturing techniques to co-infuse precured components 

(such as pultruded carbon-fiber spar caps) into blade skins in an on-site 

manufacturing environment

Research innovative manufacturing methods to produce single-piece tall 

wind turbine towers on-site

The first grand challenge is the development of the advanced materials needed to support 

future wind turbine technologies, including lightweight, long wind turbine blades. Although 

glass-fiber-reinforced plastics, steel, and cast iron are all low-cost constituents used in the 

manufacture of modern-day wind turbine components, they have shortcomings when 

considering scaling effects and the large blades, hubs, bedplates, and towers that will be 

needed to further reduce LCOE. Material innovation will be needed in the areas of low-cost 

carbon-fiber technologies, recyclability, thermoplastic composites, high stiffness to weight 

laminate and core materials, and advanced 3D printable thermosets, thermoplastics, and 

metallics. In addition, materials for electronic components including the generators, 

transformers, and power electronics will also enable improved performance and cost 

reductions of those components. Along with these developments, new tools and techniques 

for material characterization, as well as material durability analysis techniques, such as 

advanced crack propagation tools, will need to be developed.

The second grand challenge is in the development of cost-effective automation. For wind 

turbine blades, manufacturing involves significant amounts of manual labor that will need to 

be automated in the future to reduce LCOE. The automotive and aerospace industries have 

already implemented automation in their manufacturing processes to reduce costs. However, 

these industries have different price points driving cost, cycle times, scale, materials, and 

manufacturing processes that have allowed for this development. Research is needed in the 

field of automation and robotics regarding blade manufacturing, specifically, automation that 

can prepare tooling, lay down fabric and core material, assist in infusion, apply adhesive, 

trim, cut, drill, sand, paint, balance, and inspect a typical 100-plus-m and 20,000-plus-kg

blade in a more efficient manner than human labor. There also needs to be research in 

advanced positioning and metrology tools such that the blade can be located at every step of 

the manufacturing process. All of this must be implemented at a cost consistent with a price 

point of approximately $9/kg for finished composite wind turbine blades as they leave the 

factory—orders of magnitude less than the aerospace industry. 
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The third and final grand challenge is the development of the science and technology to 

enable on-site manufacturing of large wind turbine components. An example of this for onsite manufacture and assembly is for blades that will have to compete with the cost of 

advanced logistics solutions as well as potential segmented blade costs, a detailed technoeconomic model will be needed to assess the costs and viability of competing production 

approaches. These costs, coupled with required blade cycle times and quality, will drive the 

innovation needed in automation, material science, additive manufacturing, smart factory 

design, and advanced manufacturing design tools to support on-site blade manufacturing. 

The challenge will be in aligning and leveraging these innovations in a creative fashion to 

formulate the on-site factory of the future.

3.4 Breakout Group 4: Plant Controls and Operations

In recent years, there has been a fundamental shift in research thinking from a turbine-centric 

view of wind energy to a plant-centric view. Rather than each turbine being controlled and 

operated in isolation, the overall plant is operated collectively to manage flow within the 

plant and overall energy production. This approach provides new opportunities to reducing 

future wind plant costs and enabling grid operation of the future.

Innovations To Reduce LCOE

Research into improved wind power plant controls and operations provides several avenues 

to reduce LCOE, which are summarized in Table 11. Methods that most directly reduce

LCOE raise the total output power production, either through coordinated plant control or 

improvements to electrical infrastructure reducing losses. LCOE can also be reduced through 

mitigating costs—for example, by reducing loads and thereby O&M costs or reducing O&M 

through modern inspection and fault detection techniques, or reduced capital costs through 

inclusion of wind plant control benefits at the point of design through methods such as codesign (integrated design of turbine and plant hardware with software controls). Wind power 

plant design can lead to LCOE reductions as well, in terms of better optimizing the choice of 

wind turbine sizes, wind power plant layout, and so on. While some aspects of wind plant 

design are indicated in the tables and discussion, wind plant design was not a focus of this 

breakout group in the workshop and hence the upcoming discussion from the wind plant 

design perspective is cursory.
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Table 11. Innovations in Plant Control and Operations To Improve Wind Power Plant LCOE

Innovation Category Innovation Innovation LCOE Impact

Wind Power Plant Control 

and Flow Control

Wind power plant controls 

for power maximization

• Improved AEP

Wind power plant controls 

for loads reduction

• Reduced OpEx through increased 

reliability and longer 

component/turbine lifetime

Understanding inflow 

conditions and acting on 

information

• Improved AEP

• Reduced OpEx through advanced 

knowledge of loading conditions

Design for Control and CoDesignDesign for control and codesign, wind plant design 

(e.g., class 3 machines on 

class 1 sites)

• Improved AEP while keeping OpEx

approximately the same

Deployment of Sensors 

for Measuring and 

Providing Real-Time FullField Flow Estimates

Increasing the sensing of the 

flow and developing 

estimation algorithms to 

provide real-time full-field 

flow estimates are expected 

to drastically improve wind 

plant control; they will also 

provide big data for structural 

health monitoring machine 

learning and optimization

• Improved AEP 

• Reduced OpEx through advanced 

knowledge of loading conditions

Big Data, Algorithms, and 

Deployments of Robots

Big data for structural health 

monitoring, machine 

learning, optimization, and 

predictive failure

• Reduced OpEx through increased 

reliability

Real-time machine learning, 

adapting controls

• Reduced OpEx through better prediction 

of component health

• Increased AEP by supporting a plantlevel control strategy 

Drones/robotic O&M 

innovation (e.g., drones, 

vessels, access, and virtual 

reality) in-field repair

• Reduced OpEx through increased 

reliability and longer 

component/turbine lifetime

Wind power plant controls is a field of research in which the control actions of individual 

turbines are coordinated to improve the global wind power plant performance (Knudson et al.

2015; Boersma et al. 2017). Any level of coordinated control at the wind power plant level 

will require that information to be shared among turbines. Such information sharing and 

coordinated control can then be used to improve performance, or turbines can take control 

actions expecting to benefit other turbines.

One example of wind power plant control is wake steering, in which upwind turbines—that

are waking downstream turbines—implement an offset angle to the wind to cause a 

redirection of their wake to improve the power capture downstream (Bastankah and PorteAgel 2016; Gebraad et al. 2014; Dar et al. 2016; van Dijk et al. 2017; Fleming et al. 2014, 

2016; Campagnolo et al. 2016; Howland et al. 2016). This method can improve overall 
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power capture of existing wind power plants as well as to-be-installed farms. Other forms of 

wind power plant control include stationary and dynamic induction control (Munters and 

Meyers 2018; Annoni et al. 2015). In the former case, upstream turbines are derated so that 

less power is extracted from the flow, to the potential benefit of downstream machines. In the 

latter strategy, the induction is varied in a manner that promotes a faster recovery of the 

wake, which again can benefit the power capture of downstream machines.

To date, wind power plant controls research has involved open-loop set-point control, 

wherein the (e.g., yaw) set points for the individual turbines are determined at the wind 

power plant level, based on prevailing persistent wind conditions on the wind power plant. 

Demonstrations of these technologies in practice have been and are currently being explored 

in field trials (Fleming et al. 2017a, 2017b). Initial closed-loop (e.g., wake steering) control 

methods at the wind turbine level are currently being developed that will ultimately support 

closed-loop wind power plant control, and preliminary investigations of closed-loop wind 

power plant control have begun (Annoni et al. 2018; Raach et al. 2016, 2018; Campagnolo et 

al. 2016). In this case, there is feedback from a set of turbine data across the plant to improve 

the control in real time. Many avenues of research are still needed to bring the full vision of 

closed-loop wind power plant control to reality, including novel methods for sensing and 

estimating wind speeds across wind power plants.

For wind power plant control to realize its full potential, it is necessary to sufficiently sense 

the flow field on a wind power plant so that real-time estimates of the full wind power plant

flow field can be computed. These estimates will allow advanced wind power plant

controllers (such as model-predictive controllers) to optimize wind power plant performance 

metrics while ensuring that actuator and state constraints are upheld. Additional flow field 

measurements and estimates will also contribute to big data and machine-learning algorithms 

in better predicting maintenance issues.

Co-design methods attempt to capture the benefits of controls more completely, by 

synchronously designing turbines and plants along with their control algorithms. This 

bidirectional optimization can allow the benefits of control to be most effectively exploited. 

For example, the layout of a farm can be optimized by planning at the layout design phase to 

utilize wake steering (Fleming et al. 2016a). Instead of simply increasing power production, 

this allows wake steering to be used to enable higher power density and more cost-effective 

plant designs, potentially impacting LCOE in a more significant way.

As more data become available regarding wind plant operation under various atmospheric 

conditions and in different turbine operational regimes, machine-learning methods can be 

used to better predict performance and maintenance issues. Predicted problematic issues can 

then be addressed in a preventative manner, which can mitigate costs compared to addressing 

the issues after problems occur. Moreover, issues that might otherwise go unnoticed, 

including degradations in performance, such as yaw alignment errors or blade fowling, can 

be discovered. 
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Innovations To Increase System Value

Improved controls and operations also have critical opportunities to boost the system value of 

wind. This will be increasingly important as variable sources of generation come to make up 

a growing share of the energy production mix. Enhanced plant controls can be deployed to 

greatly improve the value of wind energy. Wind energy power output, within the limits of the 

power available in the wind, can respond to grid needs across time frames. Although wind 

energy today already participates in providing grid regulation services, there are numerous 

opportunities to raise the value of wind to the grid, and these are summarized in Table 12.

Table 12. Innovations in Plant Control and Operations To Improve Wind Power Plant System

Value

Innovation Category Innovation System Value Impact

Provision of Ancillary 

Services (Not 

Including Storage)

Coupling of weather forecast 

to plant ancillary services

• Provide ancillary service support

Wind turbines provide inertial 

response

Standardize 0-30-second wind 

turbine frequency reserves 

market structure

Provide probabilistic forecasts 

and implement them in grid 

operation

Respond to interarea 

oscillations

Black-start capabilities

Islanded operations

Geographic Diversity Global information retrieval 

system to enable coordinated 

planning across large 

geographic areas

• Reduced variability/uncertainty of supply

Full wind fleet data 

assimilation

• Reduced variability/uncertainty of supply

Hybrid Wind System 

Services

Demonstrate integration of 

electric vehicle/wind system 

where significant levels of each 

are present

• Reduced variability/uncertainty of supply

• Ancillary service support

• Increased capacity value

• Reduced curtailment impacts

Coupling of electric vehicle

charging and water heating, 

thermal storage with wind 

energy production

Integrate wind plant with solar 

and storage

Playing into smart grid

Regional Control of 

Wind Plants

Regional controls for windintensive areas

• Reduced variability/uncertainty of supply

• Ancillary service support

Centralized control of multiple • Increased capacity value

plants
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Innovation Category Innovation System Value Impact

Take away individual plant 

ramp rate limits and base on 

aggregate

• Reduced curtailment impacts

Transmission-based control

Aggregated distributed winds

All wind power plants 

connected via high-voltage DC 

lines

Aggregation of wind and solar 

PV for use of resource 

complementarity and 

optimization of electric 

infrastructure

In terms of ancillary services, wind energy already provides various services to power grids, 

depending on regulations and markets. However, improvements in controls and especially 

wind power plant coordinated controls, can enable higher-quality provision of services

(Fleming et al. 2016b; van Wingerden et al. 2017; Shapiro et al. 2017; Petrovic et al. 2018;

Vali et al. 2018a, 2018b, 2018c). By coordinating turbine controls, the total power output of 

the wind power plant can be controlled very accurately. As a result, wind energy can already 

be shown to be an effective source of inertial and primary type responses.

One very important subject of research is the estimation of available power in the wind, now 

and in the future. This is a difficult quantity to estimate, because wakes make the power 

dependent both on the nonhomogenous wind speed and direction. However, continual 

improvements in monitoring and estimation enable this quantity to be more and more 

accurately predicted. With this improved accuracy, wind energy can effectively contribute to 

longer timescale grid services including automatic generation control and power reserve. The 

ability of wind to increase value to the grid will enable higher penetrations, as it provides its 

own regulation services.

Hybrid wind power plant control further increases the value of wind by coupling wind 

turbines in a farm with additional generators, such as solar panels, or possibly with storage,

such as batteries. This coupling enhances value by allowing power to be generated when the 

wind is not blowing and allowing for a higher-quality power output to be produced. 

Research into this area, and the control of hybrid plants, can yield optimal combinations of 

wind, solar, and storage to maximize the complementary benefits of the individual 

subcomponents.

Regional power control provides the ability to coordinate wind power plants in a given 

geographical region. Ancillary service provision is then commanded and evaluated at this 

regional level. An important benefit is that regional variation in wind will reduce the impact 

on total power output from variations in wind in specific localities. Beyond regional control, 

coordinating wind power plant monitoring, forecasting, and control activities at a larger 

geographic, grid-wide scale can enable further benefits, including the ability to increase the 
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predictability and controllability of wind energy, and therefore value, from the grid 

perspective.

R&D Challenges To Realize Innovations

During the workshop, important R&D challenges identified for wind power plant control and 

operations included 1) a full end-to-end accurate modeling capability from the atmosphere to 

the grid, 2) accurate power system models with large amounts of wind power, storage, solar 

energy, and flexible loads, 3) implementation of wind power plant controllers and integration 

with turbine controllers, 4) field trials of wind power plant controllers and conclusive studies 

of performance, 5) and development and release of open data sets for public research. These 

challenges are outlined in greater detail in Table 13.

Table 13. Plant Control and Operations R&D Grand Challenges

R&D Grand Challenges Specific R&D Activities

Grand Challenge 1: Full End-to-End Model from the 

Atmosphere to the Grid

Validate model of control-flow interaction 

(bidirectional)

Establish hierarchy of models for solving a 

variety of problems wherein the flow, grid, 

lifetime, and controls interconnect

Model of interaction of turbine control and flow

Multiscale modeling; spatial or temporal

Engineering-level models of wind power plant

control

Models of progression from flow to structure to

loads to electrical performance and vice versa

Grand Challenge 2: Power System Model with Large 

Amount of Wind Power, Storage, Solar, Flexible 

Loads

Multiscale modeling; spatial or temporal

Simple enough, good enough, validated model

Examine how wind interacts with storage, 

location of storage, and what best serves the 

system

Models with wind, flow, storage, grid, and solar

Grand Challenge 3: Implementation of Wind Power 

Plant Controllers and Integration With Turbine 

Controllers

(Including Field Trials and Validation)

Determine how to best implement control 

strategies, such as wake steering and power 

reserve

Investigate how to best estimate current 

conditions (e.g., speed, direction, atmospheric 

stability, available power, turbine issues)

Study how to implement controls of hybrid 

plants (+ solar, + storage)

Determine what sensing is required to 

implement various types of wind power plant

control as well as what would be the value of 

new advanced sensing to control, such as from 

direct measurement of the atmosphere through 

remote-sensing technologies like lidar and radar
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R&D Grand Challenges Specific R&D Activities

Conduct repeated and public trials of wind 

power plant controls concepts to build 

confidence and validate benefits

Perform coordinated trials with utilities to 

demonstrate new grid support functionality

Grand Challenge 4: Creation of Open Data Sets for 

Use in Open Research

Establish the institutes, facilities collaborations, 

and database architecture necessary for 

collecting, storing, and distributing large open

data sets for research purposes

To realize the innovations described earlier, it is critical to have the ability to fully model 

wind power plant operation from atmospheric flow that provides the input to wind power 

plants and determines how the flow interacts with wind turbines throughout a plant. It is also 

critical to understand how the power produced by a wind power plant is injected into and 

distributed throughout the grid. Further, how wind power plants impact the atmospheric flow 

and how nearby wind power plants interact (especially at the electric grid level), are 

important areas of research. Similarly, power grid effects can influence the power demanded 

by transmission system operators on wind power plants. A comprehensive modeling tool that 

incorporates all of these capabilities will help all parties fully understand how changes in one 

part of the system can affect other portions, and only such a model can enable full system codesign and optimization. A full “end-to-end” model enables comparable research and 

detailed analysis of the impacts of advances in wind energy control on the power system.

Design of wind power plant controls and the methods by which they are integrated both with 

wind turbine and grid control activities, is an important topic of research. Although there has 

already been a large number of studies to look at various aspects of wind power plant control 

(including wake steering, induction control, and controls for grid services), there is still a 

long way to go to fully realizing the potential of these innovations. As the understanding of 

the physics and the ability to model the full end-to-end system advances, research into and 

the development of novel control strategies will be needed. In addition, field trials of wind 

power plant controls, with conclusive analysis, will be necessary to validate models and 

demonstrate that these technologies 1) will deliver the promised benefits, and 2) will do so 

without unacceptable risk in terms of adverse impacts to plant performance, reliability, and 

cost.

Further, there is a need to develop more publicly available data resources for wind power 

plants that can be used by the wind research community to validate models and test 

hypotheses. Comprehensive, high-quality, fully open data sets can provide a dramatic 

acceleration to R&D efforts and to achieving a basic scientific understanding. 

Other examples of R&D challenges not included as grand challenges include a 

mapping/coupling of the effect of wind provision of grid services to the lifetime of turbines, 

which could be used in cost-benefit analysis. Additionally, a financial analysis of the benefit 

(beyond LCOE-only analysis) would be helpful in determining what should become best 

practice. 
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Research into how controls might be used to extend the lifetime of turbines (for example, to 

40 years), could have a substantial impact on LCOE. Also, there is value in research that 

assesses the relative merits of long-life design for turbines, versus expected repowering as 

turbine technology advances and the role that controls can play in both cases. Finally, 

research into advanced electrical components for wind can yield benefits in cost, reliability,

and robustness.

3.5 Breakout Group 5: Grid Integration

The perspective of grid integration is critical to the future scenario in which renewable 

energy sources provide 80% or more of the global electricity generation, with wind energy 

providing at least 50%. Although innovations to reduce LCOE are still needed to maintain 

and increase competitiveness of wind energy with other electricity generation technologies, 

the value to the electric system from wind energy is critical to realizing the Grand Vision. 

Within the grid integration breakout group, the participants looked at the innovations that 

would be necessary both from wind turbines and plants but also other key features of the 

electricity system that would be needed to realize the Grand Vision. The group then 

identified key R&D challenges that would need to be addressed to support the development 

of the needed innovations.

Innovations To Increase System Value

The grid integration perspective focused on operation of an electricity system with over 50% 

electricity generation coming from wind energy at different timescales. Many of the 

identified innovations focused on support of electricity system reliability through the ability 

to provide short-term services to the grid. Others focused on power system operations for 

providing low-cost and reliable energy to match supply and demand over the course of the 

day, and throughout the weeks, months, and years. Table 14 summarizes the innovations 

identified by the breakout group to improve overall system value from wind energy to the 

electric system.

Table 14. Innovations in Grid Integration To Improve Wind Power Plant System Value

Innovation Category Innovation System Value Impact

Wind Turbines and 

Power Plants

Power electronics adapted to provide

grid-forming converter capabilities 

(including black start) and controls for 

islanded/weak grid operation

• Support for ancillary services

and stability

High capacity factor/oversized wind 

turbines with lower specific power wind 

turbines, overplanting/overbuilding 

(number of turbines)

• Increased capacity value

• Increased energy value

• Reduced variability/ 

uncertainty of supply

• Reduced need for grid 

infrastructure (more energy 

per unit of transmission 

capacity)

Provide ancillary services, such as

regulation, primary frequency response, 

inertia support (at plant level), and 

improved fault-ride-through capabilities 

• Grid support through ancillary 

services provision

• Reduced amount of “must 

run” thermal units
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Innovation Category Innovation System Value Impact

Turbine and plant state 

estimation/accurate estimation of 

capacity for providing services (e.g., 

available power) over different time 

periods (e.g., minutes, hours) (e.g., data, 

sensors)

• Support for ancillary services

• Reduced variability/ 

uncertainty of supply

Geographically disperse plant locations • Reduced variability/ 

uncertainty of supply

• Increased capacity value

• Increased energy value

Plant-level optimization and control • Support for ancillary services

• Reduced variability/ 

uncertainty of supply

Optimal use of storage in wind power 

plants/hybrid power plants

• Support for ancillary services

• Reduced variability/ 

uncertainty of supply

• Increased capacity value

• Increased energy value

ConverterDominated System 

and Power 

Electronics

Control interactions between converters • Reduced curtailment impacts

• Support for ancillary services

Improve efficiency converters • Reduced system losses

Accurate measurement and control of 

harmonics

• Reduced system losses 

• Improved system reliability

Control of electrical resonances/smallsignal oscillations

• Improved system stability

Improve reliability of power electronics • Improve system availability

High-voltage DC (HVDC) transmission and 

control of HVDC systems also with DC/DC 

converters

• Increase transmission 

efficiency and system 

economics

DC grid for collection grid with DC/DC 

converters

• Increase local transmission 

efficiency

Transmission High-temperature conductors • Improved utilization of 

transmission assets

• Improved siting ability

• Reduced cost of transmission

Flow control through flexible AC 

transmission systems

Low-cost, high-capacity underground 

transmission

Storage on Multiple 

Time Frames

Short-term storage for frequency control 

(seconds)

• Reduced variability/ 

uncertainty of supply

• Support for ancillary services

• Increased capacity value

• Increased energy value

• Reduced curtailment impacts

• Reduction in need for thermal 

units on line

• Reduced emissions from fossil 

fuels

• Dispatchable wind power

Hours of storage

Smoothing power (diurnal)

Seasonal storage
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Wind Power Plant Innovations

Reducing variability and uncertainty of supply can be accomplished through improved 

forecasting and wind turbine/plant state estimation (see Section 3.1 on atmospheric science 

and forecasting for more in-depth discussion of this topic). Reduced variability and 

uncertainty of energy supply is important for system operation at timescales of seconds, to 

minutes and days, or longer. Advances in turbine state estimation (accurate estimation of 

capacity for providing services) will be critical for wind power plants to provide ancillary 

services and grid-forming capability in the converter-dominated system. Accurate estimation 

of overall wind power plant availability and expected output will be made possible through 

advances in forecasting, wind plant controls, and operation and maintenance technologies 

and strategies (see previous sections for discussions of these different technology areas).

Perhaps the most critical capability required for wind power plants to become the backbone 

of the electricity grid is the ability to provide grid-forming services. Though wind power 

plants already supply grid services of various types in many markets, they typically use 

information from the electric grid (i.e., measurements of grid voltage and frequency) as input 

to their control systems. Synchronous generation, from thermal, nuclear, or hydropower 

plants, provide the services that “form” the electric grid in terms of its voltage and frequency 

characteristics. This capability for synchronous machines is tied closely to their physical 

inertia (being massive rotating mechanical machines) and a controllable voltage behind a 

reactance which together provide a voltage magnitude and frequency reference for the entire 

interconnected system. Wind power plants do not have physical inertia in the same sense that 

traditional power plants do, but with the power electronic control inherent in many turbines 

(those with doubly-fed induction generators or fully rated converter systems), the wind plants 

are able to provide “synthetic inertia” and act as “virtual synchronous generators” in the 

system (Ackermann et al. 2017). In this paradigm, wind power plants of the future are 

expected to provide grid-forming capabilities and provide a voltage and frequency reference 

for the remainder of the system. 

By developing wind power plants with further grid support functionalities (e.g., grid 

forming) they will be able to serve as the backbone of the future converter-dominated 

electricity system on a large scale and provide support for weak grids (with low amounts of 

synchronous generation), smaller grid systems, cells, or microgrids in normal and islanded 

(with the loss of the support from a larger grid system) operation. 

Closely tied to innovation around the grid-forming capability of wind farm and plant power 

electronics is the improved capability to provide a large range of ancillary services to the 

system operator to support overall system reliability. These services include reactive power 

control and voltage regulation, active power control and various forms of frequency control, 

as well as the ability to ride through various faults on the larger electric system (Ackermann 

et al. 2017; Kroposki et al. 2017) and black-start capability. Although wind power plants are 

already able to provide some of these services today, more development is needed to advance 

and improve the capabilities. While wind power plants may not need to provide all of these 

services in the future grid system, were they able to do so (and do so economically), this 

would certainly encourage more global deployment of wind energy.
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Beyond innovations related to the wind turbine and plant power electronics and their control 

are innovations related to the physical design of the turbines and plants. There is a need to 

reduce the variability and uncertainty associated with the supply of electricity from wind 

power plants at any given time. One mechanism to reduce the variability and uncertainty is to 

oversize the turbines or the overall wind power plant. In the former case, shifting to lowspecific-power machines essentially shifts the power curve of wind turbines to the left (to 

lower wind speeds) so that wind turbines are producing more energy for a higher percentage 

of the time for a given site and wind resource, which leads to a higher overall capacity factor 

for the site and potentially higher capacity value (Wiser and Bolinger 2018). 

In the latter case, “overplanting” is where the rated power of the plant is less than the sum of 

the rated power of all the turbines in the plant, or the transmission limit to the site. This is 

analogous to having higher inverter loading ratios in solar PV plants where the DC/AC ratio 

is much larger than 1. This will increase the usage of existing transmission, provide more 

megawatt-hours from wind power plants for each megawatt of transmission available, and 

thus reduce build-out and cost of transmission. Coupling either of these approaches with 

plant-level optimization and control would allow turbines to provide more reliable and ondemand electricity because some or many of the turbines in the plant could be operated in a 

“derated” state (i.e., at less than their rated power). Through coordination of turbine power 

output at the full plant level, wind power plants are also able to provide the various ancillary 

services as previously described—voltage regulation and frequency control as well (Kroposki 

et al. 2017; Milligan et al. 2015). The future wind power plant contains a collection of highly 

controllable turbines for coordinated control of energy supply and ancillary services at all 

timescales of interest (Kroposki et al. 2017; Milligan et al. 2015). 

Moving beyond individual plants, various studies have shown how geographic diversity in 

location of wind power plants can smooth out variability associated with wind electricity 

production in a bulk electricity system (Kempton et al. 2010; Grams et al. 2017). Spreading 

out wind power plants may not be optimal from an individual operator’s perspective—in 

which one tends to seek the highest wind resource sites for maximum energy production;

however, from a system operator’s perspective, locating plants in geographically distinct 

wind regimes provides more reliable overall generation both from land-based systems (for 

example, spreading plants north and south in Europe (Grams et al. 2017) or across the 

continent east to west in the United States) and from offshore systems (for example,

spreading plants along the full eastern or western seaboard of the United States [Kempton et 

al. 2010]).

Another mechanism for reducing wind power plant variability and uncertainty of energy 

supply, increasing capacity value, and increasing their capability for providing ancillary 

services is through the development of hybrid power plants including wind, storage, and 

other technologies (i.e., solar PV). On the one hand, wind and solar in many locations have 

inversely correlated diurnal cycles (i.e., in many locations wind is typically strongest at night 

and solar only operates during the day, so that having more wind energy and solar together 

can complement and smooth out their variability [Seel et al. 2018]). On the other hand, cost 

of storage technology is rapidly falling and can assist both wind and solar in mitigating the 
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variability and uncertainty of those assets (IRENA 2017a). By combining generation assets 

together, including storage, solar, and wind, into “hybrid power plants,” an individual plant 

owner can 1) develop economies of scale in terms of land usage, electrical and physical 

infrastructure, and operational expenditures, and 2) increase their system value to capitalize 

on revenue streams available through forward capacity markets (where present), 

“dispatchable” operation in peak energy pricing periods, and ancillary service markets 

(where present). However, hybrid power plants might also have higher costs and their overall 

profitability must be considered with the various revenue stream opportunities balancing 

those additional costs.

Power Electronics, Transmission, and Storage Innovations

Traditional AC overhead lines with high voltages, typical 400-500 kilovolts, are used for 

bulk power transmission on land. For subsea connections and certain land-based applications, 

DC systems are used because of the reactive power limitations associated with using AC

cables for long-distance power transmission. The AC cables produce reactive power 

themselves and thus reduce their capacity to transmit useful active power. In the past, 

interfacing DC systems with the bulk AC system relied on older technology (e.g., thyristor 

converters for transforming AC to DC current) and required connection to a strong AC grid 

for proper control. Newer converter technology uses transistors and can provide grid-forming 

functionality. The losses of these systems have also been reduced and there are still 

possibilities to reduce them further. With the achievements in power electronic development 

and the control of power flow, it is possible to have a future converter-dominated electric 

system. These innovations in technologies beyond the wind power plants themselves are 

important for a power system with a large share of renewable generation. 

In a converter-based system, the power flow is controlled by the converters. Extensive 

simulations and good models are essential components in avoiding control interactions 

between converters and achieving stable operation of the system (Yunus 2017; Max 2009).

The converters in a wind turbine as well as in a DC transmission system have to be designed 

to withstand the electric constraints and the control has to be tuned in a way to avoid 

instabilities/resonances between the wind converters and the power system. One example of 

this is the instability caused by subsynchronous resonance conditions in large wind power 

plants connected to series-compensated transmission lines (Chernet 2018). Power electronics 

enable the accurate control of the torque in the drivetrain and variable-speed operation of the 

turbine, while also damping mechanical oscillations to avoid resonances in the drivetrain and 

with the grid. Power electronics also enable the low loss and fast control advantages of DC 

technology. 

In offshore wind, as well as some instances of land-based wind, the power produced by wind 

power plants must be transmitted over long distances to a suitable grid connection point. 

Cables quickly become the preferred option for transporting the energy from wind 

installations to suitable main grid infeed points. The use of HVDC transmission facilitates the 

connection of the wind power plant to weak grids. DC is already used to transport power 

from remote wind power plants in the North Sea to grid systems in Northern Europe, and the 

next natural step is to introduce DC in wind power plant collection grids as well as to a bulk 

DC transmission system.
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A major question that must be addressed in designing DC-DC converters for offshore DC 

grids is to determine the optimal layout of future grids in terms of using radial connections, 

meshed grids, or hybrid grids based on the different converter technologies, considering the 

components and corresponding functionalities that are needed for proper and secure 

operation and control of the offshore grid. In addition, the use of energy storage systems 

should be better coordinated with the introduction of DC technology. This can be important

when integrating wind power in very weak grids (as is currently the case in many projects in 

China), but also to provide black-start capabilities to the wind power plant. Today, a wind 

power plant typically relies on its connection to the larger system for start-up and aligning its 

active and reactive power output.

 

The losses in the transmission system decrease with higher voltage for a given power 

transmission level, and correspondingly, the current decreases. The development of 

ultrahigh-voltage transmission for long-distance bulk power transportation is of great interest

but the challenges for realizing such technology are in the insulation systems. Because of the 

environmental impact of the overhead transmission power lines, it is preferable to create a 

low-cost, high-capacity, underground transmission system. Especially with the future 

development of the DC transmission system, this is a realistic development. A future 

collection and transmission grid of wind power plants would likely include a meshed DC grid 

for connection of wind power plants as well as for the transmission between different parts of 

a large AC system or between different synchronous AC systems.

Finally, the ability to store wind power over both short and long time frames will enable 

wind to be a fully dispatchable power source. Innovation in storage technology, especially 

for short-term applications, has accelerated in recent years and created a new dialogue around 

the potential for a renewables-dominated electricity system. Energy storage is a topic with a 

large scope and deserving of its own visioning study, so it will not be discussed in depth 

here. Lazard’s 2017 study on the levelized cost of various storage technologies provides a 

more complete overview of storage potential and costs for different time frames of interest 

(from very short-term grid services to seasonal storage to support of long-term variability in 

wind and/or solar assets) (Lazard 2017). In particular, seasonal storage is important to very 

large shares of wind energy in the system and this is an area where significant research 

breakthroughs are still needed.

R&D Challenges To Realize Innovations

Most of the identified R&D challenges for grid integration of wind power plants involve 

providing more support to the grid and supporting the paradigm shift to the converterdominated electric grid with very low levels of traditional synchronous generation and high 

levels of variable generation including wind and solar energy. Grand challenges for grid 

integration of wind energy include the need to develop the fundamentals for the converterdominated electric system, the controls capability for the converters themselves, the ability to 

accurately model the converter-dominated grid system across all relevant spatial and 

temporal scales, and integration of data and physical models for intelligent wind power plant 

operation in converter-dominated systems. Table 15 summarizes these challenges.
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Table 15. Grid Integration R&D Grand Challenges

R&D Grand Challenges Specific R&D Activities

Grand Challenge 1: Develop Fundamentals and 

Paradigm Design of a Converter-Dominated System

Develop a new generation of power system 

analysis and design tools

Develop accurate models of the grid-forming 

converters for all operating conditions 

Grand Challenge 2: Control of Converters with GridForming Capability in a Converter-Dominated 

System

Coordinate and control multiple grid-forming 

wind turbine converters in a single plant

Coordinate and control multiple grid-forming 

wind plants 

Establish protection aspects of converterdominated systems

Grand Challenge 3: Multiscale Modeling Across an

Entire Grid System with Accurate Modeling of 

Converters in a Converter-Dominated System 

Model the new grid, including multiple wind 

plants with grid-forming converters embedded 

in AC systems (bridge scales of slow AC and fast 

dynamic systems)

Grand Challenge 4: Intelligent Wind Power Plant 

with Integrated Data and Modeling for Active Power 

System Operation and Support

Combine data and modeling approaches for 

state estimation and accurate performance 

prediction (flow and electrical)

The first challenge around fundamentals and design of converter-dominated systems involves 

developing accurate models for converters under all operating conditions at all timescales. 

Models and simulations of power system operation under the new paradigm of the converterdominated system are needed to confidence to system operators that system reliability can be 

maintained under all future operating conditions.

The second R&D challenge is related to the converter capabilities themselves to ensure that 

they can provide the services required to support the electric system stability and reliability. 

Kroposki et al. (2017) speaks to the potential of wind power plants to provide support for 

system inertia, active power control, reactive power/voltage control, and fault ride-through. 

Although existing wind power plants are already capable of providing these services, there is 

a significant amount of work required to fully develop the power electronics and controls. In 

the future, the converters must provide the full grid-forming capability while simultaneously 

providing the full range of ancillary services to attain the necessary system stability and 

reliability. This is especially true at the full wind power plant level.

Having developed the needed hardware, controls, and associated converter models, 

significant work still needs to be done to integrate all the relevant models together for a 

complete and accurate multiscale model of the converter-dominated electric grid. This work 

will involve bringing models of wind power plants capable of accurately modeling the flow 

and electric performance of the power plant at a wide range of time intervals and all of the 

services provided not just at a single power plant level but across many plants for full system 

operation and analysis. In many ways, this R&D effort is the culmination of research 

conducted across a number of domains in wind energy for developing validated models of 

wind turbines, their components (especially power electronics), plant level flow and control, 

and grid interaction and system operation—thus building on R&D challenges identified in 

the preceding breakout sessions.
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Finally, future R&D in grid integration of wind energy will, like other wind R&D areas, start 

to incorporate more data science and integrated data and modeling approaches to create more 

intelligent wind power plants and electric power systems of the future. The significant 

complexity of wind energy systems and the larger electric systems with the range of spatial 

and temporal scales means that modeling the system purely with physics-based models will 

be challenging. As with the weather itself, there will always be aleatoric uncertainty affecting 

the system behavior and epistemic uncertainty relating to model validity. On the data side, 

again because of the sheer size and range of spatial and temporal scales involved, it is 

unlikely that a pure data approach will ever be capable of accurately representing either wind 

power plants or the larger electric system. Going forward, combined approaches leveraging 

data and modeling (e.g., as currently done in wind power plant forecasting applications) will 

be used to enable intelligent wind power plants of the future providing support at all 

necessary timescales to the converter-dominated grid.

Beyond the grand challenges identified by the group, several other challenges exist including 

the need for advancing optimization research for large-scale problems involving system 

topology and infrastructure both for system design as well as control and operations. 

Optimizing the design and operation of the electric grid system of the future will push 

beyond our current capabilities in terms of methodologies and computing resources and will 

be critical for supporting the R&D described earlier. Significant research for optimization of 

power system design and operation is available for current systems (Zheng et al. 2015;

Molzhan et al. 2017; Cole et al. 2017; IRENA 2017b; Pietzcker 2017; Holttinen 2018; IEA 

Wind 2018; Helistö et al. forthcoming,) but will need to be adapted for the future electric 

system. Another area of interest for future R&D includes the development of advanced 

materials for power electronics applications.

Market Design as a Techno-Economic Grand Challenge

One additional area that represents a grand challenge that goes beyond the purely technical is 

the market design of renewable energy and converter-dominated electric systems. Up to the 

present day, most electricity markets focused on energy as the main stream of revenue for 

generation assets with secondary opportunities associated with capacity value and system 

services. As mentioned in the discussion of the future electricity system, with more and more 

renewable energy resources on the grid, we are already seeing downward pressure on 

electricity prices and a need to develop and expand the markets for capacity value and system 

services to ensure grid reliability over all timescales (Ahlstrom et al. 2015). The market 

design has a direct influence on system operation. Therefore, research on modeling and 

analysis of the design, control, and operation of future wind power plants and the converterdominated system must go together with research on market design that will involve 

additional dimensions of economics and policy research.

3.6 Breakout Group 6: Offshore-Specific Technologies

Offshore wind energy has experienced remarkable growth over the last two decades, with 

16.3 gigawatts installed capacity in 2017, but there is reason to anticipate substantial 

advancement over the next several decades as well (WindEurope 2017; Beiter et al. 2017b). 

Some of this advancement will be in the form of evolutionary refinement of existing 
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technology (InnoEnergy 2017). In other cases, there are some grand challenges that will 

require significant innovation and associated R&D efforts (ETIP Wind 2016; van Kuik et al.

2016). To provide some context for considering these challenges, we first provide a summary

of current offshore wind energy technology.

Offshore wind turbines are conceptually divided into two main subsystems: the rotor nacelle 

assembly (RNA) and the support structure. The RNA incorporates the blades, hub, rotating 

machinery, and most of the ancillary equipment. The support structure includes the tower, 

substructure, and (for fixed-bottom turbines) foundation. For floating offshore wind turbines, 

a floating substructure is used and held in place by a station-keeping system consisting of 

mooring lines or tendons and anchors. Fixed-bottom turbines are normally located in water 

depths of less than 60 m, whereas emerging floating offshore turbines are located in waters 

deeper than this. Currently, there are some technical challenges in mooring floating offshore 

in shallow waters (e.g., 60 m–90 m) but technology solutions are expected as the designs 

mature, and it is likely that future floating turbines will be able to access shallower waters

without significant additional cost. Through this type of continued innovation, the cost of 

floating wind is anticipated to reach levels on par with (or lower than) fixed-bottom systems.

In principle, the concept of offshore wind is simple: take existing wind turbines, designed for 

use on land, and place them on support structures in the ocean and connect them electrically 

via submarine cables to a suitable land-based electrical grid. However, the situation is more 

complicated for a number of reasons. First, offshore wind support structures are massive and 

more complex than land-based towers and foundations; they have more in common with 

structures used by the offshore oil and gas industry than they do with land-based systems. 

Second, the marine infrastructure required to install, deliver power from, and operate 

offshore wind turbines and plants is expensive—as lifting capacities and port facilities need 

to be adapted for offshore wind energy applications. Because of the cost of deploying and 

operating offshore wind turbines are high, benefits of increased scale are greater. Through 

increased turbine and power plant size, the offshore wind industry has been able to make 

significant reductions in cost of energy, and this trend is likely to continue. 

The expectation for continued scaling of offshore wind turbines, together with the need to 

improve design, manufacturing, and operation for harsh environmental conditions offshore, 

form the basis of the innovation and research challenges that must be overcome (Jamieson 

2011). Although much of the experience with land-based wind energy is applicable offshore, 

there are significant differences as well. 

Innovations To Reduce LCOE

Although LCOE for offshore wind energy has fallen significantly in the last several years, 

there is still potential for further cost reductions in the future. Some of the innovations that 

will create those reductions will be applicable to all offshore systems, including both fixedbottom and floating designs; however, other reductions will be specific to certain design 

types.

For both fixed-bottom and floating technology, innovations in turbine and plant controls as 

well as operational strategies are expected. In addition, improved design criteria for site-
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specific environmental conditions will lead to offshore wind energy cost reductions. Finally, 

integrated design approaches that couple the turbine and support structure to full plant design 

will likely benefit both floating and fixed-bottom technologies (but will likely benefit 

floating wind systems more). 

Floating wind systems are presently costlier than fixed-bottom designs and have some 

specific focus areas that could improve LCOE. These areas include the industrialization of 

floating wind energy technology, use of novel materials, component innovations, novel 

support structure designs, and completely new technology configurations that could lead to 

step changes in offshore wind LCOE. Table 16 provides a review of major innovation 

categories that will reduce offshore wind energy LCOE as well as specific innovations and 

their LCOE impacts. 

Table 16. Innovations in Offshore Wind Energy Technology To Improve Wind Power Plant 

LCOE

Innovation Category Innovation LCOE Impact

Improvement of 

Manufacturing and 

Installation Process

Floating substructures 

manufactured with 

standardized, modular 

components

• Reduced CapEx through economies of 

scale

• Reduced OpEx through increased 

reliability

Large-scale manufacturing for 

technology learning offshore

• Reduced CapEx through learning

Systems that can be installed 

with limited labor at sea (selferecting)

• Reduced CapEx through less costly 

installation

Alternative installation 

techniques for fixed-bottom 

systems 

• Reduced CapEx through less costly 

design

Innovative Technology 

Development

Novel turbine concepts 

(downwind, two-bladed, 

vertical-axis wind turbine, 

airborne, superconducting, 

direct drive)

• Various impacts in AEP, CapEx, and 

OpEx depending on technology

Support structure design 

innovations that capitalize on 

rapid evolution of supply 

chains

• Reductions in CapEx

Novel tower designs (including 

lightweight, soft designs)

• Reductions in CapEx

Novel materials for support 

structure (e.g., to limit 

corrosion, increased local 

content, use of composites)

• Reductions in OpEx through increased 

reliability

• Reduced CapEx

Novel mooring designs and 

materials (including shared 

moorings)

• Reductions in CapEx

Hurricane-resilient turbines to 

open new markets

• Reductions in OpEx through increased 

reliability
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Innovation Category Innovation LCOE Impact

Novel Design Approaches Systems engineering design 

and optimization (full plant 

components including lifecycle 

costs, system-level costs, 

environmental impact)

• Reduced CapEx in all major 

components

• Reduced OpEx through optimized 

maintenance

Improved Design Criteria 

Including Site Suitability

Class-based designs with sitesuitability assessment 

(including higher-strength 

designs for tropical storms)

• Reduced OpEx through increased 

reliability

• Reduced CapEx through mass 

production

Probabilistic (reliability-based) 

design to reduce conservative 

design assumptions (e.g.,

safety factors)

• Reduced CapEx through optimized 

designs

• Reduced OpEx by derisking

Better wind characterization to 

enable hurricane-resilient 

design (needed in some 

locations, but not others)

• Reduced OpEx through increased 

reliability

• Reduced CapEx through designs 

optimized for location

Better assessment and 

modeling of soil/foundation 

interaction

• Reduced CapEx through better 

optimized designs

Smart O&M Practices Increased reliability and 

smarter, more predictive O&M 

practices (condition 

monitoring, life extension, 

robots for inspection, access, 

ability to do quayside 

maintenance)

• Reduced OpEx through increased 

reliability and longer 

component/turbine lifetime

Advanced Control 

Strategies to Lower Loads 

and Increase Performance

Advanced control strategies 

(e.g., for floating wind 

systems) at the turbine and 

farm level (including new 

actuators)

• Increased AEP and reduced OpEx

Floating systems are in a precommercial stage but have the potential to be more cost 

effective than fixed-bottom systems in the long term (Beiter et al. 2017a; James and Ros 

2015). To achieve this, the manufacturing and installation process for floating wind systems 

needs to be industrialized (i.e., developments are needed to enable, streamline, and 

standardize manufacturing of floating units and provide efficient installation protocols). For 

tension leg platforms and spars especially, the substructure cost is typically smaller than a 

fixed-bottom substructure in very deep water, and finding cost-effective installation and 

O&M strategies will be key to making these design approaches commercially viable. Costs 

could be further reduced by using commercially available components, which benefit from 

high-volume manufacturing. Ultimately, it may be possible to mass produce floating turbines 

for multiple farms and use simple tow-out procedures for both installation and maintenance, 

requiring minimal expensive effort spent on at-sea work (e.g., expensive offshore cranes). 

Fixed-bottom offshore wind turbines are already commercialized, but there is still 

opportunity for reducing their cost. In particular, the installation phase is an area of ongoing 

innovation (Sarker and Faiz 2017), with a focus on new piling and alternative installation 
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technologies that avoid transition-piece-related reliability issues, while at the same time 

reducing environmental impact. 

Currently, offshore wind turbines resemble land-based turbines in that they typically use 

horizontal-axis, three-bladed, upwind rotors on tubular towers, but have generally addressed 

the added necessity for corrosion resistance. Further, many have implemented more robust 

methods of remote health monitoring. Two of the most significant trends that differentiate 

offshore turbines from land-based wind turbines are their larger size and drivetrain 

architectures that deviate from conventional modular gearbox designs. Offshore turbines do 

not have the same transportation- and installation-driven size limitations as land-based wind 

turbines. Hence, the offshore nameplate rating has grown beyond 8 MW, with rotors as big as 

164 m in diameter—more than twice the size of most land-based turbines. They also differ 

from land-based turbines in their drivetrain configurations, especially the use of direct-drive 

generators or single-stage geared drivetrains with medium-speed generators. These drivetrain 

configurations have fewer moving parts and promise lower maintenance costs, with the 

drawback of generally being slightly heavier upfront than traditional modular gearboxes.

One exception to the conventional upwind turbine is Hitachi’s 2-MW and 5-MW downwind

turbine series, deployed successfully on proof-of-concept floating offshore projects in Japan. 

Developing downwind turbines at the same scale as upwind machines for offshore 

applications is a technology improvement that could lower cost and reduce turbine weight by 

relaxing the blade stiffness required to avoid tower strikes, allow for wake steering options at 

the plant level, and reduce demand on yaw drives (Ning and Petch 2016; Loth et al. 2017). 

Especially for floating offshore wind turbines, reducing top weight can have significant 

potential for reducing cost due to the lower buoyancy/size requirement. For this reason, twobladed wind turbines could have an advantage in an offshore market. In addition, a twobladed rotor would operate at higher tip speeds with lower solidity, which would reduce 

exposure to extreme wind loading and lower drivetrain torque, and therefore drivetrain mass. 

Much of the nacelle weight in offshore wind turbines can be attributed to the drivetrain. 

Although it may be possible to substitute some of the static components with lightweight 

(e.g., composite) materials, the heavy direct-drive generator may be a greater challenge. New 

technologies, such as superconducting permanent magnets or pseudo-direct-drive generators 

(Liu et al. 2017) are being considered as a possible weight-reduction pathway for direct-drive 

generators.

For the towers, alternative configurations may be sought in the future, including lattice 

towers, prestressed concrete towers (e.g., steel and fiber reinforced), and composite towers to 

reduce mass and facilitate fabrication, both above and below the waterline. More expensive 

glass-fiber-reinforced polymer towers have also been proposed, as they promise lighter 

system weights with net positive advantages for overall cost (Young 2017).

As floating wind designs increase in size, the mooring lines and layouts will also need to 

increase in size. For catenary approaches, the increased diameter chains run the risk of 

exceeding market availability, and also the capabilities of anchor-handling vessels, whereas

the larger layouts may lead to increased environmental impact. For these reasons, synthetic 

moorings are being investigated, in conjunction with semitaut configurations. Finally, 
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substructure designs that consider the ability to achieve mass production and installation will 

be key components in driving down the costs of the entire floating wind system. Hybrid 

approaches, such as gravity anchors or components that can change ballasting levels between 

tow-out and installation, are some ideas that can lower substructure weight and cost while 

reducing expensive labor at sea. As turbine size increases, there will also be a need to limit 

the size of the substructure to enable manufacturing in a wide variety of ports, as well as the 

ability to store completed components prior to installation.

An integrated, system-level “cradle to grave” design and optimization approach will be 

needed, which will consider all aspects of the plant, from environmental impact through 

manufacturing, deployment, and operation, to repowering and then recycling or disposing of 

those items that have been replaced. Through the application of the developed systems 

engineering tools, innovative design concepts can be evaluated and optimized to identify the 

most cost-optimal design solutions. Because of the coupled nature of floating systems, 

optimal solutions will require a combination of innovative components that best work 

together to achieve system-level cost reduction. As noted earlier, this could include novel 

turbine concepts, such as downwind and two-bladed rotors, as well as the use of novel 

materials that will lower system weight, that may have unique advantages offshore. 

Advanced control approaches will also be critical for reducing loading and optimizing power 

production for large, flexible, and floating systems, both at the turbine and farm level. And 

increased reliability and advanced O&M practices are needed to reduce costly work at sea. 

Improved understanding of wind conditions at the rotor height and the associated design 

criteria will be needed to address unique conditions in offshore regions, including hurricanes 

or other extreme events. 

Reducing uncertainties is expected to lead to significant cost reductions when combined with 

a probabilistic, reliability-based design of support structures (Jiang et al. 2017). Computeraided structural optimization can facilitate support structure design (Chew 2016; Oest 2016), 

but is still at the precommercial stage. For floating systems, a class-based approach similar to 

the methodology used for land-based turbines might enable cost reductions through highvolume production. Classes could be based not only on the wind characteristics but include 

wave conditions as well. Site-suitability assessment would still need to be performed but

could lead to the development of classed designs that are applicable to many regions. 

Hurricane-prone regions will need to be addressed, perhaps even through a special class, if

the conditions fall outside the developed classes considering that the mean wind speed, 3-

second gusts, gust factors, and shifts in wind direction can exceed the current design criteria 

for International Electrotechnical Commission (IEC) 61400-1 (2005) standard Class IA wind 

turbines. A new IEC Class T (typhoon) has been proposed that calls for a higher reference 

wind speed. Although this criterion may suffice to render turbines “hurricane reliable,” it is 

not clear whether the reliability level achieved would suffice for all hurricane categories, 

whether the wind conditions are representative of the actual physics, and whether this is 

harmonized with the level achieved by American-Petroleum-Institute-derived methods for 

the substructures. Work is needed to delineate the offshore regions of the United States

where present standards are applicable and new approaches are needed. Additional topics are 

important for derisking fixed-bottom wind turbines, including achieving a better 

understanding of extreme loads and with the uncertainties in soil properties and foundation 
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behavior under long-term cyclic loading (Kallehave et al. 2015), including changes in the 

properties during its lifetime (e.g., erosion, farm-level sand waves, and liquefaction).

Important for both fixed and floating systems is the development of O&M procedures that

are smarter, requiring less work at sea, and the development of designs that better facilitate 

cost-cutting O&M strategies. This includes feedback from optimizing maintenance and 

inspection regimes into the design process to achieve material savings while guaranteeing the 

structural reliability over the wind farm lifetime (Ambühl and Sørensen 2017). Some 

substructure concepts allow for disconnecting the platform from its moorings and towing it 

back to a harbor, which may be a safer and cheaper alternative for major component 

replacement. Alternatively, vessel arrangements suitable for main component replacement 

that minimize the relative movements of the turbine, vessel, and crane must be devised. This 

is an example of incorporating innovative O&M strategies into the engineering design of the 

turbine. 

On the operational side, advanced wind turbine control methods are being developed to 

reduce wake effects on neighboring turbines, thereby increasing the total energy yield of the 

whole wind power plant, in addition to reducing turbine loads. Fleming et al. (2016a) showed 

that wake steering could be used to achieve tighter turbine density without sacrificing plant 

AEP, than would otherwise have been possible for fixed-bottom turbines. The ability to 

control the yaw or tilt of a turbine, thereby steering the wake away from downstream 

turbines, can be leveraged for intelligent control of the entire plant. In fact, wake-steering and 

plant-level control strategies can improve plant AEP by as much as 7.6% (Fleming et al. 

2016b). This finding has not yet been verified for floating systems. In a floating turbine, there 

is an extra degree of freedom that allows passive platform yaw motion that is not under the 

surveillance of the active yaw system. Therefore, new yaw control strategies may be needed. 

Wake steering may have possible applications in floating systems, but the implementation 

and benefits may not be the same. 

Innovations To Increase System Value

Cost reduction provides an intrinsic impetus to having large offshore wind plants. 

Accordingly, the relatively large electrical output needs to be brought ashore with a minimal 

number of high-capacity submarine cables and connected to the grid at a point with enough

transmission capacity to accept that power. As more offshore wind plants are constructed, the 

integration of these plants into the overall energy system becomes progressively more 

difficult. As renewable energy achieves larger and larger shares of overall electricity 

generation, offshore wind plants will also need to supply services to the grid on par with 

conventional power plants (Hennig et al. 2014; Kakorin et al. 2014). Table 17 points to some 

key innovations that will allow offshore wind energy to provide better support to the future 

electricity system.
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Table 17. Innovations in Offshore Wind Energy Technology To Improve Wind Power Plant 

System Value

Innovation Category Innovation System Value Impact

Offshore Electrical 

Infrastructure

Offshore electrical infrastructure 

innovations (superconducting, 

HVDC, super subsea grids)

• Increased capacity value

• Increased reliability

Storage and Grid Services Power to X: energy storage 

(compressed air) or fuel production 

(hydrogen, ammonia) and how to 

address wind-powered desalination

• Support for electrification

• Reduced variability/uncertainty 

of supply

• Support for ancillary services 

• Increased capacity value

Large-scale coordinated control 

(interfarm control for storage and 

grid services)

• Reduced variability/uncertainty 

of supply

• Support for ancillary services

• Increased capacity value

Novel Approaches To 

Extract Energy Beyond 

Traditional Paradigm

Ability to move to where resource is 

optimal (floating and/or airborne)

• Reduced variability/uncertainty 

of supply

• Support for ancillary services

• Increased capacity value

• Storm avoidance

Dynamically moored systems and 

turbine control actuation to move 

systems (not just flow) to control 

plant power output

• Reduced variability/uncertainty 

of supply

Wind Forecasting Better methods for predicting 

hourly and diurnal energy 

production through better 

understanding of meteorologicalocean conditions

• Reduced variability/uncertainty 

of supply

• Realized synergies with other 

variable sources

• Reduced uncertainty in system 

design and optimized reserve 

strength 

• Increased capacity value

Integrating wind plant and power system designs, previously decoupled in most land-based 

wind systems, can provide new cost savings and more efficient and reliable power system 

operation. Offshore wind is anticipated to require new transmission infrastructure

development and therefore the opportunity to take advantage of recent innovations in power 

systems including recent advances in power electronics, active power control for wind 

turbines, energy storage integration, and HVDC or superconducting transmission systems. In 

addition, new infrastructure means the possibility of synergizing with other applications with 

similar needs to share in the cost. Industries, such as oil and gas, fishing, or even 

transportation, could better enable offshore wind commercialization while adding positive 

regional economic value. Finally, one of the motivating factors for offshore wind is the 

proximity of the resource to high-load regions. Understanding within the grid infrastructure 

how to best take advantage of colocation can also be important to maximizing the value of 

offshore wind.
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Additional avenues beyond bringing electricity back to shore could be explored to add value 

to offshore wind energy extraction. One possible approach would be to generate fuel at sea 

rather than extract and transmit electricity (Wiersema et al. 2016; World Energy Council 

Netherlands 2017) and there exist other proposals to integrate energy storage with wind 

generation also on shorter timescales (Garvey 2015). The energy extracted from the offshore 

wind turbine could be used to produce hydrogen or ammonia, which would be stored locally 

until tanker vessels could bring it to shore. Or, fuel could be used more directly in its remote 

location to power fishing or transport vessels. 

Another means of providing energy for storage and services to the grid could be achieved 

through wind power plant control. For offshore wind energy wherein smooth sea surfaces 

allow wakes of whole wind farms to propagate very long distances, wind farms affect the 

energy capture of other wind farms that are nearby. Optimizing and controlling power output 

not just across full wind plants but across collections of power plants will allow offshore 

wind energy to provide services to the grid and have more predictable and dispatchable 

energy production.

Floating wind turbines have an advantage over land-based and fixed-bottom systems in that 

they are not constrained to one position. Moving turbines inside a wind power plant could 

allow for decreasing wake effects and reducing loads. But, to gain even more energy 

extraction and reduce costs, floating wind turbines could be unmoored in deep open-ocean 

waters, allowing them to roam to where the wind resource is best while also avoiding 

extreme conditions such as tropical storms. This could also be used to move turbines closer 

to load centers in times of increased electricity need (e.g., during disaster relief or grid 

congestion). 

Finally, improved understanding of meteorological ocean (metocean) conditions over various 

timescales will enable the introduction of better wind energy forecasting capabilities for 

offshore wind. As discussed in detail in Section 3.1, many innovations are expected to 

enhance forecasting capabilities for wind energy in general, but for offshore wind energy, 

there will be additional opportunities for improved forecasting through better understanding 

of the coupled metocean physics and systems.

R&D Challenges To Realize Innovations

Fundamental research is needed to address the challenges for offshore wind and begin to 

realize the innovations needed to bring down costs. Four grand R&D challenges are 

identified in Table 18 as important areas to tackle in this regard. 
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Table 18. Offshore Wind Energy Technology R&D Grand Challenges To Realize LCOE and 

System Value Potential

R&D Grand Challenges Specific R&D Activities

Grand Challenge 1: Development and Validation 

of Multidisciplinary, Multiscale, Multifidelity, 

System Modeling Tools To Optimize Floating 

Wind Designs

Ensure modeling tools can adequately handle 

complexity—different scales, coupled phenomena, 

large machines, downwind (e.g., highly flexible, 

lead/lag behavior)

Develop improved, validated farm-level design tools 

(e.g., wake modeling offshore, large turbines)

Create a plant-level holistic design optimization process 

for floating wind systems and identifying concepts from 

this approach

Grand Challenge 2: Industrialization for 

Offshore: Manufacturing, Ports, Vessels, 

Deployment, Installation, O&M

Develop lower-cost methods to manufacture, install, 

and operate offshore wind systems (i.e., automated 

and/or improved welding)

Employ specialized access/installation vessels

Grand Challenge 3: Wind Turbine RNA Designs 

Specifically for Offshore

Research innovative drivetrain and generator concepts 

that improve LCOE for offshore systems (e.g., hydraulic 

systems, direct drive, superconducting, high-voltage DC 

generator)

Develop innovative rotor concepts (e.g., downwind and 

two-bladed)

Grand Challenge 4: Develop Full Understanding 

of Offshore Environmental Conditions

Achieve a full understanding of offshore environmental 

conditions including different turbulence levels, 

hurricane/typhoon risk, extreme waves, atmospheric 

stability, and resource assessment in the context of 

uncertainties and potential climate 

change/neighboring wind power plants

The first grand challenge is the development and validation of the modeling tools and 

procedures needed to find cost-optimal design solutions. To produce transformational cost 

reductions, multiple advancements are needed over the entire system: the turbine, tower, 

floating platform, moorings, anchors, construction, and logistics. As mentioned previously, 

this will require the development of tools that consider cradle-to-the-grave costs throughout 

the entire wind power plant through the implementation of a multidisciplinary, multiscale, 

multifidelity system design and analysis approach. The tools will need to be developed and 

validated to ensure they are capable of adequately representing both the design approaches 

used today and the innovative concepts being proposed for tomorrow. A lifetime, systemsengineering approach includes design choices that influence manufacturing and deployment 

processes and their associated costs. Executing this proposed approach in a closed-loop 

optimization framework is critical to achieving superior cost and performance gains over 

what is possible with today’s “open-loop” or stovepipe approach.

To find optimized designs, the system-level optimization tool must accurately represent the 

costs and physics of offshore wind systems. This includes representing the physics of 

offshore wind turbines at the different scales needed, validating these models, and 
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quantifying the uncertainties present. Future offshore turbines will operate at much larger 

heights in the atmospheric boundary layer than current designs, and the increasing size of the 

rotor means that turbulent fluctuations and atmospheric layering will have a greater impact 

on rotor design, performance, and reliability and need to be understood much better. The 

larger turbines will also have more flexible members and perhaps the addition of flow control 

devices. Floating systems also bring new modeling challenges including the representation of 

the hydrodynamic loading on the structure, aerodynamic loading under large motions, and 

the coupling between the two. This might also imply new aeroelastic stability issues, and the 

excitation of low-damped modes, which may lead to vibrations and increased fatigue loading.

On the metocean side, challenges also exist regarding accurately modeling extreme 

conditions, such as breaking or steep waves, atmospheric stability, and tropical storms, such 

as hurricanes.

Ensuring that accurate models are used to represent the physics at the plant level is important 

as well. Central to this is an adequate representation of the wake propagation through a farm 

and its influence both on power performance and structural loads. Plant-level control 

strategies can have a significant influence on the impact of the wakes. And, the behavior of 

floating systems will be very different from fixed-bottom systems requiring different design 

approaches for plant layout and control. As mentioned previously, the ability to move the 

turbines either through pitch and yaw control or a more active repositioning approach could 

yield significant advantages for floating system performance and loads at the farm level. 

Integrating and reducing the physics-based models to a manageable complexity to fit within a 

system-level optimization framework is a significant engineering challenge and will rely on a 

multifidelity process through which lower-fidelity models will be utilized upfront, with 

checks using higher-fidelity models.

The second grand challenge is the industrialization of the offshore wind industry. Though 

this is not purely a technical challenge, as it also involves supply chain development, there 

are underlying challenges in advanced manufacturing and automation. Although the cost for 

fixed-bottom wind has dropped significantly and is reaching cost competitiveness, prices will 

need to drop even more. Some of the ways this can be achieved is through improving the 

processes used in manufacturing and installation. For floating wind, less dependence of the 

design on site-specific seabed conditions and water depth will enable mass manufacturing. A 

class-based system for floating wind system design, considering metocean conditions of a 

site but not requiring a priori knowledge, similar to RNA design, could be used to enable 

high-volume production to reduce costs further. Developing designs that use readily available 

components could also support a mass-manufacturing approach. Engineering challenges arise 

in the context of improving manufacturing technology (e.g., robotic welding of nontrivial 

joints). The development of purpose-built boats for the transport and installation of offshore 

turbines will also be needed. Considering the transportation and installation needs in the 

design process, as is done within a systems engineering approach, will help to find costoptimal solutions. Other scientific challenges relate to the optimization of supply chain 

management.

The third grand challenge focuses on designing wind turbines (specifically the RNA) that are 

optimized for their offshore wind application—especially for floating wind energy. While an 
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off-the-shelf turbine can be placed on an offshore wind support structure (after suitable 

“marinization,” [e.g., sealing and corrosion protection]), cost advantages can be realized if 

the turbine could be better designed to suit the offshore environment. This goes back to the 

goal of designing the offshore wind system holistically—turbine and support structure—

rather than separately, while considering transportation, installation, and O&M costs. For 

fixed-bottom offshore wind systems, research can focus on increasing size, due to limited

issues in regard to transportation and installation as compared to land-based wind turbines, 

which can contribute to major cost reductions in the balance of system. Fixed-bottom 

offshore wind turbines can also benefit from research on new generator technologies with 

fewer moving parts, such as direct-drive generators or single-stage geared drivetrains with 

medium-speed generators. A contrasting need is for a lighter generator, to decrease the 

requirements on the support structure, especially for floating systems; thus, weight reduction 

options on floating drivetrains may inspire innovation to fixed-bottom configurations, 

especially as the ratings grow beyond 10 MW. New technologies, such as composites, 

higher-flux magnets, material optimization, and superconducting or pseudo-direct-drive 

generators, promise weight reductions in direct-drive generators that could reduce the weight 

penalties for floating applications. Other design requirements are more readily apparent for a 

floating wind turbine, in which the compliant nature of the support structure allows for larger 

motion of the system. Rotor concepts that are better adapted to this motion could increase 

power capture. Another focus area for research is on the turbine-level controls, which can 

provide improvements to load management for both fixed-bottom and floating systems. By 

integrating the control design upfront in the system-level offshore wind system design, better 

synergy and cost reduction pathways for the entire offshore system can be realized.

The fourth grand challenge involves developing a complete understanding of offshore 

environmental conditions to accurately characterize the metocean conditions (including 

bathymetry) at offshore locations for site assessment, system design, and model validation.

This includes understanding different turbulence levels, hurricane or typhoon risk, extreme 

waves, atmospheric stability, wind shear profiles under differing atmospheric conditions, 

resource assessment in the context of uncertainties over various timescales (diurnal, seasonal 

and annual), potential climate change, and neighboring wind power plants. In addition, there 

is much to be learned about joint occurrences of wind speeds, wind turbulence, wind shear 

(and more), wave height, wave period, and wave direction. All of these are of direct 

relevance to wind turbine design, especially as we move into more sophisticated application 

of reliability-based design for offshore, because we neither want to overdesign (unnecessarily 

costly) or underdesign the turbines (excessive failure rates). To do this, we need to have a 

better fundamental understanding of the offshore environment, presumably involving 

sophisticated measurement and analysis, but then would like to reduce the site-specific 

measurements and analysis required for specific projects. There is also much more to be done 

in the development/improvement of metocean instrumentation. Lidar technology is getting a 

lot of attention these days, but it has limitations, especially regarding turbulence and, to some 

extent, extreme winds; also, performing simultaneous measurement of wind speeds well 

above the sea surface and waves is still a challenge, especially during storms. Long-term, 

high-quality data measurements are a necessity for better understanding offshore conditions 

in the United States, including the diurnal and seasonal variations. Accurate metocean 
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condition measurements are needed for resource assessment, and also to inform sitesuitability studies for projects at a specific site. 

There are other research challenges to be addressed beyond the ones identified in Table 17. 

One important topic not mentioned thus far is the development of a relevant design standard 

for floating offshore wind systems. IEC has some material in its 61400-3 standard (IEC 

2009) relevant for offshore systems, but this is really focused on fixed-bottom designs. The 

last edition IEC 61400-3-1, still in draft form (IEC 2017a), is explicitly concerned only with 

fixed-bottom turbines, and so it is only partially relevant to floating turbines. The draft IEC 

61400-3-2 guideline (IEC 2017b) begins to discuss some of the additional needs for floating 

systems, but no formal design process has yet been established. Research is needed to define 

the appropriate load cases, including, for example, an understanding of simulation time, 

wind/wave directionality requirements, and how to adequately address tropical storms 

(hurricanes) for U.S. installations and other locations subject to tropical cyclones. As 

mentioned previously, also moving toward a reliability-based approach might enable a design 

with less uncertainty. 

Research is also needed to better understand fatigue behavior of both moorings and power 

cables, and the influence of marine growth on their health and performance. To 

commercialize floating wind, some of the initial designs may need to be developed in 

shallow water, where an economic mooring design approach has yet to be established. 

Research is needed on new materials and/or alternative configurations to enable floating 

substructure designs at shallow sites without adding cost. As turbine size grows, the use of 

catenary mooring configurations may become more difficult, both because of the large 

amount of space it will use on the seafloor, and the ability to transport the large chain. 

Research is needed on new mooring materials and configuration also for deep-water sites. 

Finally, additional research could focus on the use of shared mooring and/or anchoring 

approaches to reduce costs.

A final research topic could focus on environmental impact, including, for example, a better 

understanding of marine growth on offshore turbines, noise development and noise 

propagation during installation, and the electromagnetic shielding effect of offshore wind 

power plants. Engineering challenges are to develop novel, more environmentally friendly 

installation techniques and better corrosion protection systems (e.g., novel coatings) and to 

engineer efficient multiuse platforms.
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4 Cross-Cutting Needs for Data Science and 

Multiscale, Multidisciplinary Modeling

The previous sections set out the steps and associated grand R&D challenges that will be 

necessary to enable and support very high levels of wind energy shares in the global 

electricity system by 2050 while still maintaining low-cost energy, grid reliability, stability,

and security over time. Data science and multiscale/multidisciplinary modeling capabilities 

surfaced as common themes across the breakout groups and led to a discussion around 

providing a cross-cutting look at these topics.

Collected together, the range of temporal and geospatial scales along with the variety of 

disciplines involved makes wind energy one of the most complex technical systems in the 

world. The complexity and scale of wind power plant systems and their interaction with the 

electric grid challenge state-of-the-art research in data science, high-fidelity modeling, highperformance computing, and related fields, and motivates the need for coordinated research 

efforts. Figure 10 showcases the vast range in geospatial scales associated with the system 

from the climate to the wind power plant coupled to the electrical grid system. Though not 

illustrated, the range in temporal scales—from fast-reaction grid stability requirements to 

long-term accurate predictions of plant lifetime energy production—are just as vast. 

Figure 10. Wind power plants at every scale—from large-scale atmospheric effects to local 

climatology and topography to inter and intraplant flows and turbines responses to dynamic 

interaction with the electric grid system (Source: NREL)

Understanding and optimizing the wind energy system of the future will require collaboration 

between many different stakeholders, each working on separate aspects of common goals. In 

turn, this will be enabled by a combination of multidisciplinary and multisector data 

gathering, data science, and advanced modeling capability development that resolve the 

many different temporal and geospatial scales. Wind energy science will advance through 

coordination and collaboration across many academic disciplines while leveraging advances 

in several enabling technologies and research fields. Figure 11 provides a perspective of wind 

energy at the center of integrated research that leverages advances in 1) sensing and 

measurement technology, 2) computer and computation science, 3) big data, data science,

and digitalization, and 4) multiscale and multidisciplinary modeling.

Electric System 

Dynamics

Turbine 

Responses

Intra-plant

Flow

Mesoscale

Processes

Climate

Effects
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Figure 11. Enabling technologies/research for advancing wind energy science

Through coordinated research that leverages and integrates work within and across these 

areas, the wind energy community will develop the understanding and tools to drive 

innovation that will help realize the wind power plant of the future. This section will provide 

background in each of the areas mentioned earlier, with emphasis on their potential to impact 

and accelerate wind energy research. The discussion will then tie back to the grand 

challenges to illustrate how coordinated research within and across these areas will help 

address and overcome specific challenges.

4.1 Novel Sensing Technologies and Measurement Techniques

First, it is essential to recognize the critical role of recent and expected advancements in 

sensing technologies and measurement techniques. Current wind power plants contain many 

sensors associated with each individual turbine, meteorological equipment on-site, and the 

electrical infrastructure of the plant up to the point of interconnection. As an example, a 

typical commercial wind turbine will continuously provide 40‒50 sensor channels of data 

that can be sampled at various time intervals (Yang et al. 2013) within its SCADA system. 

Assuming storage of data at a 1-hertz rate, this would yield over 30 million separate data 

points per turbine per week. For the global fleet of wind power plant assets in 2017, one 

estimate suggested daily data generation rates of 25 trillion bytes (Bouqata 2017). Although

this may seem like a considerable number of sensors generating huge amounts of data for an 

all-encompassing view of wind power plant behavior, there are still significant gaps in our 

ability to measure the complex inflow conditions to wind power plants, the intraplant flow, 

and the responses and loading of the turbines to that flow within a wind plant. 

Understanding and predicting the behavior of wind turbines, wind power plants, and the 

electric grid will require advancing sensing technologies and measurement techniques to 

create a more complete picture of the overall flow into and through a wind power plant and 

the responses, electrical performance, and loading of all the turbines interacting with that 

flow (down to the component level). 
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Measuring Weather Variables Including Large-Scale Weather Phenomena and Wind 

Power Plant Flow and Intraplant Flow

The weather is of paramount importance for wind energy systems in that it provides the fuel 

for a wind power plant but also impacts the transmission, distribution, and consumption of 

energy. A few key technologies and research directions offer opportunities that are important 

to the design, control, and operation of a future electric power system with large shares of 

wind energy generation. For a detailed overview of recent advances in weather measurement 

technologies, see Peña Diaz et al. (2015) and Lundquist et al. (2019).

Remote Sensing of Wind by Sodar, Lidar, and Radar

Measurement systems in wind power plants have traditionally measured quantities of interest 

directly at the place they are required. For example, wind speed was measured by cup 

anemometers mounted on towers, wind direction through vanes on the same towers, and so

on. These instruments were often mounted on a few meteorological (met) towers that were 

put up before plant construction and provided local point-wise information on wind speed 

and direction, temperature, and pressure (see, e.g., Brower 2012 and Clifton, Smith, and 

Fields 2016). Met towers are still in common use today but are limited in their spatial 

coverage and the height they can reach (often met towers are less than 100 m tall, whereas

turbine blade tip heights may reach 200 m or more).

Remote-sensing devices, on the other hand, can measure the wind at some distance away 

from their physical location. The most commonly used variants of this technology are sodar, 

lidar, and radar, which respectively use sound, light, and radio emissions to actively measure 

the wind. Sodar has been popular for use in resource assessment campaigns (Peña Diaz et al. 

2015). Radar has been used for a few specialized commercial applications (Nygaard and 

Newcombe 2018). At this time, lidar appears to be the most-frequently used remote-sensing

device for wind energy applications (Emeis et al. 2007).

Commercial wind lidars were introduced in the 2000s to measure general weather conditions 

and later in the mid-2000s for specific wind energy applications. The devices provide data 

across large volumes, potentially throughout an entire gigawatt-scale wind plant. Since their 

first introduction, commercial wind lidars have reduced in cost to the point that they are now 

competitive with traditional met masts and are often used for wind resource assessment and 

power performance testing. Given the gradual reduction in costs of electronics in wind lidar

and economies of scale associated with more manufacturing, it is likely that unit costs will 

further decrease. 

Inexpensive, reliable wind lidars can improve operations ranging from the micro to the 

macroscale. For example, they could be used for wind resource assessment, wind monitoring, 

and power performance testing. Or, they can be integrated into wind turbines for feedforward controls or integrated into a plant for wind-plant-level control for plant optimization 

or power dispatch. They can also be used to increase the accuracy of data from model chains 

from large to local scale to support validation of wind plant flow models. It should be noted, 

however, that present-day lidars are not able to accurately measure turbulence intensity and 

gusts, so they cannot present a complete picture of the meteorological conditions that are 
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relevant to the design of wind turbines. As a result, further work needs to be done in this 

area.

Another remote-sensing technology that has the potential to improve measurement of wind 

plant flow is radar. Radar is also used for wind resource monitoring and research studies and 

has the potential capability to produce an even more refined and complete set of data over the 

entire flow into and through the plant at various heights and spatial resolutions (Hirth et al.

2012). Airborne- or satellite-based synthetic aperture radar have proven useful for large-scale 

measurements on complex flows in the offshore environment (Hasager et al. 2008).

However, radar systems are still very expensive, which limits their use for commercial 

application. 

Other Sensing Technologies for Weather

New opportunities based on innovative technologies, such as acoustic tomography (Arnold et 

al. 1999) and sensors placed on unmanned aerial vehicles (Giebel et al. 2010) among others 

may someday prove useful. While still early in development, ongoing research in several 

areas of weather sensing technology reveals that there is still an opportunity to improve the 

ability to measure weather data and increase the resolution, accuracy, and amount of data 

around wind plant inflow and intraplant flow conditions.

As well as benefitting from improved weather forecasts, the wind energy industry may see 

new approaches based around data fusion (i.e., the use of many different data sources of 

varying quality to achieve a picture of the whole). This approach is in its infancy but is often 

used by the automotive industry, particularly for self-driving vehicles. New vehicles 

increasingly contain sensors that monitor vehicle conditions, air temperature, visibility, road 

surface conditions, and other factors that impact vehicle safety and traffic. These data can be 

used by the vehicle itself to support autonomous decision-making (for example in selfdriving vehicle applications). Data from vehicles and other sources (e.g., cell phones and 

potentially a vast array of distributed devices) may be harnessed in the future to improve 

forecasts of weather conditions, which in turn will improve the predictability of wind energy.

Wind Turbine Response and Loading

It is necessary to collect data from the wind turbine itself to inform turbine models and 

predict its performance or maintenance requirements. Such models require information about 

the state of the wind turbine, particularly the load-bearing structures, drivetrain, and 

actuators. As mentioned, there are currently upward of 50 sensors in a wind turbine SCADA 

system that monitor different components, with over half to three-fourths of the sensors 

focused on monitoring the turbine performance in terms of power output and related 

electrical performance (Yang et al. 2013). If a turbine is being used for testing, certification 

and/or research, there may be the addition of other sensors to monitor performance and loads 

throughout the turbine (Santos and van Dam 2015), but these sensors are not common in 

commercial applications to date. For commercial applications, in addition to typical SCADA 

channels, here may also be some sensors on the turbine primarily targeted to condition 

monitoring of the drivetrain and loading on the tower or support structure; rarely are there 

sensors that provide detailed loads measurements of the turbine during commercial operation. 

For the evaluation of the turbine component health over its lifetime, ideally more detailed 
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measurement of blades, tower, and other component loads (i.e., the main shaft and bearings, 

gearbox, coupling, generator, and yaw system) would be available. As load measurement 

sensor technology advances and costs decrease, this will enable fleetwide instrumentation 

and turbine state estimation over the turbine and plant lifetime.

Several companies have recently demonstrated fiber-optic based sensors that can measure 

loads, strains, vibrations, electric fields, and temperature within wind turbines (Schröder et 

al. 2006). These systems are characterized by lower prices, higher reliability, easier 

installation, and improved safety compared to traditional sensors and may enable more 

effective turbine observations than currently possible. Cheaper and ubiquitous sensing of the 

turbine offers the potential to use these data to make wind turbine control decisions that are 

simply not possible now. These steps could result in higher reliability and more predictable 

energy compared to current wind turbines.

Wind Plant State and Beyond the Plant

Measurement and aggregation of turbine state information at the full wind power plant level 

will be increasingly important as future wind power plants provide more and more services to 

the grid system including ancillary services and grid-forming capabilities. Thus, sensing 

technologies and measurement techniques at and beyond the wind power plant/electric grid 

interconnection point will be critical. State estimation of the larger electric grid system is a 

massive research topic beyond the scope of this report. For some background on the topic, 

see Kroposki et al. (2017) and similar studies that look at state estimation, power flow 

analysis, and operation of future converter-dominated electric grid systems.

4.2 Computer and Computational Sciences

Advances in computer science and scientific computing are being leveraged to support largescale computational needs imposed by wind energy science both for management and 

analysis of huge amounts of data as well as in the ability to successfully model the wind 

energy system across physical disciplines and scales. 

In the last decade, there has been a revolution in computing power through advances in 

hardware for processing power, parallelization, memory, and other features as well as in 

software in terms of algorithm development and architectures for handling computing at 

petascale and moving toward exascales (Sprague et al. 2017). The growth in computing 

capabilities has been applied to a large range of systems from very small devices that are 

mass produced (i.e., mobile phones) to large-scale supercomputers that continue to grow in 

size every year (i.e., the largest supercomputer in 2018, the Summit, has maximum 

performance potential of 122 petaflops (https://www.top500.org )). The growth in computing 

power at both very small and very large device scales has led to a proliferation of major 

computing architectures. There are many ways to classify computing architectures and a lack 

of agreement persists amongst experts in the field. The following provides one high-level 

classification of computing architectures from a wind energy science perspective:

• High-performance, or high-throughput, computing. Associated with 

supercomputing and supercomputers in which there is significant vertical scalability 

with a single application run across numerous computing nodes. Many large-scale 
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meteorological, wind power plant flow, and grid operation models leverage this type 

of computing (Sprague et al. 2017; Womble et al. 2015; Haupt et al. 2015, 2017).

• Cloud computing. Associated with the use of many remote computing systems to 

target “embarrassingly parallel problems” that can be separated out easily into many 

parallel computational tasks with little-to-no dependency between them (and thus 

achieve horizontal scalability). These types of systems can be and are used for 

processing large amounts of data that can be processed separately (e.g., wind turbine 

and plant SCADA data). 

• Grid, or distributed, computing. A heterogenous and geographically dispersed set 

of devices/computer resources that coordinate together on a common computational 

goal. There is ambiguity in terms of the difference between cloud and grid 

computing, with a key distinction being the level or role of the distributed devices in 

terms of executing computation as part of the larger application goal. An example 

from a wind energy perspective would be an advanced wind power plant control 

system in which each turbine controller processes and analyzes internal data that are

used in a coordinated plantwide control scheme (Annoni et al. 2018; Bay et al. 2018).

These developments in computing underly the ability of the wind energy research community 

to coordinate the acquisition and management of large amounts of data, to develop both datadriven and physics-based models that can scale across all relevant temporal and geospatial 

scales, and directly support the implementation of the control and operation of wind power 

plants of the future.

4.3 Multiscale and Multidisciplinary Computational Models 

Integrated models of wind turbines, wind plants, the grid, and consumer behavior allow 

stakeholders to test the performance of current wind turbine designs and the grid for working 

conditions and extreme cases. Similarly, models of new turbines and future grid designs 

allow new solutions to be investigated. Accurate models of the full end-to-end system are 

imperative to understand how to operate and stabilize the future electricity grid dominated by 

renewable energy with wind energy contributing 50% or more of the overall generation. 

Wind Turbine and Plant Design and Analysis Tools Driven by Flow Models 

A large majority of the wind power plants in operation were designed using flow models and 

engineering design tools developed in the late 1980s, notably after the publication of the 

European Wind Atlas (Troen and Petersen 1989). At the time, limitations in computing 

power required drastic simplifications in the development of engineering models. Flow 

models were simplified by different assumptions. These conditions were used to simulate the 

mean flow under stationary conditions that would be justified in a climatological sense, 

relevant for wind resource assessment, when wind conditions are averaged over long-term 

periods of years to decades. 

Turbulence modeling in wind turbine design standards also assume stationarity and surfacelayer similarity to characterize a wind spectrum that is decoupled from the large-scale 

variations of weather processes (Kaimal et al. 1972; Veers, 1988; Mann 1994). This 

separation between microscale and mesoscale effects on the turbulence structure has been 

historically attributed to the “spectral gap” described by Van der Hoven in 1956 (Van der 
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Hoven 1956; Larsen et al. 2016, 2018), as opposed to a more generalized spectrum in this socalled “terra incognita,” wherein turbulence progressively cascades from mesoscale to 

microscale (Wyngaard 2004). Similarly, wind power plant wake models have also required 

strong approximations, such as axisymmetric similarity and terrain-wake decoupling, 

together with calibration against wake measurements to build engineering models that can be 

effectively used in the wind power plant design and optimization process (Sanz Rodrigo et al.

2017). Turbine design relies on industry standards in which wind conditions are defined in 

terms of canonical classes that can be representative of generalized wind resource and 

turbulence characteristics. 

 

These and other approximations have also facilitated the separation of modeling communities 

that would develop their models with little interaction between them. Historically, four flow 

modeling silos would develop, each one dealing with a specific range of scales from the 

perspective of different individuals with specialized scientific backgrounds: 1) a 

meteorologist, dealing with climate and large-scale weather effects, for whom a wind power 

plant is just another type of land use, 2) a wind turbine siting engineer, dealing with the flow 

at microscale affected by local topographic and wind power plant wake effects, for whom the 

wind turbine reduces to an actuator disk driven by a power law, 3) an aerodynamicist, 

specializing in rotor design aspects, for whom the inflow wind profile can be characterized 

with a simple log law in free-stream conditions (Shaw et al. 2009), and 4) a wind turbine 

structural designer, concerned with the ability of the entire structure to withstand the effects, 

both fatigue producing and ultimate, of the meteorological (or meteorological/oceanographic;

metocean, in the case of offshore) conditions to which it will be subject. 

Alongside the development of wind energy technology toward larger rotors and wind power 

plants, these physical approximations have been challenged in favor of an integrated model

chain that would produce a more realistic representation of the interplay between 

atmospheric and wind power scales. The most important uncertainties in the performance of 

large wind energy systems still reside in the highly unpredictable turbulent flow in which 

they operate. As a result, such systems require consideration of a multiscale modeling 

approach that connects disparate scales from climatic changes, which dominate the 

interannual and seasonal variability of the wind resource through atmospheric weather 

processes, to thermally stratified boundary-layer turbulence influenced by local terrain and 

vegetation effects (Sanz Rodrigo et al. 2017). 

Only recently has it become possible to bridge silos in flow modeling with advances in highperformance computing and large-eddy simulation models (Hammond et al. 2015). As 

mentioned previously, developments in computing now enable large-scale simulation across 

large spatial scales with high temporal and geospatial resolution (Sprague et al. 2017). This 

has led to a proliferation of research explicitly coupling models from the meteorology all the 

way down to individual turbine responses. The latter models will be discussed next.

Wind Turbine and Plant Design and Analysis Models 

Flow models associated with crossing all the relevant scales are critical because ultimately 

wind is the fundamental resource for the entire system. However, these fluid models feed into 

and interact with individual turbine models that couple in the physics from a broad range of 
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disciplines including aeroelastic, structural dynamics, controls, and, for offshore applications, 

hydrodynamics. Wind turbines and power plants are analogous to living systems that include

hundreds to thousands of components interacting with the wind as well as each other with 

highly coupled and nonlinear responses. Like wind flow models, there has been an evolution 

over several decades in the way each component, the full turbine, and the plant physics are 

modeled for applications of engineering analysis, design, and turbine and plant operation. At 

the same time, this modeling capability still needs improvement—for example, in offshore 

applications where models are relatively undeveloped.

Early models for wind energy applications leveraged modeling efforts for aerospace 

applications, such as helicopters. However, as experience with wind turbine technology 

grew, several wind-energy-specific dynamic analysis codes emerged that were meant to 

support accurate assessment of the machine loading under a variety of operational conditions. 

These “aero-servo-elasto-hydrodynamic” codes have evolved along with wind turbine 

technology as machines have become much larger, more flexible, and deployed in offshore 

fixed-bottom and floating applications (e.g., Jonkman and Buhl 2005). 

Recently, the focus of the wind energy scientific community has shifted from a turbinecentric view to a full wind plant design and operation view that accounts for the impact of 

intraplant flow on turbine responses. This shift has pushed the turbine modeling community 

to interact more closely with the flow modeling community to develop fully coupled models 

of turbine and plant dynamics using engineering flow models up to full large-eddy

simulations (Fleming et al. 2013; Jonkman et al. 2017, 2018). 

Through this full coupling of turbine and plant physics, the scientific community can explore 

questions never answered regarding the response and loading of individual turbines in plants 

under a range of conditions including complex terrain, atmospheric stability, heterogenous 

flow, various large-scale flow conditions for offshore wind turbines, and more. An important 

expansion in the scope of the plant-level analysis is to answer questions related to controlling 

the entire plant flow for increasing overall energy production, balancing system loads to 

improve overall plant reliability, or providing more predictable energy and services to the 

electric grid system. For the last point, coupling in models of the electric system to the rest of 

the system physics is necessary.

From Wind Turbine and Plant Models to the Full Grid System

Although developing an end-to-end modeling capability for the wind power plant as a standalone entity and treating the larger electric grid system as an exogenous entity is a huge 

challenge, it only solves part of the challenge in terms of the modeling capability necessary 

to realize the future converter-dominated electric grid system. In addition to coupling all the 

relevant disciplines together across the power plant, it is necessary to couple these models 

that accurately represent the full electric grid system dynamics at all timescales of interest 

(from short-term transients to system operation models for dispatch and commitment on the 

order of minutes to days to medium- and longer-term planning models for generation 

capacity and transmission). 
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There has already been significant effort in bridging models across all the relevant domains 

for turbine and plant physics. Similarly, within the electrical engineering community, there 

has been consistent advancement in power system models that represent the electrical 

behavior of all the various elements. However, there has yet to be large-scale efforts to 

bridge across these two areas for a truly integrated modeling capability necessary to capture 

all the interactions of the future converter-dominated electric system. 

Multiscale Model Validation

Although multiscale models produce more data and include more physics than ever before, 

this does not guarantee that they are correct. There is still a need to confirm how well those 

models match reality, which is the process of model validation. As the amount of wind 

energy on the electrical grid increases, it will be ever more important to validate models and 

explore unusual situations that might endanger supply.

High-fidelity models cannot be used directly in design, but they are used to build more 

robust, cost-effective, and physically insightful engineering models that can systematically 

decrease the uncertainty in the assessment of energy yield and design conditions. Similarly, 

the development of data-driven design and operational models (digital twins) is also limited 

by the lack of standardized databases and data screening procedures that can homogenize the 

monitoring and diagnosis of wind power plant performance. A formal process for modelchain verification and validation and uncertainty quantification between high-fidelity models, 

engineering models, and observational data is lacking, partly because of the inherent 

complexity of the full system, requiring large investments in field and laboratory experiments

(Hills et al. 2015; Sanz Rodrigo and Moriarty 2015).

One example of multidisciplinary research involving such large-scale field experiments can 

be found in the development of multiscale atmospheric models. To validate these models, the 

largest experiment related to boundary-layer meteorology in complex terrain has been 

recently accomplished in Perdigão (Portugal) (Mann et al. 2017). This experiment features 

the deployment of 50 met masts, 20 scanning lidars, and other met systems over a double-hill 

setup, through a joint-funding initiative between the New European Wind Atlas project 

(Lundtang Petersen et al. 2014) and several projects funded by the U.S. National Science 

Foundation. The New European Wind Atlas project is driven by the wind energy community 

to produce a comprehensive database of experiments that can allow wind energy modelers to 

work with a meteorologist to understand complex atmospheric physical phenomena and 

develop high-resolution turbulent models that can build the bridge across the terra incognita. 

Similar efforts have been executed in the United States under the U.S. Department of Energy

Atmosphere to Electrons (A2e) program with its Wind Forecasting Improvement Projects 

(WFIP1 and WFIP2) (Wilczak 2017), mesoscale-microscale coupling (Haupt et al. 2017), 

and scaled wind farm testing facility projects (Berg et al. 2014). These and future efforts are 

expected to provide breakthroughs in understanding around wind turbine and plant physics 

by targeting the full range of physics involved both spatially and temporally. Similar work 

remains to be done for offshore applications where understanding combined meteorological 

and oceanographic effects is critical to design and operation of wind turbines and wind 

plants.
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4.4 Digitalization, Big Data, and Information/Data Science

Addressing the grand challenges identified in the different breakout areas will require the 

wind energy industry to harness the latest in technology innovations. Wind energy 

digitalization refers to the idea that there is a whole spate of digital technologies that will 

reshape the workings of the energy industry (IEA TEM #92 2018). These digital technologies 

taken together represent the ability to gather, analyze, and act on data from wind turbines and

power plants and improve the interactions with the electrical grid and markets. These data 

will be used for historical performance analysis and validation in addition to predictive 

analysis, real-time control and operations, and even future system design. Increasingly, 

complex models will be used to understand the system and predict its performance, and 

decisions will be largely automated. This tendency toward interconnected devices that 

generate large amounts of data and act autonomously is often called “Industry 4.0” and is 

characterized by digitalization and the presence and exploitation of “big data.” For clarity, 

these concepts are defined here as they are being used and interpreted within this document:

• “Industry 4.0” (Rose 2016) is a term associated with a new paradigm in 

manufacturing and technology businesses that involves a high-level of automation, 

distributed computing, decentralized decision-making, “big data,” digitization, and 

digitalization as described in the next three bullets. Also see Figure 12.

• Big data applies to the data management, processing, and analysis of data sets that are 

too large and complex to handle with conventional techniques. Typically, the concept 

is described using the four V’s: volume, velocity, variety, and veracity (Ward and 

Barker 2013).

• Digitization is the process of changing from analog to digital form, also known as 

digital enablement. Said another way, digitization takes an analog process and 

changes it to a digital form without any different-in-kind changes to the process itself

(Gartner 2019).

• Digitalization is the use of digital technologies to change a business model and 

provide new revenue and value-producing opportunities; it is the process of moving 

to a digital business (Gartner 2019).

Figure 12. Illustration of Industry 4.0, showing the four "industrial revolutions" (Source: Rose 

2016)
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It was already noted that wind energy and the wider electrical grid is undergoing rapid 

digitalization that will lead to a very large body of data about turbines, the plant, and the grid 

(BNEF 2017). Data science, a combination of hardware and software that is designed to 

create, explore, retrieve, manipulate, and condense data from many disparate sources, will be 

a major enabler of digitalization for wind energy. Digitalization and data science are 

relatively recent technological and scientific paradigms that have enabled disruptive 

innovation in many fields and are likely to impact wind energy as well (Concolato and Chen 

2017). The effective use of data could lead to more efficient planning, better use of resources, 

and improved decision-making throughout the wind power plant life cycle, leading to a 

reduction in the cost of energy and increase in the value of wind energy. 

ETIPWind analyzed technological goals for wind energy and determined how they might be 

enabled by digitalization when tailoring information and data science to wind energy 

applications. The ETIPWind vision was presented in When Wind Goes Digital (ETIPWind 

2014). The main output from ETIPWind is a diagram showing the mind map of the 

digitalization centered around two main objectives: integration and reduction of the cost of 

energy (analogous to system value and LCOE, respectively, as considered in this report), 

which are supported by the availability of data enabled by data sharing and data 

management. The objectives of digitalization according to ETIPWind are summarized in 

Table 19. These objectives align well with the impacts from identified innovations identified 

in Section 3 across the different breakout areas.

Table 19. Objectives of Digitalization According to ETIPWind

Objectives of Digitalization

Reducing the Cost of Energy Wind Energy Integration

Digitalization will help make wind energy more 

competitive, specifically for conventional power 

plants, by applying data-driven strategies and 

decision-making. 

This approach will simultaneously decrease the cost 

of wind energy and improve the value of wind 

power. In particular:

• Improving productivity

• Forecasting and wind plants and wind 

farm control

• Decreasing O&M cost (OpEx) with better 

decision-making and efficiency

• Reducing investment cost (CapEx)

• Lifetime extension

• Better operations and trading in the 

power markets.

A set of tools to create connectivity by integrating all 

involved parties, such as wind power plant producers,

transmission system operators, distribution system 

operators, and consumers, and ensure synergy with 

other sectors in the electricity system to establish

better wind integration. In particular:

• Transmission system operator, Distribution 

System Operator integration

• Real-time grid support capabilities

• Faster and more efficient grid services 

• Synergies with other types of power 

generation

• Consumer synergies

• Sector and storage coupling.

Application of digitalization and associated capabilities to wind energy will leverage 

technology development in several enabling research areas. These technologies are organized 

into categories below and described in more detail with relation to wind energy in the text 

that follows:

● Digital workflows
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○ Analog to digital conversion of information around technology design, use, and 

operation

○ “Digital twins” that mirror the real assets in a digital format over their lifetime

○ Blockchain and distributed ledger technologies for democratizing the transition 

of digital assets

○ Automated/distributed decision-making where there is no longer a human in the 

loop

● “Internet of things”

○ Ubiquitous digital communications between large numbers of big and small 

technology assets

○ Inexpensive and fast computing available on demand

○ Connected sensors throughout technology assets

○ Edge computing where distributed technology assets are “smart” and bare some 

of the burden of system analysis and associated computation

● Data science tools

○ Distributed computing and analysis (such as methods for handling analysis and 

synthesis of big data efficiently)

○ Machine learning and artificial intelligence for creating understanding directly 

from repeated interaction with data using a large variety of algorithms

○ Computer vision and associated sensory technologies that enable a higherdimensional interaction of the computing and real world.

Digital workflows correspond to the idea that the entire wind power plant lifecycle can be 

digitized and simulated, allowing for improved efficiency, quick design iteration, scenario 

analysis, and diagnostic as well as prognostic modeling. The implementation of digital 

workflows and data standards can lead to increases in efficiency of up to 60%−80% in 

analytical processes (IEA TEM #92 2018). The idea of digital manufacturing is encapsulated 

in the industry 4.0 definition and is being aggressively pursued by several international 

initiatives (European Commission 2015). Digital manufacturing enables not only digital 

design but also a digital twin to which a whole host of scenarios can be applied. This can be 

not only digital twins of wind turbines and wind plants but also digital representations of the 

entire wind plant life cycle including the manufacturing line, supply chain, and end of life.

The internet of things relates to the ability to have a collection of computing devices that are 

interconnected, that have the ability (to a degree) to do their own computation and analysis, 

and that share information and computing resources with each other. In some ways, the

internet of things has long been present in many technical products that have computing 

capabilities distributed across many subsystems. Take a vehicle, for example, that for many 

years has had separate computing and control devices for the drivetrain, transmission, and 

infotainment (the information and entertainment system with which the vehicle passengers 

interact) that constantly share information with each other through a local vehicle intranet 

system. Within wind energy systems, many separate devices (in turbines, meteorological 

instruments, and more) collect data through sensors and may do computation and analysis of 

that data and share this across the larger system network.
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Although the potential for increased use of data science, digitalization, and related methods is 

enormous, there has already been a concerted effort by many groups to use data science and 

machine learning for wind energy research and commercial applications. Many stakeholders 

have developed approaches to mine hidden operational problems from the vast trove of 

SCADA data already being gathered in wind plants. From large-scale consulting firms, 

owner/operators, and equipment manufacturers to small-scale start-ups and research groups, 

there is a growing effort to leverage existing data sets for wind turbines and power plants to 

both identify opportunities for improved plant performance and energy production as well as 

to diagnose and predict potential failures and reduce O&M costs (Typoltova 2017). One 

example from the research community is the use of machine-learning algorithms applied to 

turbine power performance data, which can be used as an alternative means of dealing with 

the noisy data from which power curves are derived (Clifton et al. 2013). Other examples in 

the research community focusing on O&M include condition monitoring and predictive 

maintenance (Hameed et al. 2009; Garcia Marquez et al. 2012; Nabati and Thoben 2016;

Canizo et al. 2017). The recognition of the need for data-driven research has even led to 

large-scale research programs in the European Union including such projects as the ROMEO 

project (https://www.romeoproject.eu/machine-learning-iot-improve-wind-farms/) and the

VIS-Project Offshore Wind Operations & Maintenance Excellence (OWOME) project

(http://www.owi-lab.be/content/vis-project-owome-offshore-wind-operations-maintenanceexcellence).

An important consideration for machine learning and artificial-intelligence-related research is 

the responsibility to ensure transparency, fairness, and ethical treatment of automated 

decisions. There are many examples of the increased use of machine-learning and artificialintelligence tools to help inform or automate decisions that have resulted in systematic biases 

and by extension harms (O’Neill 2016). Specifically, researchers have broken these harms 

down into two main categories: allocative harm and representational harm (Barocas et al.

2017). Allocative harms correspond to the potential misallocation of resources (e.g., 

financing, insurance, employment) due to algorithm biases while representation harms relate 

to identity and perpetuation of cultural stereotypes. These include applications as broad 

ranging as credit scores, university applications, and societal policing. Another critical facet 

is that these considerations should be extended to both computer and machine ethics 

(Anderson and Anderson 2011), the distinction being whether a human is using computer 

capabilities, or a machine is using ethics to make decisions. When we consider the 

application of machine-learning/artificial-intelligence technologies, we should also consider 

the potential harm to various stakeholder groups in the wind plant lifecycle and wind plant to 

grid and market interactions. Specifically, we should be considering the impacts on wind 

plant owners, operators, manufacturers, grid operators, and consumers.

Data-Driven Modeling and Simulation

Beyond pure data science and data-driven techniques, the coupling of physics-based 

modeling approaches (as described in Section 4.3) with data-driven techniques—a process 

referred to as data assimilation—will yield a new generation of modeling capabilities for 

wind energy that leverage the advantages of each technique. On the one hand, data-driven 

techniques are successful at representing real-world conditions, but they can be limited when 

extending to applications wherein data are sparse or nonexistent. Further, physics-based 
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models that embody fundamental principles of the technology and science can predict system 

performance in a wide variety of conditions yet have difficulty when applied to situations 

that defy model assumptions. In addition, both model types can have large computational 

costs. Bringing together physics-based models and data-driven techniques through data 

assimilation may improve the combined modeling accuracy for a variety of applications and 

may also reduce computational costs. An example of such an approach is the use of data to 

improve the fidelity of less-expensive physics-based models of plant flow for design and 

control applications (King et al. 2018; Adcock et al. 2018). 

Data Management and Sharing

Regardless of whether the research application is purely data-driven or combines physicsbased and data-driven modeling, a key challenge for data science in wind energy is 

inconsistent data management across the industry, which limits the applicability of automated 

data processing and machine-learning/artificial-intelligence techniques as well as the ability 

to share data publicly across the research community and industry. This challenge highlights 

the need for more aggressive coordination across the research community and industry to 

develop frameworks, standards, and even incentivized systems for data management and 

sharing. Currently, there are a number of data standards that exist and have been catalogued 

by IEA Task 33. One apparent issue is not the lack of standards but rather the adoption and 

harmonization of existing standards such as IEC 61400-25: Communications for monitoring 

and control of wind power plants. It is not only a technical challenge to agree on the 

standards but also a social science challenge to understand how to best drive adoption and 

utilization of the standards.

Companies already recognize the value of their data in terms of building value and 

competitive advantage. The movement toward digitalization compounds this value. As a 

2017 BNEF white paper on Digitalization of Energy Systems emphasized, “The importance 

of data as a commodity cannot be overemphasized. We see data strategy and security as the 

business area with the largest degree of change and the most significant impact on 

investments and returns for all parts of the value chain. Data security, privacy, collection, 

storage and manipulation will all determine a utility’s ability to stay competitive.”

A challenge for big data within even a single organization is to know which data are 

available, how to access them, and how to synthesize them into actionable information. This 

ability requires data management and retrieval expertise, and new large infrastructures for 

storing data and retrieval systems. The technical challenge of building such an ecosystem of 

infrastructures is being addressed through investments in the information technology sector. 

However, to effectively use these systems to support wind energy research and technology 

development applications, frameworks and standards specific to wind energy technology are 

needed. Research policies, such as the European Open Science Cloud (European 

Commission 2016), are being developed to integrate public data management and computing 

infrastructures and promote open science to make sure research data can be fully exploitable 

by being findable, accessible, interoperable, and reusable (FAIR). These so-called FAIR data 

principles were introduced through a collaboration of European stakeholders in a 2014 

workshop and then published in 2016 in Nature (Wilkinson et al. 2016). 
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To organize and make wind energy data findable by the research community, the EuropeanCommission-funded project, IRPWind, supported an effort to design and develop a prototype 

of a data portal (https://sharewind.eu/). As a first step toward a data portal, the project 

produced wind-energy-specific metadata scheme and related taxonomies needed for data 

tagging (Sempreviva et al. 2017) supporting data search. ShareWind is designed as a 

metadata generator with a searchable metadata catalog and data-publishing portal. Data 

owners can generate and store the metadata in a searchable metadata catalogue and then

choose whether to upload the data and metadata on the EUDAT (https://eudat.eu/) data 

repository; or to upload data in another repository. The IEA Wind Task 37 on wind energy 

systems engineering also has an effort to create taxonomies that enable the exchange of 

model input and output formats, which is aligned with the efforts of IRPWind to further 

support interoperability and reusability (https://community.ieawind.org/task37/home ). When 

operational, ShareWind will contain the information of data available within the wind energy 

community, independently where the data are stored. This approach avoids creating an 

expensive and time-consuming community data repository. A similar portal for data sharing 

has been created in the United States under the A2e Data Archive and Portal (Sivaraman et 

al. 2014). Going forward, a key challenge will be ensuring coordination across different 

stakeholder groups and establishing a general standard that will be adopted across the 

research community and industry.

Perhaps more critical than actual data standards are practices and guidelines that enable 

wider dissemination and sharing of data. Sharing knowledge and data is widely recognized as 

being fundamental to supporting a fast track from research to innovation. However, there are 

normative and structural barriers to this sharing. Although large amounts of data about wind 

energy activities are produced (e.g., by wind power plants) every day, access to these data by 

the scientific community is restricted or regulated on a case-by-case basis through data 

licensing agreements. Research institutions and industry typically use two different models 

for data: visible data (generally covered by a nondisclosure agreement) and invisible data 

(undisclosed proprietary data). This practice of bilateral sharing of data prevents the wind 

energy community from realizing the full potential of big-data exploitation to generate 

scientific insights and systematically improve the predictive capability of models. 

An example of improving overall data sharing and management comes from the European 

Commission policy for Open Science (European Commission 2016), which supports open 

data by defining a mandatory data management plan for managing research data by 

documenting and connecting data gathered in each funded project. By following the FAIR 

data paradigm, European-Union-funded projects will guarantee data management and 

curation that supports the reproducibility of and future reuse of data. And, by 2020, the 

European Open Science Cloud will be created to incentivize data sharing, which will increase 

the ability of the research community and industry to exploit large data sets and ensure that 

critical data are accessible and used as widely as possible. The ShareWind portal is also 

designed with the purpose of facilitating data sharing. ShareWind contains only metadata 

including the information on data owners and access rights. Users will have to contact data 

owners to access the data. This allows owners maintaining control over the data whilst 

having visibility. Similarly, in the United States, the open government and open data 

initiatives have mandated full public access to government-funded research data.
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The larger utility stakeholder community in Europe is also moving to align with data 

management and sharing principles as touted by the European Commission. For example, in 

November 2017, the European Distribution Grid Operators and other stakeholders proposed a 

Protocol for a European Open Data policy in distribution systems and signed the “Tallinn eenergy Declaration” (https://www.eu2017.ee/tallinn-e-energy-declaration). This protocol 

aims to promote the digitalization of Europe’s energy systems and the use of smart solutions 

to accelerate the energy transition toward a clean and sustainable energy future. 

Data marketplace for sharing data and accelerating energy transition

As digitalization is the process to gain insights from data-enabling new services or business 

models, data are considered assets that provide a competitive advantage. However, there is 

increasing awareness that exposing the data using metadata opens the possibility to multiply 

the value of the data by attracting other stakeholders from other sectors and disciplines. Data 

marketplaces appear to be an additional possible means of encouraging collaboration and 

sharing data, as they bring together data users and owners on an existing platform— either 

public (i.e., Qlik [https://www.qlik.com/us/products/qlik-data-market]) or commercial (i.e.,

DataStreamX [https://www.datastreamx.com/])—and create new revenue opportunities for 

data owners. However, data can be traded monetarily, exchanged on a collaborative basis, or 

exchanged for services.

4.5 Intersection of the Grand Vision with Emerging Science and 

Technologies

The next generation of wind energy design and operational tools will be based on multiscale,

physics-based, data-driven and data-assimilated computational models that can produce a 

realistic characterization of weather (or metocean in the case of offshore), power plant 

performance, and grid status across all relevant scales. Full system validation will be a grand 

challenge because of the high complexity of the coupled atmospheric-power (or metoceanpower) system and the large number of operational conditions. A formal model evaluation 

process is necessary to systematically improve the physical insight of the individual models 

and coupling mechanisms that integrate the full system model chain. To this end, highfidelity experiments with extensive data collection and universal access will be required. 

Operational data from wind power plants should be accessible as well. The validation of the 

computational models (data-driven, physics-based, or a combination thereof) will depend on 

the ability to tap into the large potential using data science. By speaking a common language 

of data science, the wind energy community can reduce knowledge silos and develop more 

interdisciplinary and multidisciplinary research that can lead to drastic reductions in the cost 

of energy. Table 20 illustrates this point by linking the identified grand R&D challenges from 

the different breakout groups with the topics of sensing technology, multiscale modeling,

digitalization, and data science.
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Table 20. The Challenges and Opportunities of the Wind Energy Transition to Digitalization 

and “Openness” Toward the Grand Vision

Grand Challenges in Wind Energy R&D Opportunities Offered by Big Data, Information and 

Data Science, and Multiscale and Multidisciplinary 

Modeling and Advanced Sensing in Addressing the 

Grand Challenges

Category 1: Advanced 

Understanding of System Physics 

Within 

Relevant 

Disciplines

Atmospheric Science and Forecasting: 

• Grand Challenge 1: What Is the Wind 

Resource and How Do Wind Plants 

Change It? If We Knew the 

Atmosphere, What Could We Do?

Turbine Technology and Design: 

• Grand Challenge 1: Complete 

Understanding of Physics for Design 

and Operation of Large-Scale Machines

• Grand Challenge 2: Physical 

Understanding of the Boundary Layer 

Physics (Subviscous and Mixing Layer) 

Multiscale Characterization Driving 

Turbine Performance

Offshore-Specific Technologies: 

• Grand Challenge 4: Develop Full 

Understanding of Offshore 

Environmental Conditions

Grid Integration: 

• Grand Challenge 1: Develop 

Fundamentals and Paradigm Design of a 

Converter-Dominated System

• Grand Challenge 2: Advanced Control of 

Converters in a Converter-Dominated 

System

• Develop and deploy advanced sensing technology to 

create more accurate and higher-fidelity 

representations of the atmosphere, metocean 

conditions for offshore (including bathymetry), and 

intraplant flow. Explore the value of remotesensing technologies like lidar and radar for new 

advanced sensing (such as from direct 

measurement of the atmosphere)

• Establish comprehensive multidisciplinary (wind 

energy science) experimental campaigns measuring 

atmospheric and ocean parameters, and technical 

data at wind turbines and power plant system

levels, including turbine and plant supervisory 

control and data acquisition, turbine responses, 

condition monitoring, and plant electrical 

performance.

• Encourage open data and data-sharing strategies to 

make data stored in cloud-distributed databases 

findable and visible via metadata standards with 

related taxonomies and ontologies

o Use ontologies and taxonomies for easier

retrieval and categorize data and models 

needed to structure workflow and exchange of 

information across disciplines

• Research and validate fundamental physics within 

disciplines and at disciplinary boundaries/interfaces

o Develop a new theory and principles for system 

operation (i.e., turbine wakes, converterdominated system)
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Grand Challenges in Wind Energy R&D Opportunities Offered by Big Data, Information and 

Data Science, and Multiscale and Multidisciplinary 

Modeling and Advanced Sensing in Addressing the 

Grand Challenges

Category 2: Improved 

Modeling of the Full 

Wind Energy 

–

Grid-Integrated System 

Across 

All 

Disciplines, Spatial and 

Temporal Scales

Atmospheric Science and Forecasting: 

• Grand Challenge 2: Improved Model 

Chain to Better Predict Renewable 

Energy Resources

Plant Controls and Operations: 

• Grand Challenge 1: Full End-to-End 

Model From Atmosphere to Grid

• Grand Challenge 2: Power System 

Model with a Large Amount of Wind 

Power, Storage, Solar, Flexible Loads

Offshore-Specific Technologies: 

• Grand Challenge 1: Development and 

Validation of Multidisciplinary, 

Multiscale, High-Fidelity, System 

Modeling Tools to Optimize Floating 

Wind Designs

Grid Integration:

• Grand Challenge 3: Multiscale Modeling 

Across the Entire Grid System with 

Accurate Modeling of Converters in a 

Converter-Dominated System

• Develop data-driven, multiscale, multidisciplinary 

models

o Take advantage of advances in the “Industry 

4.0” to leverage data and data science for 

resource assessment, production forecasts, 

state estimation, controls, and condition 

monitoring for diagnostics, prognostics, and 

more

• Develop physics-based models that couple relevant 

physics across scales (geospatial and temporal) and 

disciplines

o Coupling of high-fidelity models for wind plant 

flow and wind turbine dynamics with forcing 

from larger weather models

o Modeling of the new grid with a lot of 

converters embedded in AC systems (bridge 

scales of slow AC and fast dynamic systems)

• Combine data-driven methods and physics-based 

models for end-to-end modeling chains for 

forecasting, controls, operations, and grid 

integration

o Employ uncertainty quantification for both 

measurement data and models (for 

environmental and operating conditions)

o Employ data in verification and validation

platforms to better quantify and understand 

modeling uncertainties in different situations 

and prioritize issues 
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Grand Challenges in Wind Energy R&D Opportunities Offered by Big Data, Information and 

Data Science, and Multiscale and Multidisciplinary 

Modeling and Advanced Sensing in Addressing the 

Grand Challenges

Category 3: Application and 

Demonstration of 

Data and 

Modeling 

Advances to System 

Design and 

Operation

Manufacturing and Industrialization:

• Grand Challenge 1: Advanced Materials

• Grand Challenge 2: Automation

• Grand Challenge 3: On-Site 

Manufacturing

Plant Controls and Operations: 

• Grand Challenge 3: Implementation of 

Wind Power Plant Controllers and 

Integration With Turbine Controllers 

(Including Field Trials and Validation)

• Grand Challenge 4: Creation of Open 

Data Sets for Use in Open Research

Grid Integration: 

• Grand Challenge 4: Intelligent Wind 

Power Plant with Integrated Data and 

Modeling for Active Power System 

Operation and Support

Offshore:

• Grand Challenge 2: Industrialization for 

Offshore: Manufacturing, Ports, 

Vessels, Deployment, Installation, O&M

• Leverage scientific advancements, data science, and 

improved modeling capability to:

o Enable automation in manufacturing

o Research and develop “digital twins” for the 

structures produced in an on-site 

manufacturing facility 

o Combine data and modeling approaches for 

state estimation and accurate performance 

prediction (flow and electrical)

o Utilize big data, algorithms, and deployments of 

robots/drones for O&M innovation (big data for 

structural health monitoring, machine learning, 

optimization, and predictive failure including

real-time machine learning, in-field/up-tower 

repairs, adaptive controls, and more)

o Accelerate technology learning and 

industrialization of offshore technology 

(particularly floating)

The resulting ecosystem will allow for new insights and lead to new modes of integrating 

research and capturing expertise. Obtaining a better understanding of how wind power plants 

operate will have implications for operating wind plants and managing the full life cycle. For 

example, models will also be adapted for use in the design of future wind turbines and power 

plants. Ultimately, this upgraded framework for handling data, models, and overall wind 

energy science will cover all stages of a wind power plant’s lifetime, thereby reducing the 

cost of electricity generated and increasing the value of wind energy to the electric grid 

system. A key need associated with the research efforts outlined in this report is to create the 

frameworks and mechanisms that allow effective collaboration across the relevant

stakeholder groups to realize the full potential of wind energy for the future electricity 

system.
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5 Summary

The energy sector is undergoing a paradigm shift. By 2050, many predictions identify a 

future electric grid system in which renewables make up a significant share (30% or more) of 

overall electricity generation. In the fall of 2017, wind energy experts from around the world 

came together to look at an even grander vision of the future electricity system where wind 

energy could produce a majority (>50%) of global electricity generation. This “Grand 

Vision” for wind energy pushes beyond even the most optimistic forecasts; however, to 

realize this future, significant innovation is needed to reduce the cost of wind energy and 

increase the value it provides to the electricity system.

Through a series of meetings, wind energy experts identified innovations in several areas: 

manufacturing and industrialization, turbine technology and design, atmospheric (and 

metocean for offshore) science and forecasting, plant control and operations, grid integration,

and offshore-specific technologies. Innovations in these areas would lead to reductions in 

wind cost of energy and/or improve the value that wind energy has for the electricity grid in 

providing more reliable and dispatchable energy, higher capacity value, and improved grid 

services for greater reliability and stability. The groups then discussed the R&D efforts that 

would help accelerate and enable the development of these innovations.

This report documented the findings for each innovation area to realize a future electric grid 

scenario with high shares of wind energy. The authors then synthesized the discussions on 

R&D challenges into a high-level list of grand R&D challenges by area. Throughout all the 

research areas, there were common themes that surfaced: leveraging recent advances in data 

science, digitalization, and associated technologies, and creating multiscale and 

multidisciplinary modeling capabilities that could fully capture all of the complex coupling 

and interdependencies both within the wind power plant and the full electric grid system. 

Exploring these cross-cutting themes revealed a higher-level framework that could be used to 

coordinate and integrate wind energy research across the different areas to successfully 

address the grand R&D challenges. 

The shear complexity and size of the wind energy science challenge merits such an integrated 

perspective and approach and emphasizes the need for an integrated wind energy science 

discipline. Future work, in the form of a follow-on journal article, will articulate more 

completely the different components of this discipline and discuss how execution of such an 

integrated research program will overcome the challenges set forth to realize the Grand 

Vision for Wind Energy. If successful, the resulting innovations can help realize a future 

electricity system with wind energy as its foundation.
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Appendix A: IEA Wind TEM #89 “Grand Vision for Wind 

Energy R&D” Agenda and Participant List

IEA Wind TEM #89 Agenda

Sunday, Oct. 22, 2018

The Sunday session is devoted to brainstorming and discussion of what the energy 

landscape, and wind energy contributions, will look like in 2050, as well as what the 

most challenging aspects of getting from here to there will be.

5:00 PM Future Scenario Game

During the social hour, workshop participants will be asked to participate in a casual 

brainstorming exercise around wind industry development objectives of 1) LCOE, 2) grid 

integration, and 3) deployment. For each topic, participants will be asked to think about 

different opportunities and barriers to the improvement in each objective and to rate the 

difficulty associated with each. Discussion is encouraged among the participants.

7:00 PM Grand Vision Scenario Premises

In preparation for the next day’s activities, workshop participants will engage in a guided 

discussion over the key premises behind a Grand Vision of the future for wind energy as a 

primary source of energy for the world. Topics covered will include the level of wind energy 

as a percentage of total generation, the portion of which is offshore, the level of solar and 

other renewable deployment that exists, the type of computing, data management, sensing,

and other enabling technologies that are available to the industry.

8:30 PM Adjourn for the day

Monday, October 23 (NREL, RSF, Beaver Creek Room)

The Monday meetings are organized around three main breakout sessions where attendees 

will be asked to create a list of most critical issues in achieving the needed 1) LCOE and 2) 

value to achieve the landscape discussed in the Sunday meeting. Then, in the last breakout, 

3) research, we will gather the fundamental issues needed to achieve the 2050 vision. 

Breakout sessions will be divided into small groups according to the categories below.

Schedule

8:45 AM Introductory Addresses

● Welcome; Paul Veers

● Peter Green, NREL Director, Science & Technology

● Lionel Perret and Ignacio Marti, Introduction to IEA Wind TCP and Task 11
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The meeting started with an overview of Task 11 activities, new developments, and the new 

information exchange portal.

● Joachim Peinke, University of Oldenburg: The European Academy Publication of 

Long-Term Research Challenges

This presentation highlighted the need for a new focus on long-term wind energy research. 

Recommended topic areas for research include:

● A multidisciplinary approach, including environmental and sustainability aspects

● Large scale: use of new layer of atmosphere, more focus in economics and 

environmental values

● Multiscale aspects: from millimeters to hundreds of kilometers, methods to bridge 

scales

● Big data: what to do this new knowledge, to extract the essential information, to 

identify fatigue, for example

● Validation an precision \: new to unify and validate, need a precision, not yet the case 

in aerodynamics.

● Carlo Bottasso, TU Munich The Need for Truly Open Data Sets

This presentation highlighted the value that open data sets can provide to research 

communities in advancing research and innovation. The presentation pulled in examples 

from the Rotorcraft community, which has a stronger history than the wind industry for 

sharing detailed design information. A particular project involved a heavily instrumented 

vehicle and an extensive test campaign that yielded a large public data set for research and 

several workshops. The analysis of the data had an enormous impact on education and 

research.

The wind industry needs a similar effort and this will involve collaboration. To date, most 

data sets and projects involve hypothetical rotor and turbine designs and code-to-code 

comparison rather than validation using real machine design and operational data. This will 

only take the research community so far and may limit advancement of science and 

technology for the whole industry. One comment from the audience noted that publicly

funded efforts should make a concerted effort to yield public data sets as a requirement.

9:45 AM Guiding talk

● Ryan Wiser, Lawrence Berkeley National Laboratory, LCOE projections and 

opportunities with concerted effort

Wind energy has grown exponentially over the past decades but its contributions to the 

global energy system are still small. Factors influencing growth to date have been 

innovations in turbines with larger rotors and higher capacity factors for overall very low cost 

of energy (comparable to natural gas fuel costs). However, several risks to future wind 

energy growth exist including:

● Policy instability persists and the presence of policy support for wind energy is not 
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certain

● Falling costs of natural gas and solar energy so that wind energy needs to lower costs 

in order to stay competitive

● As wind energy deployment increases, the market saturates, which reduces the overall 

potential revenue for wind plants (falling market prices and increased curtailment).

In the future, we will have to consider balancing cost and value (returning to our overarching 

objectives for the workshop of looking at decreasing LCOE and increasing system value).

10:10 AM Breakouts on Wind Plant LCOE (divided into the six breakout groups listed 

above)

In this first session, we address the classic metric of LCOE, which is still one of, if not the, 

most critical metric for evaluating a wind plant. In each breakout, workshop participants will 

evaluate technology needs for further reduction to LCOE and its main elements: energy 

production, capital costs, operational expenditures, and financing.

11:30 AM Wind Plant LCOE Breakout Reporting 

1:00 PM Guiding Talk

● Paul Veers, NREL, New paradigms for wind value beyond LCOE

The guiding talk to motivates the breakout session on increasing system value started again 

by highlighting what the future may look like with renewables providing 50% or more of the 

global energy supply. In these scenarios, wind power plants operate very differently than 

they do today. The norm of operation is curtailment and wind power plants are expected to 

provide significant support to the grid in terms of capacity value as well as grid services. In 

this future world, a greater share of the overall revenue to power plants is earned through 

capacity and service markets versus energy markets (the traditional dominant source of 

revenue). 

In addition, as deployment of wind energy grows, it will have to address various nonmarket 

challenges, such as environmental, social, and security issues. A review of an NREL study 

showed that most of the United States is affected by at least one challenge to wind power 

development (whether environmental, radar, lack of transmission, and so on). In addition, 

wind energy has the potential to gain support by providing value to the local community and 

enabling workforce development. The breakout session that followed this talk considered 

innovation opportunities to address both increased system value as well as mitigating 

challenges to deployment and providing support for economic and workforce development.

1:20 PM Breakouts on Wind Plant Value and Deployment (divided into the five 

breakout groups)

In this second session, we look more broadly at what will allow or impede massive amounts 

of wind energy deployment. In each breakout, workshop participants will address needs for 

increasing deployment that are not LCOE-centric and may include grid integration issues, 

deployment barriers, resource availability, infrastructure, and more.
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2:40 PM Wind Plant Value and Deployment Breakout Reporting

3:30 PM Guiding Talk

● Daniel Laird, NREL, Perspectives on Long-Term Research at a Fundamental Level

A short talk was used to motivate the final session around identifying R&D challenges to 

advance the future of wind energy toward the Grand Vision. The talk specifically highlighted 

some of the key research areas that are of focus to the U.S. Department of Energy Wind 

Energy Technologies Office research program. These included:

● System Management of Atmospheric Resources through Technology Wind Power 

Plants, wherein the physics across all geospatial and temporal scales are well 

understood to enable advanced wind power plant design, control, and operation

● Seamless and robust grid integration featuring wind plants that provide advanced grid 

services and energy storage

● Forecast and power system dispatch coupling atmospheric sciences, power system 

modeling, and system state estimation 

● Advanced manufacturing capabilities enabling very large turbine deployment

● Offshore floating wind technology to enable vast deployment of offshore wind 

energy.

The last breakout session will be used to identify critical research needs for enabling 

technologies that reduce LCOE and/or enable further deployment of wind energy to achieve 

the scale of the Grand Vision.

Small Groups for breakout sessions

1) Manufacturing and industrialization, installation, and logistics

2) Land-based turbine technology, design, scaling, energy access, and capture

3) Offshore turbine and foundation technology, marine construction, and access

4) Wind plant technology control, storage, and design

5) Atmospheric science, forecasting, and condition-based optimization

6) Grid integration and operations; energy transfer, stability, storage, and markets

(conducted during the IEA Wind Task 25 meeting on November 10, 2017).

Operations and reliability were addressed by each of the groups rather than as a separate 

breakout session.
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Table A1. International Energy Agency (IEA) Wind Topical Experts Meeting 89 Grand Vision for Wind Energy Technology 

Participant List (Including Side Meetings at the Utility Variable Integration Group (UVIG) 2017 Fall Technical Workshop 

and the IEA Wind Task 25 Fall Meeting)

Name Organization Country Meetings Attended

Aaron Barr Wood Mackenzie US IEA Wind TEM #89

Aaron Bloom National Renewable Energy Laboratory (NREL) US UVIG 2017 Fall Meeting

Aidan Tuohy Electric Power Research Institute US UVIG 2017 Fall Meeting

Amy Robertson NREL US IEA Wind TEM #89

Andrew Clifton WindForS - Wind Energy Research Cluster DE IEA Wind TEM #89

Andrew Oliver RES Americas US IEA Wind TEM #89

Anna Maria Sempreviva Danish Technical University (DTU) Wind Energy DK IEA Wind TEM #89

Brett Wangen Peak Reliability US UVIG 2017 Fall Meeting

Brian Smith NREL US IEA Wind TEM #89, IEA 

Wind Task 25 Meeting

Bruce Rew Southern Power Pool (SPP) US UVIG 2017 Fall Meeting

Carlo Bottasso Technical University of Munich (TUM) DE/IT IEA Wind TEM #89

Charles Meneveau John Hopkins University US IEA Wind TEM #89

Charlie Smith Energy Systems Integration Group (ESIG) US UVIG 2017 Fall Meeting, IEA 

Wind Task 25 Meeting

Charlton Clark U.S. Department of Energy (DOE) US UVIG 2017 Fall Meeting

Daniel Averbuch IFP Energies nouvelles FR IEA Wind TEM #89

Daniel Laird NREL US IEA Wind TEM #89

David Campos-Gaona University of Strathclyde UK IEA Wind Task 25 Meeting

David Edward Weir The Norwegian Water Resources and Energy 

Directorate - NVE

NO IEA Wind TEM #89

Debbie Lew GE US IEA Wind Task 25 Meeting

Derek Berry NREL US IEA Wind TEM #89

Doug Rhoda Diversified Machine Systems (DMS) US IEA Wind TEM #89

Ebba Dellwik DTU Wind Energy DK IEA Wind TEM #89

Eric Lantz NREL US IEA Wind TEM #89

Eric Smith Keystone Towers US IEA Wind TEM #89

Erik Hale Électricité de France (EDF) US IEA Wind TEM #89

Esmeralda Pita Jimenez Idaho National Engineering and Environmental 

Laboratory (INEEL)

MX IEA Wind Task 25 Meeting

Gavin Smart ORE Catapult UK IEA Wind TEM #89

Hannele Holttinen VTT Technical Research Centre of Finland FI IEA Wind Task 25 Meeting

Ignacio Marti DTU Wind Energy DK IEA Wind TEM #89

Jaime Agredano Diaz INEEL MX IEA Wind Task 25 Meeting

James Earle DMS US IEA Wind TEM #89

James Manwell University of Massachusetts at Amherst US IEA Wind TEM #89

Jan Tessmer DLR German Aerospace Center DE IEA Wind TEM #89

Jason Fields NREL US IEA Wind TEM #89

Jim Ahlgrimm U.S. DOE US IEA Wind TEM #89

Jim Baak West Stem, Inc. US UVIG 2017 Fall Meeting

Joaquim Peinke University of Oldenburg DE IEA Wind TEM #89

Jody Dillon University College of Dublin IRL IEA Wind Task 25 Meeting

John McCann Sustainable Energy Authority Of Ireland (SEAI) IRL IEA Wind TEM #89
IEA Wind TCP Task 11 Technical Report

130

John Simonelli Independent System Operator of New England 

(ISO NE)

US UVIG 2017 Fall Meeting

Johney Green NREL US IEA Wind TEM #89

Jonathan Naughton University of Wyoming US IEA Wind TEM #89

Jose Manuel Franco 

Nava

INEEL MX IEA Wind Task 25 Meeting

Joshua Paquette Sandia National Laboratories (SNL) US IEA Wind TEM #89

Katherine Dykes NREL US IEA Wind TEM #89, UVIG 

2017 Fall Meeting, IEA Wind 

Task 25 Meeting

Libing Zou Mingyang CN IEA Wind TEM #89

Lionel Perret Planair SA CH IEA Wind TEM #89

Lucy Pao University of Colorado US IEA Wind TEM #89

Mark Ahlstrom Nextera Energy US UVIG 2017 Fall Meeting

Mark O’Malley NREL US UVIG 2017 Fall Meeting

Martin Kühn ForWind - Center for Wind Energy Research DE IEA Wind TEM #89

Melinda Marquis National Oceanic and Atmospheric 

Administration (NOAA)

US IEA Wind TEM #89

Michael McMullin Midwest Independent System Operator (MISO) US UVIG 2017 Fall Meeting

Michael Muskulus National Technical University of Norway 

(NTNU)

NO IEA Wind TEM #89

Mike Derby U.S. DOE US IEA Wind TEM #89

Mike Robinson NREL US IEA Wind TEM #89

Ndaona Chokani École polytechnique fédérale de Lausanne (EPFL) CH IEA Wind TEM #89

Nick Miller GE US UVIG 2017 Fall Meeting

Nicolaos Antonio 

Cutululis

DTU Wind Energy DK IEA Wind Task 25 Meeting

Nicole Segal North American Electric Reliability Corporation 

(NERC)

US UVIG 2017 Fall Meeting

Ola Carlson Chalmers University of Technology SE IEA Wind TEM #89

Patrick Moriarty NREL US IEA Wind TEM #89

Paul Denholm NREL US UVIG 2017 Fall Meeting

Paul Fleming NREL US IEA Wind TEM #89

Paul Veers NREL US IEA Wind TEM #89, UVIG 

2017 Fall Meeting, IEA Wind 

Task 25 Meeting

Peter Green NREL US IEA Wind TEM #89

Ryan Wiser Lawrence Berkeley National Laboratory (LBNL) US IEA Wind TEM #89

Sandy Butterfield Boulder Wind Consulting US IEA Wind TEM #89

Scott Carron NREL US IEA Wind TEM #89

Stephan BARTH ForWind - Center for Wind Energy Research DE IEA Wind TEM #89

Steven Saylors Vestas US IEA Wind TEM #89

Tino Oldani Ingersoll US IEA Wind TEM #89

Xabier Munduate National Renewable Energy Center CENER ES IEA Wind TEM #89