Wind Plant Sensitivity Analysis

Abstract NREL Wind Energy Systems Engineering Tool Sensitivity Analysis and Results

Sensitivity Analysis of Offshore Wind Cost of Energy

K. Dykes, A. Ning, P. Graf, G. Scott, R. Damiani, M. Hand, R. Meadows, W. Musial, P. Moriarty, P. Veers

• National Renewable Energy Laboratory • Golden, Colorado

Introduction

OFFSHORE WINDPOWER 2012, Virginia Beach, October 911, 2012

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. NREL/PO-5000-56411

Conclusions

References

No matter the source, offshore wind energy plant cost estimates are

significantly higher than for land-based projects. For instance, an NREL

review on the 2010 cost of wind energy found baseline cost estimates for

land-based wind energy systems to be 71 dollars per megawatt-hour

($/MWh), versus 225 $/MWh for offshore systems.1 There are many ways

that innovation can be used to reduce the high costs of offshore wind

energy. However, the use of such innovation can impact the cost of energy

in counterintuitive ways because of the highly coupled nature of the

system.

In 20112012, the National Renewable Energy Laboratory (NREL) began

developing a wind energy systems engineering software tool to support

offshore wind energy system analysis.2 The tool combines engineering

and cost models to represent an entire offshore wind energy plant and to

perform system cost sensitivity analysis and optimization. Initial results

were collected by applying the tool to conduct a sensitivity analysis on a

baseline offshore wind energy system using 5-MW and 6-MW NREL

reference turbines. Results included information on rotor diameter, hub

height, power rating, and maximum allowable tip speeds.

Shown below in Figure 2, the NREL wind energy systems engineering tool

combines engineering and cost analysis models at all levels of the

turbinefrom component to plantto perform system-level analyses such

as uncertainty quantification and optimization. The initial models are

relatively simple. Ultimately, multiple levels of detail and accuracy will be

incorporated into the tool to support a variety of analyses. In addition, the

tool integrates with NREL’s and other wind energy models into an

architectural framework for analysis that is based on the National

Aeronautics and Space Administration’s (NASA’s) Open Multi-Disciplinary

Analysis and Optimization (OpenMDAO) software. In addition, the tool

incorporates the use of Sandia National Laboratories’ Design Analysis Kit

for Optimization and Terascale Applications (DAKOTA) to perform

advanced analysis ranging from optimization to uncertainty quantification.

In this first year of development, the initial tool and simplified model set

were used to investigate the sensitivity of overall wind plant performance

and cost for an offshore wind site. Key design parameters studied included

rotor diameter, hub height, power rating, and maximum allowable tip

speeds. Figure 4 shows the analysis and how information flows from

model to model.

Wind energy is a complex system that involves input from various

stakeholders and designers. Figures 1 illustrates a systems engineering

perspective of the wind energy system.

A systems engineering approach to wind plant analysis on cost and

performance was demonstrated. Subsequent work will improve individual

model fidelity as well as the overall system modeling capability for a

variety of analyses.

[1] Tegen, S.; Hand, M.; Maples, B.; Lantz, E.; Schwabe, P.; Smith, A. 

(2012). 2010 Cost of Wind Energy Review. NREL/TP-5000-52920.

[2] Dykes, K.; Meadows, R.; Felker, F.; Graf, P.; Hand, M.; Lunacek, M.; 

Michalakes, J.; Moriarty, P.; Musial, W.; Veers, P. (2011). “Applications of 

Systems Engineering to the Research, Design, and Development of Wind 

Energy Systems.” 92 pp.; NREL Report No. TP-5000-52616.

Figure 1: Wind energy system design depends on many variables for

components, turbines, and plants, and the related design decisions all

affect ultimate performance, cost, and the community.

Exogenous Economic 

Inputs

Main Plant Design Inputs 

Main Turbine Design Inputs 

Main Component Design 

Inputs 

Industry Design Inputs 

Plant Performance –

Reliability/O&M 

Turbine & Component 

Costs & Performance

Balance of Station 

Environmental Impacts 

(Habitat, wildlife, and pollution)

Grid Integration 

Impacts 

(Short-term dynamics to 

long-term planning)

Wind Cost of Energy

Main Cost Outputs 

(Capital costs, balanceof-station costs, and 

operational costs)

Main Performance 

Outputs 

(AEP)

Community Impacts 

(Visual, safety, economics, 

acoustics, and electromagnetic 

interference)

Plant Performance –

Generation/annual energy 

production (AEP)

Illustration by Rick Hinrichs, PWT Communications

The industry has recognized the highly coupled nature of wind energy

systems over the last several decades. Attempts have been made to

better integrate overall wind plant design, starting from the wind climate

and field down to the logistics of plant development and operation. But, a

design change in one aspect of the system can impact the overall system

performance and cost in significant ways. Despite the highly complex and

coupled nature of wind energy systems, the design and study of wind

plants is often a relatively decoupled process. Insight could be gleaned

from a truly integrated analysis of wind turbine and plant performance. For

this reason, NREL created a flexible software tool that uses models of

various levels of detail and accuracy to represent a full wind plant and to

enable the analysis, design, and optimization of a full wind plant system.

Figure 2: The diagram shows the

composition of the NREL wind

energy systems engineering tool as

described above. It involves

phased integration of engineering

and cost models for wind energy

within a governing architecture

based on NASA’s OpenMDAO and

Sandia’s DAKOTA software.

Architecture for the NREL Wind Energy 

Systems Engineering Tool

Subcomponents

Turbine

Plant

Grid

External Impacts

Aerodynamics

Structures

Controls & 

Electrical

Noise

Balance of Station

Operation and 

Maintenance

Illustration by Katherine Dykes, NREL

In each phase of the tool development, a critical objective is to have a full

system representation for levelized cost of energy (LCOE) analysis. A key

advantage of the tool is its ability to meet a wide range of user needs by

providing a diverse selection of models. Depending on the analysis

objectives, a variety of model configurations can be used, from a fullblown, complex optimization of a wind turbine and plant to wind plant

financial analysis for a particular site. The key attribute of the tool is that

specific models for different parts of the system can be selected based on

the particular analysis to reduce computational complexity. As illustrated in

Figure 3, the tool development entails the use of models for each aspect

of a wind plant. Subsequent development will build upon the foundation

with increasing levels of model accuracy. Objectives of the analysis in this

study include demonstration of the tool for sensitivity analysis on key

global design parameters that impact LCOE.

LCOE Analysis Initial Approach

Workflow 

Governing

Software 

(OpenMDAO)

User Inputs:

Analysis Type (i.e., Sensitivity), Fixed Parameter Input 

Conditions, Design Variables, Additional Constraints

Analysis Tool 

(i.e., DAKOTA)

Turbine Structure 

Assembly

Rotor 

Structure

Drivetrain 

Structure

Tower and 

Substructure

Balanceof-Plant 

(BOP) 

Assembly

BOP 

Model

O&M 

Assembly

O&M Tool

Finance 

Assembly

Finance 

Tool

Annual 

Energy 

Production 

(AEP) 

Assembly

Wind Plant 

LCOE

Turbine 

Capital 

Costs

BOP 

Costs O&M 

Costs Finance AEP

Inputs

Cost and Sizing Tool

Wind Plant Analysis Tool

Outputs

AEP Tool

Figure 3: The initial tool development is based

on the integration of wind plant cost models

and engineering-based wind turbine

component cost and sizing models, using a

system LCOE analysis framework. This allows

for study of how a design parameter change

in one part of the system causes changes

throughout the system to impact LCOE. For

example, changes in the rotor diameter affect

not only energy output from a single turbine,

but overall rotor weight, thereby increasing

loads from the rotor that feed through the

nacelle and into the tower. The increased

rotor loads can then affect the downstream

component size, performance, and cost. This

affects operation, maintenance, and

replacement costs. The size of turbine

components can also affect the plant logistics

and layout. The decisions concerning logistics

and plant layout will impact both balance-ofplant (BOP) and O&M costs. Thus, a full

system analysis is developed through the

proper integration of the models as shown in

Figure 3though the detail of each individual

model may change for a given analysis.

Illustration by Katherine Dykes, NREL

Illustration by Katherine Dykes, NREL

Figure 4: The sensitivity analysis is performed within the tool. The models

are integrated together using OpenMDAO and analysis is performed with

DAKOTA software.

Table 1 shows the directional impact of each design parameter on cost of

energy. Some unexpected results appear that are related to the direction

of change for each of the sensitivities, representing roughly a +/- 10% in

the overall magnitude of each of the design parameters. The impact of hub

height in increasing overall cost of energy reflects increased capital costs

that outweigh increases in energy production. Increasing the rotor

diameter and decreasing the generator size improves cost of energy, but

so does the reverse case (unexpectedly). This result reflects significant

dependencies of certain models, such as the balance of plant, on specific

design parameters and provides insight into areas of potential model

improvement. Similar findings were discovered for the maximum tip speed

sensitivity, though impacts on overall cost of energy were small in this

case because operational tip speeds at rated power were below the initial

maximum.

Design Parameter Direction Change Cost-of-Energy 

Impact

Rotor Diameter Up Down--

Down DownHub Height Up Up+

Down DownRated Power Up DownDown Down--

Maximum Tip Speed Up Up+

Down No Change

Table 1: Initial results of analysis. Directional impacts are unexpected and

reflect the simplified state of the current simple model set as opposed to

actual expected impacts from design changes.

•Rotor Speed at 

Rated

•Thrust at Rated

•Torque at Rated

•Max Root Moment

•Rotor Mass 

Properties

Turbine Model

Analysis Outputs:

• Cost of Energy & 

Levelized Cost of 

Energy

• Turbine & 

Component Masses 

& Costs

• Turbine 

performance results

• Annual Energy 

Production

• Balance of Station 

Costs

• O&M Costs

Initiate analysis:

Set up for Turbine and Plant Initial Conditions, select analysis 

type and specify workflow

(i.e. sensitivity analysis of rotor diameter, hub height, rated power or maximum 

allowable tip speed for a Virginia site with the NREL 5 MW turbine)

Drivetrain & Hub 

Model

Semi-empirical 

relationships 

relating rotor 

loads to 

component 

masses

Rotor Model Sub-optimization of structure: 

(50 year extreme wind, blade 

pitch 0)

Tower/Monopile Model

Sub-optimization of structure:

(for new rated thrust and torque)

Offshore BOS Model

Read/write to excel model calibrated for 

reference site and turbine

Check 

blade 

stress <= 

initial 5 

MW 

blade 

stress

Resi

ze 

chor

d

Check 

stress along 

tower <= 

initial 5 MW 

tower stress 

Resize 

tower & 

monopil

e 

thicknes

s / base 

diameter

•Component Masses & 

Dimensions

•Hub & 

Drivetrain Mass 

Properties

ECN Offshore O&M Model

Read/write to excel model calibrated for 

reference site and turbine

•Power 

Curve

Cost of Energy Model

Simple LCOE and COE calculation

•Turbine Capital Costs

•Annual Energy Production

•BOS Costs

•Component 

Costs, 

Masses & 

Dimensions

•O&M Costs

•O&M 

Costs

•BOS Costs

•COE & LCOE

•Thrust at Rated

•Torque at Rated

•Rotor Mass 

Properties