distributed wind resource assessment (dwra)...
TRANSCRIPT
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Distributed Wind
Resource Assessment
(DWRA) Overview
Heidi Tinnesand, Jason Fields, & Ian
Baring-Gould
National Wind Technology Center
National Renewable Energy Laboratory
Small Wind Conference
June 15, 2016
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Agenda
• Background & Motivations
• Current State of the Industry
• Key Findings:
o Challenges
o Barriers
o High-Payoff Opportunities.
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Strong Correlation: CF & COE
Figure from Alice Orrell, Pacific Northwest National Laboratory
2014 Distributed Wind Market Report
LCOE decreases with increased
production!
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Wide Range of Capacity Factors
Figure from Alice Orrell, Pacific Northwest National Laboratory
2014 Distributed Wind Market Report
Capacity factor doesn’t
correlate with turbine size,
other factors must be
considered.
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Diversity of DW Industry Stakeholders
Figure from Alice Orrell, Pacific Northwest National Laboratory
2014 Distributed Wind Market Report
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Resource Assessment Big Picture
Improper siting means sub-optimal performance and reliability.
Even the best designs may fail in the wrong environments.
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Photo from Rinspeed, Inc.
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DWRA Background
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Wind resource characterization and assessment identified through the 2014 DOE RFI as one of the top four Distributed Wind Portfolio R&D focus areas.Subsequent actions:
• Workshop developed with the help of an industry-based advisory board to obtain information in the following focus areas:
o Current State of the Industry
o Challenges and Barriers
o High-Payoff Opportunities.
• Follow-on survey completed Fall 2015
• Report published Spring 2016.
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Workshop Details
• DWT Wind Resource Assessment Stakeholder Workshop held June 18-19, 2015, in Stevens Point, WI
• Total attendees: 30
• Sectors represented:
o Turbine OEMs
o State energy programs
o Non-profits
o Consultancies
o Site assessors
o Developers.
• Key companies/perspectives
o Endurance, Bergey, Primus
o One Energy
o NWSEED, Alaska Energy Authority, Minnesota Energy Office
o Wind Advisors Team, Sagrillo Light & Power
o Small Wind Certification Council.
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State of the Industry
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DWRA State of the Industry
Two main approaches: • Model based (majority of assessments)
• Instrumentation based (500 kW+).
Key findings:• Measurements are important and valuable; however,
instrumentation is cost and time prohibitive.
• Validation and feedback for models are lacking.
• A wide range of stakeholders and economics drives variability in:o Processes
o Precision/accuracy
o Budget/time.
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Numerical Weather Prediction Models(WRF, ERA-Interim,
MERRA, CFSR)Wind Speed
Annual Average
Wind speed and direction
frequencydistribution,
Temperature,Barometric
pressure
Wind Maps
Online Data Portals(AWS, Vaisala, Vortex,Wind Prospector)
Site Specific Corrections
(rules of thumb)
Terrain Roughness Obstacles
Gross Energy Estimation
Site scaled Meteorological
Parameters
Wind Turbine
Power Curve Loss Estimation Process
Net Annual Energy
Production Estimate
LEGEND
Process Subprocess
Data
Gross Energy Estimation
Simple Loss Estimation Process
Net Annual Energy
Production Estimate
Site Specific Corrections (WAsP,
CFD, etc.)
Terrain Roughness Obstacles
Level 1-Simple
Level 2-Complex
Site Annual average
wind speed
OEM Annual Performance
Curve
Shear
Shear
DWRA Model-Based Approach
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DWRA Instrumentation-Based Approach
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Site specific meteorological
parameters(Wind speed and
direction frequencydistribution,
Temperature,Barometric pressure)
Gross Energy Estimation
Long term adjusted, site
specific meteorological
data
Wind Turbine Power Curve
Loss Estimation Process
Net Annual Energy
Production Estimate
LEGEND
Process Subprocess
Data
Onsite Measurements
(towers & remote sensing)
Long term observations
(ASOS)
Synthetic refewrence data
(WRF, ERA-Interim, MERRA, CFSR)
Measure Correlate Predict Process
Uncertainty Estimation Process
Uncertainty of Net Annual Energy
Production
Major differences
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Resource Assessment Drivers
What matters?
• Power production
• Turbine loading*
• Grid integration*
• Risk quantification.*
* Largely infeasible in current DWRA processes
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Ph
oto
by
Wa
rren
Gre
tz, N
REL
00
00
1
Key Findings
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DWRA Challenges Identified by Industry
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Tier 1:• Data access• Validation and benchmarking• Industry education and outreach.
Tier 2:• Atmospheric model input data• Measure-Correlate-Predict (MCP)• Downscaling methods• Standardization.
Tier 3:• Site suitability assessment• Low-cost instrumentation.
Note: Many of the challenges overlap and can potentially be addressed synergistically.
NREL researchers asked industry members to rank priorities according to various perspectives:• According to the
industry • According to their
individual needs • According to the
government’s role.
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Industry Survey: Impact to DW Industry
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4.08
4.15
4.50
4.69
4.75
5.17
5.62
6.07
6.08
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00
Low Cost Instrumentation
Standardization
Downscaling Methods
Site Suitability Assessment
Atmospheric Model Input Data
Measure-Correlate-Predict (MCP) Methods
Validation & Benchmarking
Data Access
Education & Outreach
Rank of Interest, based on potential impact to the distributed wind industry
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Key Conclusions
• Accuracy of current approaches wildly inconsistent or unknown
• Little verification of existing tools/rules of thumb• Few standards for processes and documentation• Limited funding or processes to understand errors or
improve assessment process – limited feedback loop• Lack of publically available and low-cost data/tools• Industry relies heavily on personal experience; limited
training and educational opportunities• Poor understanding of the cost/benefit of different fidelities
of WRA (on-site data collection, model selection)• Limited feedback between OEMs and site assessors – lack of
an ability to do more detailed site optimization• Total WRA costs (dollars and effort) must be tiered to reflect
project size and volume.
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Key Conclusions
o Limited VALIDATION, STANDARDS, and FUNDING to understand the ACCURACY and IMPACT of various WRA methods and approaches
o Limited ORGANIZED OPPORTUNITIES for resource assessment KNOWLEDGE SHARING and TRAINING.
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Tie
r 3
Tie
r 2
Impact of Solutions
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Improvement
Opportunity
Cost of
Assessment
Consumer
Confidence
Performance
Estimation
Site Suitability
Characterization
Level of Effort
Required
Data Access Major Minor Major Major Moderate
Validation &
BenchmarkingMinor Major Major Minor Moderate
Education & Outreach Major Major Major Major Low
Atmospheric Model
Input DataMajor Minor Major Minor Low/Moderate
Measure-Correlate-
Predict (MCP)Minor Minor Major Major Low
Downscaling Methods Minor Minor Major MajorModerate
/High
Standardization Minor Major Moderate Moderate Low
Site Suitability
AssessmentMinor Major Major Major
Moderate
/High
Low-Cost
InstrumentationMinor Major Major Major High
Tie
r 1
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Potential Actions: Tier 1
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Data Access:• Expand the availability of data accessible to the public
o Site to aggregate datasets (Wind Prospector) Wind maps Anemometer loan programs.
Moderate effort
Major impact on:• Cost of assessment• Performance estimation• Site suitability analysis.
Validation and Benchmarking:• Develop long-term performance monitoring
approaches• Implement a model validation process.
Moderate effort
Major impact on:• Consumer confidence• Performance estimation.
Industry Education and Outreach:• Develop a content-sharing platform • Aggregate key distributed and utility wind resource
assessment best practices• Organize annual or bi-annual DWRA workshops.
Low effort
Major impact on:• Cost of assessment• Consumer confidence • Performance estimation• Site suitability analysis.
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Resources & Next Steps
Resources
• Workshop proceedings published on OpenEI.org
http://en.openei.org/wiki/Distributed_Wind_Resource_Assessment_Workshop
• Report published on NREL’s Publications page
http://www.nrel.gov/docs/fy16osti/66419.pdf
Next Steps
• R&D on Tier 1 activities• Plan depends on budgets.
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Contact Information
Heidi Tinnesand
NREL-National Wind Technology Center
303-384-7133
http://www.nrel.gov/wind/