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Next Generation Wind Portfolio Strategy: How Important is Diversification
Scott Eichelberger, PhDOffering Manager for Wind Energy Assessment
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2015 Q1 Wind Speed Anomalies
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Simple portfolio illustration
Tools
Climate Resilient Portfolio
Agenda
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Portfolio Scenario
Project correlation – 0.90
Region correlation – 0.20
Project Size – all 100 MW
Project NCF – all 32.0%
Project IAV – all 7.0%
Pick 5 projects to invest in.
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Portfolio Scenario #1 (max correlation)
Portfolio NCF – 32%
Portfolio IAV – 6.7%
Portfolio P90/P50 - 91.4%
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Portfolio Scenario #2 (max diversity)
Portfolio NCF – 32%
Portfolio IAV – 4.2%
Portfolio P90/P50 - 94.6%
P90 improvement of 3.2% over max correlation
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Agenda
Simple portfolio illustration
Tools
Climate Resilient Portfolio
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Numerical Weather Prediction Models
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Numerical Weather Prediction Models
NWP models are able to simulate both the temporal & spatial varying components of the weather
Capture the regional variations of the wind resource, as well as the site specific flow regimes across a project site.
For more details on NWP: https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/numerical-weather-prediction
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Reanalysis Data Sets
Basis for the long term climate information within NWP simulations
Created from global observational datasets: weather balloons, weather stations, aviation data, satellites, ocean buoys
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Agenda
Simple portfolio illustration
Tools
Climate Resilient Portfolio
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Potential Projects
20 potential project sites scattered across the continental United States
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1st Scenario - Typical Portfolio – CA / TX
Build portfolio using sites with strong wind resource and significant existing development
Portfolio comprised of 1000MW in 4 locations: 2 in California and 2 in Texas, all with equal weights
250MW
250MW
250MW
250MW
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1st Scenario - Typical Portfolio – CA / TX
• Portfolio-wide, monthly-mean normalized generation values
• Shaded box bounded by P75 & P25; whiskers show minimum & maximum
values
• Red dots denote values for 2015
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Next Generation Portfolio Strategy
Problem statement: What is the distribution of projects that creates a portfolio with the least amount of variability?
To generate the climate resilient portfolio:
1. Perform NWP modeling at each of the 20 potential sites
2. Develop a wind production index (WPI) at each site based on
normalized generation data using a generic power curve
3. Solve for the portfolio weights that yield a portfolio-average WPI
time series with the minimum monthly variance*
*Please note: power price variability was not considered
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2nd Scenario - Climate Resilient Portfolio
Optimization analysis yields a climate resilient portfolio of 1000MW using 7 sites.
250MW
50MW
250MW
50MW
50MW
300MW
50MW
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2nd Scenario - Climate Resilient Portfolio
Optimization analysis yields a climate resilient portfolio of 1000MW using 7 sites.
250MW
50MW
250MW
50MW
50MW
300MW
50MW
4 sites from CA / TX portfolio are included
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2nd Scenario - Climate Resilient Portfolio
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
30405060708090
100110120130140150160
Norm
aliz
ed
Ge
ne
ration
(%
)
• Portfolio-wide, monthly-mean normalized generation values
• Shaded box bounded by P75 & P25; whiskers show minimum & maximum
values
• Red dots denote values for 2015
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Portfolio Comparison
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
30405060708090
100110120130140150160
Norm
aliz
ed
Ge
ne
ration
(%
)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
30405060708090
100110120130140150160
Norm
aliz
ed
Ge
ne
ration
(%
)
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Portfolio Comparison
• Annual-mean normalized generation values
• Climate resilient portfolio is green
• CA / TX portfolio is blue
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Portfolio Comparison
• Annual-mean normalized generation values
• Climate resilient portfolio is green
• CA / TX portfolio is blue
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Conclusion
In 2015 Q1 large portions of the US experienced low winds
At the same time other portions of the US experienced high winds
Investors that were regionally concentrated experienced very low
project returns
NWP model output may be used for optimizing portfolio diversity
Intelligent project selection can take advantage of known
variability patterns to minimize downside scenarios and yield more
stable/consistent investment returns
Thank you
Scott Eichelberger2016-04-15Analysis of Operating Wind Farms 2016Bilbao, Spain
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Min
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Quarterly Standard Deviation of Percent Departure from Normal
2% 4% 6% 8% 10% 12% 14% 16% 18%
100
80
60
40
20
YieldCo Portfolios compared to Vaisala Climate Resilient Portfolio