Optimizing Deployment of Meteorological Towers During Project Development - PowerPoint PPT Presentation

1 / 13
About This Presentation
Title:

Optimizing Deployment of Meteorological Towers During Project Development

Description:

Optimizing Deployment of Meteorological Towers During Project Development ... Kirkland, WA 98033 USA. Phone: 1 425 - 822 - 9008. Fax: 1 425 - 822 - 9022 ... – PowerPoint PPT presentation

Number of Views:19
Avg rating:3.0/5.0
Slides: 14
Provided by: steve1400
Category:

less

Transcript and Presenter's Notes

Title: Optimizing Deployment of Meteorological Towers During Project Development


1
Optimizing Deployment of Meteorological Towers
During Project Development
  • European Wind Energy
  • Conference Exhibition 2006
  • Athens
  • Steve Jones
  • Global Energy Concepts
  • 5729 Lakeview Drive, Suite 100
  • Kirkland, WA 98033 USA
  • 1 - 425 - 822 - 9008
  • sjones_at_globalenergyconcepts.com
  • March 2, 2006

2
The Issue
  • Central question for investors
  • How much energy will the project produce?
  • How windy is it?
  • Answering the question costs the developer money
  • Land lease, tower, instruments, installation,
    maintenance, data acquisition, data evaluation,
    decommissioning
  • but also provides developer access to money
  • Robust answer provides more funds with better
    terms
  • How to balance these competing issues
  • Better wind data to reduce uncertainty vs. need
    to conserve development capital
  • What guides this balance?

3
Different Uncertainty Distributions
  • Example study results
  • P50 estimate of 325 GWh/year
  • P95 estimate of 280 GWh/year (blue line)
  • With less robust measurement campaign, P95
    estimate might be only 265 GWh/year (pink line),
    reflecting less certainty in the answer

P95 estimates 95 of area under curve lies to
the right
P50 estimate
4
Uncertainties (and mitigants)
  • Usually Most Significant
  • Topographic effects (more towers)
  • Wind shear (taller towers)
  • Year-to-year variation (long-term correlation)
  • Wind turbine reliability (availability)
  • Usually Less Significant
  • Wind turbine power performance
  • Equipment mounting/installation
  • Array losses
  • Electrical losses
  • Many others

5
Topographic Uncertainty
6
How Uncertainty Impacts Value
  • Typical analysis
  • Best estimate P50 most likely outcome
  • 50 chance results will be better, 50 chance
    worse
  • 95 confident P95 1 in 20 chance of this
    outcome
  • P99 1 in 100 chance
  • One-year and long-term estimates
  • Investors consider various Pxx levels in
    evaluating down-side
  • Uncertainty used in sizing debt
  • Both P50 and P90P99 typically considered (with
    different debt service coverage ratios)
  • Lower uncertainty can increase debt (and value to
    developer)
  • 5 examples presented
  • Show range of met data acquisition programs
  • Different uncertainties go with each
  • Better data acquisition program means less
    uncertainty and thus P90P99 are closer to P50

7
Example Cases
  • Single 50-m tower, 1 year data, no long-term
    correlation
  • Single 50-m tower, 1 year data, good long-term
    correlation
  • Four 50-m towers, 1 year data, good long-term
    correlation
  • One 80-m tower, four 50-m towers, 1 year data,
    good long-term correlation
  • One 80-m tower, four 50-m towers, 4 years data,
    good long-term correlation

8
Debt Sizing Assumptions
  • Using the P50 energy production estimate 1.50
    Debt Service Coverage Ratio
  • Using the P99 (one year) energy production
    estimate 1.00 DSCR
  • Smaller of the two assumed to size the debt

9
Example Results
10
Balancing Uncertainties
  • There will always be some uncertainty
  • Overall uncertainty usually the square root of
    the sum of the squares of individual
    uncertainties
  • If any one uncertainty remains high, reducing
    uncertainty in the others is not of value in
    reducing overall uncertainty
  • Uncertainty in long-term estimates can be reduced
    much more than uncertainty in 1-year values
  • 1-year numbers largely result from wind
    variability over short (1-year) time periods

11
Balancing Uncertainty Example
12
Additional Issues
  • Terrain Site complexity helps determine
    appropriate met tower campaign
  • Turbine Suitability Met data also used to
    confirm turbine suitable to site
  • Long-Term References Nearby long-term sources
    (airports, research stations), under proper
    conditions, is often a key element in reducing
    uncertainty
  • Competition Others behavior may need to
    influence your own, if for example, competing for
    a power purchase agreement
  • Time Met towers need time to gather data.
    Installing them later in development conserves
    capital, but also means less data are available
    for the assessment
  • New Results Additional data gathering and
    analysis may increase or decrease the P50 best
    estimate

13
Contact Information
  • Steve Jones
  • Director of Utility and Investor Services
  • Gordon Randall
  • Senior Data Analyst
  • Global Energy Concepts, LLC
  • 5729 Lakeview Drive NE, Suite 100
  • Kirkland, WA 98033 USA
  • Phone 1 425 - 822 - 9008
  • Fax  1 425 - 822 - 9022
  • sjones_at_globalenergyconcepts.com
  • grandall_at_globalenergyconcepts.com
  • www.globalenergyconcepts.com
Write a Comment
User Comments (0)
About PowerShow.com