The Path to Risk Reduction

Matthew Hendrickson

Sr. Director of Energy Assessment

Risk lifecycle through project stages

A

B

C

D

Generic Project

Turbines Met Towers

This analysis follows the lifecycle of a project through the

typical development & early operation stages.

Risk associated with long term performance was assessed per

the risk relationships.

Development Operations

Risk Relationships

Met towers Spatial uncertainty

Time Temporal

uncertainty

Generation data Modeling

uncertainty

Year 0: Prospecting stage

A

B

C

D

Generic Project

Turbines

“High hopes?” “Where to put that first met?”

Met Tower Years of Data

A 0

B 0

C 0

D 0

This stage represents the decision to proceed. It is

probably the result of a prospecting effort.

Production estimates formed only from experience and

spatial maps.

Uncertainty % Energy

Measure/temporal n/a

Spatial 27.0%

Model 6.7%

Total 27.8%

6 months: Early decisions

A

B

C

D

Generic Project

Turbines Met Towers

Met Tower Years of Data

A 0

B 0

C 0.5

D 0

Uncertainty % Energy

Measure/temporal 9.9%

Spatial 8.5%

Model 6.7%

Total 14.7%

“More mets?” “Expand our position?”

Early on, decisions must be made about how quickly

and where to expand. Significant investing in land

might be the next stage.

First few months of data are better than nothing, but still

not that great.

Year 2: Time to start marketing

A

B

C

D

Generic Project

Turbines Met Towers

Met Tower Years of Data

A 0

B 1

C 2

D 0.5

Uncertainty % Energy

Measure/temporal 3.9%

Spatial 4.7%

Model 6.7%

Total 9.1%

“What’s my price?” “How risky is this?”

After several years, serious marketing is in the works.

With several years of data on several towers,

uncertainty can reach single digits.

Year 4: Let’s build it

A

B

C

D

Generic Project

Turbines Met Towers

“Did we get enough data?”

Met Tower Years of Data

A 1

B 3

C 4

D 2.5

Uncertainty % Energy

Measure/temporal 2.4%

Spatial 3.8%

Model 6.7%

Total 8.1%

“nervous”

After four years, the very significant Investment

Decision is made. It would be nice to have more time

& data, but the market doesn’t always wait.

With fours years of data on four towers, uncertainty is

still dropping, but not as fast.

Year 5 (Ops year 1): First year review

Generic Project

Turbines

“How’d we do?”

Uncertainty % Energy

Measure/temporal 6.3%

Spatial 0%

Model 0%

Total 6.3%

With one year of operations, data includes uncertain

ramp up period. All eyes are on investment

performance.

A single year of generation is better than anything that

has gone before, but there is pretty high temporal risk.

Year 6 (Ops year 2): Reforecasting

Generic Project

Turbines

“Time to update budgets?”

Uncertainty % Energy

Measure/temporal 3.2%

Spatial 0%

Model 0%

Total 3.2%

After two years, generation expectations are stabilizing

long term budgets can be revisited.

From here on out, there is only small movements in long

term expectations, provided there is a decent long term

reference.

Best Practices to Reduce Risk

» Collect high quality observations

› Aim for spatial representativeness and height

› Collect at least 1 year at each tower, preferably more

› If met towers don’t extend through plane of rotor, use remote

sensing

› The lack of observations is the biggest risk that people take

» Long Term Referencing

› Without many years from a long term reference, uncertainty is high.

› In most cases, ground based stations are not available. Synthetic

reference are well validated and can reduce uncertainty without

having to collect many years of data. But they are not all equal. Be

a discerning consumer.

Best Practices to Reduce Risk

» Spatial Modeling

› While models will never replace observations, high quality spatial

models, used with observations, can be a cost effective way of

reducing the number of met towers needed.

» Track risk

› Finally – become diligent at knowing where your risk lies and how

you can better attack it.

› Systematically identify risk in your pipeline and build your energy

assessment program around the systemic reduction of risk.

In Conclusion – I Propose The Magic 7%

» With many years of Energy Assessment experience, I’ve seen the

best and worst of projects.

» I’ve seen uncertainty estimates of very high quality with many met

towers and tip height sensors - to very low quality with almost no

observations.

» Every program should be designed to resist uncertainty until it

reaches a certain optimal level.

» I propose that 7% energy uncertainty is a target that one should

strive for. It is an achievable level that is still a stretch for some

companies.

» With 7% uncertainty, some simple economic models have a hope

of breaking even during the P95 downside case.

3TIER can help you get to 7%

» Questions?