This document discusses how weather impacts energy production and profits. It summarizes that Vaisala has been helping industries manage weather impacts for nearly 80 years through weather analysis, consulting, and tools that help understand weather's true impact. Vaisala offers measurement, assessment, and forecasting services to improve project performance, reduce uncertainty, and maximize value for energy projects.

1. The Truth about Solar
Variability
Presented By: Gwendalyn Bender

2. The most successful
companies with a financial
stake in energy rely on
Vaisala for their weather
information.
3/19/152

3. Page © Vaisala
Weather’s Impact on Energy
Weather impacts result in reduced profits, loss
of time and reduced energy output.
Are you working with a weather expert?
A better understanding of wind potential = savings
of millions of dollars in project investment &
financing
Accurate forecasts =
more profitable
scheduling & trading
decisions
With the right tools you
can accelerate the
prospecting phase by up
to 6 months
Accurately determining
uncertainty =
optimized financing
Repairing damage to
equipment caused by
weather vs. replacing can
save you over $150,000

4. Page © Vaisala
Vaisala is Your Weather Expert!
§ We have been helping
industries manage the impact of
weather for nearly 80 years
§ Our weather analysis and
consulting services are based
on proven science
§ We help you understand the
true impact of weather on your
business, allowing you to
improve efficiency and
profitability
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5. Page © Vaisala
Experience
§ Delivered over 1000 resource
assessment projects on 6 continents
§ Produced forecasts for over 130 GW
of installed wind capacity worldwide
§ Collected over 13 million hours of
commercial remote sensing wind
data for customers worldwide
§ Successfully established over 90
lightning detection networks in 45
countries
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6. Page © Vaisala
Weather Information Improves
Energy Output & Profitability
§ Vaisala offers weather expertise in
measurement, assessment, and
forecasting services for:
§ Improved project performance
§ Reduction of uncertainty
§ Maximum value and ROI
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WEATHER SOLUTIONS FOR ENERGY:
- Accurate & reliable measurements for
assessment & forecasting
- Prospecting & project feasibility tools
- Bankable energy assessment reports
- Weather monitoring for operations
- Asset management services
- Weather solutions for transmission &
distribution applications

7. Page © Vaisala
Agenda
Due diligence background
Solar pro forma and uncertainties
Solar PV technologies
Development, construction & operational risks
Solar resource assessment
Conclusion

8. Page © Vaisala
Performance example from the
wind industry
50%
60%
70%
80%
90%
100%
110%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Project Performance by First Year Commercial Operation
1-yr avg 3-yr avg
Drunsic (2012), Actual vs. Predicted Wind Project Performance: Is the Industry Closing the Gap?: [PowerPoint
slides]. Retrieved from DNV presentation presented at 2012 AWEA Wind Resource Assessment Workshop
proceedings

9. Page © Vaisala
Due diligence structure using 3TIER
3TIER is a niche consultant, focused on production yield.
As the most sensitive value driver, this is often separated
out. An additional engineer is required to perform the
remaining traditional IE capacities.
3TIER
Traditional
Engineering Partner
• Solar resource & yield
• Uncertainty analysis
• Power performance test
• Design review
• Technology evaluation
• Construction
• Permits
• Environmental
• O&M
• Financial model

10. Page © Vaisala
Agenda
Due diligence background
Solar pro forma and uncertainties
Solar PV technologies
Development, construction & operational risks
Solar resource assessment
Conclusion

11. Page © Vaisala
Sample US Solar PV Pro Forma
System Cost & Performance
Inputs
System Size (DC) (MW) 50
System Cost ($/watt DC) $4.000
Annual DC Capacity Factor 21.3%
System lifetime (Years) 35
Degradation Factor (%/yr) 1.00%
O&M Costs ($/kW) $44.0
O&M Cost Escalator (%/yr) 1.0%
Inverter replacement cost ($/W) $0.000
Inverter replacement time (Years) 15
Insurance Expense ($/kW) $0.0
Insurance Escalator (%/yr) 0.0%
Module replacement cost ($/W) $0.000
Module replacement time (Years) 40
Financing Assumptions
Inputs
Percent Financed with Equity* 60%
After-Tax WACC 8.25%
Debt Interest Rate 7.50%
Cost of Equity 10.81%
Target minimum DSCR 1.40
Debt Period in Years 20
Federal Tax Rate 35%
State Tax Rate 9%
Tax Credit Rate 30%
MACRS Term 5
Escalator 0%
Purchase price set to $0.1963/kWh – no escalation
DSCR = 1.4 P50
What happens to DSCR and owner’s unlevered IRR several downside
scenarios?

12. Page © Vaisala
Scenario results
Scenario Adjustment DSCR Unlevered
IRR
Base case as described 1.40 9.10%
Low NCF NCF * 0.90 1.24 7.95%
High CAPEX System cost * 1.1 1.25 8.05%
Low system life Lifetime * 0.90 1.40 8.93%
High O&M O&M costs * 1.1 1.38 8.91%
• Yield is often the most sensitive input into solar PV pro forma
• CAPEX is another sensitive input, but well known at the time of financing
• Markets with feed in tariff (FIT) are sometimes exposed to policy risk

13. Page © Vaisala
Uncertainties and P-values
§ Uncertainties used to derive P50, P90, P99, etc…
§ PXX values defined to be the probability that average generation will exceed
the specified generation over the associated time period and are the basis for
many financing decisions
§ Uncertainties can be modeled as normal distributions or with observed
distributions using monte carlo techniques
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
70%
73%
76%
79%
82%
85%
88%
91%
94%
97%
100%
103%
106%
109%
112%
115%
118%
121%
124%
127%
130%
probabilityofeach1%bin
% of P50
Typical Uncertainty Distribution
P90

14. Page © Vaisala
Sources of uncertainty
Sources of uncertainty
§ Measurement uncertainties – were onsite measurements taken?
§ Inter-annual variability – how variable is the resource from year to
year?
§ Irradiance model – how accurate does the irradiance models
represent the true resource?
§ Performance model – do the manufacture’s specifications capture the
reality of performance
§ Input parameters – are each of the many parameterizations of the
performance modeling software correct?
Note: 3TIER does not advise the practice of financing projects
without measurements, unless the scale is appropriately small.

15. Page © Vaisala
Agenda
Due diligence background
Solar pro forma and uncertainties
Solar PV technologies
Development, construction & operational risks
Solar resource assessment
Conclusion

16. Page © Vaisala
§ Worldwide, the vast majority of new
solar plants are photovoltaic.
§ Utility-scale photovoltaic plants
range upward from 5 MW capacity.
§ The largest PV plant currently in
operation is the 250 MW Agua
Caliente Plant near Yuma, AZ
§ The Charanka complex in Gujarat
India is close behind with about 213
MW built and operated by more than
a dozen separate companies.
Agua Caliente
Utility Scale Photovoltaic

17. Page © Vaisala
§ By choice of “technology” we mean
§ Choice of module type
– Monocrystalline
– Polycrystalline
– Thin film & CIGS
– Concentrating PV also under consideration
§ Choice of mounting
– Fixed at tilt angle
– 1-axis tracking
– 2-axis tracking
PV Technology

18. Page © Vaisala
Modules
1. Monocrystalline
• Expensive but rugged and
efficient
2. Polycrystalline
• A good choice
3. Thin film (including CIGS)
• Cheapest
• Least efficient
• Requires the largest
amount of land area
Mounting
1. Fixed Mount
• Cheapest but least
efficient
• Most adaptable to
different types of terrain
2. 1-axis tracking
• ~25% more energy than
fixed
• Requires more land and
moderate slopes
3. 2-axis tracking
• Most expensive option
• ~35-40% more energy
than fixed
Technology Selection boils down to one from column A and one from
Column B
Technology Selection

19. Page © Vaisala
§ What is the most important thing to know about a solar plant?
§ The annual yield is the amount of electrical energy that will be generated by the plant during
an “average” year.
§ Measured in GWh (or GHw/yr)
§ What is the second most important thing to know about a solar plant?
§ The capacity is the amount of power the plant would instantaneously generate if it were
exposed to standard irradiance and weather conditions.
§ Measured in MW, capacity is a measure of power, not energy.
§ Why are these the most important things to know?
§ Because revenue is proportional to yield, but …
§ Costs are proportional to capacity.
§ Capacity is a firm number based on the design, but yield varies year to year with weather and
other factors, so you need to know both the yield and the uncertainty on the yield.
§ Never assume that capacity and yield are proportional. You need to know both.
Photovoltaic basics

20. Page © Vaisala
§ The biggest design consideration in planning a new
photovoltaic plant is the choice of module technology.
§ There are three kinds of solar modules used in commercial
electric generation.
§ Monocrystalline
§ Polycrystalline
§ Thin film
Photovoltaic module technology

21. Page © Vaisala
§ Even the very best
monocrystalline panels
manufactured (for commercial
power applications) today are only
about 20% efficient.
§ Polycrystalline and thin-film
panels are even less efficient
(about 14% and 9% respectively)
and are priced to reflect the fact
that you need more of them to
generate the same amount of
energy as a monocrystalline
panel.
Photovoltaic module technology

22. Page © Vaisala
§ A second critical design
parameter is whether
the solar modules will
be fixed in place, or
mounted on a system to
track the sun.
§ Fixed installations cost
less, but are also the
least efficient at
harvesting energy.
Fixed vs. tracking

23. Page © Vaisala
§ One-axis tracking systems
use pivots and motors to
track the sun from east to
west across the sky, but
do not adjust for the way
that the sun’s height
above the horizon varies
with the seasons.
§ 1-axis tracking systems
are typically 30% more
efficient than fixed
systems.
Single axis tracking

24. Page © Vaisala
§ 2-axis tracking systems
always keep their modules
pointed directly toward the
sun and are the most
efficient type of mounting
§ 2-axis trackers are
35%-40% more efficient
that fixed mounts.
§ They are substantially
more expensive to install
and maintain than either
fixed or 1-axis systems.
Dual axis tracking

25. Page © Vaisala
Agenda
Due diligence background
Solar pro forma and uncertainties
Solar PV technologies
Development, construction & operational risks
Solar resource assessment
Conclusion

26. Page © Vaisala
§ Incorrect yield assessment
§ Causes both cost and revenue
projections to be off
§ Failure to recognize all permitting and
regulatory requirements and secure
permits in a timely manner
§ Expect permitting to take longer than
construction
§ Failure to secure transmission rights
§ Failure to obtain a PPA
§ Plants selling on the open market are
much riskier.
Development Risks

27. Page © Vaisala
§ Failure of EPC or module
contractors to complete work as
specified and on time.
§ Inability to built plant to spec due
to site conditions, site access.
§ Weather
§ Errors in site survey.
§ Installing rows too close can
significantly impact
production
§ Lack of qualified workers or labor
unrest
Construction Risks

28. Page © Vaisala
§ Unexpectedly low power production
§ Could be weather or plant issues
§ Most plants have acceptance plans and
warranties
§ Volcanic eruptions and agricultural burning
can impact production
§ Curtailment by power purchaser.
§ Contract must specify details
§ Failure of power purchaser
§ Very rare.
Operating Risks

29. Page © Vaisala
§ Falling prices for electricity
§ Plants with PPA have no worries
§ Much more significant if plant
sells on the spot market.
§ Damaging weather
§ Hail, windstorms, wildfires
– Should all be covered in contract
and insurances
Operating Risks

30. Page © Vaisala
§ Many solar plants will never generate at full capacity during
their lifetime
§ Because capacity is defined at a standard irradiance (1000
W/m2) which is never achieved at many locations during
most years.
§ This is not really a concern, since revenue is proportional to
yield, not capacity.
§ Although mono, poly, and thin films have very different
efficiencies, the module costs per watt are usually very similar.
§ This is not coincidental, it is the free market at work.
§ Mounting infrastructure costs, however, are proportional to the
number of modules, so mounts for a 50 MW thin film plant will be
larger than costs for a 50 MW monocrystalline plant.
Be aware of…

31. Page © Vaisala
Agenda
Due diligence background
Solar pro forma and uncertainties
Solar PV technologies
Development, construction & operational risks
Solar resource assessment
Conclusion

32. Page © Vaisala
The Myth of Solar Variability
3/19/1532

33. Page © Vaisala
Solar Resource Doesn’t Vary Much Year
to Year…..
3/19/1533
1998 2000 2002 2004 2006 2008 2010
Year
232
236
240
244
248
252
W/m2
Mean NREL GHI = 240.3 Mean 3TIER GHI = 241.7

34. Page © Vaisala
…..In Some Locations But Not Others…
3/19/1534
1998 2000 2002 2004 2006 2008 2010
Year
148
152
156
160
164
168
172
176
W/m2
Mean NREL GHI = 169.21 Mean 3TIER GHI = 164.6

35. Page © Vaisala
..But Everywhere is Effected by Variability
3/19/1535
DNI
Variability in
India by
month and
year.
Based on
3TIER
Service’s
Solar Dataset

36. Page © Vaisala
Resource Variability is a Key
Component to Project Performance
3/19/1536

37. Page © Vaisala
So Are Other Kinds of Weather Events
3/19/1537
Retrieved from www.nytimes.com
Retrieved forum.solar-electric.com. Hail damage

38. Page © Vaisala
And Soiling
3/19/1538
15% soiled <1% soiled

39. Page © Vaisala
And Climatic Events
3/19/1539

40. Page © Vaisala
And Shading
3/19/1540
Retrieved from www.renoble.com and www.pveducation.org

41. Page © Vaisala
And Equipment Maintenance
3/19/1541
Mehta (2014): [PowerPoint
slides] Retrieved from Intersolar
India 2014 proceedings

42. Page © Vaisala
And Site Maintenance
3/19/1542
Retrieve from http://www.fwi.co.uk/business/small-sheep-breeds-solution-to-solar-land-use.htm

43. Page © Vaisala
And It Isn’t Always About
Underperformance
3/19/1543
Schnitzer (2014): [PowerPoint slides] Retrieved from AWS Truepower webinar given November 2014
Money lost to not being able to
sell the energy produced over a
fixed amount
Money lost to receiving lower price
than agreed on in the PPA for “over
production”

44. Page © Vaisala
-30
-20
-10
0
10
20
1975 1980 1985 1990 1995 2000 2005 2010
Eugene, OR
1978–2009
DNI GHI
Anomaly(%)
Year
Convergence time
5%
13 years
To Understand Variability You Can Use
Ground Stations
§ For GHI it might take only 2–3
years of measurement to be
within ±5% of the long-term
mean.
§ For DNI it takes much longer, up
to 5–15 years.
3/19/1544
Gueymard (2010): [PowerPoint Slides] Retrieved from 3TIER webinar presented January 2011
-30
-20
-10
0
10
20
1975 1980 1985 1990 1995 2000 2005 2010
Eugene, OR
1978–2009
DNI GHI
Anomaly(%)
Year
Annual Resource

45. Page © Vaisala
Or You Can Use Satellite Modeled Data
§ Modeled from satellite
observations with other key
inputs
§ Available as long-term
records at high spatial and
temporal resolutions
§ Because it is a modeled
dataset the uncertainty is
higher than ground stations
(GHI is 5-10% rather than
2-3%) but the data is
available today for any
location in the world
3/19/1545

46. Page © Vaisala
Length of
Observations
Training Yr
Uncertainty
Blind Yr
Uncertainty
0 Month 9.0 9.0
3 Months 7.01 7.86
6 Months 4.83 6.29
9 Months 2.61 4.23
12 Months 0.58 2.43
From 9% to 2.4% uncertainty.
Even Better Is To Use Both

47. Page © Vaisala
Understanding Resource Variability Is
Key in Project Financing
System Cost & Performance
Inputs
System Size (DC) (MW) 50
System Cost ($/watt DC) $4.000
Annual DC Capacity Factor 21.3%
System lifetime (Years) 35
Degradation Factor (%/yr) 1.00%
O&M Costs ($/kW) $44.0
O&M Cost Escalator (%/yr) 1.0%
Inverter replacement cost ($/W) $0.000
Inverter replacement time (Years) 15
Insurance Expense ($/kW) $0.0
Insurance Escalator (%/yr) 0.0%
Module replacement cost ($/W) $0.000
Module replacement time (Years) 40
Financing Assumptions
Inputs
Percent Financed with Equity* 60%
After-Tax WACC 8.25%
Debt Interest Rate 7.50%
Cost of Equity 10.81%
Target minimum DSCR 1.40
Debt Period in Years 20
Federal Tax Rate 35%
State Tax Rate 9%
Tax Credit Rate 30%
MACRS Term 5
Escalator 0%
• Yield is often the most sensitive input into solar PV pro forma
• CAPEX is another sensitive input, but well known at the time of financing
• Markets with feed in tariff (FIT) are sometimes exposed to policy risk

48. Page © Vaisala
Observations
Equipment Design
Uncertainty
Environment
Derates and Losses
Net Energy
Questions?
Satellite Data