When determining energy output from utility scale solar projects when is it appropriate to use just a TMY and when should you use a long-term time series? This presentation compares both methodologies at 20 project sites to help answer the question.

1. Solar Energy Assessments:
When is a Typical
Meteorological Year Good
Enough?
Authored by: Dr. Sophie Pelland, Charles Maalouf, Renée Kenny,
Dr. Louise V. Leahy, Brad Schneider and Gwendalyn Bender
Presented by: Gwendalyn Bender
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2. 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
§ Acquired 3TIER Inc in 2013
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3. Page © Vaisala
Agenda
§ Introduction
§ Identifying when a TMY resource based energy assessment is good enough
compared to a long-term resource assessment
§ Methodology
§ Methodology for long-term energy assessments as baseline, TMY and adjusted
TMY methods
§ Results
§ Results for 18 projects, Evaluation of extreme cases
§ Conclusions
§ Adjusted TMY results within 1% of full time series results; exceptions are
locations where inter-annual variability of resource dominates other uncertainties
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4. Page © Vaisala
Introduction
§ Why investigate what kind of resource files to use in energy
estimates?
§ Long-term resource data is becoming increasingly available, as
well as the ability to process it in photovoltaic simulations tools
§ Many clients still ask for energy assessments based on Typical
Meteorological Year (TMY) files as inputs
§ There is a need to be able to guide clients as to whether and
when a TMY analysis is “good enough” for their needs
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5. Page © Vaisala
Methodology –
§ This study is based on eighteen energy assessments that Vaisala conducted for
Megawatt-scale photovoltaic projects. Projects are located in various parts of North
America, South America and Asia.
§ They include fourteen projects with single-axis horizontal East-West trackers and four
with fixed (or seasonally varying) orientations. Some projects are at the pre-construction
stage, while others are already operational.
§ All projects were modeled with 3TIER Services resource data and the energy
calculations were run in PVsyst.
§ Probability-of-exceedance values were generated corresponding to the year-1 yield of
the projects. This is the first year of operation for new projects and the upcoming year
for operational projects.
§ Specifically, P50, P75, P90 and P99 values were calculated. These indicate,
respectively, the energy yield which a PV project has a 50%, 75%, 90% and 99%
probability of exceeding during year-1.
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6. Page © Vaisala
Observations
P-values yields and performance metrics
Modeling: in-house and PVsyst
P50 yields and performance metrics
Energy Modeling Methodology
Satellite Data and
NWP model data
PV system design and specifications
Uncertainty analysis
§ Hourly meteorological data was either
TMY or 16-19 years (1997-2016)
§ Inter-annual variability uncertainty treated
differently in TMY and full time series
analyses
§ Uncertainties not associated with the
inter-annual variability in the solar
resource were treated identically across
all approaches.
§ P-values from the two approaches
compared

7. Page © Vaisala
Uncertainties other than inter-annual
variability
§ Resource modeling: Resource modeling uncertainty captures the
uncertainties related to the accuracy of the satellite derived irradiance
data utilized in the energy assessment, excluding uncertainties
associated with climate variability.
§ Power modeling: Power modeling uncertainty considers each step in
converting solar irradiance estimates into energy estimates
§ Aging: The rate at which photovoltaic systems experience
degradation is subject to uncertainty. Vaisala uses technology-specific
median long-term degradation rates.
§ These uncertainties were combined with the inter-annual distributions
in year-1 yield to generate an overall cumulative distribution function
from which P-values were obtained.
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Methodology – Full Time Series
Simulations
§ For each project, PVsyst was run in batch mode to generate annual energy
yields for each year of weather data, returning 16 to 19 year-1 yields.
§ These multiple year-1 yields were then used to construct a probability
distribution of year-1 yields using kernel density estimation (KDE).
§ Kernel density estimation is a non-parametric method of estimating the
probability density function of a random variable (Silverman, 1998). Kernel
density estimators are a generalization over empirical histograms, which are
often used.
§ The key in the KDE approach is selecting the bandwidth (analog to
histogram bin width). In this analysis, we selected bandwidths using a cross-
validation approach, where data points were withheld one at a time, and the
bandwidth leading to the maximum total log-likelihood over withheld data
points was selected.
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9. Page © Vaisala
Methodology – TMY Simulations
§ Vaisala creates TMY datasets using an empirical approach that selects 4-day
samples from the full time series to create a “typical year” of data with 8760
hours, while preserving the monthly and annual means of either global
horizontal irradiance (GHI) or direct normal irradiance (DNI).
§ The process is iterated until the monthly and annual means of both GHI and
DNI in the TMY dataset match the means of the full time series to within
roughly 0.5% or less.
§ The TMY datasets were used as inputs to PVsyst for each of the eighteen
projects. The resulting year-1 yield was interpreted as the mean of a normal
distribution of year-1 yields.
§ In order to estimate the standard deviation of this distribution, different proxies
for the standard deviation in year-1 yield were evaluated, the best of which
was found to be the standard deviation in annual GHI.
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Comparing year-1 yield distributions
from TMY and full time series
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§ KDE distributions tend to have fatter tails
§ KDE distributions often have different shapes than normal distributions, so
not possible to match these up completely

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Methodology – TMY Adjustment
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§ As can be seen from the top figure, the standard
deviation in GHI tends to systematically
underestimate the standard deviation in energy.
§ We therefore considered two versions of the
TMY approach: one in which the standard
deviation in GHI was used directly and another,
which we refer to as “TMY-adjusted”, in which
corrections to the standard deviation in GHI
were made to partly compensate for biases.
§ These corrections were developed on the first
ten projects that we analyzed (training data set).
Next eight projects were used as a testing data
set on which to independently validate the
corrections.

12. Page © Vaisala
Results – 10 Training Cases
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TMY TMY-adjusted
Project # P50 P75 P90 P99 P75 P90 P99
1 0.8% 1.0% 1.3% 1.5% 0.8% 0.8% 0.7%
2 -0.7% 0.0% 0.7% 1.7% -0.7% -0.4% -0.2%
3 0.1% 0.5% 0.9% 1.8% 0.1% 0.1% 0.2%
4 0.4% 0.7% 0.8% 0.9% 0.4% 0.2% 0.0%
5 0.5% 0.9% 1.1% 1.1% 0.5% 0.4% 0.1%
6 -0.2% 0.0% 0.3% 0.9% -0.2% -0.4% -0.4%
7 0.2% 0.4% 0.5% 0.9% 0.2% 0.0% -0.2%
8 0.0% 0.0% 0.1% 0.2% 0.0% -0.2% -0.3%
9 -0.2% 0.2% 0.6% 1.7% -0.2% -0.1% 0.1%
10 -0.1% 0.2% 0.6% 1.2% -0.1% -0.1% 0.0%
Mean 0.1% 0.4% 0.7% 1.2% 0.1% 0.0% 0.0%
Stnd dev 0.4% 0.4% 0.4% 0.5% 0.4% 0.4% 0.3%
Max 0.8% 1.0% 1.3% 1.8% 0.8% 0.8% 0.7%
Min -0.7% 0.0% 0.1% 0.2% -0.7% -0.4% -0.4%

13. Page © Vaisala
Results – 8 Testing Cases
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TMY TMY-adjusted
Project # P50 P75 P90 P99 P75 P90 P99
11 -0.3% 0.0% 0.3% 1.0% -0.2% -0.2% 0.0%
12 0.3% 0.4% 0.4% 0.5% 0.2% 0.1% -0.2%
13 0.4% 0.7% 1.0% 1.7% 0.5% 0.5% 0.7%
14 0.2% 0.7% 1.0% 2.0% 0.3% 0.4% 0.7%
15 -0.3% -0.2% -0.1% 0.3% -0.4% -0.5% -0.6%
16 0.0% 0.1% 0.2% 0.3% -0.1% -0.2% -0.4%
17 0.5% 0.9% 1.2% 1.8% 0.6% 0.6% 0.7%
18 0.0% 0.2% 0.4% 0.7% -0.1% -0.3% -0.6%
Mean 0.1% 0.3% 0.6% 1.0% 0.1% 0.1% 0.0%
Stnd dev 0.3% 0.4% 0.5% 0.7% 0.4% 0.4% 0.6%
Max 0.5% 0.9% 1.2% 2.0% 0.6% 0.6% 0.7%
Min -0.3% -0.2% -0.1% 0.3% -0.4% -0.5% -0.6%

14. Page © Vaisala
Analysis of Extreme Cases
§ Since our analysis is based on a fairly small sample of eighteen projects, it may not
pick up extreme cases where the difference between the TMY approach and the full
time series approach is most pronounced.
§ We conducted two analyses to try to expand our results to capture extreme cases
where the differences between the two approaches should be most pronounced when
inter-annual variability is large relative to other uncertainties.
§ In order to explore this, two hypothetical projects (one tracking, one fixed) were
simulated at a location near Pades, Romania, where inter-annual variability is high. All
other uncertainties were set to realistic minimum values.
§ The second extreme case analysis consisted of calculating P-values for each project
neglecting all uncertainties except inter-annual variability. This essentially mimics the
case where other uncertainties are negligible compared to inter-annual variability.
§ In the extreme case scenarios differences in the P90 reach 3.6% in the unadjusted
case and 2.0% in the adjusted case, while differences in the P99 reach 5.3% in the
unadjusted case and 3.2% in the adjusted case.
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Conclusion
§ Differences in the P50 closely reflect differences between the TMY means and the long-term
time series means of GHI and DNI. In the case of the 3TIER Services TMY, this difference is
usually less than 0.5%.
§ For other P-values, our analysis shows that using the standard deviation in GHI as a proxy for
inter-annual variability in the yield tends to systematically underestimate uncertainty, but also
that this bias can be removed through simple corrections.
§ With the “adjusted-TMY” approach, differences in P-values were within 1% or less for the 18
projects analyzed.
§ In “extreme cases” where inter-annual variability dominates, this can reach about 2-3% in the
adjusted case (3-5% in the unadjusted case). In these cases, we recommend the full time series
analysis.
§ When is TMY “good enough”? It depends…
§ What TMY dataset is being considered
§ User requirements as to what constitutes an acceptable difference between TMY and full time series analyze
§ Relative size of inter-annual variability and of other uncertainties.
§ One way to decide whether or not a TMY approach is appropriate is to ask whether or not
differences on P-values of the order of 1% or less are acceptable for a given project. If P-values
are being used to secure financing on Megawatt-scale projects, then the small added complexity
involved in running a full time series will probably seem worth the effort!
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16. Questions?
gwendalyn.bender@vaisala.com