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 BenderPresented by: Gwendalyn Bender

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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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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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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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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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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

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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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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.

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Results – 10 Training Cases

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TMY TMY-adjustedProject# P50 P75 P90 P99 P75 P90 P991 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%

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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%

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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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Questions?gwendalyn.bender@vaisala.com