UPDATED FINAL
Availability Evaluation Report
Prepared For:
SunPower Corporation
Prepared By:
January 14, 2013
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
BEW Engineering - Experience and Qualifications
BEW Engineering, a wholly owned subsidiary of DNV, is a
recognized leader in solar photovoltaic engineering services and
our team has well over 200 years of combined experience in this
area over the last three decades. BEW has merged into DNV
KEMA Renewables, Inc. under the banner DNV KEMA Energy &
Sustainability. BEW has provided technical services on
commercial and utility scale projects totaling more than 10 GW of
PV. This extensive project experience has allowed for the development of effective energy analysis, EPC
selection, design review, independent engineering, system design, financial analysis, performance
prediction and other important tools which provide an efficient approach to the evaluation of these
systems. BEW’s experience in technology advancement with module manufacturers, inverter suppliers,
balance of system component suppliers, and mounting and tracking system manufacturers provides a
unique perspective toward integrating these components within a given project. Experience with
national and international codes and standards development and work with the U.S. Department of
Energy and National Laboratories keeps BEW at the leading edge of standards and technology
developments. Forensic experience has also assisted BEW in improving PV system implementation
practices that impact real world systems. This extensive experience allows BEW to provide engineering
services in an efficient and cost effective manner.
BEW has a staff of 55, with specialists in the areas of transmission and distribution systems, high grid
penetration of renewable energy, curtailment risk assessment, structural and wind-loading calculations
for rooftop and ground-mount systems, civil engineering, electrical engineering, codes and standards,
PV system design and optimization, solar resource assessments, PV system performance analysis, and
power electronics design and verification testing.
BEW Engineering
Independent Engineering Report
Page 2
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
Biographies of Key Contributors
Jessica Forbess, M.Eng., Senior Engineer, PV Solar
Ms. Forbess performs independent engineering reviews on behalf of clients such as third-party
developers, integrators, and investment banks. She has been involved in the photovoltaic solar industry
since 2008, with an emphasis on PV performance analysis, utility-scale PV system operations and
maintenance, PV system design, energy management and data acquisition systems.
Prior to joining BEW, Ms. Forbess was a Performance Engineer with the EPC division of First Solar, Inc., a
leading solar module manufacturer, project developer and EPC, providing performance testing, analysis
and monitoring for the largest PV plants in North America and the US in 2009 and 2010. She began her
career in telecommunications as a field and test engineer rolling out flexible software-based cell
network infrastructure. She completed her BSEE from the Massachusetts Institute of Technology in
1997, and her M.Eng. in EECS also from MIT in 2000.
Jeffrey D. Newmiller, Principal Engineer, PV Solar
Mr. Newmiller specializes in a number of areas of PV with his prime focus on analyzing performance
data, developing performance monitoring systems, troubleshooting PV systems, and reviewing system
designs for photovoltaic solar power systems. His engineering know how has been applied to various PV
systems from 1 to 100,000kW. Mr. Newmiller has worked with PV since 1992 and has participated in the
development of IEEE1547, UL1741, California Rule 21, and updates for the National Electric Code
Articles 690 and 705. He has provided technical support to the California Energy Commission by
managing the CEC Eligible Equipment Lists of Inverters and PV Modules for the first five years of
implementation, and provided reviews of compliance with equipment certification requirements for
California Rule 21. Mr. Newmiller has analyzed and suggested improvements on the modeling
capabilities of several clients’ in-house PV performance modeling tools. Mr. Newmiller received BS and
MS degrees in Mechanical Engineering (with focus on engineering measurement theory and practice)
from the University of California at Davis.
BEW Engineering
Independent Engineering Report
Page 3
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
Table of Contents
1.
Executive Summary ......................................................................................................................... 5
2.
Introduction .................................................................................................................................... 7
3.
Overview ......................................................................................................................................... 9
Variability .......................................................................................................................................... 10
4.
Review of SunPower Availability Algorithm .................................................................................... 12
5.
Review SunPower Historical Availability ......................................................................................... 15
Time-weighted and Energy-weighted Availability............................................................................... 15
Availability by Subset ......................................................................................................................... 16
Variability .......................................................................................................................................... 18
BEW Engineering
Independent Engineering Report
Page 4
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
1. Executive Summary
SunPower has engaged BEW to conduct a review of a subset of SunPower’s utility-scale PV plant
operational data with the intent of quantifying expected availability for staffed SunPower solar plants.
SunPower asserts that the availability of SunPower plants with full O&M service from SunPower on-site
staff will be at least 99%, and has provided supporting data and calculations. From a financial point of
view, 99% availability means that the project revenue may be reduced by no more than 1% annually due
to plant outages in a p(50) estimate. BEW has reviewed the data, discussed the data validation process
with SunPower, and replicated the calculations. BEW has performed additional calculations to
statistically compare the availability when external forced outages are included as energy lost, and
quantified the variability of annual availability. BEW has confirmed SunPower’s assertion on a p(50)
basis, and has further analysis on the annual and ten-year average variability. This is based on the
assumption that power plant service and maintenance activities are consistently performed throughout
the life of the project.
BEW confirms SunPower’s assertion of 99% availability with the data in Table 1 below. It indicates that a
single year p(50) energy estimate can reasonably use 99.5% availability. The data supporting the tenyear average availability is less complete. BEW has calculated the historic ten year average availability
assuming O&M procedures and manufacturer support remain constant, Years 2 through 8 are
reasonably characterized in the current data, and failure rates in Years 9 and 10 are similar to Year 1.
BEW underlines that this assumes that failure rates are not significantly greater in Years 9 and 10 than in
Year 1. Our estimate results in a ten-year average annual availability of 99.7% at a p(50) level, and 99.3%
at p(90) level including all outages. A more detailed analysis including the assumptions we made, and
confidence intervals around each cumulative probability level is found in Section 5.
Table 1: Single year and ten-year cumulative probability estimates
Single Year Availability
10 Year Average Availability
Penalized
Cumulative
for all
Probability
outages
External
outages
removed
Penalized for
all outages
External
outages
removed
P50
99.5%
99.7%
99.7%
99.8%
P75
98.8%
99.2%
99.5%
99.7%
P90
97.2%
98.3%
99.3%
99.5%
P95
95.2%
97.3%
99.2%
99.5%
BEW Engineering
Independent Engineering Report
Page 5
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
The p(50) in all cases shown in Table 1 above is significantly higher than the targeted 99% availability.
Because the single year p(90) reduction in energy is similar to that typically seen in solar resource
variability assessment, BEW recommends that it be included in a project finance downside uncertainty
analysis. The ten-year average annual availability estimate has a higher p(90) value of 99.3%.
BEW Engineering
Independent Engineering Report
Page 6
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
2. Introduction
SunPower has engaged BEW to conduct a review of a subset of SunPower’s utility-scale PV plant
operational data with the intent of quantifying expected availability for staffed SunPower solar plants.
SunPower asserts that the availability of SunPower plants with full O&M service from SunPower on-site
staff will be at least 99%, and has provided supporting data and calculations. From a financial point of
view, 99% availability means that the project revenue may be reduced by no more than 1% annually due
to plant outages in a p(50) estimate. BEW has reviewed the data, discussed the data validation process
with SunPower, and replicated the calculations. BEW has performed additional calculations to
statistically compare the availability when external forced outages are included as energy lost, and
quantified the variability of annual availability. BEW has confirmed SunPower’s assertion on a p(50)
basis, and has further analysis on the annual and ten-year average variability. This is based on the
assumption that power plant service and maintenance activities are consistently performed throughout
the life of the project.
This report analysis covers data from sixteen solar plants in six countries, and includes all utility-scale PV
plants monitored by SunPower and operated by on-site staff. The following table provides a detailed list
of the solar projects, which ranged in size from 6.1 MWDC to 45 MWDC. The earliest Commercial
Operation Date was January 1, 2005, and the most recent plant began operating commercially in
January 2012. The data used in the availability analysis was recorded from January 1, 2008 to June 6,
2012. Data from plants becomes available at different times, with the oldest data being from Nellis in
January 2008, data from Bavaria starting in September 2009, and the rest following after.
BEW Engineering
Independent Engineering Report
Page 7
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
Table 2: SunPower staffed Utility Scale Projects
Site
Project
Country
M0190
M0311
M0407
M0505
M0672
M0693
M0695
M0696
M0726
M0815
M0818
M0827
M0837
M0853
M0865
M0869
Bavaria Solar 1 / Muehlhausen
GE Energy Financial Services / Serpa
United States Air Force / Nellis Air Force Base
360 Corporate / Olivenza
Florida Power and Light / FPL DeSoto
Sunshire S.r.l / Tolentino
Cassiopea PV Srl (SunRay Renewable Energy)
Florida Power and Light / FPL Space Coast
Exelon Generation Company, LLC / Cook County
Montalto Centauro
Xcel - Greater Sandhill I
Andromeda PV Srl (SunRay Renewable Energy)
Amherstburg Solar Farm
Montalto Andromeda
Iberdrola Alamosa
Iberdrola Copper Crossing
DE
PT
US
ES
US
IT
IT
US
US
IT
US
IT
CA
IT
US
US
Commercial
Date of
Operation
1/1/2005
1/17/2007
10/15/2007
11/24/2008
9/15/2009
7/1/2010
6/1/2010
9/28/2010
3/23/2010
9/18/2010
9/23/2010
3/31/2011
7/1/2011
3/31/2011
1/6/2012
9/1/2011
kWp
6,269
10,980
14,056
18,000
27,604
5,013
24,008
10,000
10,000
8,800
20,072
45,000
20,000
6,100
35,100
23,000
SunPower provided the data in Staffed_Availability_Study_(Energy-Weighted)_2012_07_25.xlsx.
Additional information was provided in discussions with SunPower including interactive sessions with
their SCADA management system, OSI PI.
BEW Engineering
Independent Engineering Report
Page 8
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
3. Overview
At a portfolio level, staffed SunPower plants have achieved at least 99% availability both on a timeweighted basis and an energy-weighted basis when forced grid outages are excluded. When grid
outages are included, the portfolio maintains 99% availability on an energy-weighted basis, though not
on a time-weighted basis as shown in Figure 1.
Portfolio Availability
Time Weighted
Energy Weighted
Target
100.0%
99.5%
99.0%
99.03%
99.19%
99.28%
98.82%
98.5%
98.0%
Penalized for All Energy Lost
Not Penalized for Grid Outages
Figure 1: Portfolio Availability
While the figure above indicates there is minimal difference between time-weighted availability and
energy-weighted availability, a more detailed review by plant shows that there is a difference that
exceeds 1% in specific instances. Because the primary goal of tracking availability in most contexts is to
quantify and minimize the energy lost through operational failure, BEW recommends using estimated
energy to weight the operating time, which more closely represents energy lost. If estimated energy
isn’t available, BEW considers weighting by irradiance to be sufficiently accurate for the purposes of
improving the calculation of availability over a flat time-weighted calculation.
The energy-weighting process in this report only considers the energy on a daily basis, and therefore
does not consider the difference in an outage at the end of the day compared to an outage at noon. The
analysis in this report does take into account the difference between an outage on a cloudy winter day
and a sunny summer day. BEW expects that daily weighting is sufficient for this analysis, because we
expect outages to be either unbiased as to time of day, or slightly biased to the ends of the day rather
than the peak. Two very typical inverter failures are failure to start up in the morning, and failures due
to high temperatures. Start-up failures are typically resolved by on-site staff before peak irradiance, and
high temperature failures are slightly more likely to occur a few hours after peak irradiance.
BEW Engineering
Independent Engineering Report
Page 9
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
Variability
An individual solar plant may operate at a much lower availability than the portfolio average. It may
operate at a higher availability as well, but as availability cannot exceed 100%, the distribution of annual
availability is not symmetric as a typical normal distribution would be.
To calculate the variability of availability, BEW used a logarithmic transform of the data, a set of all of
the annual availabilities for all plants, with a total of 37 data points. We did not assume that the data
was perfectly log-normal, and therefore a kernel estimate was used to curve-fit the probability
distribution that best reflects the available information.
BEW confirms SunPower’s assertion of 99% availability with the data in Table 1 below. It indicates that a
single year p(50) energy estimate can reasonably use 99.5% availability. The data supporting the tenyear average availability is less complete. BEW has calculated the historic ten year average availability
assuming O&M procedures and manufacturer support remain constant, Years 2 through 8 are
reasonably characterized in the current data, and failure rates in Years 9 and 10 are similar to Year 1.
BEW underlines that this assumes that failure rates are not significantly greater in Years 9 and 10 than in
Year 1. Our estimate results in a ten-year average annual availability of 99.7% at a p(50) level, and 99.3%
at p(90) level including all outages. A more detailed analysis including the assumptions we made, and
confidence intervals around each cumulative probability level is found in Section 4.
Table 3: Single year and ten-year cumulative probability estimates
Single Year Availability
10 Year Average Availability
Penalized
Cumulative
for all
Probability
outages
External
outages
removed
Penalized for
all outages
External
outages
removed
P50
99.5%
99.7%
99.7%
99.8%
P75
98.8%
99.2%
99.5%
99.7%
P90
97.2%
98.3%
99.3%
99.5%
P95
95.2%
97.3%
99.2%
99.5%
The p(50) in all cases shown in Table 1 above is significantly higher than the targeted 99% availability.
Because the single year p(90) reduction in energy is similar to that typically seen in solar resource
variability assessment, BEW recommends that it be included in a project finance downside uncertainty
BEW Engineering
Independent Engineering Report
Page 10
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
analysis. The ten-year average annual availability estimate has a higher p(90) value of 99.3%. The data
in Table 1 is shown graphically in Figure 2.
Cumulative Probaility
Availability Variability
Penalized for all outages (1 year)
External outages removed (1 year)
Penalized for all outages (10 year)
External outages removed (10 year)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
84%
86%
88%
90%
92%
94%
96%
98%
100%
Availability
Figure 2: Estimated single year and ten year availability probability distribution
BEW Engineering
Independent Engineering Report
Page 11
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
4. Review of SunPower Availability Algorithm
The power industry typically calculates availability on a time-weighted basis, as most power plants run
at a fairly consistent capacity factor. Annual availability is calculated by dividing the hours the plant was
generating energy by the number of hours in a year. Exceptions are allowed for planned maintenance,
force majeure conditions, and other contingencies.
Contracts in the solar industry commonly also use a time-weighted basis to calculate availability.
Minimal data from utility-scale solar plants has been available to calculate whether a time-weighted
basis would tend to over- or under-estimate the energy lost through outages, since the lost energy at
any given point in time is highly variable. Annual availability is calculated on an energy-weighted basis by
dividing the generated energy by the total possible generated energy based on the available irradiance
at the site. SunPower has provided data to enable BEW to calculate availability for each basis.
SunPower has described their availability algorithm in discussions with BEW, including demonstrating
specific examples directly from the SCADA management system, OSI PI. We have not validated the
actual software implementation of the availability calculation in the SunPower data collection system.
An Availability data point is calculated automatically in the OSI PI system used to monitor the plants. It is
calculated on a per-inverter basis at 15 minute increments when the average POA irradiance is greater
than 100 W/m2over the 15 minute interval. If an inverter generates any energy in a 15 minute period, its
Availability is marked as 1 for that period. If an inverter generates no energy in a 15 minute period, its
Availability is marked as 0 for that period. BEW finds this algorithm to be generally reasonable for utilityscale PV plants, but notes the following limitations to the algorithm:
1. 100 W/m2 is higher than ideal for a threshold of inverter operation. A typical PVSim model of a
single axis tracking system showed 0.5% of the annual generation occurred during hours with
less than 100 Wh/m2. While it is unlikely that the inverter would be configured such that it
would fail to generate under those conditions while operating correctly in all others, it is an
upper bound on the potentially missing energy for a typical single axis tracking system. BEW has
not performed an in-depth review of the inverter performance at different levels of detection,
but verified that operational data from a single inverter showed only a 0.06% lower annual timeweighted availability when the threshold of inverter operation was 50 W/m2 instead of 100
W/m2 and a 0.14% lower annual time-weighted availability when the threshold of inverter
operation was 25 W/m2 instead of 100 W/m2. The comparisons would show a lower variation in
energy-weighted availability, given the small amount of energy involved at the edges of the day.
2. Inverter outages lasting less than 15 minutes are not detected. This includes inverters that trip
off line and recover within the 15 minute period. BEW expects that these outages are monitored
through alarms, and that the actual energy lost is minimal, given their short duration.
3. If the POA irradiance does not exceed 100 W/m2, no Availability is calculated. BEW has observed
6,054 out of 326,423 inverter-days in the data where this was the case for the entire day, which
BEW Engineering
Independent Engineering Report
Page 12
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
is 1.9% of the total data. The low POA values are likely due to missing or bad data through
communication errors, or very cloudy winter days. These days were not included in the analysis.
4. No DC or tracker motor availability is included. This analysis does not consider whether the
inverters are operating with reduced capacity. BEW does not regard this lack of data as a
deficiency, as it is not typically used as a metric in contractual agreements, and we expect
significant DC capacity or tracker failures would be detected through more typical contractual
metrics like performance index. We note these issues as metrics that should be tracked in the
future for improved O&M cost and availability forecasting. A few causes of reduced capacity are:
a. Open fuses at the DC feeder or string level
b. Poor alignment of modules through tracker failures. No information was available on
the percentage of time the modules were aligned appropriately.
The Availability data was averaged over each day to create a Daily Availability between 0 and 1 for each
inverter on each day, and exported. OSI PI also calculates the Daily Expected kWh for each inverter using
environmental data. The Expected kWh was exported to calculate an Energy-Weighted Availability along
with a Time-Weighted Availability. The rest of the analysis to quantify expected availability of a staffed
SunPower solar plant was performed outside of OSI PI.
The Time-Weighted Availability was evaluated by calculating the average of the Daily Availability over
different categories, including age of plant, country of plant, and each plant on an annual and lifetime
basis.
The Energy-Weighted Availability was evaluated by calculating the Lost kWh by subtracting the Daily
Availability from 1 and multiplying by the Daily Expected kWh. Then Energy-Weighted Availability was
evaluated over the same set of categories by dividing the total Lost kWh by the total Daily Expected kWh
and subtracting from 1 for each category. This calculation is expressed in the following set of equations.
𝐴𝑣𝑎𝑖𝑙𝐸 = 1 −
𝐴𝑣𝑎𝑖𝑙𝐸 = 1 −
∑ 𝐿𝑜𝑠𝑡 𝑘𝑊ℎ𝐷𝑎𝑖𝑙𝑦
∑ 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑘𝑊ℎ𝐷𝑎𝑖𝑙𝑦
∑ [�1 − 𝐴𝑣𝑎𝑖𝑙𝐷𝑎𝑖𝑙𝑦 � × 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑘𝑊ℎ𝐷𝑎𝑖𝑙𝑦 ]
∑ 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑘𝑊ℎ𝐷𝑎𝑖𝑙𝑦
In addition, specific outages were excluded in the analysis outside of OSI PI. The exclusions fell into two
general categories, Forced and Communication Error. BEW has reviewed the causes identified by
SunPower and in general agrees that they are reasonable and typical exclusions in the industry.
It is typical to remove outages requested or caused by the utility or the grid operator from Availability
calculations for O&M contracts as well as force majeure events. However, BEW recommends reviewing
Availability from an energy generation estimation stand-point, no matter the cause, as when energy is
not generated it may affect the project revenue stream. Power purchase agreements and
interconnection agreements may include compensation for lost revenue due to outages caused by the
utility, but the circumstances that are covered vary between agreements.
BEW Engineering
Independent Engineering Report
Page 13
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
BEW believes that most of the grid outages in the excluded data are not typical annual events, and
might be considered more likely in the first years or in a subset of 20 years on a given distribution or
transmission line. Because only three plants are more than four years old, BEW is not able to quantify
the likelihood of grid outages in later years of plant operation versus early years.
Some of the Communication Errors were due to no communication, and SunPower has demonstrated
that they verified that the inverters were generating energy though the inverters were not reporting
directly to OSI PI. Other Communication Errors were due to erroneous readings of irradiance at night.
BEW has no concerns about excluding Communication Error events from calculation.
BEW has performed the availability analysis for two different sets of exclusions:
1) The best case, where all outages identified by SunPower were removed from the data set
2) The worst case, where only the Communication Errors were removed, and therefore SunPower
was penalized for all energy lost
BEW Engineering
Independent Engineering Report
Page 14
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
5. Review SunPower Historical Availability
Time-weighted and Energy-weighted Availability
BEW reviewed both the time-weighted and energy-weighted availability for the overall portfolio, with
and without the exclusions. The time-weighted availability was similar to the energy-weighted
availability at a portfolio level.
Portfolio Availability
Time Weighted
Energy Weighted
Target
100.0%
99.5%
99.0%
99.03%
99.19%
99.28%
98.82%
98.5%
98.0%
Penalized for All Energy Lost
Not Penalized for Grid Outages
Figure 3: Comparison of time-weighted and energy-weighted availability over the portfolio
However, when the difference in the two techniques was examined on a per plant level, larger
differences are found. Because the primary goal of tracking availability is to quantify and minimize the
energy lost through operational failure, BEW recommends using energy-weighted availability. Discussion
of availability below focuses on the energy-weighted availability.
BEW Engineering
Independent Engineering Report
Page 15
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
Availability by Site
Time-Weighted
Energy-Weighted
Target
100.0%
99.5%
99.0%
98.5%
98.0%
97.5%
97.0%
Figure 4: Comparison of time-weighted and energy-weighted availability at the plant level
Availability by Subset
BEW has reviewed the availability data over a number of different categories. BEW has plotted the
energy-weighted availability for the worst case availability, which includes energy lost from all external
outages, and for the best case availability, where days with externally caused outages were removed.
In Figure 5 below, the availability of each plant was calculated for the period from September 29, 2009
onward. The majority of the plants achieved greater than 99% availability even when external outages
were included in the calculation. Only four plants did not achieve 99% availability with external outages
included. Only one plant did not achieve 98% availability with external outages included, and it achieved
98% availability when the external outages were excluded.
BEW Engineering
Independent Engineering Report
Page 16
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
Availability by Plant
w/ External Outages
w/o External Outages
Target
100%
98%
96%
94%
92%
90%
Figure 5: Energy-weighted availability by plant
In Figure 6 below, the availability of the plants in each country was calculated for the period from
September 29, 2009 onward. Only one country did not achieve 99% availability with external outages
included. The single poor performing plant in Figure 5 is the only plant in Portugal, so the poor
performance should not necessarily be regarded as a general problem with availability in Portugal.
Availability by Country
w/ External Outages
w/o External Outages
Target
100%
98%
96%
94%
92%
90%
CA
DE
ES
IT
PT
Figure 6: Energy-weighted availability by country
BEW Engineering
Independent Engineering Report
Page 17
US
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
In Figure 7 below, the availability of the plants at each year of their life was calculated for the period
from September 29, 2009 onward. The majority of the years achieved greater than 99% availability even
when external outages were included in the calculation. Year 5 did not achieve 99% availability with
external outages included because the single poor performing plant in Figure 5 was one of only three
plants with data at year 5.
Availability by Plant Age
w/ External Outages
w/o External Outages
Target
100%
98%
96%
94%
92%
90%
1
2
3
4
5
6
7
8
Figure 7: Energy-weighted availability by plant age
Variability
As is evident in the figures above, an individual solar plant may operate at a much lower availability than
the portfolio average. It may operate at a higher availability as well, but as availability may not exceed
100%, the distribution of annual availability is not symmetric as a typical normal distribution would be.
For the variability analysis, it is assumed that the service and maintenance activities are consistent
throughout the period of deployment of the PV power plant. This is a critical assumption and if the level
of maintenance were to be reduced then the availability would be expected to drop and the application
of this analysis would be inappropriate.
To calculate the variability of availability, BEW used a logit 1 transform of the data, a set of all of the
annual availabilities for all plants, with a total of 37 data points. Data where availability was recorded as
1
Logit is defined as log of odds ratio, or logit(p) = ln[p/(1-p)], with corresponding inverse expit(q) = eq/(1+eq).
BEW Engineering
Independent Engineering Report
Page 18
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
100% was re-coded as 99.999% (a value larger than all non-100% values) to permit use of the transform.
This invalidates probability analysis at the high extremes of availability, but provides appropriate
weighting to support analysis at low levels of availability. We did not assume that the data was perfectly
logit-normal, and therefore a kernel estimate was used to curve-fit the probability distribution that best
reflects the available information. Because of the logit transform, the cumulative probability graph
shown in Figure 8 cannot be translated directly to read that p(50) is approximately 95% of the mean
availability. Rather, the p(50) is approximately 95% of the mean of the logit transform of availability. For
example, to find p(90) or the availability for which 90% of cases will be greater, the intersection of 100%
- 90% = 10% cumulative probability with the red kernel estimate curve is 66.6% of the mean of the logit
of the data (5.760), so
𝑝(90) =
𝑒 0.617× 5.760
= 97.2%
1 + 𝑒 0.617 × 5.760
Figure 8 and Figure 9 show the translated probabilities, with corresponding means of logit availability,
5.760 and 6.082.
Penalized for All Outages
SunPower annual availability variability for staffed plants
years 2009 Q4 through 2012 Q2
Sample
Cumulative Probability
100%
Kernel Estimate
Normal
80%
60%
40%
20%
0%
0%
50%
100%
150%
200%
Percent of Mean of Logit(Availability)
250%
Figure 8: Variability of availability including all outages
It is good statistical practice in any industry to only use a normal distribution fit when the data can be
shown to meet the criteria of a normal distribution. A normal distribution fit is plotted with the kernel
estimation fit in Figure 8 and Figure 9. There is a significant difference in the two fits shown in both
figures. In both figures, the kernel estimation fit is more conservative at the p(90) level, but at
BEW Engineering
Independent Engineering Report
Page 19
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
approximately the p(70) level, the normal distribution is the more conservative. The kernel estimation fit
is more conservative again starting at approximately the p(10) level. When all external outages are
removed, the bottom of the distribution is pulled in closer to the mean, as we would expect. Figure 9
below shows that this tighter distribution skews the distribution further from the normal fit than when
all of the outages are included. Therefore it is important to use the kernel estimation fit to calculate the
cumulative probability function.
All External Outages Removed
SunPower annual availability variability for staffed plants
years 2009 Q4 through 2012 Q2
Sample
Kernel Estimate
Normal
Cumulative Probability
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0%
50%
100%
150%
200%
Percent of Mean of Logit(Availability)
250%
Figure 9: Variability of availability without external grid outages
Table 4 and Table 5 below show the transformed cumulative probabilities. Since a limited number of
samples were used to estimate the variability, estimates for P-levels far from the center of the
distribution are less well-defined. Confidence intervals obtained from bootstrapping the available data
are computed to illustrate that conclusions from this data regarding conservative downside analyses
(e.g. P95) are considerably more uncertain than conclusions regarding more likely outcomes (P50). In
general, larger sample sizes which provide more information on the shape of the tails of the distribution
can reduce the uncertainty of the downside estimates.
BEW Engineering
Independent Engineering Report
Page 20
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
Table 4: Single year availability including all outages
Cumulative
Availability
Probability
Upper
Confidence
Interval (p95)
Lower
Confidence
Interval (p95)
P50
99.5%
99.3%
99.7%
P75
98.8%
98.1%
99.3%
P90
97.2%
94.1%
98.5%
P95
95.2%
88.1%
97.8%
Table 5: Single year availability without external grid outages
Cumulative
Availability
Probability
Upper
Confidence
Interval (p95)
Lower
Confidence
Interval (p95)
P50
99.7%
99.5%
99.8%
P75
99.2%
98.8%
99.5%
P90
98.3%
97.2%
99.0%
P95
97.3%
95.8%
98.6%
BEW expects that a well-maintained solar plant will have a higher 10 year average annual availability
than the single year variability analysis shows. However, SunPower does not have enough data collected
to calculate the 10 year average annual availability without making some assumptions, as the majority
of their utility plants have been operating for less than five years.
In order to calculate the 10 year average annual variability, we have assumed that Years 9 and 10 in a
plant’s life will be similar to Year 1, which had the lowest average availability 2, as might be expected due
to infant mortality of components. Assuming SunPower’s O&M planned maintenance and response
times remain at the current levels, or improve, and assuming that inverter component parts are
2
The exception is Year 5, which had too few plants to average to be considered statistically significant as an
individual Year.
BEW Engineering
Independent Engineering Report
Page 21
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
available as quickly as they have been in Year 1, the time to repair after any failure should be similar to
that in Year 1. We also assume that the failure rates in Year 9 and 10 are similar to those in Year 1. We
assume that Years 2 through 8 will have a different average failure rate, lower on average, and
interchangeable through those years.
Figure 10and Figure 11 show the translated probabilities, with corresponding means of logit availability,
5.760 and 6.082.
Penalized for All Outages
SunPower 10-year average annual availability variability for staffed plants
years 2009 Q4 through 2012 Q2
Normal
Cumulative Probability
Kernel Estimate
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
60%
70%
80%
90%
100%
110%
120%
130%
Percent of Mean of Logit(Availability)
Figure 10: Ten-year average annual availability variability including all outages
BEW Engineering
Independent Engineering Report
Page 22
140%
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
All External Outages Removed
SunPower 10-year average annual availability variability for staffed plants
years 2009 Q4 through 2012 Q2
Normal
Cumulative Probability
Kernel Estimate
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
60%
70%
80%
90%
100%
110%
120%
130%
140%
Percent of Mean of Logit(Availablity)
Figure 11: Ten-year average annual availability variability without external outages
Table 6 and Table 7 below show the transformed cumulative probabilities for a ten year average annual
availability. The confidence levels reported below don’t capture the uncertainty in the variability
calculation, as the uncertainty comes from the assumptions used to characterize the availability in Years
4 through 10, rather than widely spread data and low sample sizes. In general, larger sample sizes which
provide more information on the shape of the tails of the distribution can reduce the uncertainty of the
downside estimates.
BEW Engineering
Independent Engineering Report
Page 23
2303 Camino Ramon, Suite 220, San Ramon, CA 94583
+1 (925) 867-3330 Fax +1 (925) 867-3331 – www.bewengineering.com
Table 6: Ten-year availability including all outages
Cumulative
Availability
Probability
Upper
Confidence
Interval (p95)
Lower
Confidence
Interval (p95)
P50
99.7%
99.7%
99.7%
P75
99.5%
99.5%
99.5%
P90
99.3%
99.3%
99.4%
P95
99.2%
99.1%
99.2%
Table 7: Ten-year availability without external outages
Cumulative
Availability
Probability
Upper
Confidence
Interval (p95)
Lower
Confidence
Interval (p95)
P50
99.8%
99.7%
99.8%
P75
99.7%
99.6%
99.7%
P90
99.5%
99.5%
99.6%
P95
99.5%
99.4%
99.5%
BEW Engineering
Independent Engineering Report
Page 24