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Webinar May 22, 2012 CSI 2010 Impact Evaluation Addendum
© 2011, Itron Inc. 2 Introduction Scope: Additional analyses to the 2010 CSI Impacts Report Not meant to stand alone Includes SGIP and CSI systems Section 5: PV Performance over Time. This section quantifies the effects of ownership, incentive type, and module material on PV performance over time using two different methods. This section also provides estimates of PV degradation for each of those groupings. Section 7: Analysis of Interval Billing Data. This section uses interval billing data for CSI customers in PG&E territory to expand and revisit the interval billing analysis previously included in Section 7.2 of the 2010 CSI Impact Evaluation.
© 2011, Itron Inc. 3 Logistics Everyone but the presenters will be muted If you have a question please type it in at the question box; will address these appropriately at the end of each section At the end of the presentation there will be a brief Q&A if time allows A recording of this webinar and the slides will be available afterwards
© 2011, Itron Inc. 4 Terminology EPBB – Estimated Performance Buy Back; CSI up front incentive based on estimated system performance PBI – Performance Based Incentive; Payment per kWh produced for five years for CSI systems SGIP – Self Generation Incentive Program; incentives were based on installed capacity TPO – Third Party Owner; can be either lease or PPA
5 PV PERFORMANCE OVER TIME - DEGRADATION
© 2011, Itron Inc. 6 PV Performance Over Time Objective is quantify the effects of Ownership, Incentive type, and Material type on PV system performance over time Builds on trend analysis in the 2010 Impacts Report with more sophisticated and in-depth analysis
© 2011, Itron Inc. 7 What is Degradation System Degradation: refers to overall change in system performance over time. This definition explicitly includes all factors that may lead to reduction in system performance over time. The definition includes the traditional ‘degradation’ that is largely due to module degradation but adds such factors as Soiling, Maintenance, System availability, Fire, theft, etc. Equipment Degradation: refers to change in system performance with minimization of the affects of soiling and system outages. This is intended to be much closer to the definition of ‘PV degradation’ found in many other studies.
© 2011, Itron Inc. 8 An Extreme Case of Soiling There are many factors that affect system performance beyond module degradation
© 2011, Itron Inc. 9 Methods Day Substitution for System Degradation. Weather normalization is accomplished by substituting sunny and cloudy days such that solar resource variability is removed and annual percentage changes in system performance can be calculated. Linear Regression System Degradation. Weather normalization is accomplished by using monthly irradiance and temperature as independent variables in the regression analysis. We use the regression model to estimate the effects of ownership, incentive, and PV technology on degradation. Equipment Degradation. In this analysis we further filter the data used by the system degradation model to minimize the affects of soiling and system availability.
© 2011, Itron Inc. 10 Data Preparation & Merging PV Performance Data – Processed metered data Panel & System characteristics –PowerClerk (CSI) and SGIP tracking data Exclude tracking systems Plane of Array (POA) Irradiance from Solar Anywhere (Clean Power Research) Global Horizontal & Direct Normal estimated from solar geometry & cloud cover Panel azimuth & tilt and Perez sky model Rainfall and Temperature from CIMIS Only used for regression
© 2011, Itron Inc. 11 DAY SUBSTITUTION
© 2011, Itron Inc. 12 Steps in Day Substitution Develop Statewide Monthly Reference Solar Resource Weather Normalization via Day Substitution Calculate Performance Changes Estimate accuracy of performance change results
© 2011, Itron Inc. 13 Reference Solar Resource - Statewide Median of all actual values for individual systems
© 2011, Itron Inc. 14 Weather Normalization via Day Substitution Example of actual May 2009 data for a CCSE PV system
© 2011, Itron Inc. 15 Example Site-Specific System Degradation Rates Average Year1 to Year 2 change: -2.8% Many site-specific results deviate substantially from average
© 2011, Itron Inc. 16 Example Site-Specific Data July Daily CF for Year 1 & Year 2 Site-specific change: -39%
© 2011, Itron Inc. 17 Example Site-Specific Data July Daily CF for Year 2 Apparent temporary outage reduced CF approximately 50%
© 2011, Itron Inc. 18 Average Annual System Degradation Rates using Day Substitution Program-ChangeUncertainty (%/Year) Ownership(%/Year)ErrorLCLUCL EPBB-Host EPBB-TPO PBI-Host PBI-TPO SGIP-Host Difference from zero significant for 2 groups EPBB-TPO & SGIP-Host Difference between groups significant in 2 cases EPBB-TPO degrade faster than EPBB-Host EPBB-TPO degrade faster than PBI-TPO
© 2011, Itron Inc. 19 Key Findings Many site-specific results deviate substantially from averages Substantial quantities of data are required to produce accurate estimates of important differences Example: 90% confidence that EPBB-Host degradation rate is 0.X% (±10%) faster than EPBB-TPO degradation rate Caution is needed when comparing these averages to other references that are not subject to the same types of confounding factors Module warranties Controlled studies Results of Day Substitution and Regression analyses are aligned Differences between the results are not significantly different when several years of data are available
© 2011, Itron Inc. 20 LINEAR REGRESSION
© 2011, Itron Inc. 21 Data Filtering System Degradation > POA 700 – 1300 W/m 2 > 0 ≤ CF Interval ≤ 1.15 > Explicitly includes outages and potentially soiled panels Equipment Degradation > POA 700 – 1300 W/m 2 > 0 < CF Interval ≤ 1.15; total outages excluded > 7 Days or less since 0.2” of rain in a day > Intent is to minimize effects of outages (system availability) and soiling
© 2011, Itron Inc. 22 Data By Incentive Type and Age Majority of CSI data is for systems less than two years old 3 years is often held as the recommended bare minimum of data for degradation studies
© 2011, Itron Inc. 23 Monthly Mean Capacity Factors During High Sun Only metered systems with 12 months or more of data Age based on time from estimated date of operation
© 2011, Itron Inc. 24 Regression Model Due to the presence of serial correlation, our analysis used an autoregressive error model in the AUTOREG procedure in SAS software to correct for the correlated errors. Dependent variable is monthly capacity factor Independent variables are the parameters of interest or expected influence CF monthly = a 1 +a 2 +……a 38
© 2011, Itron Inc. 25 Regression Parameters ParameterIdentifier (i)Range of ValuesDescription of Parameter Intercept Unit Age Months System Age POA700 – 1300 Average Monthly Plane of Array irradiance striking each system over the month during high sun hours Temperature Average Monthly Temperature (F) during high sun hours PV Technology Monocrystalline, Polycrystalline, thin film, Hybrid Type of PV panel technology Month1-12Month of the year Incentive TypeEPBB, PBI, SGIP OwnershipHost-Owned, TPO PV Technology* Unit Age Months 27, 28, 29 Monocrystalline, thin film, Hybrid Interaction of the PV Panel Technology and Age (‘base’ as Polycrystalline) Ownership * Unit Age Months 30TPO Interaction of Ownership and Age Incentive Type * Unit Age Months 31EPBB Interaction of Incentive Type and Age 32PBI Incentive Type * Ownership EPBB*TPO, PBI*TPO, SGIP*TPO Interaction of Incentive Type and Ownership Ownership *Incentive Type * Unit Age Months 36EPBB*TPO Interaction of Ownership and Incentive Type and Age 37 PBI*TPO 38 SGIP*TPO
© 2011, Itron Inc. 26 Parameter Estimates from System Level Regression (these are monthly) Before Autocorrelation Adjustment, Durbin-Watson = 0.56 Post Autocorrelation Adjustment Durbin-Watson =1.996 R 2 = 0.65 ‘Base’ is SGIP, Host Owned, Polycrystalline PBI and TPO have positive affects ParameterEstimate Standard ErrorT Value Approx PR > |t| Unit Age Months <.0001 HybridPvTech* Unit Age Months <.0001 MonoCsiPvTech* Unit Age Months ThinFilmPvTech* Unit Age Months <.0001 TPO* Unit Age Months EPBB* Unit Age Months PBI* Unit Age Months <.0001 EPBB* TPO*Unit Age Months PBI* TPO*Unit Age Months
© 2011, Itron Inc. 27 Calculating Annual Change from Parameter Estimates Model has a mix of incentive types, ownership types, and module technologies Annual rates of change for each grouping is a mix of incentive, ownership, and module technology age effects for example; EPPB participants that are host owned with thin film technology; DegRate EPBB-Host-ThinFilm = * (EPPBB = 1) * (ThinFilmPVTech = 1) Or: DegRate EPBB-Host-ThinFilm = a33 + a28 EPBB Host degradation rates are a mix of different technologies age affects, so EPBB Host rate is weighted by the proportion of each module type
© 2011, Itron Inc. 28 Average Annual System Degradation Rates using Linear Regression Statistically Significant Differences; PBI > EPBB > SGIP TPO has a positive effect Crystalline modules appear more stable than thin film or hybrid
© 2011, Itron Inc. 29 Data Availability after filtering for Equipment Degradation Panels likely to see soiling late summer/early fall Including only ‘clean’ panels; 7 Days or less since 0.2” of rain in a day Excluding full outages
© 2011, Itron Inc. 30 Average Annual Equipment Degradation Rates using Linear Regression EPBB now appears better than PBI (not significantly) TPO still has a significant positive effect Hybrid modules appear to perform worse than others using these filters
© 2011, Itron Inc. 31 System Degradation Method Comparison Methods don’t necessarily agree but they don’t disagree
© 2011, Itron Inc. 32 Key Findings Overall rates vary for the regression approach from +0.71% (for PBI TPO systemsThird Party Ownership is beneficial for long term performance -2.12% for SGIP host owned systems These are somewhat higher than usually referenced as degradation (~0.%~1%) but since they encompass soiling & availability that is to be expected Extrapolation beyond the time frame of available data could be troublesome Third party ownership appears to slow degradation in a statistically significant way for this sample Incentive Results at the system level; PBI systems showed minimal degradation (~0.03% improvement in fact) EPBB systems showed a change of ~-1 %/year SGIP systems showed a change of ~-2%/year Crystalline modules appear to degrade more slowly over time then thin film or hybrid May be highly influenced by first year non linear drop for thin films Future analysis should allow a much finer and accurate assessment and care should be taken when extrapolating beyond the period of available data. Additionally, future years should allow the EPBB sample to be drawn from a truly random sample.
33 CSI 2010 IMPACT EVALUATION ADDENDUM – INTERVAL BILLING DATA
© 2011, Itron Inc. 34 Interval Data Analysis Hourly profile for a bill with net exports:
© 2011, Itron Inc. 35 Interval Data Analysis, cont. Hourly profile for a bill without net export:
© 2011, Itron Inc. 36 Value of Interval Data On their own, interval data can show: Likelihood of export Grid usage Amount PV export Combined with generation data, interval data can also show: PV usage Share of PV exported PV as share of total usage
© 2011, Itron Inc. 37 Interval Data Sites PG&ESCECCSE Residential Commercial Gov./Non-Profit 1197 Interval and PV Generation Data PG&ESCECCSE Residential Commercial Gov./Non-Profit Interval Data Only
© 2011, Itron Inc. 38 Residential Export Probability
© 2011, Itron Inc. 39 Commercial Export Probability
© 2011, Itron Inc. 40 Government/Non-Profit Export Probability
© 2011, Itron Inc. 41 SCE Residential Export Probability by Export Group
© 2011, Itron Inc. 42 CCSE Residential Export Probability by Export Group
© 2011, Itron Inc. 43 SCE Commercial Export Probability by Export Group
© 2011, Itron Inc. 44 CCSE Commercial Export Probability by Export Group
© 2011, Itron Inc. 45 Comparison of Residential Average Hourly Grid kWh
© 2011, Itron Inc. 46 Comparison of Commercial Average Hourly Grid kWh
© 2011, Itron Inc. 47 Comparison of Gov./Non-Profit Average Hourly Grid kWh
© 2011, Itron Inc. 48 Residential Export Magnitude Quarter Program Administrator SCECCSE Average Export kWh Average Percent of Capacity Average Export 90th Percentile Percent of Capacity 90th Percentile Average Export kWh Average Percent of Capacity Average Export 90th Percentile Percent of Capacity 90th Percentile Jan - Mar % % % % Apr - Jun % % % % Aug - Sep % % % % Oct - Dec % % % % All % % % %
© 2011, Itron Inc. 49 Commercial Export Magnitude Quarter Program Administrator PG&ESCECCSE Averag e Export kWh Averag e Percent of Capacit y Averag e Export 90th Percent ile Percent of Capacit y 90th Percent ile Averag e Export kWh Averag e Percent of Capacit y Averag e Export 90th Percent ile Percent of Capacit y 90th Percent ile Averag e Export kWh Averag e Percent of Capacit y Average Export 90th Percenti le Percent of Capacit y 90th Percenti le Jan - Mar % % % % % % Apr - Jun % % % % % % Aug - Sep % % % % % % Oct - Dec % % % % % % All % % % % % %
© 2011, Itron Inc. 50 Government/Non-Profit Export Magnitude Quarter Program Administrator PG&ESCECCSE Averag e Export kWh Averag e Percent of Capacit y Averag e Export 90th Percent ile Percent of Capacit y 90th Percent ile Averag e Export kWh Averag e Percent of Capacit y Averag e Export 90th Percent ile Percent of Capacit y 90th Percent ile Averag e Export kWh Averag e Percent of Capacit y Average Export 90th Percent ile Percent of Capacit y 90th Percent ile Jan - Mar % % % % % % Apr - Jun % % % % % % Aug - Sep % % % % % % Oct - Dec % % % % % % All % % % % % %
© 2011, Itron Inc. 51 Residential Export as Percent of Capacity
© 2011, Itron Inc. 52 Commercial Export as Percent of Capacity
© 2011, Itron Inc. 53 Government/Non-Profit Export as Percent of Capacity
54 INTERVAL AND PV GENERATION DATA
© 2011, Itron Inc. 55 Residential Average Hourly Usage, Generation, and Exports Customer Segment/Quarter Grid kWh Used PV kWh Exported Total PV kWh Generation PV kWh Used Total kWh Used Percent of PV kWh Exported PV Generation as Percent of Total kWh Used SCE Jan - Mar %55.4% Apr - Jun %80.5% Jul - Sep %57.9% Oct - Dec %45.7% All %59.9% CCSE Jan - Mar %74.2% Apr - Jun %106.0% Jul - Sep %82.4% Oct - Dec %59.3% All %80.0%
© 2011, Itron Inc. 56 Commercial Average Hourly Usage, Generation, and Exports Customer Segment/Quarter Grid kWh Used PV kWh Exported Total PV kWh Generation PV kWh Used Total kWh Used Percent of PV kWh Exported PV Generation as Percent of Total kWh Used PG&EJan - Mar %37.1% Apr - Jun %57.5% Jul - Sep %39.7% Oct - Dec %25.6% All %39.9% SCEJan - Mar %12.7% Apr - Jun % Jul - Sep %15.6% Oct - Dec %11.0% All %14.4% CCSEJan - Mar %20.9% Apr - Jun %27.6% Jul - Sep %23.7% Oct - Dec %15.8% All %22.0%
© 2011, Itron Inc. 57 Residential Usage Profiles
© 2011, Itron Inc. 58 Residential Usage Profiles, Cont.
© 2011, Itron Inc. 59 Non-Residential Usage Profiles
© 2011, Itron Inc. 60 Summary The residential segment shows more homogeneity, though CCSE and SCE do have some differences Seasonal pattern is intuitive and consistent Exported quantities often represent close to the full system capacity The non-residential customer segments show substantial differences among the program administrators PG&E less likely to export Exports represent a smaller fraction of system capacity For sites with PV generation data, the analysis shows that residential participants export the majority of electricity generated by their systems For non-residential, the share exported varies by PA, but is never more than half of the generated electricity
© 2011, Itron Inc. 61 Thank You CPUC for sponsoring this work Clean Power Research for providing Solar Anywhere data Third Party Data providers for performance data Contact info; Stephan Barsun Kurt Scheuermann Collin Elliot
© 2011, Itron Inc. 63 Comparison of System and Equipment Degradation
© 2011, Itron Inc. 64 Average Annual System Degradation Rates using Linear Regression PBI TPO got better over time PBI > EPBB > SGIP TPO has a positive effect Crystalline modules appear more stable System GroupingDifferenceUncertainty (%/Year) Significant Difference Between Groups (%/Year) Standard ErrorLCLUCL EPBB -1.05%0.19%-1.36%-0.74% Yes PBI 0.03%0.19%-0.28%0.33% SGIP -2.03%0.11%-2.21%-1.85% Host Owned -1.33%0.10%-1.50%-1.16% Yes TPO -0.16%0.17%-0.44%0.12% EPBB-Host -1.33%0.21%-1.68%-0.98% No EPBB-TPO -0.66%0.27%-1.11%-0.22% PBI-Host -0.40%0.22%-0.76%-0.03% No PBI-TPO 0.71%0.26%0.29%1.14% SGIP -Host -2.12%0.11%-2.30%-1.94% No SGIP -TPO -1.26%0.27%-1.72%-0.81% Hybrid - PV -2.85%0.32%-3.38%-2.33% Mono vs. Poly No, Hybrid vs. Thin Film No, others Yes Mono Crystalline PV -0.72%0.16%-0.98%-0.45% Poly Crystalline PV -0.63%0.12%-0.82%-0.43% Thin Film PV -3.04%0.27%-3.48%-2.60%
© 2011, Itron Inc. 65 Average Annual Equipment Degradation Rates using Linear Regression EPBB now appears better than PBI; filtering issue? TPO still has a positive effect Hybrid modules appear to perform worse using these filters System GroupingDifferenceUncertainty (%/Year) Significant Difference Between Groups (%/Year) Standard ErrorLCLUCL EPBB -0.32%0.41%-1.00%0.36% No PBI -0.91%0.35%-1.48%-0.33% SGIP -1.85%0.14%-2.08%-1.62% Host Owned -1.66%0.15%-1.91%-1.42% Yes (p=0.0086) TPO -0.18%0.29%-0.66%0.30% EPBB-Host -1.42%0.48%-2.21%-0.63% No EPBB-TPO 0.81%0.54%-0.09%1.70% PBI-Host -1.27%0.43%-1.99%-0.56% PBI-TPO -0.48%0.44%-1.22%0.25% SGIP -Host -1.87%0.14%-2.10%-1.64% SGIP -TPO -1.68%0.35%-2.26%-1.10% Hybrid - PV -2.30%0.51%-3.15%-1.46% Hybrid vs. Others Yes, Other Combos No Mono Crystalline PV -1.40%0.24%-1.80%-1.00% Poly Crystalline PV -0.98%0.19%-1.30%-0.66% Thin Film PV -1.35%0.44%-2.08%-0.63%
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