The document was not produced by the CAISO and therefore does not necessarily reflect its views or opinion.

Transcription

1 Version No. 1.0 Version Date 2/25/2008 Externally-authored document cover sheet Effective Date: 4/03/2008 The purpose of this cover sheet is to provide attribution and background information for documents posted to the California ISO website that were not authored by CAISO. Project Name Author Company Author Name Author Title Title of document Solar Symposium Sunpower Corp Adriane Kimber Mono-Crystalline PV System Modeling Date submitted 1/28/09 Other Comments Notes This document was submitted to the California Independent System Operator (CAISO) for posting to the CAISO website in conjunction with a Stakeholder-involved initiative or similar activity. The document was not produced by the CAISO and therefore does not necessarily reflect its views or opinion. Created by: IP&S Updated by: EA/ComPR/IPS/rls CAISO Page 1 of 1

2 Mono-Crystalline PV System Modeling January 29, 2008

3 Corporate Overview Incorporated in 1985 HQ in Silicon Valley, California Nasdaq: SPWRA, SPWRB Manufacturing: Philippines, US 400+ MW large systems >500 systems monitored 10 yrs operational experience on 1+ MW power plants 2008 revenue $1.4 billion est. 2

4 SunPower Solar Cells Mono-crystalline Silicon Cell Back Cell Front Positive and negative electrical contacts are made using alternating inter-digitated fingers that conduct current to buses on the cell edges By locating the electrical contacts on the back surface, SunPower achieves conversion efficiencies of 22%+, up to 50% higher than conventional solar cells and 2-4 times thin film 3

5 Product Families Roof Integrated Systems Fixed Tilt Systems PowerGuard SunTile T10 Roof Tile SunPower Trackers T0 Tracker T20 Tracker 4

6 SunPower Applications Residential Retrofit New Production Homes Commercial & Public Power Plants 5

7 SunPower Energy Management > 400 MW monitored > 500 systems monitored > 75 power plants >1MW 24/7/365 real-time monitoring OSI Soft PI-based data platform Guaranteed performance Regional service centers to provide rapid outage restoration response Systems Control Center 6

8 Lanai SunPower Power Plant Control Technology 1.5 MW SunPower power plant = 30% of peak load Dedicated January 2009, complete summer 2009 Remotely operated by MECO (local utility) from Maui Ramped according to schedule and maintain set point Curtailment is measured at point of interconnection Control achieved at inverters Storage added as modular system completed summer 2009 Reactive power delivered and consumed as needed 7

9 8 Performance Data Set Points with Ramp Rates

10 9 Performance Data Power Factor Control

11 R&D System Performance Group 10

12 Modeling PV Two Major Parts How much light is on the modules (and how hot are they)? Ideally, measure this directly (calibrated, matched reference cell) For forecasting, will have to estimate this SunPower uses Perez irradiance decomposition models in PVGrid What will the modules/system do with this light and temp? Models for this are very accurate on an annual basis some claim accuracy of better than 2% 1 when loss factors are well known (more on this later) Experience seasonal, hourly variability in accuracy, but welldeveloped PV module models have mean average error less than 3% for sub-hourly data 2 again, when loss factors are well known Müller, et al, 22 nd European PVSEC, Milan, Marion, et al, 33 rd IEEE Specialists Conference, San Diego, 2008

13 C-Si Technology and Modeling Crystalline Silicon (c-si) technology behavior is wellunderstood Mature technology 35+ years of terrestrial field exposure/measurements with technologies similar to those in use today, 10 years at SunPower Differences between c-si technologies include: Light-induced/initial degradation Temperature coefficients of cell, heat transfer of module Spectral response (efficiency at various wavelengths) Optical properties of module In general, models of c-si technology performance are relatively advanced and accurate 12

14 SunPower Modeling and Simulation PVGrid What is PVGrid? PVGrid is a software tool used to generate projections of PV system energy production Why did we create it? To ensure that we have the flexibility and control we need to generate accurate projections of SunPower s existing and future products and systems How is PVGrid s accuracy evaluated? We use measured data from our 450+ fielded systems to validate our model accuracy. In addition, Independent Engineers like BEW have vetted our model, comparing it with respected models such as PVSyst. 13

15 PVGrid Structure 14

16 PVGrid Validation Example: 2 MW T0 Tracker, Bavaria, Germany Total Measured Energy: 1,451,916 kwh Total Energy Projected by PVGrid v11: 1,458,279 kwh Difference, Measured to PVGrid v11 Projection: +0.4% 15

17 PV Model Inputs External loss factors include soiling, shade, snow - these will require direct measurement for accurate forecasting. Also long-term system degradation (varies by technology) 16

18 SunPower Dynamic Soiling Model Derived from Measured System Data Three key elements: Soiling rate - empirically-derived, climate, environment, and sitespecific (average of 0.2% per day in CA) Cleaning threshold - amount of rain required to fall in one day to fully clean the PV system (5 8 mm in CA) Grace Period - number of days after a rain before significant soiling degradation begins Here, estimated annual loss with no washing is ~ 8% 17

19 Soiling Rates Vary from Year to Year (Performance Index based on LICOR) 18

20 Possible Strategies for Forecasting Soiling Losses For any site: Develop site-specific soiling rate from recent measured degradation trend Can develop this trend based on comparison to sensor that soils relatively slowly (e.g. LICOR) use this rate to forecast near future performance For staffed sites: Direct measurement by comparison between clean and dirty reference cells or modules Direct measurement by comparison between current on two test strings (one that is washed frequently, one that is not) Cleaning schedule should be communicated to ISO Rain forecasts should be part of forecasting equation 19

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22 Reference Cells in Modeling Matched reference cells can reduce model uncertainty On the plus side Plane-of-array measurements eliminate need to model sun position and array geometry Spectral matching eliminates need to correct for spectral response mismatch between sensor and array Optical matching eliminates need to correct for optical loss differences between sensor and array On the other hand May need to make your own (c-si cells readily available, but not sure about a-si, CdTe, etc) Can t use directly for forecasting 21