Understanding Tracker Accuracy and its Effects on CPV

Understanding Tracker Accuracy and its Effects on CPV M. Davis, T. Williams (GreenMountain Engineering) M. Martínez, D. Sanchéz (ISFOC) Presented at the 5 th International Conference on Solar Concentrators Palm Desert, CA, Nov. 16-19, 2008

Presentation Outline Background Accuracy Measurement Accuracy Specs & Reporting Real-world Data Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (2/29)

About GreenMountain Engineering Engineers dedicated to the advancement of cleantech Engineering design services and problem-solving, from R&D labs to commercial-scale manufacturing Since 2003, over 60 client projects in PV, 30 in CPV Modules & Receivers Trackers & Controllers Manufacturing Tools Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (3/29)

(Most) CPV Requires Tracking There is no single solution that has been proven best for all applications there is still room for innovation in tracking. Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (4/29)

Why Understand Tracker Accuracy? There are two different reasons to have good methods of characterizing tracker accuracy: Technically relevant tracker accuracy metrics help predict system energy production and Levelized Cost of Energy (LCOE)*. Standardized, repeatable, generalized methods of characterization allow side-by-side comparison between different tracker designs. Trackers are a significant component of system cost avoiding overdesign and allowing more assembly/installation variation can reduce cost. * See, for example, M. Campbell et al., The Drivers of the Levelized Cost of Electricity for Utility-Scale Photovoltaics, SunPower Corp 2008 Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (5/29)

Presentation Outline Background Accuracy Measurement Accuracy Specs & Reporting Data Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (6/29)

Tracking Accuracy Measurement A variety of methods have been used to measure pointing accuracy. Many are essentially optical methods. image sensor, algorithms Calibration plays an important role in the accuracy of these methods. Sun Optics (pinhole, lenses, filters) For further discussion, see for example: I. Luque-Heredia, et al. A Sun Tracking Error Monitor [ ], EUPVSEC 2005 C. Cancro, et al. Field Testing the PhoCUS Solar Tracker [ ], ICSC 2007 M. Davis, et al. Machine Vision [ ] for Characterizing Tracker Performance, IEEE PVSC 2008 Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (7/29)

Measuring Tracker Accuracy Today 6 Trac-Stat SL1 A diagnostic instrument for measuring the performance of solar trackers 0.02 accuracy Datalogging capability Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (8/29)

A Note on Reference Frames Pointing errors reported in a sensor s reference frame are not the same as errors in the tracker s axes of motion or earth reference frame. Vector transformations can be performed based on current tracker angle or even just time of day and location (via sun position calculations) Transforming from sensor reference frame: bˆ aˆ = cˆ aˆ ( c 1) aˆ c ( kˆ aˆ) c ( aˆ ( kˆ GND = x + y + z (for certain assumptions about system rotation, otherwise there are multiple possible solutions) aˆ)) Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (9/29)

Presentation Outline Background Accuracy Measurement Accuracy Specs & Reporting Real-world Data Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (10/29)

Accuracy Spec -- Metrics Desirable in an accuracy spec: Correlates with system energy production Assesses full tracker performance (mechanical, controller, algorithms, calibration) Measureable, on-site, in real installations Undesirable: Too conservative (promotes tracker overdesign) Too optimistic (based on a perfect day, etc) Arbitrarily penalizes certain tracker architectures in ways not related to real-world performance Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (11/29)

Accuracy not a single number? Having a single number for a spec (inverter efficiency, module watts, and so on) is simplest. DC-DC Regulator Efficiency However, the goal of much of the PV and CPV industry is largescale deployments. At this scale, it is reasonable to expect the purchaser to look at and interpret a more detailed set of specs. Inverter Efficiency Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (12/29)

A Side Note on Convolution Various data sets with matching or nested dependent variables can be convolved or combined. For example, in the context of spectral performance: solar spectrum optics transmissivity cell spectral responsivity nm nm nm cell output cell output, by wavelength B 18.2 G 18.6 R 21.8 nm Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (13/29)

Accuracy Specs Not to Use Ephemeris calculation accuracy (<<0.01, but this doesn t matter if there are other uncompensated errors) Motor encoder resolution not really accuracy Worst-case accuracy (too conservative as an only spec) Simulated accuracy Mean pointing accuracy (skewed unfairly by horizonpointing performance) Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (14/29)

Accuracy Specs Mixed Usefulness Mean pointing accuracy over the subset of a day for which the sun elevation is above (10 ) While this avoids penalizing a tracker for not being able to point directly at the horizon, this choice of 10 degrees is arbitrary and will penalize certain trackers disproportionately. Median pointing accuracy While a median could be used to remove outliers, this is a roundabout way of doing it. 95 th percentile accuracy (the pointing accuracy the tracker exceeds over 95% of sunrise-to-sunset hours) This is another way to remove outliers from a data set, and has the advantage of being fairly simple to compute and explain, and fairly relevant, but is also somewhat arbitrary and not necessarily bestcoupled to energy production. Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (15/29)

Accuracy Specs Potentially Useful? Graphs of tracking error as functions of Sun elevation and azimuth (can be determined by time of day and location, without other sensors) Tracker position (on local tracker axes) Ambient temperature Wind speed and direction DNI, GNI These provide data sets that can potentially be combined with site assessment data collected at different sites (or from existing databases), where trackers have not yet been installed. Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (16/29)

Sample High-Level Workflow (this slide is just a conceptual example the process itself is not easy and there are many difficultto-measure variables to include) DNI Error Tracker Testing Data Raw Analyzed t t Error Error Elevation Wind Cumulative DNI % Cumulative DNI % Data from Site Assessment Wind Elevation Energy Power Module Data Angle Annual Energy Projections Temp Error DNI % t t Temp Temp Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (17/29)

Other Accuracy Concerns Weight* & Wind** Wind Speed (m/s) Vs. Deflection (deg) 0.6 0.5 45 degrees elevation 60 degrees elevation Deflection [deg] 0.4 0.3 0.2 0.1 Error bars show backlash 0 0 5 10 15 20 25 30 35 Wind Speed [m/s] Image: GreenMountain Engineering Image: SOLARGENIX * For more information, see for example: A. Hakenjos, et al. Field Performance FLATCON High Concentration PV Systems, EUPVSEC 2007 ** For more information, see for example I Luque-Heredia, et al. CPV Tracking Systems: Performance Issues, Specifications & Design, ICSC 2007 Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (18/29)

Presentation Outline Background Accuracy Measurement Accuracy Specs & Reporting Data Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (19/29)

Data Sources & Notes ISFOC testing Several HCPV manufacturers who are SL1 customers provided data anonymously. GreenMountain testing Data taken between March 08 and November 08 (primarily Oct 08 and later) Not champion data data from a range of conditions. A few manufacturers included disclaimers such as: data was before optimal tracker alignment and calibration had been performed, or without the newest control algorithms. Very preliminary data/results additional statistical analysis and interpretation will be performed in the future. Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (20/29)

System Acceptance Angles Correlation, though not necessarily causation. 120% Could indicate that the effective acceptance angle of the system (influenced by panel-to-panel misalignment and tracker deflection) is much tighter than an individual module s acceptance angle. 100% CPV Array Power (normalized) 80% 60% 40% 20% 0% 0.00 0.05 0.10 0.15 0.20 0.25 Tracking Error (degrees) (Relative to Tracking Error @ moment of Max Array Power) Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (21/29)

Tracking Error vs Irradiance 2.0 Not a CPV-optimized tracker, global irradiance 1.8 1.6 1.4 One 1.0 0.9 0.8 CPV tracker, DNI Tracking Error (Degrees) [Variation from the Median] 1.2 1.0 0.8 0.6 Two 0.7 0.4 Tracking Error (Degrees) [Variation from Median] 0.6 0.5 0.4 0.3 0.2 0.1 0.2 0.0 0 100 200 300 400 500 600 700 800 900 Global Irradiance (W/m^2) Slight correlation shown between irradiance and tracking accuracy. 0.0 0 100 200 300 400 500 600 700 800 900 1000 Direct Normal Irradiance (W/m^2) Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (22/29)

Tracking Errors, Various Trackers 3.0 2.5 A wide range of accuracies (some could be partially sensor or tracker misalignment) Increased error at low sun elevations Significant scatter for brief periods of time Tracking Error (Degrees) 2.0 1.5 1.0 A B C D E F G H 0.5 0.0 0 10 20 30 40 50 60 70 Sun Elevation (Degrees) Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (23/29)

Tracking Errors, Various Trackers Tracking Error (Degrees) [Variation from Median] 1.4 1.2 1.0 0.8 0.6 This graph subtracts median tracking error components (optimistic, assumes perfect initial alignment/calibration of the tracker). Tracking variation of 0.1-0.2 from median is common, more is present on some trackers (but does it occur during unimportant low-dni times?). A B C D E F G H 0.4 0.2 0.0 0 10 20 30 40 50 60 70 Sun Elevation (Degrees) Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (24/29)

Tracking Error (Ratio of Daily Median) 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 The P Plot Concept 900 [ just demonstration data, from non-cpv system ] 800 9:36 10:48 12:00 13:12 14:24 15:36 16:48 Time of Day DNI [W/m^2] 700 600 500 400 300 200 100 0 900 800 700 600 500 400 300 Cumulative DNI 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0.01 0.1 1 10 Tracking Error (Ratio of Daily Median) 200 100 0 900 800 700 600 500 400 300 200 100 0 7:00 8:06 9:13 10:20 11:26 12:33 13:40 14:46 Time of Day Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (25/29)

Cumulative DNI 100% 90% 80% Cumulative DNI energy 70% 60% 50% 40% 30% 20% 10% Times when the tracking error was less than 0.2 correspond to approximately 90% of this day s total direct irradiance (DNI-seconds). Note that the somewhat optimistic calculation of tracking error is being used here and in the following graph. This is an normalization process that assumes that the median error in each axis is caused by issues which can be calibrated out (for example, installation alignment of the tracker or sensor). 0% 0.01 0.1 1 10 Maximum Tracking Error (degrees, log scale) [ normalized to be variation from Median Tracking Error ] Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (26/29)

DNI vs Time as weighting 100% 90% 80% Cumulative DNI energy Cumulative Time About 5% of the time, tracking error was above 0.3 70% 60% 50% 40% Tracking error was < 0.1 nearly 45% of the day, but only 13% of the day s DNI energy occurred during these times. However, this >0.3 error corresponded to only about 1% of the day s total DNI energy 30% 20% 10% 0% 0.01 0.1 1 Maximum Tracking Error (degrees, log scale) [ normalized to be variation from Median Tracking Error ] Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (27/29)

Conclusions It is possible to measure tracking accuracy in the field. Examining and analyzing detailed data sets can provide more insight than a single numerical value. Without optimal alignment, tuning, and calibration, CPV tracking errors of 0.1 to 2.0 have been observed. Tracking is not as trivial as just calculate the sun ephemeris position. The need for future tracker cost reduction can be assisted by a careful understanding of performance and design trade-offs. Image: GreenMountain Engineering Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (28/29)

Acknowledgments ISFOC, and especially María Martínez, for supporting us in this work. CPV manufacturers and Trac-Stat customers who were willing to anonymously share real field data. The ICSC-5 Conference Committee. All of the previous contributors to the shared body of CPV technical knowledge whose work we ve drawn on. [ This document was originally presented with additional verbal commentary to explain particular slides. For further information or details, contact Max Davis at mdavis@greenmountainengineering.com ] Understanding CPV Tracking Accuracy ICSC5, 2008-11-19 (29/29)