Field data requirements for the validation of PV module performance models

Transcription

1 Field data requirements for the validation of PV module performance models Dipl phys. Gabi Friesen 4th PV Performance Modelling and Monitoring Workshop

2 OUTLINE Introduction Test issues and requirements Typical measurement uncertainties Conclusions Outlook

3 FOCUS OF THE PRESENTATION PV Performance Modeling Collaborative -

4 Who is interested in module field testing? Planners kwh/wp, kwh/m² PV module manufacturers ΔkWh/Wp (benchmarking) R&D (product development) kwh/year, kwh loss /year Software developers kwh±x% Standardisation groups kwh rating Financial kwh lifetime / Equipment manufacturers kwh test requirements

5 Why do we need accurate field data? Laboratory testing short term harmonised standard test conditions Field testing long term real operating conditions - site dependent prediction PV MODEL Yield Lifetime Failure Degradation validation IEC , IEC60891, IEC61853 (power rating), IEC61215/IEC61646 (design approval), IEC61730 (safety approval) Clear test procedures with uncertainty declarations Measurable repeatability (reproducible test conditions) Easy comparability (round robin s) IEC61724 (PV system performance monitoring) not fully applicable to module testing! ISSUES Not harmonized test practices Missing uncertainty declarations Time/weather specific results and uncertainties Degradation issues

6 Why test in different climates? Different local conditions irradiance temperature humidity UV exposure spectrum soiling temperature variations different module performance and degradation rates To better understand the differences reliable, accurate and comparable measurements are needed!

7 How reliable are today module field data? Literature reports typical combined measurement uncertainties for kwp/wp of around±5%. The measurement uncertainties exceeds the technological differences measured with high level equipment, but on the other hand there are no detailed studies on the uncertainties! High discrepancies in measurement practice, understanding, uncertainty determination and reporting can easily lead to contradictory results. Modules ranked as the best in one study comes out as average in another study! Blind modeling round robins demonstrated large spread in predictions, which requires further validation. Unfortunately most of the available field data are reported without any measurement uncertainty making validation of models very difficult. Field data are affected by reversible and not reversible degradation effects which are not always taken into account when comparing different technologies. Most studies are limited at first-year energy yields, but what is more relevant is the life-time energy yield. Harmonization of testing practices and reporting is the key!

8 Field testing requirements test device test equipment Generally the focus is on the measurement equipment used for testing! test site test/data processing

9 Field testing requirements test device reference power (kwh/wp) selection of modules (tolerance) test device selection procedure which considers statistical distribution test equipment hardware definition hardware accuracy test site non- homogeneities hardware exposure test/data processing data quality data processing & reporting W p ±t 1 kwh/w p ±t 2 full electrical characterisation accord. IEC61853 sorting of defect or damaged modules min. sample number (cross correlation) stabilisation of all modules stability check over time (annual degradation rate) reference module (stored in the dark) Module sampling!

10 Field testing requirements test device selection of modules reference power (kwh/wp) test equipment hardware definition hardware accuracy test site non- homogeneities hardware exposure test/data processing data quality data processing & reporting test equipment regularly calibrated high precision instruments and sensors class A solar simulator for STC meas. same solar simulator for all STC meas. (repeatability dominates the uncertainty) max. measurement interval (1-5 min) MPP loading multiple sensors for cross verification check for transient effects (IV & MPPT) Synchronisation of measurements (meteo/ module parameters) 4 wire measurements proper grounding High standard equipment!

11 Field testing requirements test device selection of modules reference power (kwh/wp) test equipment hardware definition hardware accuracy test site non- homogeneities hardware exposure test site stand configuration (modules mounted top or next to each other, min distance from ground) module mounting distribution and number of sensors alignment control of modules and sensors irradiance homogeneity (reflections) temperature/ventilation homogeneity albedo shadows/horizont test/data processing data quality data processing & reporting Site characterisation!

12 Field testing requirements test device selection of modules reference power (kwh/wp) test equipment hardware definition hardware accuracy test site non- homogeneities hardware exposure test/data processing data quality data processing & reporting test/data processing maintenance cleaning procedure error/quality markers alert system cross verification (stability check) uncertainty calculation harmonised reporting Harmonisation, uncertainty calculation & quality control!

13 Field testing requirements test device selection of modules reference power (kwh/wp) test equipment hardware definition hardware accuracy test site non- homogeneities hardware exposure test/data processing data quality data processing & reporting The consideration of all these aspects allows: better comparability of data with clear measurement uncertainties. better validation studies and models. higher confidence in technology benchmarking.

14 Uncertainty contributions in field data Typical uncertainties in Performance Ratio (PR) measurements. PR I,V meas.; 0.25% ΔPR (ranking) I,V meas.; 0.25% irradiance meas.; 3.0% MPP extraction; 1.0% test site nonuniformity ; 1.0% irradiance meas.; 3.0% MPP extraction; 0.5% test site nonuniformity ; 1.0% other; 0.5% other; 0.50% product variability (TC, LLE, SR, ); 2.0% STC rating; 2.0% product variability (TC, LLE, SR, ); 0.5% STC rating; 1.0% combined measurement uncertainty (k=2) 4.5% combined measurement uncertainty (k=2) 1.5%

15 Conclusions Module field testing data are affected by many measurement issues which has to be quantified and reduced to: increase the confidence into new PV module models (validation studies). increase the confidence in a future energy rating standard (demonstration of the validity of calculated rankings). The main uncertainties are coming from the experimental design, the power rating, the irradiance measurement and the product variability. A standardized method for the assessment of uncertainties and the reporting of results is needed. A standardized method for the measurement of degradation parameters is needed to support the development of new degradation models.

16 (ΣPm/Pm,stc)/(ΣG/1000) back of module temperature (Tbom) IEA TASK 13 initiative Survey on outdoor measurements and its uncertainties best practice guideline Creation of an open-source reference data base Presentation of climate and technology specific performance figures Apr-10 Jul-10 Oct-10 Feb-11 May-11 Aug-11 Dec-11 Mar-12 Jun-12 Sep-12 Jan-13 Source: Report IEA-PVPS T13-02:2014, May

17 Thank you for your attention!