9.2 30.4 6.3 5.0 1.0 12.5 17.5 10.0 8.1 Relative importance (calibration-based formulation) [%] Datalogger Calibration uncertainty Non-stability Non-linearity Temperature response Maintenance zero off-set a zero off-set b Directional response EVALUATION OF UNCERTAINTY SOURCES AND PROPAGATION FROM IRRADIANCE SENSORS TO PV YIELD Francesco Mariottini*, Jiang Zhu, Thomas R. Betts, Ralph Gottschalg Centre for Renewable Energy Systems Technology (CREST), Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Leicestershire, LE11 3TU, United Kingdom, *Corresponding author: f.mariottini@lboro.ac.uk, +44(0) 01509 635353 AIM HOW (3): directional response and temperature dependency WHY HOW Energy production uncertainty affects financial costs and bankability 1) Reducing uncertainty of (global) irradiance measurements through pyranometers. 2) Assessing impact of reduced expanded uncertainty on the evaluation of PV performance in a solar farm 1) Data quality management 2) almost clear-sky days sampling for uncertainty assessment. 3) Use of calibration data for point- based directional response and temperature dependency of uncertainty. Better understanding and evaluation of systematic and random effects needed. Including (and not limited to): • Physically possible and extreme rare limits (BSRN checks). • Exclusion of days with disconnections checked against sunrise and sunset paths HOW (1): data quality RESULTS SUBJECTS Incoherencies on uncertainty: from 4.7% to 25.3% for solar data but from 3% to 12% on measurements & modelling HOW (2): almost-clear sky days CREST: calibrated ventilated Kipp & Zonen CMP21 pyranometers with Pt-100 temperature sensor COM: fielded pyranometer, CMP21 assumed (datasheet specifications) Relevance of clear sky conditions for energy production and thermal offset in irradiance measurements (ISO 9060) but rare in UK. Almost clear sky days, closest to: • Diffuse fraction of 0.2 • Pearson coefficient r (against Perez’s clear sky model) of 0.95 • Irradiance deviation of 5% Interpolated values (based on calibration certification) filled into the formulation of uncertainty from JCGM 100:2008. Calibration-based formulation reduces relative importance of directional and temperature uncertainties Relative importance: 𝑢𝑖∗𝑐𝑖 𝑢 σ𝑖 𝑢𝑖∗𝑐𝑖 𝑢 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721452. MAIN CONCLUSIONS By applying a few calibration-based information, irradiance uncertainty is reduced by about 40%. Input data Time resolution [s] Percentage deviation [%] Average expanded uncertainty [W/m2] COM datasheet- based 60 ± 2.87 13.83 3600 ± 3.07 13.83 CREST calibration- based 60 ± 2.01 7.93 3600 ± 2.18 7.94 CREST datasheet- based 60 ± 3.51 13.84 3600 ± 3.79 13.84 Calculation of uncertainty for the 20 selected almost-clear sky days identified between the 3/6/2015 and 3/1/2016. Identification of an almost-clear sky day. Monitored data against calculation through Perez’s model based on Linke Turbidity from Meteonorm and EU project SODA. 6062 6116 6420 6367 6050 6100 6150 6200 6250 6300 6350 6400 6450 Yearly AC energy production (COM datasheet-based) [MWh] Yearly AC energy production (CREST calibration-based) [MWh] Annual energy production (minute average) Effects of uncertainty on estimation of yearly performance (10/8/15- 10/8/16) of a PV solar farm (7389 kWp) based on found deviations. Future independent calibrations will better estimate uncertainty dependency on environmental parameters in irradiance measurements. 70.10 70.69 74.12 73.53 70.00 70.50 71.00 71.50 72.00 72.50 73.00 73.50 74.00 74.50 Performance Ratio (COM datasheet- based) [%] Performance Ratio (CREST calibration- based) [%] Performance Ratio (minute average) Example of identification of “suspect” disconnections against sunrise and sunset paths for a CREST pyranometer. FUTURE STEPS 5.0 18.4 3.8 3.0 15.2 7.6 10.6 6