PVMPC-Clean-Power-Research-Data-Sources-and-Validation-24062020

Copyright 2020 Clean Power Research, L.L.C. SATELLITE IRRADIANCE MODEL ACCURACY IMPROVEMENTS ACCESS TO LATEST INPUTS AND 20-YEAR VALIDATION 2020 PV Systems Symposium Webinar June 24, 2020 Data Reduce risk on your solar project Get the most accurate, bankable solar resource data. SystemCheck Validate PV system performance Automatically monitor and assess performance of PV systems and fleets. FleetView Effectively integrate solar into your grid Plan for solar adoption on your distribution system with site-to- feeder-specific PV production. Forecast Forecast solar power Reliably predict production from utility- scale PV with the most accurate, solar-specific forecast. Today’s presentation ▪ Motivation ▪ Input and validation data ▪ V3.4 model performance ▪ Key results Need for consistent and real-time solar data is increasing ❖ Benchmarking performance ❖ Solar resource tuning ❖ Weather trends Why temporal consistency matters Short Validation 1998 2020 Annu al Inso lation Resource Data Dataset B Dataset A On-site data Correlation On-site data Dataset B Dataset A Correlation Tuned Resource Data Why temporal consistency matters Short Validation Complete Validation 1998 2020 Annu al Inso lation On-site data On-site data Dataset B Dataset A SolarAnywhere Correlation Correlation Resource Data Quality and volume of data enable more accurate models ❖ New satellites ❖ Numerical weather models ❖ 20 years of ground measurements ❖ Leveraging software techniques New satellites offer better performance Comparison of GOES-13 and GOES-16 However, maintaining consistency is critical Half Hourly RMSE of GHI for Western Validation Stations Ground measurements provide an excellent long-term reference, but different biases must be considered Detecting Calibration Drift at Ground Truth Stations A Demonstration of Satellite Irradiance Models’ Accuracy Richard Perez1, James Schlemmer1, Adam Kankiewicz2, John Dise2, Alemu Tadese2 Thomas Hoff2 1 Atmospheric Sciences Research Center, SUNY, Albany, New York, 12203, USA 2 Clean Power Research, Napa, California, 94558, USA Today’s presentationDirectional response present in pyranometer data Today’s presentationIndirectly measured GHI shows better alignment of clear sky irradiance Today’s presentation ▪ Motivation ▪ Input and validation data ▪ V3.4 model performance ▪ Key results Long-term bias errors provide a quick view of accuracy https//www.solaranywhere.com/validation/leadership-bankability/data-validation/ Annual statistics are more important for many use cases https//www.solaranywhere.com/validation/leadership-bankability/data-validation/ SolarAnywhere v3.4 shows 18 reduction in distribution of annual errors in North America https//www.solaranywhere.com/validation/leadership-bankability/data-validation/ and excellent consistency https//www.solaranywhere.com/validation/leadership-bankability/data-validation/ Today’s presentation ▪ Motivation ▪ Input and validation data ▪ V3.4 model performance ▪ Key results Histogram 20-year Change in GHICLR Mean 0.2 SD 0.5 ΔGHICLR 20-yr AoD AoD Effect of Shutting Coal Assets M. Perez et al., Observed recent trends in the solar resource across North America changing cloud cover, AOD, and the implications for PV yield, IEEE PVSC 2020. Histogram 20-year Change in GHI Mean 0.6 SD 1.5 ΔGHI20-yr M. Perez et al., Observed recent trends in the solar resource across North America changing cloud cover, AOD, and the implications for PV yield, IEEE PVSC 2020. For more information, contact Patrick Keelin Lead Product Manager pkeelincleanpower.com Mark Grammatico Senior Technical Account Executive markgcleanpower.com www.cleanpower.com Thank you