Irradiance and cloud optical properties from solar photovoltaic systems

. Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented 5 that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. Specifically, the aerosol (cloud) optical depth is inferred during clear sky (completely overcast) conditions. The method is tested on data from two measurement campaigns that took place in Allgäu, Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 minute resolution, the hourly global horizontal irradiance is 10 extracted with a mean bias error compared to concurrent pyranometer

[1]  S. Meilinger,et al.  Impact of Aerosols on Photovoltaic Energy Production Using a Spectrally Resolved Model Chain: Case Study of Southern West Africa , 2022, SSRN Electronic Journal.

[2]  J. Betcke,et al.  Surface solar irradiation retrieval from MSG/SEVIRI based on APOLLO Next Generation and HELIOSAT‑4 methods , 2022, Meteorologische Zeitschrift.

[3]  S. Geiss,et al.  Entwicklung innovativer satellitengestützter Methoden zur verbesserten PV-Ertragsvorhersage auf verschiedenen Zeitskalen für Anwendungen auf Verteilnetzebene : Schlussbericht , 2021 .

[4]  Dazhi Yang,et al.  Worldwide validation of 8 satellite-derived and reanalysis solar radiation products: A preliminary evaluation and overall metrics for hourly data over 27 years , 2020 .

[5]  C. Reise,et al.  Silicon Sensors vs. Pyranometers – Review of Deviations and Conversion of Measured Values , 2020 .

[6]  K. Pfeilsticker,et al.  Dynamic model of photovoltaic module temperature as a function of atmospheric conditions , 2020 .

[7]  Caio Felippe Abe,et al.  Computing Solar Irradiance and Average Temperature of Photovoltaic Modules From the Maximum Power Point Coordinates , 2020, IEEE Journal of Photovoltaics.

[8]  Radiation Transmission through Glazing , 2020, Solar Engineering of Thermal Processes, Photovoltaics and Wind.

[9]  Jamie M. Bright,et al.  An analytical approach for estimating the global horizontal from the global tilted irradiance , 2019, Solar Energy.

[10]  B. Mayer,et al.  Quantifying the bias of radiative heating rates in numerical weather prediction models for shallow cumulus clouds , 2019, Atmospheric Chemistry and Physics.

[11]  Ana M. Gracia-Amillo,et al.  Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data , 2018 .

[12]  Susanne Crewell,et al.  Bias correction of a novel European reanalysis data set for solar energy applications , 2018 .

[13]  Boudewijn Elsinga,et al.  Inverse photovoltaic yield model for global horizontal irradiance reconstruction , 2017 .

[14]  Vasco Medici,et al.  An unsupervised method for estimating the global horizontal irradiance from photovoltaic power measurements , 2017, 1706.06878.

[15]  Benjamin K. Smith,et al.  Photovoltaic system derived data for determining the solar resource and for modeling the performance of other photovoltaic systems , 2017 .

[16]  Mario Paolone,et al.  Photovoltaic-Model-Based Solar Irradiance Estimators: Performance Comparison and Application to Maximum Power Forecasting , 2017, IEEE Transactions on Sustainable Energy.

[17]  Lucien Wald,et al.  Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat-4 method , 2017 .

[18]  Bernhard Wille-Haussmann,et al.  Projection of power generation between differently-oriented PV systems , 2016 .

[19]  Martin Braun,et al.  A probabilistic approach to the estimation of regional photovoltaic power generation using meteorological data , 2016 .

[20]  Alessandro Salvini,et al.  A fast and effective procedure for sensing solar irradiance in photovoltaic arrays , 2016, 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC).

[21]  Arve Kylling,et al.  The libRadtran software package for radiative transfer calculations (version 2.0.1) , 2015 .

[22]  Lars Klüser,et al.  APOLLO_NG - a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels , 2015 .

[23]  Yves-Marie Saint-Drenan,et al.  An empirical approach to parameterizing photovoltaic plants for power forecasting and simulation , 2015 .

[24]  Nicholas A. Engerer,et al.  KPV: A clear-sky index for photovoltaics , 2014 .

[25]  Romeo Ortega,et al.  An Estimator of Solar Irradiance in Photovoltaic Arrays With Guaranteed Stability Properties , 2014, IEEE Transactions on Industrial Electronics.

[26]  F. Chenlo,et al.  Analysis of spectral effects on the energy yield of different PV (photovoltaic) technologies: The case of four specific sites , 2014 .

[27]  K. Mertens.  Photovoltaics: Fundamentals, Technology, and Practice, 2nd Edition , 2014 .

[28]  W. Beckman,et al.  Radiation Transmission through Glazing: Absorbed Radiation , 2013 .

[29]  M. Baldauf,et al.  Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities , 2011 .

[30]  E. Dunlop,et al.  A power-rating model for crystalline silicon PV modules , 2011 .

[31]  D. Lüthi,et al.  Intercomparison of aerosol climatologies for use in a regional climate model over Europe , 2011 .

[32]  Claudia Emde,et al.  New secondary-scattering correction in DISORT with increased efficiency for forward scattering , 2011 .

[33]  E. Skoplaki,et al.  ON THE TEMPERATURE DEPENDENCE OF PHOTOVOLTAIC MODULE ELECTRICAL PERFORMANCE: A REVIEW OF EFFICIENCY/ POWER CORRELATIONS , 2009 .

[34]  B. Mayer.  Radiative transfer in the cloudy atmosphere , 2009 .

[35]  David Faiman,et al.  Assessing the outdoor operating temperature of photovoltaic modules , 2008 .

[36]  Sigifredo Gonzalez,et al.  Performance Model for Grid-Connected Photovoltaic Inverters , 2007 .

[37]  Andreas Macke,et al.  Enhanced solar global irradiance during cloudy sky conditions , 2007 .

[38]  Bernhard Mayer,et al.  Atmospheric Chemistry and Physics Technical Note: the Libradtran Software Package for Radiative Transfer Calculations – Description and Examples of Use , 2022 .

[39]  R. Pincus.  A First Course on Atmospheric Radiation , 2004 .

[40]  William E. Boyson,et al.  Photovoltaic array performance model. , 2004 .

[41]  Govindasamy Tamizhmani,et al.  Photovoltaic Module Thermal/Wind Performance: Long-Term Monitoring and Model Development for Energy Rating , 2003 .

[42]  K. T. Kriebel,et al.  The cloud analysis tool APOLLO: Improvements and validations , 2003 .

[43]  A. Feijt,et al.  Accuracy of Cloud Optical Depth Retrievals from Ground-Based Pyranometers , 2000 .

[44]  E. Clothiaux,et al.  The k-distribution method and correlated-k approximation for a shortwave radiative transfer model. , 1999 .

[45]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[46]  B. McArthur,et al.  Baseline surface radiation network (BSRN/WCRP) New precision radiometry for climate research , 1998 .

[47]  P. Koepke,et al.  Optical Properties of Aerosols and Clouds: The Software Package OPAC , 1998 .

[48]  R. Boers.  Simultaneous retrievals of cloud optical depth and droplet concentration from solar irradiance and microwave liquid water path , 1997 .

[49]  Wim Turkenburg,et al.  A simple model for PV module reflection losses under field conditions , 1996 .

[50]  E. J. Gough,et al.  PVUSA model technical specification for a turnkey photovoltaic power system , 1995 .

[51]  K. Stamnes,et al.  Estimations of Cloud Optical Thickness from Ground-Based Measurements of Incoming Solar Radiation in the Arctic , 1994 .

[52]  Yongxiang Hu,et al.  An Accurate Parameterization of the Radiative Properties of Water Clouds Suitable for Use in Climate Models , 1993 .

[53]  M. King,et al.  Determination of the optical thickness and effective particle radius of clouds from reflected solar , 1990 .

[54]  K. Stamnes,et al.  Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. , 1988, Applied optics.

[55]  Steven A. Ackerman,et al.  A Shortwave Parameterization Revised to Improve Cloud Absorption , 1984 .

[56]  William Shockley,et al.  The theory of p-n junctions in semiconductors and p-n junction transistors , 1949, Bell Syst. Tech. J..

[57]  J. Saiz,et al.  Right‐sided non‐recurrent laryngeal nerve without any vascular anomaly: an anatomical trap , 2021, ANZ journal of surgery.

[58]  William A. Beckman,et al.  Improvement and validation of a model for photovoltaic array performance , 2006 .

[59]  Grant W. Petty,et al.  A First Course in Atmospheric Radiation , 2004 .

[60]  Dirk Uwe Sauer,et al.  Untersuchungen zum Einsatz und Entwicklung von Simulationsmodellen für die Auslegung von Photovoltaik-Systemen , 1994 .

[61]  P. Ineichen,et al.  Dynamic global-to-direct irradiance conversion models , 1992 .

[62]  S. Klein,et al.  Calculation of the monthly-average transmittance-absorptance product , 1979 .

[63]  D. L. Evans,et al.  Cost studies on terrestrial photovoltaic power systems with sunlight concentration , 1977 .