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Effective Solar Resource Methodologies for Sustainable PV Applications

  • David S. RennéEmail author
Chapter
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Abstract

Through focused R&D efforts, favorable policies, and access to private capital financing, solar photovoltaic (PV) technologies have experienced phenomenal growth in the marketplace during the past three decades. During this time, the global installed capacity of PV has doubled every 3–4 years, so that at the time of this writing, global capacity is approaching 300 GW [1]. This growth is attributed to the positive feedback cycle of improved efficiencies and reliability resulting from public and private R&D investments, which brings down the commercial costs and promotes positive policy making to further expand markets and encourage new investments. One major factor in stimulating the growth of this key carbon-free technology has been the constant improvement in understanding of the solar resource available to these technologies. Solar resource availability and its characteristics are among the most important factors to be considered as further investments are contemplated. During the past three decades, as R&D in improving cell and panel efficiencies and reliability has paid dividends, so has the concurrent R&D in improving our understanding of the solar “fuel” available to these technologies. This understanding not only provides guidance on optimal sites for which to install systems but also on how to optimize the design and operation of systems at any site.

Keywords

Aerosol Optical Depth Flat Plate Collector Solar Resource Typical Meteorological Year Global Horizontal Irradiance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    REN21 (The Renewable Energy Policy Institute of the 21st Century, Global Status Report (2016), http://www.ren21.net/status-of-renewables/global-status-report/
  2. 2.
    F. Vignola, C. Grover, N. Lemon, A. MacMahan, Building a bankable solar radiation dataset. Sol. Energy 86, 2218–2229 (2012)CrossRefGoogle Scholar
  3. 3.
    M. Sengupta, A. Habte, S. Kurtz, A. Dobos, S. Wilbert, E. Lorenz, T. Stoffel, D. Renné, D. Myers, S. Wilcox, P. Blanc, R. Perez, Best practices handbook for the collection and use of solar resource data for solar energy applications. Technical Report NREL/TP-5D00-63112, National Renewable Energy Laboratory, Golden, Colorado, USA, February 2015Google Scholar
  4. 4.
    R.C. Temps, K.L. Coulson, Solar radiation incident up on slopes of different orientations. Sol. Energy 19, 179–184 (1977)CrossRefGoogle Scholar
  5. 5.
    J.E. Hay, Study of shortwave radiation on non-horizontal surfaces. Report 79-12, Downsview, ON. Atmospheric Environment Service (1979)Google Scholar
  6. 6.
    T.M. Kluchar, Evaluation of models to predict insolation on tilted surfaces. Sol. Energy 23, 111–114 (1979)CrossRefGoogle Scholar
  7. 7.
    B.Y.H. Liu, R.C. Jordan, Daily insolation on surfaces tilted towards equator. ASHRAE Trans. 67, 526–541 (1961)Google Scholar
  8. 8.
    D.T. Reindl et al., Evaluation of hourly tilted surface radiation models. Sol. Energy 45, 9–17 (1990)CrossRefGoogle Scholar
  9. 9.
    C.A. Gueymard, D. Myers, Validation and ranking methodologies for solar radiation models, in Modeling solar radiation at the earth’s surface, ed. by V. Badescu (Springer, New York, 2008)Google Scholar
  10. 10.
    W. Marion, S. Wilcox, Solar radiation data manual for flat-plate and concentrating collectors. NREL/TP-463-5607 (National Renewable Energy Laboratory, Golden, CO, 1994)Google Scholar
  11. 11.
    S. Wilcox, D. Myers, Evaluation of radiometers in full-time use at the National Renewable Energy Laboratory Solar Radiation Research Laboratory. NREL/TP-550-44627 (National Renewable Energy Laboratory, Golden, CO, 2008)CrossRefGoogle Scholar
  12. 12.
    A. Habte, S. Wilcox, T. Stoffel, Evaluation of radiometers deployed at the National Renewable Energy Laboratory’s Solar Radiation Research Laboratory. Technical Report NREL/TP-5D00-60896, National Renewable Energy Laboratory, Golden, Colorado (USA), February 2014Google Scholar
  13. 13.
    N. Geuder, M. Hanussek, J. Halle, R. Affolter, S. Wilbert, Comparison of corrections and calibration procedures for rotating shadowband irradiance sensors, SolarPACES Conference. Granada, Spain, 2011Google Scholar
  14. 14.
    D. Cano, J. M. Monget, M. Albuisson, H. Guillard, N. Regas, L. Wald, A method for the determination of the global solar radiation from meteorological satellite data. Sol. Energy 37(1), 31–39 (1986)Google Scholar
  15. 15.
    S. Renné, David, Richard Perez, Antoine Zelenka, Charles Whitlock, Roberta DiPasquale, in Use of weather and climate research satellites for estimating solar resources. Chapter 5, ed. by D. Yogi Goswami, Karl W. Böer. Advances in solar energy, Vol 13 (American Solar Energy Society, Boulder, CO). p. 457 (1999)Google Scholar
  16. 16.
    R. Perez, P. Ineichen, K. Moore, M. Kmiecik, C. Chain, R. George, F. Vignola, A new operational model for satellite-derived irradiances: Description and validation. Sol. Energy 73(5), 307–317 (2002)CrossRefGoogle Scholar
  17. 17.
    C.A. Gueymard, Clear-sky irradiance predictions for solar resource mapping and large-scale applications: Improved validation methodology and detailed performance analysis of 18 broadband radiative models”. Sol. Energy 86, 2145–2169 (2012)CrossRefGoogle Scholar
  18. 18.
    P. Ineichen, A broadband simplified version of the SOLIS Clear-Sky Model. Sol. Energy 82, 758–762 (2008)CrossRefGoogle Scholar
  19. 19.
    T.E. Hoff, R. Perez, Modeling PV fleet output variability. Sol. Energy 86(2012), 2177–2189 (2012)CrossRefGoogle Scholar
  20. 20.
    CAISo (California Independent System Operator), 2013: Fast facts: What the duck curve tells us about managing a green grid. 250 Outcropping Way, Folsom, CA 95630. www.caiso.com
  21. 21.
    S.E. Haupt, A public-private academic partnership to advance solar forecasting. National Solar Conference, 2013 (American Solar Energy Society, Baltimore, MD, 2013)Google Scholar
  22. 22.
    J. Kleissl (ed.), Solar energy forecasting and resource assessment (Academic, London, 2013), 504 pp.Google Scholar
  23. 23.
    E. Lorenz, J. Kuehnert, D. Heinemann, K.P. Nielsen, J. Remund, S. C. Mueller, Comparison of Irradiance Forecasts Based on Numerical Weather Prediction Modes with Different Spatio-Temporal Resolutions, Conference Proceedings, 31st EUPVSEC 2015, Hamburg, Germany (2015)Google Scholar
  24. 24.
    S. Wilcox, W. Marion, Users Manual for TMY3 Data Sets. NREL/TP-581-43156 (Golden, Colorado, USA, National Renewable Energy Laboratory, 2008)CrossRefGoogle Scholar
  25. 25.
    A. Dobos, P. Gilman, M. Kasberg et al., P50/P90 Analysis for Solar Energy Systems Using the Systems Analysis Model. Conference Paper NREL/CP-6A20-54488, June 2012. National Renewable Energy Laboratory, Golden, Colorado. Presented at World Renewable Energy Congress, Denver, 2012Google Scholar
  26. 26.
    C.A. Gueymard, A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects. Renew. Sustain. Energy Rev. 39, 1024–1034 (2014)CrossRefGoogle Scholar
  27. 27.
    J. Polo, S. Wilbert, J.A. Ruiz-Arias, R. Meyer, C. Gueymard, M. Suri, L. Martín, T. Mieslinger, P. Blanc, I. Grant, J. Boland, P. Ineichen, J. Remund, R. Escobar, A. Troccoli, M. Sengupta, Integration of ground measurements to model-derived data. IEA-SHC Task 46 Solar Resource Assessment and Forecasting, Final Report for Subtask B3 (2015), To be available on http://task46.iea-shc.org
  28. 28.
    C.A Gueymard, Stephen M. Wilcox, Spatial and temporal variability in the solar resource: Assessing the value of short-term measurements at potential solar power plant sites. Proceedings, Solar 2009, Buffalo, New York, American Solar Energy Society Proceedings, 2009Google Scholar
  29. 29.
    Schumann, Kathrin, Hans Georg Beyer, Kaushal Chhatbar, Richard Meyer, 2011: Improving satellite-derived solar resource analysis with parallel ground-based measurements. Proceedings, Solar World Congress, Kassel, Germany, 2011. International Solar Energy SocietyGoogle Scholar
  30. 30.
    T. Mieslinger, F. Ament, K. Chhatbar, R. Meyer, A new method for fusion of measured and model-derived solar radiation time-series. Proceedings, SHC 2013: International Conference on Solar Heating and Cooling for Buildings and Industry. Energy Procedia 48, 1617–1626 (2014)CrossRefGoogle Scholar
  31. 31.
    M. Schnitzer, C. Thuman, P. Johnson, Reducing uncertainty in solar energy estimates (AWS Truepower, Albany, NY, 2012). 21pp.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Dave Renné Renewables, LLCBoulderUSA

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