Short-term irradiance variability: Preliminary estimation of station pair correlation as a function of distance |
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| Authors: | Richard Perez Sergey Kivalov Jim Schlemmer Karl Hemker Thomas E Hoff |
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| Affiliation: | 1. Atmospheric Sciences Research Center, The University at Albany, 251 Fuller Rd., Albany, NY 12203, USA;2. Clean Power Research, 10 Glen Court, Napa, CA 94558, USA;1. Department of Chemical and Biological Engineering, Iowa State University, 617 Bissell Road, Ames, IA 50011, USA;2. Bioeconomy Institute, Iowa State University, 617 Bissell Road, Ames, IA 50011, USA;1. Solar Energy Research Institute of Singapore, National University of Singapore, Singapore S117574, Singapore;2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore;3. Department of Industrial and System Engineering, National University of Singapore, Singapore;1. Fenner School of Environment and Society, The Australian National University, Forestry Building 48, Linnaeus Way, Canberra, ACT 0200, Australia;2. National ICT Australia, Canberra Research Laboratory, Canberra, Australia;3. Research School of Physics and Engineering, ANU, Canberra, Australia;4. Space Science Institute, Boulder, CO, USA;1. Department of Energy & Environment, TERI University, New Delhi, India;2. GIZ GmbH, New Delhi, India;3. Energy Meteorology Group, University of Oldenburg, Oldenburg, Germany;4. National Institute of Wind Energy, Chennai, India |
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| Abstract: | In this article, we report on the correlation between the irradiance variability observed at two neighboring sites as a function of their distance, and of the considered variability time scale. Correlation is the factor that determines whether the combined relative fluctuations of two solar systems add up when correlation is high, or attenuate when correlation is low.Using one-dimensional virtual networks in 24 US locations and cloud motion derived from satellites as experimental evidence, we observe station pair correlations for distances ranging from 100 m to 100 km and from variability time scales ranging from 20 s to 15 min.Within the limits of the assumptions from one-dimensional virtual networks, results show that the relationship between correlation, distance and time scale is predictable and largely independent of location and prevailing insolation conditions. Further, results indicate that the distance at which station pairs become uncorrelated is a quasi linear function of the considered time scale. |
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