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Solar variability of four sites across the state of Colorado

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  • Lave, Matthew
  • Kleissl, Jan

Abstract

Solar Global Horizontal Irradiance (GHI) fluctuates on both short (seconds to hours) and long (days to months) timescales leading to variability of power produced by solar photovoltaic (PV) systems. Under a high PV penetration scenario, fluctuations on short time scales may require a supplementary spinning power source that can be ramped quickly, adding significant external cost to PV operation. In order to examine the smoothing effect of geographically distributed PV sites, GHI timeseries at 5min resolution at four sites across the state of Colorado were analyzed. GHI at the four sites was found to be correlated due to synchronous changes in the solar zenith angle. However, coherence analysis showed that the sites became uncorrelated on time scales shorter than 3h, resulting in smoother average output at short time scales. Likewise, extreme ramp rates were eliminated and the spread in ramp rate magnitude was significantly reduced when all four sites were averaged. Nevertheless, even for the averaged output, high frequency fluctuations in PV power output are relatively larger in magnitude than fluctuations expected from wind turbines. Our results allow estimation of the ancillary services required to operate distributed PV sites.

Suggested Citation

  • Lave, Matthew & Kleissl, Jan, 2010. "Solar variability of four sites across the state of Colorado," Renewable Energy, Elsevier, vol. 35(12), pages 2867-2873.
  • Handle: RePEc:eee:renene:v:35:y:2010:i:12:p:2867-2873
    DOI: 10.1016/j.renene.2010.05.013
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    References listed on IDEAS

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    1. Denholm, Paul & Margolis, Robert M., 2007. "Evaluating the limits of solar photovoltaics (PV) in traditional electric power systems," Energy Policy, Elsevier, vol. 35(5), pages 2852-2861, May.
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    5. Rowlands, Ian H. & Kemery, Briana Paige & Beausoleil-Morrison, Ian, 2014. "Managing solar-PV variability with geographical dispersion: An Ontario (Canada) case-study," Renewable Energy, Elsevier, vol. 68(C), pages 171-180.
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    12. Lappalainen, Kari & Wang, Guang C. & Kleissl, Jan, 2020. "Estimation of the largest expected photovoltaic power ramp rates," Applied Energy, Elsevier, vol. 278(C).
    13. Aina Maimó-Far & Alexis Tantet & Víctor Homar & Philippe Drobinski, 2020. "Predictable and Unpredictable Climate Variability Impacts on Optimal Renewable Energy Mixes: The Example of Spain," Energies, MDPI, vol. 13(19), pages 1-25, October.
    14. Roy, Sanjoy, 2015. "Statistical estimates of short duration power generated by a photovoltaic unit in environment of scattered cloud cover," Energy, Elsevier, vol. 89(C), pages 14-23.
    15. Zhenyu Wang & Cuixia Tian & Qibing Zhu & Min Huang, 2018. "Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition," Energies, MDPI, vol. 11(1), pages 1-21, January.
    16. Diesendorf, Mark & Elliston, Ben, 2018. "The feasibility of 100% renewable electricity systems: A response to critics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 318-330.
    17. Lappalainen, Kari & Valkealahti, Seppo, 2017. "Output power variation of different PV array configurations during irradiance transitions caused by moving clouds," Applied Energy, Elsevier, vol. 190(C), pages 902-910.
    18. Chu, Yinghao & Coimbra, Carlos F.M., 2017. "Short-term probabilistic forecasts for Direct Normal Irradiance," Renewable Energy, Elsevier, vol. 101(C), pages 526-536.
    19. Haurant, P. & Muselli, M. & Gaillard, L. & Oberti, P., 2022. "A new methodology to analyse and optimize territorial compensations of solar radiation intermittency: A case study in Corsica Island (France)," Renewable Energy, Elsevier, vol. 185(C), pages 598-610.
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