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Evaluating the variability of photovoltaics: A new stochastic method to generate site-specific synthetic solar data and applications to system studies

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  • Tang, Yuchen
  • Cheng, John W.M.
  • Duan, Qinwei
  • Lee, Cheuk Wing
  • Zhong, Jin

Abstract

The power output of solar photovoltaics (PV) may have sharp fluctuations and its impact has to be carefully evaluated before integrating significant PV facilities into the power grid. Variability of solar resources is best measured by ground-based measurements. However, distributed ground-measured solar data is not available everywhere, and it would take considerable cost and time to obtain such data. Therefore, it is important and beneficial to model and estimate the variability of distributed PV generation even with insufficient solar data at each location. This study proposes a new methodology to generate spatially-distributed synthetic PV data based on detailed ground measurements collected at a few sites. The synthetic PV data is examined with specific criteria and the feasibility for simulating spatially-distributed PV generation is verified. A case study for Hong Kong is conducted using both the real and synthetic solar data. It is demonstrated that individual PV facilities can have significant fluctuations on a minute-by-minute basis, but the fluctuations can be significantly reduced if PV facilities are more spatially-distributed. The improvement to the estimation of solar variability with the proposed method is illustrated and the significance of its applications is discussed.

Suggested Citation

  • Tang, Yuchen & Cheng, John W.M. & Duan, Qinwei & Lee, Cheuk Wing & Zhong, Jin, 2019. "Evaluating the variability of photovoltaics: A new stochastic method to generate site-specific synthetic solar data and applications to system studies," Renewable Energy, Elsevier, vol. 133(C), pages 1099-1107.
    • Handle: RePEc:eee:renene:v:133:y:2019:i:c:p:1099-1107
    DOI: 10.1016/j.renene.2018.10.102
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    References listed on IDEAS

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    1. 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.
    2. Troy, Niamh & Denny, Eleanor & O'Malley, Mark, 2010. "Base-load cycling on a system with significant wind penetration," MPRA Paper 34848, University Library of Munich, Germany.
    3. Peng, Jinqing & Lu, Lin, 2013. "Investigation on the development potential of rooftop PV system in Hong Kong and its environmental benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 149-162.
    4. Shaker, Hamid & Zareipour, Hamidreza & Wood, David, 2016. "Impacts of large-scale wind and solar power integration on California׳s net electrical load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 761-774.
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    Cited by:

    1. Hasanain A. H. Al-Hilfi & Ahmed Abu-Siada & Farhad Shahnia, 2020. "Combined ANFIS–Wavelet Technique to Improve the Estimation Accuracy of the Power Output of Neighboring PV Systems during Cloud Events," Energies, MDPI, vol. 13(7), pages 1-15, April.
    2. Huy, Phung Dang & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tan, Kang Miao & Ekanayake, Janaka B., 2020. "Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage," Energy, Elsevier, vol. 195(C).
    3. Keeratimahat, Kanyawee & Bruce, Anna & MacGill, Iain, 2021. "Analysis of short-term operational forecast deviations and controllability of utility-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 167(C), pages 343-358.

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