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Estimation of the largest expected photovoltaic power ramp rates

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  • Lappalainen, Kari
  • Wang, Guang C.
  • Kleissl, Jan

Abstract

Photovoltaic (PV) systems are prone to irradiance variation caused by cloud shadows leading to fluctuations in generated power. Since these fluctuations can be harmful to the operation of power grids, there is a need to restrict the largest PV power ramp rates (RR). This article proposes a method to estimate the largest expected PV power RRs. The only inputs of the method are the minimum PV system dimension and the measurements of point irradiance and cloud shadow velocity. Since cloud shadows cause the largest power RRs for well-designed large-scale PV power plants, the relation between the largest RRs in irradiance and power during partial cloud shading events was studied based on irradiance measurements. The largest RRs in PV power are estimated from RRs in the average irradiance across the PV system. The proposed method was validated using measured data of 57 days from two PV systems. It showed superior performance compared to an existing method enveloping the RR in the measured power over 99.99% of the time. The method can be used in design and component sizing of PV power plants.

Suggested Citation

  • Lappalainen, Kari & Wang, Guang C. & Kleissl, Jan, 2020. "Estimation of the largest expected photovoltaic power ramp rates," Applied Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920311387
    DOI: 10.1016/j.apenergy.2020.115636
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    References listed on IDEAS

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    6. João Martins & Sergiu Spataru & Dezso Sera & Daniel-Ioan Stroe & Abderezak Lashab, 2019. "Comparative Study of Ramp-Rate Control Algorithms for PV with Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-15, April.
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    8. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Wen, Huiqing & Yan, Ke & Kirtley, James, 2020. "Power ramp-rates of utility-scale PV systems under passing clouds: Module-level emulation with cloud shadow modeling," Applied Energy, Elsevier, vol. 268(C).
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    Cited by:

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    4. Lappalainen, Kari & Valkealahti, Seppo, 2021. "Experimental study of the maximum power point characteristics of partially shaded photovoltaic strings," Applied Energy, Elsevier, vol. 301(C).
    5. Gonzalez-Moreno, A. & Marcos, J. & de la Parra, I. & Marroyo, L., 2022. "A PV ramp-rate control strategy to extend battery lifespan using forecasting," Applied Energy, Elsevier, vol. 323(C).
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    9. Mohammad Junaid Khan & Divesh Kumar & Yogendra Narayan & Hasmat Malik & Fausto Pedro García Márquez & Carlos Quiterio Gómez Muñoz, 2022. "A Novel Artificial Intelligence Maximum Power Point Tracking Technique for Integrated PV-WT-FC Frameworks," Energies, MDPI, vol. 15(9), pages 1-35, May.
    10. Zhao, Xiaohu & Huang, Guohe & Lu, Chen & Zhou, Xiong & Li, Yongping, 2020. "Impacts of climate change on photovoltaic energy potential: A case study of China," Applied Energy, Elsevier, vol. 280(C).
    11. Polleux, Louis & Guerassimoff, Gilles & Marmorat, Jean-Paul & Sandoval-Moreno, John & Schuhler, Thierry, 2022. "An overview of the challenges of solar power integration in isolated industrial microgrids with reliability constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    12. Paletta, Quentin & Arbod, Guillaume & Lasenby, Joan, 2023. "Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions," Applied Energy, Elsevier, vol. 336(C).
    13. Kies, Alexander & Schyska, Bruno U. & Bilousova, Mariia & El Sayed, Omar & Jurasz, Jakub & Stoecker, Horst, 2021. "Critical review of renewable generation datasets and their implications for European power system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
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