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Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems

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  • Martin Hofmann

    (Valentin Software GmbH, Stralauer Platz 34, 10243 Berlin, Germany
    Institute for Meteorology and Climatology, Leibniz Universität Hannover, Herrenhäuser Straße 2, 30419 Hannover, Germany)

  • Gunther Seckmeyer

    (Institute for Meteorology and Climatology, Leibniz Universität Hannover, Herrenhäuser Straße 2, 30419 Hannover, Germany)

Abstract

We analyze the output of various state-of-the-art irradiance models for photovoltaic systems. The models include two sun position algorithms, three types of input data time series, nine diffuse fraction models and five transposition models (for tilted surfaces), resulting in 270 different model chains for the photovoltaic (PV) system simulation. These model chains are applied to 30 locations worldwide and three different module tracking types, totaling in 24,300 simulations. We show that the simulated PV yearly energy output varies between −5% and +8% for fixed mounted PV modules and between −26% and +14% for modules with two-axis tracking. Model quality varies strongly between locations; sun position algorithms have negligible influence on the simulation results; diffuse fraction models add a lot of variability; and transposition models feature the strongest influence on the simulation results. To highlight the importance of irradiance with high temporal resolution, we present an analysis of the influence of input temporal resolution and simulation models on the inverter clipping losses at varying PV system sizing factors for Lindenberg, Germany. Irradiance in one-minute resolution is essential for accurately calculating inverter clipping losses.

Suggested Citation

  • Martin Hofmann & Gunther Seckmeyer, 2017. "Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems," Energies, MDPI, vol. 10(10), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1495-:d:113271
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    References listed on IDEAS

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    5. Martin Hofmann & Gunther Seckmeyer, 2017. "A New Model for Estimating the Diffuse Fraction of Solar Irradiance for Photovoltaic System Simulations," Energies, MDPI, vol. 10(2), pages 1-21, February.
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    Cited by:

    1. Mayer, Martin János, 2022. "Benefits of physical and machine learning hybridization for photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. AL-Rasheedi, Majed & Al-Khayat, Mohammad, 2024. "Variable renewable energy modeling system to study challenges that impact electrical load at different penetration levels: A case study on Kuwait's load profile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    3. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    5. Riyad Mubarak & Eduardo Weide Luiz & Gunther Seckmeyer, 2019. "Why PV Modules Should Preferably No Longer Be Oriented to the South in the Near Future," Energies, MDPI, vol. 12(23), pages 1-16, November.
    6. Manni, Mattia & Jouttijärvi, Sami & Ranta, Samuli & Miettunen, Kati & Lobaccaro, Gabriele, 2024. "Validation of model chains for global tilted irradiance on East-West vertical bifacial photovoltaics at high latitudes," Renewable Energy, Elsevier, vol. 220(C).
    7. David Leitão & João Paulo N. Torres & João F. P. Fernandes, 2020. "Spectral Irradiance Influence on Solar Cells Efficiency," Energies, MDPI, vol. 13(19), pages 1-18, September.

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