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Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review

Author

Listed:
  • John Boland

    (Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide 5000, Australia
    These authors contributed equally to this work.)

  • Sleiman Farah

    (Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide 5000, Australia
    These authors contributed equally to this work.)

  • Lei Bai

    (Industrial AI Research Centre, UniSA STEM, University of South Australia, Adelaide 5000, Australia
    These authors contributed equally to this work.)

Abstract

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms in the last 15 years, as well as in roof top solar, both domestic and commercial. For example, in 2020, 27% of the electricity in Australia was from renewable sources, and in South Australia almost 60% was from wind and solar. In the literature, there has been extensive research reported on solar and wind resource, entailing both point and interval forecasts, but there has been much less focus on the forecasting of output from wind and solar systems. In this review, we canvass both what has been reported and also what gaps remain. In the case of the latter topic, there are numerous aspects that are not well dealt with in the literature. We have added discussion on the value of forecasts, rather than just focusing on forecast skill. Further, we present a section on how to deal with conditionally changing variance, a topic that has little focus in the literature. One other topic may be particularly important in Australia at the moment, but may become more widespread. This is how to deal with the concept of a clear sky output from a solar farm when the field is oversized compared to the inverter capacity, resulting in a plateau for the output.

Suggested Citation

  • John Boland & Sleiman Farah & Lei Bai, 2022. "Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review," Energies, MDPI, vol. 15(1), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:370-:d:718113
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    References listed on IDEAS

    as
    1. John Boland & Sleiman Farah, 2021. "Probabilistic Forecasting of Wind and Solar Farm Output," Energies, MDPI, vol. 14(16), pages 1-15, August.
    2. Alfredo Nespoli & Emanuele Ogliari & Sonia Leva & Alessandro Massi Pavan & Adel Mellit & Vanni Lughi & Alberto Dolara, 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques," Energies, MDPI, vol. 12(9), pages 1-15, April.
    3. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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    Cited by:

    1. Artem Stopochkin & Inessa Sytnik, 2022. "Algorithm for Rapid Estimation of the Performance of Small Rooftop Solar PV Use by Households," Energies, MDPI, vol. 15(11), pages 1-29, May.

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