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The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting

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  • Stefano Alessandrini

    (Research Applications Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80307-3000, USA)

  • Tyler McCandless

    (Research Applications Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80307-3000, USA)

Abstract

One way to mitigate the variability of wind and solar power generation is to install the corresponding plants in nearby locations. For example, in Kuwait, the facility at Shagaya Renewable Energy Park is located in a desert area with both photovoltaic panels and wind turbines that allow the continuous generation of renewable energy throughout the day. The National Center for Atmospheric Research (NCAR) has developed a system to generate probabilistic wind and solar predictions for the Shagaya facility. These predictions are based on the analog ensemble technique that post-processes the wind speed and solar irradiance predictions based on a combination of multiple models including the Weather Research and Forecasting (WRF) numerical model. The ensemble forecasts have 20 members and are generated independently at each wind and solar power production facility. Here we present a method based on the Schaake Shuffle (SS) technique to pair the ensemble members from the independent systems to obtain a unique ensemble prediction of the aggregated wind and solar generation. After reordering through the SS technique, the corresponding paired solar and wind power members can be summed to build a unique ensemble of combined generation that is statistically consistent, as verified by the presented metrics.

Suggested Citation

  • Stefano Alessandrini & Tyler McCandless, 2020. "The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting," Energies, MDPI, vol. 13(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2503-:d:358844
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    1. Roulston, M.S. & Kaplan, D.T. & Hardenberg, J. & Smith, L.A., 2003. "Using medium-range weather forcasts to improve the value of wind energy production," Renewable Energy, Elsevier, vol. 28(4), pages 585-602.
    2. P. Pinson, 2012. "Very-short-term probabilistic forecasting of wind power with generalized logit–normal distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 555-576, August.
    3. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    4. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    5. AL-Rasheedi, Majed & Gueymard, Christian A. & Al-Khayat, Mohammad & Ismail, Alaa & Lee, Jared A. & Al-Duaj, Hamad, 2020. "Performance evaluation of a utility-scale dual-technology photovoltaic power plant at the Shagaya Renewable Energy Park in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    6. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
    7. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    8. Simone Sperati & Stefano Alessandrini & Pierre Pinson & George Kariniotakis, 2015. "The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation," Energies, MDPI, vol. 8(9), pages 1-26, September.
    9. Alessandrini, S. & Sperati, S. & Pinson, P., 2013. "A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data," Applied Energy, Elsevier, vol. 107(C), pages 271-280.
    10. Widén, Joakim & Carpman, Nicole & Castellucci, Valeria & Lingfors, David & Olauson, Jon & Remouit, Flore & Bergkvist, Mikael & Grabbe, Mårten & Waters, Rafael, 2015. "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 356-375.
    11. Santos-Alamillos, F.J. & Pozo-Vázquez, D. & Ruiz-Arias, J.A. & Von Bremen, L. & Tovar-Pescador, J., 2015. "Combining wind farms with concentrating solar plants to provide stable renewable power," Renewable Energy, Elsevier, vol. 76(C), pages 539-550.
    12. 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:

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    3. Georgios E. Arnaoutakis & Georgia Kefala & Eirini Dakanali & Dimitris Al. Katsaprakakis, 2022. "Combined Operation of Wind-Pumped Hydro Storage Plant with a Concentrating Solar Power Plant for Insular Systems: A Case Study for the Island of Rhodes," Energies, MDPI, vol. 15(18), pages 1-23, September.
    4. Mitrentsis, Georgios & Lens, Hendrik, 2022. "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," Applied Energy, Elsevier, vol. 309(C).

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