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Forecasting of solar power ramp events: A post-processing approach

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  • Abuella, Mohamed
  • Chowdhury, Badrul

Abstract

The growing integration level of wind and solar energy resources introduces new regulating and operating challenges in the electric grid. Ramp-rate limits of conventional power plants in the generation mix impose an operating constraint on renewable energy sources to the point that, at high integration levels, the ramp-rates of wind and solar resources must be managed by situational awareness tools that are based on forecasts, especially the ramp events forecasts. To leverage such tools, a post-processing adjusting approach is developed in this paper for improving the capability of hour-ahead combined forecasts of solar power to capture ramp events. The performance evaluation is conducted with several evaluation metrics that consider the accuracy of forecasts in terms of ramp events. The results of a case study demonstrate the efficacy of the adjusting approach. Probabilistic forecasts are also generated to quantify the uncertainty associated with the solar power ramp event forecasts and the uncertainty analysis is carried out.

Suggested Citation

  • Abuella, Mohamed & Chowdhury, Badrul, 2019. "Forecasting of solar power ramp events: A post-processing approach," Renewable Energy, Elsevier, vol. 133(C), pages 1380-1392.
    • Handle: RePEc:eee:renene:v:133:y:2019:i:c:p:1380-1392
    DOI: 10.1016/j.renene.2018.09.005
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    6. Sujan Ghimire & Ravinesh C Deo & Nawin Raj & Jianchun Mi, 2019. "Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction," Energies, MDPI, vol. 12(12), pages 1-39, June.
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    8. Yürüşen, Nurseda Y. & Uzunoğlu, Bahri & Talayero, Ana P. & Estopiñán, Andrés Llombart, 2021. "Apriori and K-Means algorithms of machine learning for spatio-temporal solar generation balancing," Renewable Energy, Elsevier, vol. 175(C), pages 702-717.
    9. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).
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