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Article Dans Une Revue IEEE Transactions on Power Systems Année : 2022

Reliable Provision of Ancillary Services from Aggregated Variable Renewable Energy Sources through Forecasting of Extreme Quantiles

Résumé

Virtual power plants aggregating multiple renewable energy sources such as Photovoltaics and Wind are promising candidates for the provision of balancing ancillary services. A requisite for the provision of these services is that forecasts of aggregated production need to be highly reliable in order to minimize the risk of not providing the service. Yet, a reliability greater than 99% is unattainable for standard forecasting models. This work proposes alternative models for the day-ahead prediction of the lowest quantiles (0.1% to 0.9 %) of renewable Virtual power plant production. The proposed approaches derive conditional quantile forecasts of aggregated Wind/PV/Hydro production, obtained from tailored parametric models and machine learning models, including a Convolutional Neural Network architecture for predicting extremes. Reliability deviation is reduced up to 50 % and probabilistic skill score up to 18% compared to Quantile Regression Forest. Forecasting models are subsequently applied to the provision of downward reserve capacity by a renewable Virtual power plant. Increased forecasting reliability leads to a higher reliability of the reserve capacity, but reduces the average reserve volume offered by the renewable aggregation.
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Dates et versions

hal-03759187 , version 1 (23-08-2022)
hal-03759187 , version 2 (02-09-2022)

Identifiants

Citer

Simon Camal, Andrea Michiorri, George Kariniotakis. Reliable Provision of Ancillary Services from Aggregated Variable Renewable Energy Sources through Forecasting of Extreme Quantiles. IEEE Transactions on Power Systems, 2022, pp.1-15. ⟨10.1109/TPWRS.2022.3198839⟩. ⟨hal-03759187v2⟩
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