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Forecasting and optimization of ancillary services provision by renewable energy sources

Abstract : As variable renewable energy plants penetrate significantly the electricity generation mix, they are expected to contribute to the supply of reserve power, albeit the high uncertainty levels on their production. A solution to reduce the uncertainty consists in aggregating renewable plants dispersed over several climates to obtain a smoother production profile and operate them within a Virtual Power Plant control system. In this thesis, a series of probabilistic forecasting models are proposed to assess the capacity of a variable renewable Virtual Power Plant to provide ancillary services with maximum reliability: these models are adapted decision-tree regression models, recurrent and convolutional neural networks, as well as distributions dedicated to extremely low quantiles. The combination of energy sources (Photovoltaics, Wind, Run-of-river Hydro) is considered in detail. Optimal strategies for the joint offer of energy and ancillary services by a variable renewable Virtual Power Plant are later defined, based on production forecasts and market uncertainties. Offer strategies explore several modelling options:dependence between renewable production and prices via a copula, controlled rate of reserve underfullfilment with a chance-constraint optimization, and finally offer of multiple ancillary services thanks to a Lagrangian formulation.
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Submitted on : Wednesday, October 21, 2020 - 11:59:20 AM
Last modification on : Friday, October 23, 2020 - 3:11:02 AM

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  • HAL Id : tel-02973808, version 1

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Simon Camal. Forecasting and optimization of ancillary services provision by renewable energy sources. Electric power. Université Paris sciences et lettres, 2020. English. ⟨NNT : 2020UPSLM016⟩. ⟨tel-02973808⟩

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