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Forecasting and optimization of ancillary services provision by renewable energy sources
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.