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, Toutefois la forte incertitude de la production variable est un obstacle à la fourniture de ces services qui requièrent une fiabilité élevée. L'agrégation de centrales renouvelables dispersées et contrôlées par une centrale virtuelle permet de diminuer cette incertitude en profitant du foisonnement entre les centrales. Cette thèse propose plusieurs modèles de prévision probabiliste afin d'évaluer la capacité d'une centrale virtuelle renouvelable variable à offrir des services système avec une fiabilité maximale: ces modèles sont des adaptations d'arbres de décisions, de réseaux de neurones récurrents et convolutifs, ainsi que de distributions dédiées aux quantiles extrêmement faibles. Une attention particulière est portée à la combinaison de sources d'énergie (Photovoltaïque, éolien, hydraulique au fil de l'eau). Ensuite, des stratégies d'offre optimale d'énergie et de réserve par une agrégation renouvelable sont établies en utilisant les prévisions de production et en considérant les incertitudes associées aux différents marchés. Ces stratégies explorent plusieurs options de modélisation: dépendence entre production renouvelable et prix par une copule, Les énergies renouvelables variables prennent une part croissante de la production raccordée aux réseaux électriques

O. Prévision, Machine Learning, Energies renouvelables, Services système, Centrale virtuelle, Agrégation