Data-driven stochastic optimization approaches with applications in power systems
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In this thesis, we focus on data-driven stochastic optimization problems with an emphasis in power systems applications. On the one hand, we address the inefficiencies in maintenance and operations scheduling problems which emerge due to disregarding the uncertainties, and not utilizing statistical analysis methods. On the other hand, we develop a partially adaptive general purpose stochastic programming approach for effectively modeling and solving a class of problems in sequential decision-making.