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With increasing penetration of renewable energy into the power grid and its intermittent nature, it is crucial and challenging for system operators to provide reliable and cost effective daily electricity generation scheduling. In this dissertation, we present our recently developed innovative modeling and solution approaches to address this challenging problem. We start with developing several optimization-under-uncertainty models, including both stochastic and robust optimization ones, to solve reliability unit commitment problems for Independent System Operators (ISOs) so as to ensure power system cost efficiency while maintaining a high utilization of renewable energy. Then, we extend our research to study data-driven risk-averse two-stage stochastic program, for which the distribution of the random variable is within a given confidence set. By introducing a new class of probability metrics, we construct the …