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Considering recent development of deregulated energy markets and the intermittent nature of renewable energy generation, it is important for power system operators to ensure cost effectiveness while maintaining the system reliability. To achieve this goal, significant research progress has recently been made to develop stochastic optimization models and solution methods to improve reliability unit commitment run practice, which is used in the day-ahead market for ISOs/RTOs to ensure sufficient generation capacity available in real time to accommodate uncertainties. Most stochastic optimization approaches assume the renewable energy generation amounts follow certain distributions. However, in practice, the distributions are unknown and instead, a certain amount of historical data are available. In this research, we propose a data-driven risk-averse stochastic unit commitment model, where risk aversion stems …