Unit commitment (UC) seeks the most cost effective generator commitment schedule for an electric power system to meet net load while satisfying the operational constraints on transmission system and generation resources. This problem is challenging because of the high level of uncertainty in net load which results from load uncertainty and renewable generation uncertainty. This dissertation addresses topics in modeling and computational aspects of considering risk in UC problems.

We investigate and compare the performance of stochastic programming and robust optimization as the most widely studied approaches for unit commitment under net load

uncertainty. We explicitly account for risk, via conditional value at risk (CVaR), in the stochastic programming objective function and by employing a CVaR-based uncertainty set in the robust optimization formulation. The numerical results indicate that the stochastic program with CVaR evaluated in a low probability tail is able to achieve better cost-risk trade-offs than the robust formulation. The CVaR-based uncertainty set similarly

outperforms an uncertainty set based only on ranges.

Being able to solve UC problem in short amount of time on a daily basis is one of the challenges power system operators face. Therefore, we also adopt a branch-and-cut approach to improve the solution algorithm for robust optimization formulation of the UC problem.

Finally, we present an asymptotic approximation for the CVaR of cost in a power system accounting for generating unit outages. This approximation provides a fast computation of the risk emanating from a set of committed generators due to their imperfect reliability.