Robust and stochastic optimization have gained a lot of attention recently to deal with the uncertainties caused by the integration of variable generation. The concept of robust optimization is to optimize the system against the worst-case scenario. Such a philosophy is consistent with the current operating practice, for example, the N-1 protection operating criteria. Stochastic optimization has been used for decades in the power industry in mid-term hydrothermal scheduling, and a variety of algorithms have been developed for addressing the problem, including Benders decomposition, Lagrangian relaxation and progressive hedging. However, both approaches present challenges that need to be addressed in order to implement them in industrial scale problems within operationally acceptable time frames. This panel session will discuss the following topics: Recent advancement of the theory of robust and stochastic optimization, potential areas of application in markets, and system operation and planning, industry experience and computational challenges.