  In this paper, we discuss an application of the SDDP type algorithm to nested
risk-averse formulations of Stochastic Optimal Control (SOC) problems. We
propose a construction of a statistical upper bound for the optimal value of
risk-averse SOC problems. This outlines an approach to a solution of a long
standing problem in that area of research. The bound holds for a large class of
convex and monotone conditional risk mappings. Finally, we show the validity of
the statistical upper bound to solve a real-life stochastic hydro-thermal
planning problem.
