  We analyze a potentially risk-averse convex stochastic optimization problem,
where the control is deterministic and the state is a Banach-valued essentially
bounded random variable. We obtain strong forms of necessary and sufficient
optimality conditions for problems subject to equality and conical constraints.
We propose a Moreau--Yosida regularization for the conical constraint and show
consistency of the optimality conditions for the regularized problem as the
regularization parameter is taken to infinity.
