  We study merchant energy production modeled as a compound switching and
timing option. The resulting Markov decision process is intractable.
State-of-the-art approximate dynamic programming methods applied to realistic
instances of this model yield policies with large optimality gaps that are
attributed to a weak upper (dual) bound on the optimal policy value. We extend
pathwise optimization from stopping models to merchant energy production to
investigate this issue. We apply principal component analysis and block
coordinate descent in novel ways to respectively precondition and solve the
ensuing ill conditioned and large scale linear program, which even a
cutting-edge commercial solver is unable to handle directly. Compared to
standard methods, our approach leads to substantially tighter dual bounds and
smaller optimality gaps at the expense of considerably larger computational
effort. Specifically, we provide numerical evidence for the near optimality of
the operating policies based on least squares Monte Carlo and compute slightly
better ones using our approach on a set of existing benchmark ethanol
production instances. These findings suggest that both these policies are
effective for the class of models we investigate. Our research has potential
relevance for other commodity merchant operations settings.
