Title: Simulation Methods for Stochastic Storage Problems: A Statistical Learning Perspective,
Abstract: We consider solution of stochastic storage problems through regression Monte
Carlo (RMC) methods. Taking a statistical learning perspective, we develop the
dynamic emulation algorithm (DEA) that unifies the different existing
approaches in a single modular template. We then investigate the two central
aspects of regression architecture and experimental design that constitute DEA.
For the regression piece, we discuss various non-parametric approaches, in
particular introducing the use of Gaussian process regression in the context of
stochastic storage. For simulation design, we compare the performance of
traditional design (grid discretization), against space-filling, and several
adaptive alternatives. The overall DEA template is illustrated with multiple
examples drawing from natural gas storage valuation and optimal control of
back-up generator in a microgrid.