  In this study, a microgrid with storage (battery, hot water tank) and solar
panel is considered. We benchmark two algorithms, MPC and SDDP, that yield
online policies to manage the microgrid, and compare them with a rule based
policy. Model Predictive Control (MPC) is a well-known algorithm which models
the future uncertainties with a deterministic forecast. By contrast, Stochastic
Dual Dynamic Programming (SDDP) models the future uncertainties as stagewise
independent random variables with known probability distributions. We present a
scheme, based on out-of-sample validation, to fairly compare the two online
policies yielded by MPC and SDDP. Our numerical studies put to light that MPC
and SDDP achieve significant gains compared to the rule based policy, and that
SDDP overperforms MPC not only on average but on most of the out-of-sample
assessment scenarios.
