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In this study, a microgrid with storage (battery, hot water tank) and solarpanel is considered. We benchmark two algorithms, MPC and SDDP, that yieldonline policies to manage the microgrid, and compare them with a rule basedpolicy. Model Predictive Control (MPC) is a well-known algorithm which modelsthe future uncertainties with a deterministic forecast. By contrast, StochasticDual Dynamic Programming (SDDP) models the future uncertainties as stagewiseindependent random variables with known probability distributions. We present ascheme, based on out-of-sample validation, to fairly compare the two onlinepolicies yielded by MPC and SDDP. Our numerical studies put to light that MPCand SDDP achieve significant gains compared to the rule based policy, and thatSDDP overperforms MPC not only on average but on most of the out-of-sampleassessment scenarios.

Optimization of a domestic microgrid equipped with solar panel and  battery: Model Predictive Control and Stochastic Dual Dynamic Programming  approaches