  Microgrids are local energy systems that integrate energy production, demand,
and storage units. They are generally connected to the regional grid to import
electricity when local production and storage do not meet the demand. In this
context, Energy Management Systems (EMS) are used to ensure the balance between
supply and demand, while minimizing the electricity bill, or an environmental
criterion. The main implementation challenges for an EMS come from the
uncertainties in the consumption, the local renewable energy production, and in
the price and the carbon intensity of electricity. Model Predictive Control
(MPC) is widely used to implement EMS but is particularly sensitive to the
forecast quality, and often requires a subscription to expensive third-party
forecast services. We introduce four Multistage Stochastic Control Algorithms
relying only on historical data obtained from on-site measurements. We
formulate them under the shared framework of Multistage Stochastic Programming
and benchmark them against two baselines in 61 different microgrid setups using
the EMSx dataset. Our most effective algorithm produces notable cost reductions
compared to an MPC that utilizes the same uncertainty model to generate
predictions, and it demonstrates similar performance levels to an ideal MPC
that relies on perfect forecasts.
