  The optimal operation of water reservoir systems is a challenging task
involving multiple conflicting objectives. The main source of complexity is the
presence of the water inflow, which acts as an exogenous, highly uncertain
disturbance on the system. When model predictive control (MPC) is employed, the
optimal water release is usually computed based on the (predicted) trajectory
of the inflow. This choice may jeopardize the closed-loop performance when the
actual inflow differs from its forecast. In this work, we consider - for the
first time - a stochastic MPC approach for water reservoirs, in which the
control is optimized based on a set of plausible future inflows directly
generated from past data. Such a scenario-based MPC strategy allows the
controller to be more cautious, counteracting droughty periods (e.g., the lake
level going below the dry limit) while at the same time guaranteeing that the
agricultural water demand is satisfied. The method's effectiveness is validated
through extensive Monte Carlo tests using actual inflow data from Lake Como,
Italy.
