Scenario-based model predictive control of water reservoir systems
This work addresses water management for reservoir operators by providing a more robust control method, though it is incremental as it applies an existing stochastic MPC framework to a new domain.
The authors tackled the challenge of optimal water reservoir operation under uncertain inflows by proposing a stochastic model predictive control approach that uses scenarios generated from past data, resulting in improved performance that counteracts drought periods and meets agricultural water demands as validated with real data from Lake Como.
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.