Scenario-based model predictive control of water reservoir systems

TL;DR

This paper introduces a novel scenario-based stochastic model predictive control method for water reservoir systems that uses past data to generate plausible inflow scenarios, improving robustness and performance under uncertainty.

Contribution

It is the first to apply a scenario-based stochastic MPC approach to water reservoirs, enhancing control robustness against inflow uncertainties.

Findings

Improved drought mitigation and water level regulation.

Guarantees of agricultural water demand satisfaction.

Validated effectiveness through extensive Monte Carlo simulations.

Abstract

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.