Hourly operation of a regulated lake via Model Predictive Control

TL;DR

This paper introduces a receding-horizon Model Predictive Control approach for the online regulation of a lake, improving upon traditional offline methods by enabling more frequent and adaptive water level management.

Contribution

It presents a novel receding-horizon control policy for lake regulation, compares its tuning to benchmark methods, and offers a daily alternative approach validated through simulations.

Findings

The proposed MPC approach effectively manages lake levels in simulations.

The daily alternative policy performs well under certain inflow assumptions.

Numerical results demonstrate improved regulation performance over traditional methods.

Abstract

The optimal operation of regulated lakes is a challenging task involving conflicting objectives, ranging from controlling lake levels to avoid floods and low levels to water supply downstream. The traditional approach to operation policy design is based on an offline optimization, where a feedback control rule mapping lake storage into daily release decisions is identified over a set of observational data. In this paper, we propose a receding-horizon policy for a more frequent, online regulation of the lake level, and we discuss its tuning as compared to benchmark approaches. As side contributions, we provide a daily alternative based on the same rationale, and we show that this is still valid under some assumptions on the water inflow. Numerical simulations are used to show the effectiveness of the proposed approach. We demonstrate the approach on the regulated lake Como, Italy.