Model Predictive Control of a Large-scale River Network

Abstract

This study investigates the use of Model Predictive Control (MPC) for regulation of river flows. During the past decade, MPC has emerged for controlling open water systems, such as irrigation and drainage channels. Compared to a full river network, irrigation and drainage systems are of relatively small scale. The aim of the present work is to investigate MPC as a tool for control of releases from gates and dams in a large-scale river network using the Murrumbidgee River in New South Wales, Australia, as case study. The Murrumbidgee River has around 1300 kilometers of river reaches, and the travel time through the valley is of the order of one month. The research has focused on four points: 1) Configuring linear surrogate models to describe the characteristics of reaches and weir pools; 2) Formulating the control problem and its objectives; 3) Using MPC with a receding horizon to solve the control problem; and 4) Testing the accuracy of the calculated control action, by using it as forcing in a detailed hydraulic model. The tests show that a reliable computation of optimal releases from regulators throughout the river is obtained, despite the linear approximation of the dynamics. The tests also show that the computation time for setting up and solving the optimization problem is no more than a few minutes on today's laptops.