Stochastic Model Predictive Control and Sewer Networks

TL;DR

This paper evaluates Chance-Constrained Model Predictive Control (CC-MPC) versus classical MPC in sewer networks, demonstrating CC-MPC's superior ability to prevent weir overflow under uncertainty, using a case study of Barcelona's sewer system.

Contribution

The paper introduces a CC-MPC formulation tailored for sewer networks and compares its performance to traditional MPC, highlighting improved overflow prevention under uncertain inflows.

Findings

CC-MPC provides better statistical guarantees against weir overflow.

A simple backup strategy enhances CC-MPC feasibility.

Case study confirms improved performance of CC-MPC over classical MPC.

Abstract

In this work, an evaluation of Chance-Constrained Model Predictive Control (CC-MPC) in sewer systems over the use of the classical deterministic Model Predictive Control (MPC) is presented. The focus of this evaluation is on the avoidance of weir overflow when uncertainty is present. Furthermore, the design formulation of CC-MPC is presented with a comparison to the design of MPC. For the evaluation, a simplified model of the Barcelona sewer network case study is utilized. Our comparison shows that for sewer systems with uncertain inflows, a CC-MPC allows for better statistical guarantees for avoiding weir overflow, than relying on a deterministic MPC. A simple back-up strategy in case of infeasible optimization program was also apparent for the CC-MPC based on the results of the analysis.