Chance-constrained stochastic MPC of Astlingen urban drainage benchmark network

Jan Lorenz Svensen, Congcong Sun, Gabriela Cembrano, Vicenç Puig

Research output: Contribution to journalJournal articlepeer-review

Abstract

In urban drainage systems (UDS), a proven method for reducing the combined sewer overflow (CSO) pollution is real-time control (RTC) based on model predictive control (MPC). MPC methodologies for RTC of UDSs in the literature rely on the computation of the optimal control strategies based on deterministic rain forecast. However, in reality, uncertainties exist in rainfall forecasts which affect severely accuracy of computing the optimal control strategies. Under this context, this work aims to focus on the uncertainty associated with the rainfall forecasting and its effects. One option is to use stochastic information about the rain events in the controller; in the case of using MPC methods, the class called stochastic MPC is available, including several approaches such as the chance-constrained MPC(CC-MPC) method. In this study, we apply CC-MPC to the UDS. Moreover, we also compare the operational behavior of both the classical MPC with perfect forecast and the CC-MPC based on different stochastic scenarios of the rain forecast. The application and comparison have been based on simulations using a SWMM model of the Astlingen urban drainage benchmark network. From the simulations, it was found that CSO volumes were larger when CC-MPC had overestimating forecast biases, while for MPC they increased with any presence of forecast biases.
Original languageEnglish
Article number104900
JournalControl Engineering practice
Volume115
Number of pages10
ISSN0967-0661
DOIs
Publication statusPublished - 2021

Keywords

  • Astlingen benchmark network
  • CSO
  • Stochastic MPC
  • Chance-Constrained
  • Real-Time Control

Fingerprint

Dive into the research topics of 'Chance-constrained stochastic MPC of Astlingen urban drainage benchmark network'. Together they form a unique fingerprint.

Cite this