  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
method. In this study, we apply stochastic MPC to the UDS using the
chance-constrained method. Moreover, we also compare the operational behavior
of both the classical MPC with perfect forecast and the chance-constrained 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.
