Chance-constrained stochastic MPC of Astlingen urban drainage benchmark network

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 (MPC). MPC methodologies RTC of UDSs in literature rely computation optimal strategies deterministic rain forecast. However, reality, uncertainties exist rainfall forecasts which affect severely accuracy computing strategies. Under this context, work aims to focus uncertainty associated with forecasting and its effects. One option use stochastic information about events controller; case using methods, class called available, including several approaches such as chance-constrained method. study, we apply UDS Moreover, also compare operational behavior both classical perfect forecast different scenarios The application comparison have been simulations SWMM Astlingen benchmark network.