HAPPy to Control: A Heuristic And Predictive Policy to Control Large Urban Drainage Systems

Abstract Model Predictive Control (MPC) of Urban Drainage Systems (UDS) has been established as a cost‐effective method to reduce pollution. However, the operation of large UDS (containing over 20 actuators) can only be optimized by oversimplifying the UDS dynamics, potentially leading to a decrease in performance and reduction in users' trust, thus inhibiting widespread implementation of MPC procedures. A Heuristic And Predictive Policy (HAPPy) was set up, relying on the dynamic selection of the actuators with the highest impact on the UDS functioning and optimizing those in real‐time. The remaining actuators follow a pre‐set heuristic procedure. The HAPPy procedure was applied to two separate UDS in Rotterdam with the control objective being the minimization of overflow volume in each of the two cases. Results obtained show that the level of impact of the actuators on the UDS functioning changes during an event and can be predicted using a Random Forest algorithm. These predictions can be used to provide near‐global optimal actuator settings resulting in the performance of the HAPPy procedure that is comparable to a full‐MPC control and outperforming heuristic control procedures. The number of actuators selected to obtain near‐global optimal settings depends on the UDS and rainfall characteristics showing an asymptotic real‐time control (RTC) performance as the number of actuators increases. The HAPPy procedure showed different RTC dynamics for medium and large rainfall events, with the former showing a higher level of controllability than the latter. For medium events, a relatively small number of actuators suffices to achieve the potential performance improvement.