Assessing the effectiveness of a real-time control method for Marina Reservoir management

Abstract

Marina Reservoir is one of the largest fresh water body in Singapore, recently constructed with the purpose of increasing the drinking water supply and Singapore's self-sufficiency. Besides this strategic role, the reservoir also serves for floods control and lifestyle attraction. The largest portion of the inflow volumes comes from five main uncontrolled catchments, which show fast-varying discharge variations due to the strong storms affecting this region. Moreover, highly paved areas cause rapid runoff processes, with a concentration time lower than one hour. Such hydrological context creates difficulties in satisfying the operational objectives and provides a challenging environment for the application of real-time control methods. With the purpose of evaluating the potential of real-time control for the optimal operation of Marina Reservoir, this work adopts Model Predictive Control (MPC), a form of deterministic control that employs the current state of the system, the future inflows trajectory furnished by a predictive model and a further model describing the internal dynamics of the controlled sub-system to determine an optimal control sequence over a finite prediction horizon. The rationale behind the choice of MPC is that this method is characterized by reduced computational requests and the capability of exploiting any exogenous information, as precipitation or evaporation measurements, that allow for an accurate prediction of the inflow events, which is crucial for an effective management of fast-varying hydrological systems. In the present application, Marina Reservoir catchment is described with a data-driven rainfall-runoff model (in the form of M5 model trees) that fully exploits the hydro-meteorological information available in real-time, thus enhancing the accuracy of the inflow prediction. The model is combined with a non-linear MPC scheme that optimizes the barrage operation according to the different operational objectives. Preliminary results show the effectiveness of the proposed control method, which outperforms the currently-used operating rules.