Control of rivers with flood avoidance

Abstract

River control and decision support system can help improving water resource management. Rivers often have two modes of operations: normal operations, which include maintaining water levels and flows at their reference points, and flood avoidance. This work formulates the river control problem as a Stochastic Model Predictive Control (S-MPC), where a Multiple Chance-Constrained optimisation Problem (M-CCP) is solved at every time step. The M-CCP accommodates the constraints related to normal river operations and flood avoidance as two probabilistic constraints. We use the soft probabilistic constraints, because a hard constraint on the water level that depends on uncertain forecasts of the unregulated flows can cause infeasibility of the optimisation problems. We employ the Optimisation and Testing algorithm, proposed in [1], to solve the M-CCP and present simulation results of the control of the upper part of Murray River in Australia.