A randomised approach to Multiple Chance-Constrained Problems: An application to flood avoidance

摘要

One of the major risks associated with rivers is flooding, and a desirable way to manage rivers is to reduce the risk of severe floods without affecting the normal river operations. The flood risks are mainly contributed by uncertain inflows from tributaries. Due to uncertain in- and out-flows, the river control problem is formulated in this paper as a Multiple Chance-Constrained optimisation Problem (M-CCP), within a Stochastic MPC setting. M-CCPs are difficult to solve and this paper proposes an optimisation and testing algorithm to find approximate solutions of such problems. The algorithm is a significantly improved version of our previous proposal in [1]. Each step of the algorithm is supported with rigorous probabilistic bounds, and the usefulness of the algorithm is demonstrated on a simulated river example.