Abstract
This paper proposed an economic reliabilityaware model predictive control (MPC) for the management of drinking water transport networks (DWNs) that includes a new goal to increase the system and components reliability based on a finite horizon stochastic optimization problem with joint probabilistic (chance) constraints. The proposed approach is based on a single-layer economic optimization problem with dynamic constraints. The inclusion of components and system reliability in the MPC model using an LPV modelling approach aims at maximizing the availability of the system by estimating system reliability. The solution of the optimization problem related to the MPC problem is obtained by solving a series of Quadratic Programming (QP) problem. The use of chance-constraint programming allows computing an optimal policy based on a desirable risk acceptability level and managing dynamically volume tank stocks to cope with non-stationary flow demands. Finally, the proposed approach is applied to a part of a real drinking water transport network of Barcelona for demonstrating the performance of the method.
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