Operational Predictive Optimal Control of Barcelona Water Transport Network

Abstract

This paper describes the application of model-based predictive control (MPC) techniques to the supervisory flow management in large-scale drinking water networks including a telemetry/telecontrol system. MPC is used to generate flow control strategies (set-points for the regulatory controllers) from the sources to the consumer areas to meet future demands, optimizing performance indexes associated to operational goals such as economic cost, safety storage volumes in the network and smoothness of the flow control actions. The designed management strategies are applied to a model of a real case study: the drinking water transport network of Barcelona (Spain).

Economic MPC for the management of drinking water networks

“…Moreover, assuming the water demand and the energy price as periodically time-varying signals, this paper uses the results in [9], [11], [15], [20], and adds periodic terminal constraints to the MPC controller developed in [14] to equip it with guarantees of recursive feasibility and convergence. Even when this paper uses a small-scale case study, the proof of concept may be potentially extended to large-scale DWNs.…”

“…Recent studies on control of DWNs are focused on the design of MPC controllers that directly optimise a (nonstandard) economic cost function, see e.g., [13], [14], not to obtain steady-state set-points but target trajectories for low-level PID controllers. This latter is the case of interest of this paper, where a stationary operation is not beneficial and possible periodicity of time-varying parameters can be exploited for economic profitability.…”

A service reliability model predictive control with dynamic safety stocks and actuators health monitoring for drinking water networks

“…Hence, MPC is a suitable technique to control a DWN due to its capability to deal efficiently with multi-variable dynamic constrained systems and predict the proper actions to achieve the optimal performance according to a user defined cost function. Specifically, the interest is to minimize the following objectives [15]:…”

“…Relevant works apply MPC to DWNs and general SC in different schemes such as: centralized, decentralized, distributed, hierarchical, and robust, see, e.g., [1], [11], [15], [16], [21], [25]. Reliability assurance of supply chains is mainly associated with demand forecasting and safety stocks allocation in storage units as a countermeasure to secure network performance against forecast inaccuracy, (see, e.g., [7], [6], [13], [19], [22]).…”

Adaptive multilevel neuro-fuzzy Model Predictive Control for Drinking Water Networks

“…To determine the amount of safety water stocks, an inventory planning strategy is addressed here to enrich previous control approaches [4], [7], [23], with replenishment policies [24]. The goal is to dynamically allocate the minimal volume in each storage unit to avoid stockouts.…”