A Robust Predictive Control Method for Pump Scheduling in Water Distribution Networks

TL;DR

This paper introduces a robust model predictive control approach for pump scheduling in water distribution networks, effectively handling uncertainties and demand errors to optimize costs and ensure system reliability.

Contribution

It develops a novel RMPC method with affine disturbance policies and reduced computational complexity, tailored for water network pump scheduling.

Findings

Outperforms traditional MPC in constraint satisfaction and reliability.

Achieves comparable economic performance with reduced computational effort.

Successfully applied to a real-world water network in Denmark.

Abstract

Water utilities aim to reduce the high electrical costs of Water Distribution Networks (WDNs), primarily driven by pumping. However, pump scheduling is challenging due to model uncertainties and water demand forecast errors. This paper presents a Robust Model Predictive Control (RMPC) method for optimal and reliable pump scheduling, extending a previous efficient robust control method tailored to our model. A linear model with bounded additive disturbances is used to represent tank water level evolution, with uncertainty bounds derived from WDN simulation and demand data. At each time step, a pump scheduling policy, affine in past disturbances, is optimized to satisfy system constraints over a prediction horizon. The resulting policies are then applied in a receding horizon fashion. The optimization problem is formulated to require $\mathcal{O}(N^6)$ computations per iteration with an interior-point method, which is reduced to $\mathcal{O}(N^3)$ by reformulating it into a sparse form. When evaluated on a model representing the water distribution network of Randers, a medium-sized town in Denmark, the method surpasses nominal and constraint-tightening model predictive control (MPC) approaches in terms of meeting constraints and provides comparable economic outcomes.