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

This paper proposes a Robust Model Predictive Control (RMPC) method for energy-efficient and reliable pump scheduling in Water Distribution Networks (WDNs), accounting for model uncertainties and demand forecast errors. Building on a previous robust control approach, this extended method uses a linear model with bounded disturbances and optimizes affine disturbance-based pump schedules over a receding horizon. The optimization complexity is reduced from ${\mathcal{O}}\left({{N^6}}\right)$ to ${\mathcal{O}}\left({{N^3}}\right)$ via a sparse reformulation. When applied to the Randers WDN in Denmark, the method surpasses traditional MPC variants in meeting constraints while maintaining comparable economic performance.

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Publication Details

Published
2025-12-09
DOI
https://doi.org/10.1109/cdc57313.2025.11312981
Primary Topic
Water Systems and Optimization
Type
article
Field-Weighted Citation Impact
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A Robust Predictive Control Method for Pump Scheduling in Water Distribution Networks

Carsten Skovmose Kallesøe, Eric C. Kerrigan, Mirhan Ürkmez, Jan Dimon Bendtsen et al.
Water Systems and Optimization
article

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

Carsten Skovmose Kallesøe, Eric C. Kerrigan, Mirhan Ürkmez, Jan Dimon Bendtsen, John Leth
article en

Abstract

This paper proposes a Robust Model Predictive Control (RMPC) method for energy-efficient and reliable pump scheduling in Water Distribution Networks (WDNs), accounting for model uncertainties and demand forecast errors. Building on a previous robust control approach, this extended method uses a linear model with bounded disturbances and optimizes affine disturbance-based pump schedules over a receding horizon. The optimization complexity is reduced from ${\mathcal{O}}\left({{N^6}}\right)$ to ${\mathcal{O}}\left({{N^3}}\right)$ via a sparse reformulation. When applied to the Randers WDN in Denmark, the method surpasses traditional MPC variants in meeting constraints while maintaining comparable economic performance.

Imperial College London (GB), Aalborg University (DK)
Affordable and clean energy
Openalex Percentile: Top 34%
Water Systems and Optimization
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