Paper Info
Title
Receding Horizon Control for Drinking Water Networks: The Case for Geometric Programming
Abstract
Optimal, network-driven control of water distribution networks (WDNs) is very difficult: valve and pump models form nontrivial, combinatorial logic; hydraulic models are nonconvex; water demand patterns are uncertain; and WDNs are naturally of large scale. Prior research on control of WDN addressed major research challenges, yet either i) adopted simplified hydraulic models, WDN topologies, and rudimentary valve/pump modeling or ii) used mixed-integer, nonconvex optimization to solve WDN control problems. The objective of this article is to develop tractable computational algorithms to manage WDN operation, while considering arbitrary topology, flow direction, an abundance of valve types, control objectives, hydraulic models, and operational constraints-all while only using convex, continuous optimization. Specifically, we propose new geometric programming (GP)-based model predictive control (MPC) algorithms, designed to solve the water flow equations and obtain WDN controls, i.e., pump/valve schedules alongside heads and flows. The proposed approach amounts to solving a series of convex optimization problems that graciously scale to large networks. The proposed approach is tested using a 126-node network with many valves and pumps and is shown to outperform traditional, rule-based control. The developed GP-based MPC algorithms, as well as the numerical test results, are all included on Github.
Year
DOI
Venue
2020
10.1109/TCNS.2020.2964139
IEEE Transactions on Control of Network Systems
Keywords
DocType
Volume
Geometric programming,model predictive control,pump and valve control,water distribution networks
Journal
7
Issue
ISSN
Citations 
3
2325-5870
1
PageRank 
References 
Authors
0.35
0
4
Name
Order
Citations
PageRank
Shen Wang1143.03
Ahmad F. Taha23914.71
Nikolaos Gatsis334037.15
Marcio H. Giacomoni462.03