Pump scheduling optimization in water distribution system based on mixed integer linear programming

•A novel linearization method for the bivariate nonlinear functions is proposed.•A strategy that can adaptively adjust the number of breakpoints is developed.•The computational efficiency and the objective value are improved. The energy consumption in water distribution systems (WDSs) is significant...

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Veröffentlicht in:European journal of operational research Jg. 313; H. 3; S. 1140 - 1151
Hauptverfasser: Shao, Yu, Zhou, Xinhong, Yu, Tingchao, Zhang, Tuqiao, Chu, Shipeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 16.03.2024
Schlagworte:
Linear programming
Linear relaxation
Operation optimization
Pump scheduling
Water distribution systems
Linear programming
Linear relaxation
Operation optimization
Water distribution systems
Pump scheduling
ISSN:0377-2217, 1872-6860
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Zusammenfassung:•A novel linearization method for the bivariate nonlinear functions is proposed.•A strategy that can adaptively adjust the number of breakpoints is developed.•The computational efficiency and the objective value are improved. The energy consumption in water distribution systems (WDSs) is significant. Improving the efficiency of pump operation can significantly reduce energy costs. However, optimal pump operation is a nonconvex mixed-integer nonlinear programming (MINLP) problem, which can be challenging to solve. A feasible approach is to linearize the problem and convert it into a mixed-integer linear programming (MILP) problem. However, this approach introduces many auxiliary variables, which can lead to inefficiency in finding the optimal solution due to the expanded search space. To address this issue, we propose a novel method for linearization of the original MINLP problem and a strategy that can adaptively adjust the number of piecewise linearization breakpoints. By reducing the number of auxiliary variables, our approach achieved competitive computing efficiency and the ability to save energy costs, as demonstrated in two benchmark instances. Furthermore, in a realistic large-scale WDS, our approach saved 9.83% more energy costs than the genetic algorithm and achieved a gap of only 7.36% from the lower bound.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2023.08.055