Bi-objective design-for-control for improving the pressure management and resilience of water distribution networks - Ulusoy, Aly-Joy; Mahmoud, Herman A.; Pecci, Filippo; Keedwell, Edward C.; Stoianov, Ivan (Water research (Oxford), 2022)
•Optimal water network design and control combines continuous and integer variables.•Pressure-induced leakage and network resilience are conflicting objectives.•Complementary advantages of evolutionary algorithms and gradient-based methods.•Novel hybrid method efficiently solves bi-objective MINLPs for large water networks. [Display omitted] This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN), while minimizing pressure induced pipe stress and leakage. Both evolutionary algorithms (EA) and gradient-based mathematical optimization approaches are investigated for the solution of the resulting large-scale non-linear (NLP) and bi-objective mixed-integer non-linear programs (BOMINLP). While EAs have been successfully applied to solve discrete network design problems for large-scale WDNs, gradient-based mathematical optimization methods are more computationally efficient when dealing with large search spaces associated with continuous variables in optimal network control problems. Considering the advantages of each method, we propose a sequential hybrid method for the optimal design-for-control of large-scale WDNs, where a refinement stage relying on gradient-based mathematical optimization is used to solve continuous optimal control problems corresponding to design solutions returned by an initial EA search. The proposed method is applied to compute the Pareto front of a bi-objective design-for-control problem for the operational network BWPnet, where we consider reopening closed connections between isolated supply areas. The results show that the considered design-for-control strategy increases the resilience of BWPnet while minimizing pressure induced leakage. Moreover, the refinement stage of the proposed hybrid method efficiently improves the coarse approximation computed by the initial EA search, returning a continuous and even Pareto front approximation.