Enhancing cooperative distributed model predictive control for the water distribution networks pressure optimization

Abstract

In water distribution networks (WDNs), the supply pressure to users should be homogeneous and close to the lower bound so as to reduce leakages and energy consumption while meeting users’ water demand. The large-scale features and incomplete structure information of modern WDNs increase the difficulty in modeling and pressure optimization. In this paper, an enhancing cooperative distributed model predictive control (EC-DMPC) strategy is proposed to solve this problem with enhanced operating performance. Firstly, an efficient modeling method and a partitioning method based on operation data are proposed to decompose the WDNs and construct the subsystem models. These methods are based on k-Shape clustering and canonical correlation analysis with considering the mechanism of WDNs. In order to increase the flexibility of coordination while guaranteeing the global optimization performance, a neighbourhood optimization coordination strategy is adopted, where each subsystem considers the performance of itself and that of strongly affected subsystems. Moreover, a robust invariant set and a robust invariant set control law are utilized to handle the disturbance caused by the ignored weak couplings. Part of Shanghai’s WDNs is used as the study case to demonstrate the application of the proposed methods. A comparative experiment study is developed. Results have shown the efficiency of the proposed methods. By the coordination of the EC-DMPC strategy, when the water demand of users changes, the users’ pressure can be maintained homogeneous, and the average pressure decreases, which is beneficial in the reduction of leakages and energy consumption. The distributed strategy could also improve the flexibility and scalability for large-scale WDNs.