Abstract-Urban water supply networks are large-scale systems that transport potable water over vast geographical areas to millions of consumers. A safe and efficient operation of these networks is crucial, as without it living in today's cities would be impossible. To achieve an adequate operation, these networks are equipped with actuators like pumps and valves, which are used to maintain water pressures and flows within safe margins. Currently, these actuators are controlled in a decentralized way using local controllers that only use local information and that do not take into account the presence of other controllers. As a result, water supply networks regularly experience pressure drops and interruptions of water supply when there is an unexpected increase in water demand. To improve performance the actions of the local controllers should be coordinated. Implementing a centralized control scheme is not tractable due to the large-scale nature of these networks. Therefore, this paper proposes the application of a distributed control scheme for control of urban water supply networks. The scheme is based on local model predictive control (MPC) strategies and a parallel coordination scheme that implements cooperation among the local MPC controllers. A simulation study based on a part of the urban water supply network of Bogotá, the capital of Colombia, illustrates the potential of the approach.

In this paper water distribution systems and sewer systems are considered. Both can be seen as complementary networks: water distribution systems supply the commodity to consumers while sewer systems drain the wastewater which is produced by domestic usage and industrial facilities. In the water distribution case an application of a distributed control scheme for management of urban water supply networks is proposed. The scheme is based on local model predictive control (MPC) and a parallel coordination that implements cooperation among local MPC controllers. For the sewer system case a nonlinear predictive control is presented. To deal with the nonlinear and nondifferentiable features of the used predictor model, a direct search method is proposed to solve the optimization problem. Both cases are based on the Bogotá's networks and results illustrate the potential of the approaches.

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