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Installation of (micro-)turbines in water supply networks has become an attractive strategy forsmall hydropower production in urban environment. It has virtually no impact on ecosystemsand offers side-benefits such as leakage reduction by turning excess pressure intohydropower. Our research aims at improving the modelling and optimization of existingtools for sizing and selecting the optimal location of turbines in urban water supplysystems. A critical component of the analysis is the hydraulic model of the system. In mostexisting studies, the flow variables in the water supply network are computed under theassumption of a quasi-steady flow. Here, we opt for an in-house hydraulic modelwhich achieves a more realistic description of time-varying flow. We will presentcomparisons of the two approach to assess the potential benefits of the unsteadycomputation. For optimizing the size and location of turbines in the network, most research usesmeta-heuristics [1-3]. In this research, we explore an alternative operational strategy, basedon the detailed hydraulic computation of the water supply network prior to the installation ofturbines, followed by formal optimization techniques. Both the hydraulic model and theoptimization technique are implemented in the CasADi framework for nonlinear optimizationand optimal control [4]. The operationality of our developments is shown for a range of standard case studies aswell as a real-world case study representing the urban water supply system of Liege,Belgium, which includes 3,600 km of pipes and more than 200 hydraulic structures, such asinterconnected reservoirs. Monitoring data from around 700 gauges throughout the watersupply network are used to provide a deep understanding of the system operation and avalidation of the hydraulic model. Acknowledgement This research is supported partly by the Belgian Fund for Scientific Research F.R.S-FNRSthrough the program Joint WATER JPI and by the ERDF project Wal-e-Cities. References [1] Fecarotta, O. and Aonghus, M. (2017). Optimal location of pump as turbines (PATs)in water distribution networks to recover energy and reduce leakage. Water Resour. Manage.,31, 31–5043. [2] Meirelles, G., Brentan. B., Izquierdo. J., Ramos H. and Luzivotto E., Jr. (2018). TrunkNetwork Rehabilitation for Resilience Improvement and Energy Recovery in WaterDistribution Networks. Water Resour. Manage., 10(6), 693. [3] Samora, I., Franca, M.J., Schleiss, A.J. and Ramos, H.M. (2016): Simulated annealingin optimization of energy production in a water supply network. Water Resour. Manage.,30(4): 1533– 1547. [4] Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., and Diehl, M. (In Press, 2018).CasADi – A software framework for nonlinear optimization and optimal control.Mathematical Programming Computation. [ABSTRACT FROM AUTHOR]