District heating networks (DHNs) play a central role in the decarbonization of urban energy systems, but their increasing size and operational complexity pose significant computational challenges when detailed network models are embedded in optimization frameworks. This thesis proposes and applies a multi-stage methodology for network reduction and pumping station optimization to the district heating network of Turin, one of the largest DHNs in Europe. In the first part of the work, a physically consistent reduction of the original hydraulic model of the Turin DHN is developed in a MATLAB environment. The reduction is carried out through three successive steps: the series reduction of pipes and removal of hydraulically negligible branches, the aggregation of nodes via a Heavy Edge Matching approach to form hydraulically equivalent super-nodes, and the clustering of active user nodes to reduce local demand density while preserving global flow extraction.