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District heating networks have become widespread due to their ability to distribute thermal energy efficiently, which leads to reduced carbon emissions and improved air quality. Additional benefits can derive from novel demand side management strategies, which can efficiently balance demand and supply. However, their implementation requires detailed knowledge of heating network characteristics, which vary remarkably depending on urban layout and system amplitude. Moreover, extensive data about the energy distribution and thermal capacity of different areas are seldom available. For this purpose, the present work proposes a novel procedure to develop a fast scale-free model of large-scale district heating networks for system optimization and control. Each network community is represented and modeled as an aggregated region. Its physics-based model is identified starting from a limited amount of data available at the main substations and includes heat capacity and heat loss coefficients. The procedure is demonstrated and validated on the network of Va center dot steras, Sweden, showing results that are in agreement with data from the literature. Thus, the model is well suited for real-time optimization and predictive control. In particular, the possibility to easily estimate the heat storage potential of network communities allows demand side management solutions to be applied in several conditions.