Saletti, C.; Zimmerman, N.; Morini, M.; Kyprianidis, K.; Gambarotta, A. A Control-Oriented Scalable Model for Demand Side Management in District Heating Aggregated Communities. Applied Thermal Engineering 2022, 201, 117681, doi:10.1016/j.applthermaleng.2021.117681.
Saletti, C.; Zimmerman, N.; Morini, M.; Kyprianidis, K.; Gambarotta, A. A Control-Oriented Scalable Model for Demand Side Management in District Heating Aggregated Communities. Applied Thermal Engineering 2022, 201, 117681, doi:10.1016/j.applthermaleng.2021.117681.
Saletti, C.; Zimmerman, N.; Morini, M.; Kyprianidis, K.; Gambarotta, A. A Control-Oriented Scalable Model for Demand Side Management in District Heating Aggregated Communities. Applied Thermal Engineering 2022, 201, 117681, doi:10.1016/j.applthermaleng.2021.117681.
Saletti, C.; Zimmerman, N.; Morini, M.; Kyprianidis, K.; Gambarotta, A. A Control-Oriented Scalable Model for Demand Side Management in District Heating Aggregated Communities. Applied Thermal Engineering 2022, 201, 117681, doi:10.1016/j.applthermaleng.2021.117681.
Abstract
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. The characteristics of these networks vary remarkably depending on the urban layout and system amplitude. Moreover, extensive data about the energy distribution and thermal capacity of different areas are seldom available. Design, optimization and control of these systems are enabled by the availability of fast and scalable models of district heating networks. This work addresses this issue by proposing a novel method to develop a scale-free model of large-scale district heating networks. Starting from coarse data available at the main substations, a physics-based model of the system aggregated regions is developed by identifying the heat capacity and heat loss coefficients. The model validation on the network of Västerås, Sweden, shows compatibility with literature data and can therefore be exploited for system design, optimization and control-oriented applications. In particular, the possibility to estimate the heat storage potential of network regions allows new smart management strategies to be investigated.
Keywords
District Heating Network; reduced-order model; building heat capacity; scalability; gray box model
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.