Application of different modeling approaches to a district heating network

The diffusion of district heating (DH) technologies is closely related to the improvement in design and management techniques. Holistic modeling of DH networks represents one of the most promising tools in this regard, since it is aimed at both identifying the size of new plants and optimizing existing ones, with a significant reduction in the time, costs and complexity of real-field tests. The aim of this work is to present the application of two innovative modeling procedures to a real case, i.e. a branch of the Parma University Campus network with a length of about 2 km, 12 connected buildings and an overall heating requirement of 11.2 MW. The first procedure is based on a dedicated library of energy system components developed by the authors in the Matlab®/Simulink® environment, which can be used to assemble models of systems and grids following different configurations. Power generation, conversion and distribution systems together with buildings and generic users can be simulated, considering the thermal and pressure losses of the heat transfer fluid. Typical disturbances can be considered, e.g. solar irradiance, environmental temperature variations and metabolic heat gain from people. Furthermore, a realistic control strategy has been applied to valve opening and boiler operation, in order to fulfil the heat demand profile of buildings in terms of indoor temperatures. Results show that the simulation tool provides a detailed representation of the real system behavior and allows for a comprehensive analysis of the evolution of the main thermodynamic parameters of the heat transfer fluid. The second procedure is based on a tool able to simulate a complex energy distribution network considering the contemporary flows of electrical, thermal and cooling energy. This approach allows both the energy distribution and its generation to be optimized by adopting an optimization procedure based on a genetic algorithm that allows for the load allocation of all the generation systems minimizing the costs of energy production and the associated environmental impact. The results of this second approach, applied to the case study of the Parma University Campus network, show a decrease in electricity consumption for pumping coupled with a reduction in CO2 emissions. The two approaches allow a complete description of a complex smart grid by considering all its aspects from the optimal load distribution of the energy systems up to the implementation of control strategies for the management of the room temperatures at the final users