Management of a district heating network using model predictive control with and without thermal storage

District heating and cooling networks are a key infrastructure to decarbonise the heating and cooling sector. Besides the design of new networks according to the principles of the 4th and 5th generation, operational aspects may significantly contribute to improve the efficiency of existing networks from both economic and environmental standpoints. This article is the second step of a work that aims to exploit the flexibility of existing networks and improve their economic and environmental performance, using the district heating network of Verona as a case study. In particular, the first part of the research demonstrated through numerical simulations that the thermal inertia of the water contained in the pipes can be used to shift the heat production of the generators over time by acting on the flow rate circulating in the network. This article shifts the focus from the heat distribution side to the heat supply. A model predictive control strategy was formulated as a MILP optimization problem to schedule the heat supply of the cogeneration plants, heat pump and gas boilers as a function of heat load, waste heat production and electricity price forecasts. Computer simulations of considered district heating network were carried out executing the optimization with a rolling-horizon scheme over two typical weeks. Results show that the proposed look-ahead control achieves a reduction in the operational costs of about 12.5% and 5.8%, respectively in a middle season and a winter representative week. Increasing the flexibility of the system with a centralized heat storage tank connected to the CHP and HP units, these percentage rise to respectively 20% and 6.3%. In the warmest periods, when the total installed power of the CHP and HP plants is sufficient to supply the entire heat demand during the peak, and the modulation of these plants has a higher impact, the cost reduction related to the additional thermal energy storage is more relevant.