District heating networks (DHNs) are crucial infrastructures for the implementation of energy efficiency and CO2 reduction plans, especially in countries with continental climate. DHNs are often complex systems and their energy performance may be largely affected by the operating conditions.
Optimal operation of DHNs involves the optimization of the pumping system. This is particularly important for large networks and for low temperature networks. A common practice to perform optimization consists in using a phyical model. Nevertheless, simulation and optimization of DHNs may involve large computational resources, because of thire possible large extension and the number of scenarios to be examined. A reduced model, obtained from the physical model, can be effectively applied to multiple simulations of a network, with significant reduction of the computational time and resources.
In this paper, a large district heating system, which supplies heating to a total volume of buildings of about 50 million of cubic meters, is considered. The use of a reduced model based on proper orthogonal decomposition (POD) is investigated. Various operating conditions corresponding to partial load operation are analyzed using a fluid-dynamic model of the network. Results show that optimal settings are not particularly regular with respect to load variation. This means that any variation in the thermal load generally involves changes in the set points of all groups. For this reason, a sensitivity analysis is performed using the POD model in order to check the opportunities to limit the number of variations in the pumping settings without significant penalization of the total pumping power.
The proposed approach is shown to be very effective for the optimal management of complex district heating systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.