Optimal operation of district heating networks through demand response

Abstract

In this paper, an optimization method aiming at minimizing the thermal peaks in district heating networks is proposed. The method relies on a thermo-fluid dynamic model of both the supply and return networks and permits to analyze the opportunities for thermal peak shaving through "virtual storage". The latter is obtained through variation of the thermal request profiles of the users. The presence of a peak in the morning is due to the shut-down or attenuation of the heating systems during the night, which lead to a dramatical increase of the thermal request early in the morning. The peak compromises a full exploitation of cogeneration and renewable plants that are able to cover just a portion of the maximum load. Consequently, boilers have to be used, leading the system to a performance reduction and to an increase of primary energy consumption. Moreover, the peak makes the possibility of network extension quite difficult, because of the limitation on mass flow rates in the pipes. For this reason, a model is developed to make the thermal profile as flat as possible. The model is applied to a portion of the Turin district heating network, which is the largest network in Italy. Results show that reductions between 20% and 42% are possible, depending on the maximum changes in the possible schedules.

Flexibility From Distributed Multienergy Systems

“…Similarly to relaxation of constraints in some energy vector operation to facilitate overall MES flexibility, it may also be possible to relax some of the physical constraints that characterise multi-vector network operation, for example in terms of pressure for gas networks, temperature for thermal networks, water velocity for water networks, etc. (see for example relevant ideas in [76] and [77]). Of course, again this relaxation should still be carried out within acceptable, secure limits and not for a sustained duration.…”

Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review

“…Using heat stored in the buildings connected to DH networks to reduce the DH demand peaks (i.e., using the buildings for so-called 'virtual' storage) is one of the strategies that has attracted increasing attention during the last years, especially in Italy [25,[69][70][71][72][73][74] and in Nordic countries, such as Finland [30,[75][76][77], Denmark [78][79][80][81][82][83][84][85], and Sweden [54,86,87]. These countries are characterized by well-developed DH sectors and a large proportion of buildings with high thermal inertia.…”

Towards future infrastructures for sustainable multi-energy systems: A review

“…The concept of demand response was coined in the electric field representing a promising option to meet renewables fluctuations due to the progress on information technology, control and data science; it allows a better use of transmission and distribution networks, increasing overall system efficiency [199]. However, some recent works show the interest of demand response in thermal networks [200][201][202][203]. Demand response in DH systems (also called virtual storage) consists of modifying the settings of the heating systems in buildings: the time the heating systems are switched on and off or the set point temperature.…”