Energy and Buildings, Vol.158, 1317-1327, 2018
An active control strategy for district heating networks and the effect of different thermal energy storage configurations
The work presented in this paper relates to a small scale district heating network heated by a gas-fired CHP. In most common situations, such a CHP is heat driven operated, meaning that the CHP will switch on whenever heat is needed, while not taking into account the demand of electricity at that time. In this paper however, an active control strategy is developed, aiming to maximize the profit of the CHP, selling its electricity on the spot market. The CHP will therefore switch on at moments of high electricity prices. Nevertheless, since there never is a perfect match between the demand of heat and the demand of electricity, thermal energy storage is included in the network to overcome this difference. Here, three different storage concepts are compared: (1) a central buffer tank next to the CHP; (2) small storage vessels distributed over the different connected buildings; and (3) the use of the thermal mass of the buildings as storage capacity. Besides the development of the control algorithms based on model predictive control, a simulation model of the network is described to evaluate the performance of the different storage concept during a representative winter week. The results show that the presented control algorithm can significantly influence the heat demand profile of the connected buildings. As a result, active control of the CHP can drastically increase the profit of the CHP. The concept with the distributed buffers gives the best results, whereas the profit for the thermal mass concept is only marginally smaller. Since in this latter case no significant investment costs are needed, the conclusion for this case study is that the use of thermal mass of buildings for demand side management in district heating systems is very promising. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:District heating;Demand side management;Thermal energy storage;CHP;Operational management;Active control;Model predictive control