Smart Control for Coupled District Heating Networks

District heating (DH) networks will play an important role in transitioning into a carbon natural economy. When communities grow, so do the respective DH networks, and often the opportunity for coupling different DH networks together arises. This creates the need for high-level control concepts that allow for the handling of such coupled DH networks. These networks are often operated by different owners with different economic interests. Any high-level control concept for coupled DH networks must respect this multi-owner energy system structure.
Optimization-based energy management systems (EMS) are a promising high-level control approach for coupled DH networks. These rely on mathematical optimization to devise an optimal operation plan for all production units, taking varying prices, future demand and yield predictions, and operational constraints into account. However, extending an optimization-based EMS for coupled DH networks with a multi-owner structure is non-trivial. This contribution aims to provide an EMS algorithm that supports this task.
The presented concepts were tested on the real-world example of the three DH networks of Leibnitz, Austria. The three DH networks are operated by two owners and incorporate biomass boilers (3,2MW and 2,4 MW), industrial waste heat (~6MW) and a gas backup boiler (8MW). The annual heat demand of the three networks ranges from ~4GWh to ~14GWh. The two networks that are operated by the same owner are directly hydraulically connected, and the third one is connected via a bidirectional heat transfer station (4MW). However, during the test runs the full transfer capacity could not be reached since the pressure conditions did not allow it. The temperatures in all three networks are similar, making heat exchange easier. The goal was to reduce the overall cost and CO2 emissions of the energy system. Preliminary test results show that the proposed control concept is able to achieve a reduction in CO2 emissions by 35% and a reduction in fuel costs by 7%.