Model predictive control for a heat booster substation in ultra low temperature district heating systems

District heating systems may support an increased penetration of stochastic renewable energy technologies and a reduction in centralized combined heat and power plants to reduce carbon dioxide emissions. Ultra low temperature district heating minimizes transport heat losses while enabling the utilization of low-grade surplus heat. Local heat booster substations can heat water to useable temperatures using a heat pump and a hot water tank for storage and flexible operation. This paper proposes a hybrid model predictive control strategy in which an existing heat booster substation is modelled and its charging schedule optimized in real-time over a 24-h forecasted prediction horizon. This enables load shifting whereby scheduling of the heat pump minimizes operation costs. The realisation of energy flexibility can support greater utilization of renewable energy sources and surplus heat in energy supply systems to reduce primary energy consumption. The linear hybrid model predictive controller was successfully implemented in a real 22-flat multifamily building in Copenhagen to verify the control strategy. A comparison of the proposed model predictive control scheduling to the standard rule-based control showed average savings of 23 % on the electricity costs. • Modelling of a heat booster substation (HBS) for a cost-effective optimal operation. • Field test of a hybrid Economic MPC (EMPC) in the HBS with real-time feed-back. • Average saving of 23 % by comparing the proposed hybrid EMPC to a rule-based control. • Load shifting of EMPC for HBS enabling renewables integration in ULTDH.