Model Predictive Control of Ground Coupled Heat Pump Systems in Office Buildings

摘要

Ground coupled heat pump (GCHP) systems combined with low-temperature heat emission systems such as concrete core activation (CCA) have a primary energy savings potential of more than 50% compared to conventional installations for space heating and cooling. In colder climates, such as in Belgium, the ground is used as a heat source for the heat pump (HP) and as a heat sink for passive cooling (PC). Because of the high investment cost of the ground loop heat exchangers, GCHP systems are often designed for based load operation. A conventional backup installation is added to cover the peak loads. Currently, however, the energy savings potential of CCA-GCHP systems is rarely realized in practice. This is mainly due to the fact that current control strategies are based on a static system representation and do not optimally combine the different sublevels (building, installation and ground). This work presents a model predictive control (MPC) strategy which optimizes the system operation from an integrated system’s perspective with maximization of thermal comfort, minimization of energy cost and a long term sustainable use of the ground as control objectives. The analyses show that optimal system operation maximizes the share of the HP and the PC in covering the heating and cooling demand. This is achieved by controlling the heat fluxes to and from the ground such that the fluid temperature inside the ground loop heat exchangers remains within the tolerance temperature band. The ground thereby optimally serves as a good dissipator of heat and cold, rather than as a seasonal storage device. The results indicate that MPC can realize energy cost savings of up to 20-30% compared to the conventional heating curve/cooling curve-based control strategies. MPC uses the CCA thermal mass to make optimal use of the variations in electricity price (through load shifting) and to minimize the use of the expensive backup system (through peak load reduction). Additionally, reduction in the peak load enables smaller installation sizes resulting in significant savings in the investment cost.