Comparison and implementation of MPC and simple predictive control into a heat pump system

Abstract

Heat pumps in combination with thermal energy storage systems offer the potential to response to fluctuating renewable energy sources, e.g. photovoltaics. To fully exploit this flexibility and financial potential, predictive control strategies are needed. Since an additional effort due to detailed knowledge and programming skills is required to create the model predictive control (MPC) strategies, a fast and easy implementation is prevented. Therefore, a second model-based approach is developed with a predictive but rule-based control. This simplified approach uses predictive models as well but energy balancing to determine the heat pump operation and the state of charge of thermal storage units throughout the day. In this paper, two predictive approaches were compared with two rule-based controls and evaluated for their potential for PV self-consumption and cost savings in annual simulations. In addition, one rule-based PV optimized control (PVC) and the predictive approaches, MPC and the simple predictive control (SPC), are implemented in the real operation in a plus energy building. In simulation, the best result is achieved by the MPC with a cost saving of 8.3 % due to a high PV energy consumption but mainly to the best efficiency with a SPF of 4.5. Despite the predictive approach of SPC, SPC and PVC achieve very similar results with cost savings of 2.5 % and 0.8 %. Since the costs of PV include taxes, these moderate cost savings are achieved. Excluding these taxes, there are significantly higher cost savings of up to 34 % for MPC. In real operation, differences between simulation results and measured data become apparent. This gap between the set point output of the simulation and the set point input of the real components poses a challenge to the implementation of efficient and cost-effective control like the MPC.