Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages

Abstract

We present the design, implementation, and experimental validation of a supervisory model predictive control approach for the simultaneous optimization of thermal and electrical building energy supplies. The system comprises a heat pump, a photovoltaic system, an electric battery, and a thermal storage tank. The system uses a phase change slurry as both the heat storage and heat transfer medium, which, beyond an increased storage capacity, results in an improved performance of the heat pump and an increased load-shifting potential. In contrast to approaches that use volumetric flow rates as inputs, the supervisory model predictive control used here determines optimal energy flows. As a result, the online optimization problems do not require impractical computational power in spite of the system complexity. We show the control can be implemented on low-cost embedded hardware and validate it with an experimental demonstrator comprising an installation of the complete supply system, including all hydraulic and all electrical components. Experimental results demonstrate the feasibility of both, the heat pump based heating system with a phase change slurry, and the optimal control approach. The main control objectives, i.e., thermal comfort and maximum self-consumption of solar energy, can be met. In addition, the system and its controller provide a load shifting potential.