A low-temperature heating system is studied in this paper. It consists of hydronic under-floor heating pipes and an air/ground source heat pump. The heat pump in such a setup is conventionally controlled only by feed-forwarding the ambient temperature. Having shown >10% cut-down on electricity bills by involving feedback control in a previous study, this paper has continued the same line of argument and has investigated effects of a priori knowledge on weather forecast and electricity price profile to alleviate the total electricity cost subject to constraints on resident's thermal comfort. A two level hierarchical control structure is chosen for this purpose. While local PI controllers at the bottom level maintain individual temperature set-points of the rooms, a model predictive controller at the top level minimizes water supply temperature, and hence maximizes the heat pump's coefficient of performance. At the same time, it determines the actual temperature set-points of the rooms by deviating from the user-defined set-points within a thermal tolerance zone. Simulations results confirm significant cut-down on electricity bills without sacrificing resident thermal comfort. The proposed control strategy is a leap forward towards balanced load control in Smart Grids where individual heat pumps in detached houses contribute to preserve load balance through intelligent electricity pricing policies.
Ieee Conference on Decision and Control (cdc), 2012, p. 7583-7588
Optimization; Predictive control for linear systems; Energy systems
51st IEEE Conference on Decision and Control (CDC), 2012