Economic COP optimization of a heat pump with hierarchical model predictive control

Abstract

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.