Economic evaluation of a hybrid heating system in different climate zones based on model predictive control

Abstract

Thermal energy storage (TES) systems allow the user to store energy produced from renewable energy sources during lower consumption periods, and to consume it when the energy demand is higher. Moreover, the use of TES systems encourages the user to take advantage of the time-of-use (ToU) tariff structures in the electricity market. Framed in this goal, the present paper evaluated the economic impact of a novel model predictive control (MPC) strategy that employed an inner control algorithm (ICA) to contribute in the cost electricity reduction of 17 different climate zones while the accuracy in results was improved and the computational effort was maintained. The studied system was composed of an air-to-water heat pump (HP), photovoltaic (PV) panels, and a water TES tank. Its response was based on forecasting the weather conditions, grid electricity price, and heating demand as a dynamic controller. Its economic benefits were compared against a rule-based control (RBC) method and an ON/OFF strategy. The results showed that a good operation of the system was strongly linked with proper sizing of the water TES tank and PV panels, especially in the coldest zones. Nevertheless, using the proposed MPC strategy with its ICA, the system was able to reduce the energy cost between 16% and 22% in most heating demanding climates compared to the RBC strategy and between 17% and 46% compared to the ON/OFF one. Moreover, the energy purchased from the electricity grid during on-peak periods decreased dramatically with the MPC strategy, reaching around 90% reduction in almost all studied climates.