Day-Ahead Predictive Control of Heat Pump and Energy Storage Systems With Integrated Photovoltaics
Abstract
Thermal energy storage (TES) became a crucial element in building heating, ventilation, and air conditioning (HVAC) systems. The feature of adding energy flexibility for performing optimal heating control, load shaving/shifting, and energy price-dependent management are among the TES essential foreseen roles. However, driving TES within a realistic operation environment to fulfill these targets might not be straightforward. This need becomes critical when a renewable energy system, with intermittent behavior, is integrated or when load fluctuations are present. This work represents a simulation study for one-day ahead predictive management addressing the heating cost optimization for an HVAC-integrated heat pump with a photovoltaic (PV) system. Using real data collected for a building situated in University College Dublin in Ireland, the proposed TES predictive management strategy using a PV partially-powered heat pump could show a heating cost reduction of more than 20 % in winter compared to the baseline scenario where no optimization-driven TES exists.
Keywords
Thermal energy storage, predictive management, photovoltaic, HVAC