Abstract：

To promote the efficient utilization of distributed photovoltaic (PV) systems in buildings through air-source heat pump (ASHP) heating, this paper proposes a demand response method for ASHP heating water temperature. This method is based on the prediction of PV power generation, ASHP energy consumption, heating load, and indoor temperature. It aims to optimize both operational costs and indoor thermal comfort, utilizing the particle swarm optimization (PSO) algorithm to determine the heating water temperature setpoints for the next 24 hours, thereby guiding the operation of the ASHP system. Taking a low-carbon residential building in Qingdao as a case study, the effectiveness of this method is validated through simulation. The results demonstrate that the optimization method, by scheduling the ASHP heating water temperature and fully leveraging the building’s thermal storage potential, effectively coordinates the electricity consumption of the ASHP, the building’s distributed PV power generation, and the grid power supply. It shows significant advantages under typical high, medium, and low load conditions, with the PV utilization rate and the PV self-sufficiency rate increasing by up to 29.94% and 30.21% respectively, and the operational cost is reduced by 10.59% to 42.67%. This method effectively enhances the energy utilization efficiency and economic performance, providing a new technical pathway for the low-carbon transformation of building heating systems.

Keywords:air-source heat pump; water temperature optimization control; photovoltaic utilization rate; demand response; model prediction