Optimal Flexible Heating Scheduling of Air Source Heat Pumps for Residential Buildings: Application Performance in China’s New Power System

This chapter delves into the optimal flexible heating scheduling of air source heat pumps (ASHPs) for residential buildings, focusing on their application performance within China's evolving power system. The study introduces an optimal flexible heating scheduling (OFHS) method designed to enhance energy flexibility and reduce operating costs by shifting heating energy consumption from peak to off-peak periods. A room dynamic prediction model incorporating thermal resistances and capacitances (RC) network technology is developed to forecast indoor air and return water temperatures. Additionally, an ASHP heating effect prediction model using deep neural networks is established to accurately predict power consumption during demand response regulation. The chapter also presents a rolling optimization formulation for solving water supply temperature setpoints, aiming to minimize indoor temperature deviation and heating operating costs. A case study in Shandong Province, China, demonstrates the effectiveness of the OFHS control method compared to two benchmark methods. The results show a significant reduction in total thermal energy and operating costs, along with an improvement in the coefficient of performance (COP) of ASHPs. The study concludes that the OFHS method is feasible and enhances the flexibility of residential ASHP systems, supporting their integration into China's new power system with a high proportion of renewable energy.