An online simulation-based collaborative optimization control method for solar thermal energy, heat pumps and building operations

The setting of operational parameters plays a crucial role in enhancing the performance of solar-heat pump heating systems. However, these parameter settings are typically kept constant in practice, lacking effective dynamic adjustments to adapt to fluctuations in building demands, solar energy, and heat pump performance, subsequently resulting in low system efficiency. In this paper, we propose a novel day-ahead optimization control method using online simulation and a genetic algorithm to maximize solar energy utilization and heat pump efficiency while maintaining building thermal comfort. The simulation model was developed using Dymola and encapsulated as an Functional Mock-up (FMU) model, which is then utilized by the optimization program developed in Python for online simulation. The critical operational parameters, such as the solar collector startup temperature difference, the solar heating startup temperature difference, and the heat pump set temperature, can be optimized for the subsequent day according to weather forecasting. The effectiveness of this method was validated through simulation experiments on a solar-ground source heat pump heating system of a near-zero-energy office building in Beijing, China. The results show that in the whole heating season, the solar energy utilization increased by 15.1 %, and the energy consumption decreased by 15.5 %. Therefore, this study offers a promising solution for solar-heat pump heating systems to enhance solar energy utilization and reduce energy consumption.