Abstract

The medium-depth ground source heat pump (MD-GSHP) heating technology is considered a clean energy solution with the potential to mitigate air pollution issues attributed to winter heating in northern China. However, it currently suffers from problems such as a mismatch between heat supply and demand and a lack of optimal operation. To overcome these limitations, this study focuses on a 3000 m2 residential building in Xi’an and implements two configurations: coupling MD-GSHP heating systems with heat storage tank and shallow borehole heat exchanger (BHE). Additionally, an optimization strategy based on the improved particle swarm algorithm is proposed, integrating chaos optimization and dynamic parameters. Finally, noteworthy outcomes were revealed by optimizing the MD-GSHP heating systems under different objective functions. In system coupled with shallow BHE, the variation trend of flow in the medium-depth BHE was similar to that of heating load when the heating cost was taken as the objective function. After the optimization, heating cost reduced by 10.02%, while system coefficient of performance (COP) and medium-depth geothermal utilization coefficient increased by 10.43% and 2.00%, respectively. For system coupled with heat storage tank, the optimized flow exhibited a generally decreasing-increasing-decreasing trend, with the peak preceding the heating load curve. This resulted in a 12.82% reduction in heating cost, along with increases of 10.35% and 1.96% in system COP and the medium-depth geothermal utilization coefficient, respectively. Comprehensive consideration of heating cost and system COP suggested a favorable weight of 2.8 for continuous system operation. In conclusion, when prioritizing heating cost, the system coupled with heat storage tank yields greater economic benefits compared to the system coupled with shallow BHE. The findings of this study can provide a guidance for the applications of the MD-GSHP heating technology.

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