Multi-objective optimization of solar-assisted ground-source heat pumps for minimizing life-cycle cost and climate performance in heating-dominated regions

Abstract

The use of solar-assisted ground-source heat pumps (SGSHPs) with serial and parallel configurations can reduce the energy consumption compared with conventional ground-source heat pumps (GSHPs). However, the environmental and economic issues associated with SGSHPs, which involve high initial costs and CO2 emissions, remain major barriers to their application. Therefore, SGSHPs should be carefully designed considering both the economic and environmental circumstances to guarantee their competitiveness over GSHPs. In this study, multi-objective optimization of SGSHPs with serial and parallel configurations was conducted to minimize the life-cycle cost (LCC) and climate performance (LCCP). Based on the estimated energy consumption using Transient System Simulation Tool (TRNSYS), the design parameters and refrigerants of the SGSHPs were optimized using an artificial neural network and multi-objective genetic algorithm in the four heating-dominated regions. The optimized SGSHP with a parallel configuration exhibited lower LCC and LCCP by up to 7% and 10%, respectively, compared to that with a serial configuration. Furthermore, as building insulation technology improves, the optimal system size decreases. In addition, R134a is the best option for minimizing LCC owing to its high performance, whereas R1234yf is the best option for minimizing LCCP in Helsinki and Stockholm, showing up to a 37% reduction in LCCP.